We propose Orpheus, a novel programming model for communicating agents based on information protocols and realized using cognitive programming. Whereas traditional models are focused on reactions to handle incoming messages, Orpheus supports organizing the internal logic of an agent based on its goals. We give an operational semantics for Orpheus and implement this semantics in an adapter to help build agents. We use the adapter to demonstrate how Orpheus simplifies the programming of decentralized multiagent systems compared to the reactive programming model.

Warning: This paper may contain triggering language for some readers, especially survivors of sexual violence. Survivors of sexual violence sometimes share their experiences on social media, revealing their feelings and emotions and seeking advice. On platforms such as Reddit, some stories can be long---up to 40,000 characters. We posit that such long stories are demanding for helpers to read and respond to. Prior research has indicated that parts of these stories describing the incident, the effects on the poster, and advice requested by the poster are important. Highlighting those parts can draw helpers' attention toward key information and assist them in reading and responding to long stories. We first examine the stories posted on Reddit for the prevalence of these parts. Second, we develop a computational model to highlight these parts of a story. On ten-fold cross-validation of a dataset, our model achieves a macro F1 score of 0.82. In addition, we contribute METHREE, a dataset comprising 8,947 labeled sentences for these parts from Reddit stories. A survey of users who are helpers on some relevant subreddits shows that the parts highlighted by our tool represent important information and assist them while reading and responding to long stories. We find that these tool-generated highlights statistically significantly reduce the demandingness of long stories. Moreover, almost all helpers felt that highlighted stories are helpful and easier to read, understand, and respond to than nonhighlighted ones. In particular, on a 4-point Likert scale, there is about 0.7 point reduction in demandingess when stories were presented with highlights.

Argumentative stance classification plays a key role in identifying authors' viewpoints on specific topics. However, generating diverse pairs of argumentative sentences across various domains is challenging. Existing benchmarks often come from a single domain or focus on a limited set of topics. Additionally, manual annotation for accurate labeling is time-consuming and labor-intensive. To address these challenges, we propose leveraging platform rules, readily available expert-curated content, and large language models to bypass the need for human annotation. Our approach produces a multidomain benchmark comprising 4,498 topical claims and 30,961 arguments from three sources, spanning 21 domains. We benchmark the dataset in fully supervised, zero-shot, and few-shot settings, shedding light on the strengths and limitations of different methodologies.

An interaction protocol specifies how the member agents of a decentralized multiagent system may communicate to satisfy their respective stakeholders' requirements. We focus on information protocols, which are fully declarative specifications of interaction and support asynchronous communication. We offer Mambo, an approach for protocol design. Mambo identifies common patterns of requirements, provides a notation to express them, and a verification procedure. Mambo incorporates heuristics to generate small internal representations for efficiency. Experimental results demonstrate Mambo's effectiveness on practical protocols.

Protocols model multiagent systems (MAS) by capturing the communications between its agents. Belief-Desire-Intention (BDI) architectures provide an attractive way for organizing an agent in terms of cognitive concepts. Current BDI approaches, however, lack adequate support for engineering protocol-based agents. We describe Argus, an approach that melds recent advances in flexible, declarative communication protocols with BDI architectures. For concreteness, we adopt Jason as an exemplar of the BDI paradigm and show how to support protocol-based reasoning in it. Specifically, Argus contributes (1) a novel architecture and formal operational semantics combining protocols and BDI; (2) a code generation-based programming model that guides the implementation of agents; and (3) integrity checking for incoming and outgoing messages that help ensure that the agents are well-behaved. The Argus conceptual architecture builds quite naturally on top of Jason. Thus, Argus enables building more flexible multiagent systems while using a BDI architecture than is currently possible.

We study (public) microtransit, a type of transportation service wherein a municipality offers point-to-point rides to residents, for a fixed, nominal fare. Microtransit exemplifies practical resource allocation problems that are often over-constrained in that not all ride requests (pickup and dropoff locations at specified times) can be satisfied or satisfied only by violating soft goals such as sustainability, and where economic signals (e.g., surge pricing) are not applicable—they would lead to unethical outcomes by effectively coercing poor people. We posit that instead of taking rider preferences as fixed, shaping them prosocially will lead to improved societal outcomes. Prosociality refers to an attitude or behavior that is intended to benefit others. This paper demonstrates a computational approach to prosociality in the context of a (public) microtransit service for disadvantaged riders. Prosociality appears as a willingness to adjust one’s pickup and dropoff times and locations to accommodate the schedules of others and to enable sharing rides (which increases the number of riders served with the same resources). This paper describes an interdisciplinary study of prosociality in microtransit between a transportation researcher, psychologists, a social scientist, and AI researchers. Our contributions are these: (1) empirical support for the viability of prosociality in microtransit (and constraints on it) through interviews with drivers and focus groups of riders; (2) a prototype mobile app demonstrating how our prosocial intervention can be combined with the transportation backend; (3) a reinforcement learning approach to model a rider and determine the best interventions to persuade that rider toward prosociality; and (4) a cognitive model of rider personas to enable evaluation of alternative interventions.

Inciting speech seeks to instill hostility or anger in readers or motivate them to take action against a target group. Whereas hate speech in social media has garnered much attention, inciting speech has not been well studied in domains such as religion. We address two aspects of religious incitement: 1) what rhetorical strategies are used in it?; and 2) do the same strategies apply across disparate social contexts and targets? We identify inciting speech against Muslims but demonstrate the generality of the construct vis à vis other targets. We adopt existing datasets of Islamophobic WhatsApp posts and hateful and offensive posts (Twitter and Gab) against other targets. Our methods include: 1) qualitative analysis revealing rhetorical strategies; and 2) an iterative process to label the data, yielding a tool to detect incitement. Incitement applies three rhetorical strategies focused, respectively, on the target group's identity, their imputed misdeeds, and an exhortation to act against them. These strategies carry distinct textual signatures. Our tool (with additional verification) reveals that inciting sentences appear in non-Islamophobic posts and in other contexts (e.g., posts against certain gender identities), indicating the generality of incitement as a concept. Incitement reflects a wide swath of malicious speech omitted from traditional analyses. Understanding and identifying incitement can facilitate online moderation and thus concomitantly reduce harm in real life.

Online storytelling is an essential channel for users to express their experiences and opinions and therefore influence online society. Yet, despite its importance, approaches to story understanding on social media have not advanced sufficiently. Specifically, current approaches can carry out high-level, aggregate analyses on a corpus of stories but do not provide a way of understanding individual stories. We consider a major source of social behavior, app reviews, which surprisingly are rarely studied in social media research. We observe that app reviews often contain one or more stories. These stories exhibit complex structures and are often presented via events that are not placed in their natural order. Accordingly, we introduce Saga, an approach that carries out a deep analysis of the event-based structures and substructures arising in app reviews. Saga’s main contribution is how it goes beyond the state of the art in identifying fine-grained story (sub)structures. In addition, it supports querying stories (and their containing app reviews) according to these (sub)structures. These specific (sub)structures help identify stories that serve different information goals. Saga is evaluated both computationally on a publicly available data source and via a human study validating the helpfulness in addressing various information goals.

We propose Exanna, a framework to realize agents that incorporate values in decision making. An Exanna agent considers the values of itself and others when providing rationales for its actions and evaluating the rationales provided by others. Via multiagent simulation, we demonstrate that considering values in decision making and producing rationales, especially for norm-deviating actions, leads to (1) higher conflict resolution, (2) better social experience, (3) higher privacy, and (4) higher flexibility.

Moral reasoning reflects how people acquire and apply moral rules in particular situations. With social interactions increasingly happening online, social media provides an unprecedented opportunity to assess in-the-wild moral reasoning. We investigate the commonsense aspects of morality empirically using data from a Reddit subcommunity (i.e., a subreddit), r/AmITheAsshole, where an author describes their behavior in a situation and seeks comments about whether that behavior was appropriate. A commenter judges and provides reasons for whether an author or others’ behaviors were wrong. We focus on the novel problem of understanding the moral reasoning implicit in user comments about the propriety of an author’s behavior. Specifically, we explore associations between the common elements of the indicated rationale and the extractable social factors. Our results suggest that a moral response depends on the author’s gender and the topic of a post. Typical situations and behaviors include expressing anger emotion and using sensible words (e.g., f-ck, hell, and damn) in work-related situations. Moreover, we find that commonly expressed reasons also depend on commenters’ interests.

Political news is often slanted toward its publisher’s ideology and seeks to influence readers by focusing on selected aspects of contentious social and political issues. We investigate political slants in news and their influence on readers by analyzing election-related news and readers’ reactions to the news on Twitter. To this end, we collected election-related news from six major U.S. news publishers who covered the 2020 U.S. presidential election. We computed each publisher’s political slant based on the favorability of its news toward the two major parties’ presidential candidates. We find that the election-related news coverage shows signs of political slant both in news headlines and on Twitter. The difference in news coverage of the two candidates between the left-leaning (left) and right-leaning (right) news publishers is statistically significant. The effect size is larger for the news on Twitter than for headlines. And, news on Twitter expresses stronger sentiments than the headlines. We identify moral foundations in readers’ reactions to the news on Twitter based on the moral foundation theory. Moral foundations in readers’ reactions toleftandrightdiffer statistically significantly, though the effects are small. Further, these shifts in moral foundations differ across social and political issues. User engagement on Twitter is higher forrightthan forleft. We posit that an improved understanding of slant and influence can enable better ways to combat online political polarization.

Psychological studies on morality have proposed underlying linguistic and semantic factors. However, current empirical studies often lack the nuances and complexity of real life. This article examines how well the findings of prior studies generalize to a corpus of over 30000 narratives of tense social situations submitted to a popular social media forum. A poster describes interpersonal moral situations or misgivings; other users judge from the post whether the poster (protagonist) or an opposing side (antagonist) is morally culpable. We extend and apply natural language processing (NLP) techniques to understand the effects of descriptions of the people involved in these posts. We conduct extensive experiments to investigate the effect sizes of features to understand how they affect the assignment of blame on social media. Our findings show that aggregating psychological theories enables understanding real-life moral situations. We also find evidence of bias blame assignment on social media, such as that males are likelier to receive blame no matter whether they are protagonists or antagonists.

This work aims to develop a surveillance mission system using unmanned aerial vehicles (UAVs) or drones when Denial-of-Service (DoS) attacks are present to disrupt normal operations for mission systems. In particular, we introduce the concept of cyber deception using honey drones (HDs) to protect the mission system from DoS attacks. HDs exhibit fake vulnerabilities and employ stronger signal strengths to lure DoS attacks, unlike the legitimate drones called mission drones (MDs) deployed for mission execution. This research formulates an optimization problem to identify an optimal set of signal strengths of HDs and MDs to best prevent the system from DoS attacks while maximizing mission performance under the resource constraints of UAVs. To solve this optimization problem, we leverage deep reinforcement learning (DRL) to achieve these multiple objectives of the mission system concerning system security and performance. Particularly, for efficient and effective parallel processing in DRL, we utilize a DRL algorithm called the Asynchronous Advantage Actor-Critic (A3C) algorithm to model attack-defense interactions. We employ a physical engine-based simulation testbed to consider realistic scenarios and demonstrate valid findings from the realistic testbed. The extensive experiments proved that our HD-based approach could achieve up to a 32% increase in mission completion, a 20% reduction in energy consumption, and a 62% decrease in attack success rates compared to existing defense strategies.

With the emergence of new generation of digital technologies, e.g., artificial intelligence, blockchain, cloud computing, big data, edge computing, 5G/6G, VR/AR/MR, and the Internet of Things, an exciting era of metaverse is coming. Interacted and linked with the physical world, metaverse offers a platform of a new social ecosystem, dealing with digital twins and empowering virtual-reality symbiosis. In metaverse, social activities and business processes are performed based on the sequences of workflow or service processes. Bridging both the virtual space and the real world, such metaverse services are more complicated and present many new challenges and research topics. In this paper, the concept and characteristics of metaverse services are presented, the key technologies and typical use cases are reviewed, and the future challenges and opportunities of metaverse services are also discussed.

Understanding an online argumentative discussion is essential for understanding users' opinions on a topic and their underlying reasoning. A key challenge in determining completeness and persuasiveness of argumentative discussions is to assess how arguments under a topic are connected in a logical and coherent manner. Online argumentative discussions, in contrast to essays or face-to-face communication, challenge techniques for judging argument relevance because online discussions involve multiple participants and often exhibit incoherence in reasoning and inconsistencies in writing style. We define relevance as the logical and topical connections between small texts representing argument fragments in online discussions. We provide a corpus comprising pairs of sentences, labeled with argumentative relevance between the sentences in each pair. We propose a computational approach relying on content reduction and a Siamese neural network architecture for modeling argumentative connections and determining argumentative relevance between texts. Experimental results indicate that our approach is effective in measuring relevance between arguments, and outperforms strong and well-adopted baselines. Further analysis demonstrates the benefit of using our argumentative relevance encoding on a downstream task, predicting how impactful an online comment is to certain topic, comparing to encoding that does not consider logical connection.

Conversations among online users sometimes derail, i.e., break down into personal attacks. Derailment interferes with the healthy growth of communities in cyberspace. The ability to predict whether an ongoing conversation will derail could provide valuable advance, even real-time, insight to both interlocutors and moderators. Prior approaches predict conversation derailment retrospectively without the ability to forestall the derailment proactively. Some existing works attempt to make dynamic predictions as the conversation develops, but fail to incorporate multisource information, such as conversational structure and distance to derailment. We propose a hierarchical transformer-based framework that combines utterance-level and conversation-level information to capture fine-grained contextual semantics. We propose a domain-adaptive pretraining objective to unite conversational structure information and a multitask learning scheme to leverage the distance from each utterance to derailment. An evaluation of our framework on two conversation derailment datasets shows an improvement in F1 score for the prediction of derailment. These results demonstrate the effectiveness of incorporating multisource information for predicting the derailment of a conversation.

The power of norms in both human societies and sociotechnical systems arises from the facts that (1) societal norms, including laws and policies, characterize acceptable behavior in high-level terms and (2) they are not hard controls and can be deviated from. Thus, the design of responsibly autonomous agents faces an essential tension: these agents must both (1) respect applicable norms and (2) deviate from those norms when blindly following them may lead to diminished outcomes. We propose a conceptual foundation for norm deviation. As a guiding framework, we adopt Habermas's theory of communicative action comprising objective, subjective, and practical validity claims regarding the suitability of deviation. Our analysis thus goes beyond previous studies of norm deviation and yields reasoning guidelines uniting norms and values by which to develop responsible agents.

Unmanned Aerial Vehicles (UAVs) have been used for surveillance operations, search and rescue missions, and delivery services. Given their importance and versatility, they naturally become targets for cyberattacks. Denial-of-Service (DoS) attacks are commonly considered to exhaust their resources or crash UAVs (or drones). This work proposes a unique proactive defense using honey drones (HD) for UAVs during surveillance operations. These HDs use lightweight virtual machines to lure and redirect potential DoS attacks. Both the choice of target by the attacker and the HD's deceptive tactics are influenced by the strength of the radio signal. However, a critical trade-off exists in that stronger signals can deplete battery life, while weaker signals can negatively affect the connectivity of a drone fleet network. To address this, we formulate an optimization problem to select the best strategies for an attacker or defender in selecting their signal strength level. We propose a novel HD-based defense to identify the optimal setting using deep reinforcement learning (DRL) or game theory and compare their performance with that of non-HD-based methods, such as Intrusion Detection Systems and ContainerDrone. Our experiments demonstrate the unique benefits and superior efficacy of each HD-based defense across various attack scenarios.

Artificial intelligence raises important social and ethical concerns, especially about accountability, autonomy, dignity, and justice. We focus on the specific concerns arising from how the emerging autonomous vehicle (AV) technology will affect professional drivers. We posit that we must engage with stakeholders to understand the implications of a technology that will affect the stakeholders’ lives, livelihoods, or wellbeing. We conducted nine in-depth interviews with professional drivers, with at least two years of driving experience, to understand the ethical and societal challenges from the drivers’ perspective during the predicted widespread implementation of AVs. Safety was the most commonly discussed issue, which was mentioned by all drivers (17 times by truck drivers and 18 times by Uber/Lyft drivers). We find that although drivers agree that AVs will significantly impact future transportation systems, they are apprehensive about the prospects of reskilling for other jobs and want their employers to be straightforward in how the introduction of AVs will affect them. Additionally, drivers dismiss the suggestions that driving jobs are unsatisfying and potentially unhealthy and thus should be eliminated. These findings should be considered seriously in decision-making about questions of socioeconomic justice, and could be useful to policymakers as they shape relevant regulations.

Existing defensive deception (DD) approaches apply game theory, assuming that an attacker and defender play the same, full game with all possible strategies. However, in deceptive settings, players may have different beliefs about the game itself. Such structural uncertainty is not naturally handled in traditional game theory. In this work, we formulate an attackdefense hypergame where multiple advanced persistent threat (APT) attackers and a single defender play a repeated game with different perceptions. The hypergame model systematically evaluates how various DD strategies can defend proactively against APT attacks. We present an adaptive method to select an optimal defense strategy using hypergame theory for strategic defense as well as machine learning for adaptive defense. We conducted in-depth experiments to analyze the performance of the eight schemes including ours, baselines, and existing counterparts. We found the DD strategies showed their highest advantages when the hypergame and machine learning are considered in terms of reduced false positives and negatives of the NIDS, system lifetime, and players’ perceived uncertainties and utilities. We also analyze the Hyper Nash Equilibrium of given hypergames and discuss the key findings and insights behind them.

This paper focuses on a dynamic aspect of responsible autonomy, namely, to make intelligent agents be responsible at run time. That is, it considers settings where decision making by agents impinges upon the outcomes perceived by other agents. For an agent to act responsibly, it must accommodate the desires and other attitudes of its users and, through other agents, of their users. The contribution of this paper is twofold. First, it provides a conceptual analysis of consent, its benefits and misuses, and how understanding consent can help achieve responsible autonomy. Second, it outlines challenges for AI (in particular, for agents and multiagent systems) that merit investigation to form as a basis for modeling consent in multiagent systems and applying consent to achieve responsible autonomy.

Malicious reconnaissance is a critical step for attackers to collect sufficient network knowledge and choose valuable targets for intrusion. Defensive deception (DD) is an essential strategy against threats by misleading attackers' observations and beliefs. Honeypots are widely used for cyber deception that aims to confuse attackers and waste their resources and efforts. Defenders may use low-interaction honeypots or high-interaction honeypots. In this paper, we consider a hybrid honeypot system that balances the use of the two levels of honeypot complexity, where high-interaction honeypots are more capable of deceiving skilled attackers than low-interaction honeypots. We present a two-player hypergame model that characterizes how a defender should deploy low and high-interaction honeypots to defend the network against malicious reconnaissance activities. We model the tradeoff of each player and characterize their best strategies within a hypergame framework that considers the imperfect knowledge of each player toward their opponent. Finally, our numerical results validate the effectiveness of the proposed honeypot system.

Norms help regulate a society. Norms may be explicit (represented in structured form) or implicit. We address the emergence of explicit norms by developing agents who provide and reason about explanations for norm violations in deciding sanctions and identifying alternative norms. These agents use a genetic algorithm to produce norms and reinforcement learning to learn the values of these norms. We find that applying explanations leads to norms that provide better cohesion and goal satisfaction for the agents. Our results are stable for societies with differing attitudes of generosity.

Abstract We conceptualize a decentralized software application as one constituted from autonomous agents that communicate via asynchronous messaging. Modern software paradigms such as microservices and settings such as the Internet of Things evidence a growing interest in decentralized applications. Constructing a decentralized application involves designing agents as independent local computations that coordinate successfully to realize the application’s requirements. Moreover, a decentralized application is susceptible to faults manifested as message loss, delay, and reordering. We contribute Mandrake , a programming model for decentralized applications that tackles these challenges without relying on infrastructure guarantees. Specifically, we adopt the construct of an information protocol that specifies messaging between agents purely in causal terms and can be correctly enacted by agents in a shared-nothing environment over nothing more than unreliable, unordered transport. Mandrake facilitates (1) implementing protocol-compliant agents by introducing a programming model; (2) transforming protocols into fault-tolerant ones with simple annotations; and (3) a declarative policy language that makes it easy to implement fault-tolerance in agents based on the capabilities in protocols. Mandrake’s significance lies in demonstrating a straightforward approach for constructing decentralized applications without relying on coordination mechanisms in the infrastructure, thus achieving some of the goals of the founders of networked computing from the 1970s.

Multi-agent systems provide a basis for developing systems of autonomous entities and thus find application in a variety of domains. We consider a setting where not only the member agents are adaptive but also the multi-agent system viewed as an entity in its own right is adaptive. Specifically, the social structure of a multi-agent system can be reflected in the social norms among its members. It is well recognized that the norms that arise in society are not always beneficial to its members. We focus on prosocial norms, which help achieve positive outcomes for society and often provide guidance to agents to act in a manner that takes into account the welfare of others. Specifically, we propose Cha, a framework for the emergence of prosocial norms. Unlike previous norm emergence approaches, Cha supports continual change to a system (agents may enter and leave) and dynamism (norms may change when the environment changes). Importantly, Cha agents incorporate prosocial decision-making based on inequity aversion theory, reflecting an intuition of guilt arising from being antisocial. In this manner, Cha brings together two important themes in prosociality: decision-making by individuals and fairness of system-level outcomes. We demonstrate via simulation that Cha can improve aggregate societal gains and fairness of outcomes.

Defensive deception techniques have emerged as a promising proactive defense mechanism to mislead an attacker and thereby achieve attack failure. However, most game-theoretic defensive deception approaches have assumed that players maintain consistent views under uncertainty. They do not consider players’ possible, subjective beliefs formed due to asymmetric information given to them. In this work, we formulate a hypergame between an attacker and a defender where they can interpret the same game differently and accordingly choose their best strategy based on their respective beliefs. This gives a chance for defensive deception strategies to manipulate an attacker’s belief, which is the key to the attacker’s decision-making. We consider advanced persistent threat (APT) attacks, which perform multiple attacks in the stages of the cyber kill chain (CKC) where both the attacker and the defender aim to select optimal strategies based on their beliefs. Through extensive simulation experiments, we demonstrated how effectively the defender can leverage defensive deception techniques while dealing with multi-staged APT attacks in a hypergame in which the imperfect information is reflected based on perceived uncertainty, cost, and expected utilities of both the attacker and defender, the system lifetime (i.e., mean time to security failure), and improved false-positive rates of intrusion detection.

research-article Share on Introduction to the Special Issue on Multiagent Systems and Services in the Internet of Things Authors: Andrei Ciortea University of St. Gallen, Switzerland University of St. Gallen, Switzerland 0000-0003-0721-4135Search about this author , Xiaomin Zhu National University of Defense Technology, China National University of Defense Technology, China 0000-0003-1301-7840Search about this author , Calton Pu Georgia Institute of Technology, USA Georgia Institute of Technology, USA 0000-0002-6616-8987Search about this author , Munindar P. Singh North Carolina State University, USA North Carolina State University, USA 0000-0003-3599-3893Search about this author Authors Info & Claims ACM Transactions on Internet TechnologyVolume 22Issue 4November 2022 Article No.: 99pp 1–3https://doi.org/10.1145/3584744Published:03 March 2023Publication History 0citation0DownloadsMetricsTotal Citations0Total Downloads0Last 12 Months0Last 6 weeks0 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access

The study of autonomous agents has a long history in the Multiagent System and the Semantic Web communities, with applications ranging from automating business processes to personal assistants. More recently, the Web of Things (WoT), which is an extension of the Internet of Things (IoT) with metadata expressed in Web standards, and its community provide further motivation for pushing the autonomous agents research agenda forward. Although representing and reasoning about norms, policies, and preferences is crucial to ensuring that autonomous agents act in a manner that satisfies stakeholder requirements, normative concepts, policies, and preferences have yet to be considered as first-class abstractions in Web-based multiagent systems. Towards this end, this article motivates the need for alignment and joint research across the Multiagent Systems, Semantic Web, and WoT communities, introduces a conceptual framework for governance of autonomous agents on the Web, and identifies several research challenges and opportunities.

We introduce and formalize a concept of a maintenance commitment, a kind of social commitment characterized by states whose truthhood an agent commits to maintain. This concept of maintenance commitments enables us to capture a richer variety of real-world scenarios than possible using achievement commitments with a temporal condition. By developing a rule-based operational semantics, we study the relationship between agents' achievement and maintenance goals, achievement commitments, and maintenance commitments. We motivate a notion of coherence which captures alignment between an agents' achievement and maintenance cognitive and social constructs, and prove that, under specified conditions, the goals and commitments of both rational agents individually and of a multiagent system are coherent.

Historically, enterprise network reconnaissance is an active process, often involving port scanning. However, as routers and switches become more complex, they also become more susceptible to compromise. From this vantage point, an attacker can passively identify high-value hosts such as the workstations of IT administrators, C-suite executives, and finance personnel. The goal of this paper is to develop a technique to deceive and dissuade such adversaries. We propose HoneyRoles, which uses honey connections to build metaphorical haystacks around the network traffic of client hosts belonging to high-value organizational roles. The honey connections also act as network canaries to signal network compromise, thereby dissuading the adversary from acting on information observed in network flows. We design a prototype implementation of HoneyRoles an OpenFlow SDN controller and evaluate its security using the PRISM probabilistic model checker. Our performance evaluation shows that HoneyRoles has a small effect on network request completion time, and security analysis demonstrates that once an alert is raised, HoneyRoles can quickly identify the compromised switch with high probability. In doing so, we show that role-based network deception is a promising approach for defending against adversaries in compromised network devices.

A decentralized application involves multiple autonomous principals, e.g., humans and organizations. Autonomy motivates (i) specifying a decentralized application via a protocol that captures the interactions between the principals, and (ii) a programming model that enables each principal to independently (from other principals) construct its own protocol-compliant agent. An agent encodes its principal's decision making and represents it in the application. We contribute Deserv, the first protocol-based programming model for decentralized applications that is suited to the cloud. Specifically, Deserv demonstrates how to leverage function-as-a-service (FaaS), a popular serverless programming model, to implement agents. A notable feature of Deserv is the use declarative protocols to specify interactions. Declarative protocols support implementing stateful agents in a manner that naturally exploits the concurrency and autoscaling benefits offered by serverless computing.

A flexible communication protocol is necessary to build a decentralized multiagent system whose member agents are not coupled to each other's decision making. Information-based protocol languages capture a protocol in terms of causality and integrity constraints based on the information exchanged by the agents. Thus, they enable highly flexible enactments in which the agents proceed asynchronously and messages may be arbitrarily reordered. However, the existing semantics for such languages can produce a large number of protocol enactments, which makes verification of a protocol property intractable. This paper formulates a protocol semantics declaratively via inference rules that determine when a message emission or reception becomes enabled during an enactment, and its effect on the local state of an agent. The semantics enables heuristics for determining when alternative extensions of a current enactment would be equivalent, thereby helping produce parsimonious models and yielding improved protocol verification methods.

Interaction-Oriented Programming (IOP) refers to multiagent concepts, languages, and programming models for engineering applications that are characterized by interactions between autonomous parties. Such applications arise in domains such as e-commerce, health care, and finance. Owing to the autonomy of the principals involved, such applications are conceptually decentralized. We demonstrate how to specify a decentralized application flexibly and how to engineer correct, fault-tolerant endpoints (agents) for the principals in a straightforward manner. Notably, the entire application is realized as agents communicating over an unordered, unreliable messaging infrastructure (our implementations in fact use UDP). IOP departs from traditional distributed systems approaches that rely on guarantees in the application's communication infrastructure, e.g., for ordering and fault tolerance. Notably, IOP shows how to address application semantics, the holy grail of distributed systems.

Changes in the framing of topical news are known to foreshadow significant public, legislative, and commercial events. Automated detection of framing changes is, therefore, an important problem, which existing research has not considered. Previous approaches are manual surveys that rely on human effort and are consequently limited in scope. This article systematizes the discovery of framing changes through a fully unsupervised computational method that seeks to isolate framing change trends over several years. We demonstrate our approach by isolating framing change periods that correlate with previously known framing changes. We have prepared a new data set, consisting of over 12 000 articles from seven news topics or domains, in which earlier surveys have found framing changes. Finally, our work highlights the predictive utility of framing change detection, by identifying two domains in which framing changes foreshadowed substantial legislative activity, or preceded judicial interest.

Specifying a normative multiagent system (nMAS) is challenging, because different agents often have conflicting requirements. Whereas existing approaches can resolve clear-cut conflicts, tradeoffs might occur in practice among alternative nMAS specifications with no apparent resolution. To produce an nMAS specification that is acceptable to each agent, we model the specification process as a negotiation over a set of norms. We propose an agent-based negotiation framework, where agents’ requirements are represented as values (e.g., patient safety, privacy, and national security), and an agent revises the nMAS specification to promote its values by executing a set of norm revision rules that incorporate ontology-based reasoning. To demonstrate that our framework supports creating a transparent and accountable nMAS specification, we conduct an experiment with human participants who negotiate against our agent. Our findings show that our negotiation agent reaches better agreements (with small p -value and large effect size) faster than a baseline strategy. Moreover, participants perceive that our agent enables more collaborative and transparent negotiations than the baseline (with small p -value and large effect size in particular settings) toward reaching an agreement.

Engineering a decentralized multiagent system (MAS) requires realizing interactions modeled as a communication protocol between autonomous agents. We contribute Clouseau, an approach that takes a commitment-based specification of an interaction and generates a communication protocol amenable to decentralized enactment. We show that the generated protocol is (1) correct—realizes all and only the computations that satisfy the input specification; (2) safe—ensures the agents' local views remain consistent; and (3) live—ensures the agents can proceed to completion.

Commitments and requests are a hallmark of collaborative communication, especially in organizational settings.Identifying specific tasks being committed to or requests from emails and chat messages can enable important downstream tasks, such as producing to-do lists, reminders, and calendar entries.State-of-the-art approaches for task identification rely on large annotated datasets, which are not always available, especially for domain-specific tasks.Accordingly, we propose Liṅ, an unsupervised approach of identifying tasks that leverages dependency parsing and VerbNet.Our evaluations show that Liṅ yields comparable or more accurate results than supervised models on domains with large training sets, and maintains its excellent performance on unseen domains.

A user's review of an app often describes the user's interactions with the app. These interactions, which we interpret as mini stories, are prominent in reviews with negative ratings. In general, a story in an app review would contain at least two types of events: user actions and associated app behaviors. Being able to identify such stories would enable an app's developer in better maintaining and improving the app's functionality and enhancing user experience.

Understanding how people change their views during multiparty argumentative discussions is important in applications that involve human communication, e.g., in social media and education. Existing research focuses on lexical features of individual comments, dynamics of discussions, or the personalities of participants but deemphasizes the cumulative influence of the interplay of comments by different participants on a participant’s mindset. We address the task of predicting the points where a user’s view changes given an entire discussion, thereby tackling the confusion due to multiple plausible alternatives when considering the entirety of a discussion.

Communication protocols are central to engineering decentralized multiagent systems. Modern protocol languages are typically formal and address aspects of decentralization, such as asynchrony. However, modern languages differ in important ways in their basic abstractions and operational assumptions. This diversity makes a comparative evaluation of protocol languages a challenging task. We contribute a rich evaluation of diverse and modern protocol languages. Among the selected languages, Scribble is based on session types; Trace-C and Trace-F on trace expressions; HAPN on hierarchical state machines, and BSPL on information causality. Our contribution is four-fold. One, we contribute important criteria for evaluating protocol languages. Two, for each criterion, we compare the languages on the basis of whether they are able to specify elementary protocols that go to the heart of the criterion. Three, for each language, we map our findings to a canonical architecture style for multiagent systems, highlighting where the languages depart from the architecture. Four, we identify design principles for protocol languages as guidance for future research.

We address the problem of engineering a sociotechnical system (STS) with respect to its stakeholders’ requirements. We motivate a two-tier STS conception composed of a technical tier that provides control mechanisms and describes what actions are allowed by the software components, and a social tier that characterizes the stakeholders’ expectations of each other in terms of norms. We adopt agents as computational entities, each representing a different stakeholder. Unlike previous approaches, our framework, D ESEN , incorporates the social dimension into the formal verification process. Thus, D ESEN supports agents potentially violating applicable norms—a consequence of their autonomy. In addition to requirements verification, D ESEN supports refinement of STS specifications via design patterns to meet stated requirements. We evaluate D ESEN at three levels. We illustrate how D ESEN carries out refinement via the application of patterns on a hospital emergency scenario. We show via a human-subject study that a design process based on our patterns is helpful for participants who are inexperienced in conceptual modeling and norms. We provide an agent-based environment to simulate the hospital emergency scenario to compare STS specifications (including participant solutions from the human-subject study) with metrics indicating social welfare and norm compliance, and other domain dependent metrics.

Zhe Zhang, Munindar Singh. Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). 2019.

We study how emotions influence norm outcomes in decision-making contexts. Following the literature, we provide baseline Dynamic Bayesian models to capture an agent's two perspectives on a directed norm. Unlike the literature, these models are holistic in that they incorporate not only norm outcomes and emotions but also trust and goals. We obtain data from an empirical study involving game play with respect to the above variables. We provide a step-wise process to discover two new Dynamic Bayesian models based on maximizing log-likelihood scores with respect to the data. We compare the new models with the baseline models to discover new insights into the relevant relationships. Our empirically supported models are thus holistic and characterize how emotions influence norm outcomes better than previous approaches.

Commitments capture how an agent relates to another agent, whereas goals describe states of the world that an agent is motivated to bring about. Commitments are elements of the social state of a set of agents whereas goals are elements of the private states of individual agents. It makes intuitive sense that goals and commitments are understood as being complementary to each other. More importantly, an agent's goals and commitments ought to be coherent, in the sense that an agent's goals would lead it to adopt or modify relevant commitments and an agent's commitments would lead it to adopt or modify relevant goals. However, despite the intuitive naturalness of the above connections, they have not been adequately studied in a formal framework. This article provides a combined operational semantics for goals and commitments by relating their respective life cycles as a basis for how these concepts (1) cohere for an individual agent and (2) engender cooperation among agents. Our semantics yields important desirable properties of convergence of the configurations of cooperating agents, thereby delineating some theoretically well-founded yet practical modes of cooperation in a multiagent system.

Advances in AI techniques and computing platforms have triggered a lively and expanding discourse on ethical decision making by autonomous agents. Much recent work in AI concentrates on the challenges of moral decision making from a decision-theoretic perspective, and especially the representation of various ethical dilemmas. Such approaches may be useful but in general are not productive because moral decision making is as context-driven as other forms of decision making, if not more. In contrast, we consider ethics not from the standpoint of an individual agent but of the wider sociotechnical systems (STS) in which the agent operates. Our contribution in this paper is the conception of ethical STS founded on governance that takes into account stakeholder values, normative constraints on agents, and outcomes (states of the STS) that obtain due to actions taken by agents. An important element of our conception is accountability, which is necessary for adequate consideration of outcomes that prima facie appear ethical or unethical. Focusing on STS provides a basis for tackling the difficult problems of ethics because the norms of an STS give an operational basis for agent decision making.

We propose Limbic, an unsupervised probabilistic model that addresses the problem of discovering aspects and sentiments and associating them with authors of opinionated texts.Limbic combines three ideas, incorporating authors, discourse relations, and word embeddings.For discourse relations, Limbic adopts a generative process regularized by a Markov Random Field.To promote words with high semantic similarity into the same topic, Limbic captures semantic regularities from word embeddings via a generalized Pólya Urn process.We demonstrate that Limbic (1) discovers aspects associated with sentiments with high lexical diversity; (2) outperforms stateof-the-art models by a substantial margin in topic cohesion and sentiment classification.

Norms describe the social architecture of a society and govern the interactions of its member agents. It may be appropriate for an agent to deviate from a norm; the deviation being indicative of a specialized norm applying under a specific context. Existing approaches for norm emergence assume simplified interactions wherein deviations are negatively sanctioned. We investigate via simulation the benefits of enriched interactions where deviating agents share selected elements of their contexts. We find that as a result (1) the norms are learned better with fewer sanctions, indicating improved social cohesion; and (2) the agents are better able to satisfy their individual goals. These results are robust under societies of varying sizes and characteristics reflecting pragmatic, considerate, and selfish agents.

This article addresses the challenge of planning coordinated activities for a set of autonomous agents, who coordinate according to social commitments among themselves. We develop a multi-agent plan in the form of a commitment protocol that allows the agents to coordinate in a flexible manner, retaining their autonomy in terms of the goals they adopt so long as their actions adhere to the commitments they have made. We consider an expressive first-order setting with probabilistic uncertainty over action outcomes. We contribute the first practical means to derive protocol enactments which maximise expected utility from the point of view of one agent. Our work makes two main contributions. First, we show how Hierarchical Task Network planning can be used to enact a previous semantics for commitment and goal alignment, and we extend that semantics in order to enact first-order commitment protocols. Second, supposing a cooperative setting, we introduce uncertainty in order to capture the reality that an agent does not know for certain that its partners will successfully act on their part of the commitment protocol. Altogether, we employ hierarchical planning techniques to check whether a commitment protocol can be enacted efficiently, and generate protocol enactments under a variety of conditions. The resulting protocol enactments can be optimised either for the expected reward or the probability of a successful execution of the protocol. We illustrate our approach on a real-world healthcare scenario.

relies in large part on the efforts of peer reviewers.Since the review assignments for any article or issue are kept anonymous to promote impartiality, we acknowledge their efforts en masse here.This list includes all those who submitted at least one review from

We propose Kont, a formal framework for comparing normative multiagent systems (nMASs) by computing tradeoffs among liveness (something good happens) and safety (nothing bad happens). Safety-focused nMASs restrict agents' actions to avoid undesired enactments. However, such restrictions hinder liveness, particularly in situations such as medical emergencies. We formalize tradeoffs using norms, and develop an approach for understanding to what extent an nMAS promotes liveness or safety. We propose patterns to guide the design of an nMAS with respect to liveness and safety, and prove their correctness. We further quantify liveness and safety using heuristic metrics for an emergency healthcare application. We show that the results of the application corroborate our theoretical development.

Traditionally, distributed computing concentrates on computation understood at the level of information exchange and sets aside human and organizational concerns as largely to be handled in an ad hoc manner. Increasingly, however, distributed applications involve multiple loci of autonomy. Research in multiagent systems (MAS) addresses autonomy by drawing on concepts and techniques from artificial intelligence. However, MAS research generally lacks an adequate understanding of modern distributed computing. In this Blue Sky paper, we envision decentralized multiagent systems as a way to place decentralized intelligence in distributed computing, specifically, by supporting computation at the level of social meanings. We motivate our proposals for research in the context of the Internet of Things (IoT), which has become a major thrust in distributed computing. From the IoT's representative applications, we abstract out the major challenges of relevance to decentralized intelligence. These include the heterogeneity of IoT components; asynchronous and delay-tolerant communication and decoupled enactment; and multiple stakeholders with subtle requirements for governance, incorporating resource usage, cooperation, and privacy. The IoT yields high-impact problems that require solutions that go beyond traditional ways of thinking. We conclude with highlights of some possible research directions in decentralized MAS, including programming models; interaction-oriented software engineering; and what we term enlightened governance.

Demand-side management (DSM) is an important theme in studies of the Smart Grid and offers the possibility of leveling power consumption with its attendant benefits of reducing capital expenses. This paper develops an algorithmic mechanism that reduces peak total power consumption and encourages prosocial behavior, such as expressing flexibility in one's power consumption and reporting preferences truthfully. The objective is to provide a tractable, budget-balanced mechanism that promotes truth-telling from households. The resulting mechanism is theoretically and empirically proven to be ex ante budget-balanced, weakly Pareto-efficient, and weakly Bayesian incentive-compatible. A simulation study verifies that the mechanism could largely reduce the computational complexity that the optimal allocation requires, while maintaining approximately the same performance. A user study with 20 subjects further shows the effectiveness of the mechanism in preventing participants from defecting and incentivizing them to reveal flexible preferences.

Policy design is an important part of software development. As security breaches increase in variety, designing a security policy that addresses all potential breaches becomes a nontrivial task. A complete security policy would specify rules to prevent breaches. Systematically determining which, if any, policy clause has been violated by a reported breach is a means for identifying gaps in a policy. Our research goal is to help analysts measure the gaps between security policies and reported breaches by developing a systematic process based on semantic reasoning. We propose SEMAVER, a framework for determining coverage of breaches by policies via comparison of individual policy clauses and breach descriptions. We represent a security policy as a set of norms. Norms (commitments, authorizations, and prohibitions) describe expected behaviors of users, and formalize who is accountable to whom and for what. A breach corresponds to a norm violation. We develop a semantic similarity metric for pairwise comparison between the norm that represents a policy clause and the norm that has been violated by a reported breach. We use the US Health Insurance Portability and Accountability Act (HIPAA) as a case study. Our investigation of a subset of the breaches reported by the US Department of Health and Human Services (HHS) reveals the gaps between HIPAA and reported breaches, leading to a coverage of 65%. Additionally, our classification of the 1,577 HHS breaches shows that 44% of the breaches are accidental misuses and 56% are malicious misuses. We find that HIPAA's gaps regarding accidental misuses are significantly larger than its gaps regarding malicious misuses.

The notion of commitment is widely studied as a high-level abstraction for modeling multiagent interaction. An important challenge is supporting flexible decentralized enactments of commitment specifications. In this paper, we combine recent advances on specifying commitments and information protocols. Specifically, we contribute Tosca, a technique for automatically synthesizing information protocols from commitment specifications. Our main result is that the synthesized protocols support commitment alignment, which is the idea that agents must make compatible inferences about their commitments despite decentralization.

Crowd RE is an emerging avenue for engaging the general public or the so called crowd in variety of requirements engineering tasks. Crowd RE scales RE by involving, potentially, millions of users. Although humans are at the center of Crowd RE, automated techniques are necessary (1) to derive useful insights from large amounts of raw data the crowd can produce; and (2) to drive the Crowd RE process, itself, by facilitating novel workflows combining crowd and machine intelligence.To facilitate automated techniques for Crowd RE, first, we showcase a crowd-acquired dataset, consisting of requirements and their ratings on multiple dimensions for the smart homes application domain. Our dataset is unique in that it contains not only requirements, but also the characteristics of the crowd workers who produced those requirements including their demographics, personality traits, and creative potential. Understanding the crowd characteristics is essential to developing effective Crowd RE processes. Second, we outline key challenges involved in automating Crowd RE and describe, how our dataset can serve as a foundation for developing such automated techniques.

Existing approaches to compute team process measures are primarily based on survey ratings, semantic classification of communications, and social network analyses. Although existing approaches reveal important information about team performance, they face specific limitations. Survey methodologies are in general unreliable, biased, and not dynamic; communication classifications are often a-theoretical; and social network analytics ignore the meanings of messages. Accordingly, we develop a better-defined formal empirical approach for computing team process measures. Our contribution builds on existing work in semantic classification of messages in broadcast communications and proposes a general set of meanings of messages for team processes. Using the meanings of messages, we propose formal approaches to compute team process measures. We evaluate these measures using a military data set and find the following. First, our text mining approach to infer meanings of messages significantly improves over the bag of words approach and yields macroaverage and microaverage F-measures of 70% and 80%, respectively. Second, compared with baseline measures such as degree centrality, cognitive processes remain significantly stable with time, whereas measures such as affective process significantly increase with time.

Social network services (SNSs) enable users to conveniently share personal information. Often, the information shared concerns other people, especially other members of the SNS. In such situations, two or more people can have conflicting privacy preferences; thus, an appropriate sharing policy may not be apparent. We identify such situations as multiuser privacy scenarios . Current approaches propose finding a sharing policy through preference aggregation. However, studies suggest that users feel more confident in their decisions regarding sharing when they know the reasons behind each other’s preferences. The goals of this paper are (1) understanding how people decide the appropriate sharing policy in multiuser scenarios where arguments are employed, and (2) developing a computational model to predict an appropriate sharing policy for a given scenario. We report on a study that involved a survey of 988 Amazon Mechanical Turk (MTurk) users about a variety of multiuser scenarios and the optimal sharing policy for each scenario. Our evaluation of the participants’ responses reveals that contextual factors, user preferences, and arguments influence the optimal sharing policy in a multiuser scenario. We develop and evaluate an inference model that predicts the optimal sharing policy given the three types of features. We analyze the predictions of our inference model to uncover potential scenario types that lead to incorrect predictions, and to enhance our understanding of when multiuser scenarios are more or less prone to dispute.

TOIT's success in maintaining its standards of quality and timeliness relies in large part on the efforts of peer reviewers.Since the review assignments for any article or issue are kept anonymous to promote impartiality, we acknowledge their efforts en masse here.This list includes all those who submitted at least one review over the two-year period from January 1, 2015 to December 31, 2016.

User location is crucial in understanding the dynamics of user activities, especially in relating their online and offline aspects. However, users' social media activities, such as tweets sent, do not always reveal their location. We consider the problem of estimating geo-tags for tweets and develop a comprehensive approach that incorporates textual content, the user's personalized behavior, and the user's social relationships. Our approach, Percimo, considers the two major kinds of communal attachment, which have distinct computational ramifications. We evaluate Percimo via three geo-social graphs based on the mutual-follow relationships of Twitter users, their geographical distance (computed from their geotagged tweets), and their preferences for location categories (collected from Foursquare). We find that Percimo yields a smaller prediction error than the two state-of-the-art approaches we compare with.

Context: The security goals of a software system provide a foundation for security requirements engineering. Identifying security goals is a process of iteration and refinement, leveraging the knowledge and expertise of the analyst to secure not only the core functionality but the security mechanisms as well. Moreover, a comprehensive security plan should include goals for not only preventing a breach, but also for detecting and appropriately responding in case a breach does occur. Goal: The objective of this research is to support analysts in security requirements engineering by providing a framework that supports a systematic and comprehensive discovery of security goals for a software system. Method: We develop a framework, Discovering Goals for Security (DIGS), that models the key entities in information security, including assets and security goals. We systematically develop a set of security goal patterns that capture multiple dimensions of security for assets. DIGS explicitly captures the relations and assumptions that underlie security goals to elicit implied goals. We map the goal patterns to NIST controls to help in operationalizing the goals. We evaluate DIGS via a controlled experiment where 28 participants analyzed systems from mobile banking and human resource management domains. Results: Participants considered security goals commensurate to the knowledge available to them. Although the overall recall was low given the empirical constraints, participants using DIGS identified more implied goals and felt more confident in completing the task. Conclusion: Explicitly providing the additional knowledge for the identification of implied security goals significantly increased the chances of discovering such goals, thereby improving coverage of stakeholder security requirements, even if they are unstated.

Recent data breaches in domains such as healthcare where confidentiality of data is crucial indicate that breaches often originate from misuses, not only from vulnerabilities in the technical (software or hardware) architecture. Current requirements engineering (RE) approaches determine what access control mechanisms are needed to protect sensitive resources (assets). However, current RE approaches inadequately characterize how a user is expected to interact with others in relation to the relevant assets. Consequently, a requirements analyst cannot readily identify misuses by legitimate users. We adopt social norms as a natural, formal means of characterizing user interactions whereby potential misuses map to norm violations. Our research goal is to help analysts identify misuse cases by formal reasoning about norm enactments. We propose Nane, a formal framework for identifying such misuse cases using a semiautomated process. We demonstrate how Nane enables monitoring of potential misuses on a healthcare scenario.

As a creative discipline, Requirements Engineering (RE), lends importance to understanding the associated human factors. Crowd RE, the approach of acquiring requirements from members of the public-the so-called crowd-emphasizes human factors further. We investigate how human personality and creative potential influence a requirement acquisition task. These factors are of specific importance to Crowd RE because (1) crowd workers are generally not trained in RE, and (2) a key motivation in engaging them is to benefit from their creativity. We propose a sequential Crowd RE process, where workers in one stage review requirements from the previous stage and produce additional requirements. To reduce potential information overload in this process, we propose strategies for selecting requirements from one stage to expose to workers in later stages. We conducted a study on Amazon Mechanical Turk tasking 300 workers with creating requirements via the above sequential process (in the domain of smart home applications for concreteness) and tasking an additional 300 workers to rate the creativity (novelty and usefulness) of those requirements. Our findings offer insights on how to carry out Crowd RE effectively. First, we find that a crowd worker's (1) creative potential, and personality traits of openness and conscientiousness have significant positive influence on the novelty of the worker's ideas, and (2) personality traits of agreeableness and conscientiousness have significant positive influence, but extraversion has significant negative influence on the usefulness of the worker's ideas. Second, we find that exposing a worker to ideas from previous workers cognitively stimulates the worker to produce creative ideas. Third, we identify effective strategies based on personality traits and creative potential for selecting a few requirements from a pool of previous requirements to stimulate a worker.

The overarching vision of social machines is to facilitate social processes by having computers provide administrative support. We conceive of a social machine as a sociotechnical system (STS): a software-supported system in which autonomous principals such as humans and organizations interact to exchange information and services. Existing approaches for social machines emphasize the technical aspects and inadequately support the meanings of social processes, leaving them informally realized in human interactions. We posit that a fundamental rethinking is needed to incorporate accountability, essential for addressing the openness of the Web and the autonomy of its principals. We introduce Interaction-Oriented Software Engineering (IOSE) as a paradigm expressly suited to capturing the social basis of STSs. Motivated by promoting openness and autonomy, IOSE focuses not on implementation but on social protocols, specifying how social relationships, characterizing the accountability of the concerned parties, progress as they interact. Motivated by providing computational support, IOSE adopts the accountability representation to capture the meaning of a social machine's states and transitions.

We propose Cupid, a language for specifying commitments that supports their information-centric aspects, and offers crucial benefits. One, Cupid is first-order, enabling a systematic treatment of commitment instances. Two, Cupid supports features needed for real-world scenarios such as deadlines, nested commitments, and complex event expressions for capturing the lifecycle of commitment instances. Three, Cupid maps to relational database queries and thus provides a set-based semantics for retrieving commitment instances in states such as being violated, discharged, and so on. We prove that Cupid queries are safe. Four, to aid commitment modelers, we propose the notion of well-identified commitments, and finitely violable and finitely expirable commitments. We give syntactic restrictions for obtaining such commitments.

In QoS-aware service selection, a service requester seeks to maximize its utility by selecting a service provider that charges the lowest service price while meeting the requester's QoS requirements. In existing selection approaches, a service requester focuses on finding providers based on their QoS and thereby ignores their service prices that could change with their QoS. High QoS may provide more benefits, but may require a high service price. As a result, the highest QoS may not produce the maximum utility. A service requester and candidate service providers have a conflicting interest over service prices. Since a provider would not reveal its minimum acceptabl price, it is important for a requester to predict the minimum price for a service that meets its QoS requirements. We propose a collaborative approach to predicting a provider's minimum price for a desired QoS based on prior usage experience. The experimental results show our approach can find the optimal service providers efficiently and effectively.

This paper reports on an ongoing interdisciplinary study of analytic workflow, describing our preliminary understanding and findings as well as some directions for further investigation and validation. Specifically, we exploit knowledge from organizational psychology to develop a computational organizational model. Our proposed organizational model provides a framework to understand the impact of organizational level variables and worker characteristics on workflow performance, providing a view to create justifiable interventions to improve performance. To evaluate the viability of the model, we develop a multiagent simulation framework and design an experimental study.

A stakeholder's beliefs influence his or her goals. However, a stakeholder's beliefs may not be consistent with the goals of all stakeholders of a system being constructed. Such belief-goal inconsistencies could manifest themselves as conflicting goals of the system to be. We propose Arg-ACH, a novel approach for capturing inconsistencies between stakeholders' goals and beliefs, and resolving goal conflicts. Arg-ACH employs a hybrid of (1) the analysis of competing hypotheses (ACH), a structured analytic technique, for systematically eliciting stakeholders' goals and beliefs, and (2) rational argumentation for determining belief-goal inconsistencies to resolve conflicts. Arg-ACH treats conflicting goals as hypotheses that compete with each other and the winning hypothesis as a goal of the system to be. Arg-ACH systematically captures the trail of a requirements engineer's thought process in resolving conflicts. We evaluated Arg-ACH via a study in which 20 subjects applied Arg-ACH or ACH to resolve goal conflicts in a sociotechnical system concerning national security. We found that Arg-ACH is superior to ACH with respect to completeness and coverage of belief search; length of belief chaining; ease of use; explicitness of the assumptions made; and repeatability of conclusions across subjects. Not surprisingly, Arg-ACH required more time than ACH: although this is justified by improvements in quality, the gap could be reduced through better tooling.

We introduce a high-level abstraction of location called place . A place derives its meaning from a user's physical space, activities, or social context. In this manner, place can facilitate improved user experience compared to the traditional representation of location, which is spatial coordinates. We propose the Platys framework as a way to address the special challenges of place-aware application development. The core of Platys is a middleware that (1) learns a model of places specific to each user via active learning , a machine learning paradigm that seeks to reduce the user-effort required for training the middleware, and (2) exposes the learned user-specific model of places to applications at run time, insulating application developers from dealing with both low-level sensors and user idiosyncrasies in perceiving places. We evaluated Platys via two studies. First, we collected place labels and Android phone sensor readings from 10 users. We applied Platys' active learning approach to learn each user's places and found that Platys (1) requires fewer place labels to learn a user's places with a desired accuracy than do two traditional supervised approaches, and (2) learns places with higher accuracy than two unsupervised approaches. Second, we conducted a developer study to evaluate Platys' efficiency in assisting developers and its effectiveness in enabling usable applications. In this study, 46 developers employed either Platys or the Android location API to develop a place-aware application. Our results indicate that application developers employing Platys, when compared to those employing the Android API, (1) develop a place-aware application faster and perceive reduced difficulty and (2) produce applications that are easier to understand (for developers) and potentially more usable and privacy preserving (for application users).

The sentiment captured in opinionated text provides interesting and valuable information for social media services.However, due to the complexity and diversity of linguistic representations, it is challenging to build a framework that accurately extracts such sentiment.We propose a semi-supervised framework for generating a domain-specific sentiment lexicon and inferring sentiments at the segment level.Our framework can greatly reduce the human effort for building a domainspecific sentiment lexicon with high quality.Specifically, in our evaluation, working with just 20 manually labeled reviews, it generates a domain-specific sentiment lexicon that yields weighted average F-Measure gains of 3%.Our sentiment classification model achieves approximately 1% greater accuracy than a state-of-the-art approach based on elementary discourse units.

How an agent trusts another naturally depends on the outcomes of their interactions. Previous approaches have treated the outcomes in a domain-specific way. We propose an approach relating trust to the domain-independent notion of commitments. We conduct an empirical study to evaluate our approach, in which subjects read emails extracted from the Enron dataset (augmented with some synthetic emails for completeness), and estimate trust between each pair of communicating participants. We propose a probabilistic model for trust based on commitment outcomes and show how to train its parameters for each subject based on the subject's trust assessments. The results are promising, though imperfect. Our main contribution is to launch a research program into computing trust based on a semantically well-founded account of agent interactions.

We address the challenge of requirements engineering for sociotechnical systems, wherein humans and organizations supported by technical artifacts such as software interact with one another. Traditional requirements models emphasize the goals of the stakeholders above their interactions. However, the participants in a sociotechnical system may not adopt the goals of the stakeholders involved in its specification. We motivate, Protos, a requirements engineering approach that gives prominence to the interactions of autonomous parties and specifies a sociotechnical system in terms of its participants' social relationships, specifically, commitments. The participants can adopt any goal they like, a key basis for innovative behavior, as long as they interact according to the commitments. Protos describes an abstract requirements engineering process as a series of refinements that seek to satisfy stakeholder requirements by incrementally expanding a specification set and an assumption set, and reducing requirements until all requirements are accommodated. We demonstrate this process via the London Ambulance System described in the literature.

Link prediction on social media is an important problem for recommendation systems. Understanding the interplay of users' sentiments and social relationships can be potentially valuable. Specifically, we study how to exploit sentiment homophily for link prediction. We evaluate our approach on a dataset gathered fro Twitter that consists of tweets sent in one month during U.S. 2012 political campaign along with the "follows" relationship between users. Our first contribution is defining a set of sentiment-based features that help predict the likelihood of two users becoming "friends" (i.e., mutually mentioning or following each other) based on their sentiments toward topics of mutual interest. Our evaluation in a supervised learning framework demonstrates the benefits of sentiment-based features in link prediction. We find that Adamic-Adar and Euclidean distance measures are the best predictors. Our second contribution is proposing a factor graph model that incorporates a sentiment-based variant of cognitive balance theory. Our evaluation shows that, when tie strength is not too weak, our model is more effective in link prediction than traditional machine learning techniques.

Contracts are legally binding descriptions of business service engagements. In particular, we consider business events as elements of a service engagement. Business events such as purchase, delivery, bill payment, and bank interest accrual not only correspond to essential processes but are also inherently temporally constrained. Identifying and understanding the events and their temporal relationships can help a business partner determine what to deliver and what to expect from others as it participates in the service engagement specified by a contract. However, contracts are expressed in unstructured text and their insights are buried therein. Our contributions are threefold. We develop a novel approach employing a hybrid of surface patterns, parsing, and classification to extract 1) business events and 2) their temporal constraints from contract text. We use topic modeling to 3) automatically organize the event terms into clusters. An evaluation on a real-life contract dataset demonstrates the viability and promise of our hybrid approach, yielding an F-measure of 0.89 in event extraction and 0.90 in temporal constraints extraction. The topic model yields event term clusters with an average match of 85 percent between two independent human annotations and an expert-assigned set of class labels for the clusters.

We address the problem of usage license administration in federated settings. This problem arises whenever organizations, such as educational or research groups or institutions, share resources for business and scientific reasons. In such settings, each user's usage of a licensed resource is typically supported by the user's organization. License administration involves satisfying legal requirements while applying organizational strategies for effective resource usage, and carrying out suitable accounting and audit controls. We propose an approach, Licit, wherein an agent represents each resource sharing site and administers licenses in collaboration with other agents. We show how to represent a variety of usage licenses formally as executable policies and provide a simple information model using which each party can specify both the attributes involved in its licenses and how to resolve them. Our architecture naturally accommodates a variety of site-specific (i.e., custom) strategies for license administration. Licit has been implemented in a popular open source framework for virtual computing, and yields performance results indicating its practical feasibility.

Commitments help model interactions in multiagent systems in a computationally realizable yet high-level manner without compromising the autonomy and heterogeneity of the member agents. Recent work shows how to combine commitments with goals and apply planning methods to enable agents to determine their actions. However, previous approaches to modeling commitments are confined to propositional representations, which limits their applicability in practical cases. We propose a first-order representation and reasoning technique that accommodates templatic commitments and goals that may be applied repeatedly with differing bindings for domain objects. Doing so not only leads to a more perspicuous modeling, but also supports many practical patterns.

Given a process specification, it is a complex task to dynamically select constituent services and compose them in an execution plan to satisfy users' non-functional preferences. Process scheduling approaches assume users can clearly specify their non-functional preferences and there are formulas (e.g., utility functions) to compute process level QoS from the QoS of constituent services and their connections. However, these assumptions are not always true. Users' preferences can be subjective, implicit, vague, mixed and different for various types of processes. Besides, not all the preferences for example easy-to-use can be computed using formulas. We proposed a machine learning based approach to evolutionarily learn user preferences according to their ratings on historical execution plans, recommend existing or generate new execution plans for business processes that adapt to user preferences.

Increasingly, software engineering involvesopensystems consisting of autonomous and heterogeneous participants oragentswho carry out loosely coupled interactions. Accordingly, understanding and specifying communications among agents is a key concern. A focus on ways to formalizemeaningdistinguishes agent communication from traditional distributed computing: meaning provides a basis for flexible interactions and compliance checking. Over the years, a number of approaches have emerged with some essential and some irrelevant distinctions drawn among them. As agent abstractions gain increasing traction in the software engineering of open systems, it is important to resolve the irrelevant and highlight the essential distinctions, so that future research can be focused in the most productive directions. This article is an outcome of extensive discussions among agent communication researchers, aimed at taking stock of the field and at developing, criticizing, and refining their positions on specific approaches and future challenges. This article serves some important purposes, including identifying (1) points of broad consensus; (2) points where substantive differences remain; and (3) interesting directions of future work.

No abstract available.

A (business) protocol describes, in high-level terms, a pattern of communication between two or more participants, specifically via the creation and manipulation of the commitments between them. In this manner, a protocol offers both flexibility and rigor: a participant may communicate in any way it chooses as long as it discharges all of its activated commitments. Protocols thus promise benefits in engineering cross-organizational business processes. However, software engineering using protocols presupposes a formalization of protocols and a notion of therefinementof one protocol by another. Refinement for protocols is both intuitively obvious (e.g.,PayViaCheckis clearly a kind ofPay) and technically nontrivial (e.g., compared toPay,PayViaCheckinvolves different participants exchanging different messages). This article formalizes protocols and their refinement. It develops Proton, an analysis tool for protocol specifications that overlays a model checker to compute whether one protocol refines another with respect to a stated mapping. Proton and its underlying theory are evaluated by formalizing several protocols from the literature and verifying all and only the expected refinements.

We understand a sociotechnical system as a multistakeholder cyber-physical system. We introduce governance as the administration of such a system by the stakeholders themselves. In this regard, governance is a peer-to-peer notion and contrasts with traditional management, which is a top-down hierarchical notion. Traditionally, there is no computational support for governance and it is achieved through out-of-band interactions among system administrators. Not surprisingly, traditional approaches simply do not scale up to large sociotechnical systems. We develop an approach for governance based on a computational representation of norms in organizations. Our approach is motivated by the Ocean Observatory Initiative, a thirty-year $400 million project, which supports a variety of resources dealing with monitoring and studying the world's oceans. These resources include autonomous underwater vehicles, ocean gliders, buoys, and other instrumentation as well as more traditional computational resources. Our approach has the benefit of directly reflecting stakeholder needs and assuring stakeholders of the correctness of the resulting governance decisions while yielding adaptive resource allocation in the face of changes in both stakeholder needs and physical circumstances.

No abstract available.

No abstract available.

Trust is a natural mechanism by which an autonomous party can deal with the inherent uncertainty regarding the behaviors of other parties and the uncertainty in the information it shares with those parties. Trust is thus crucial in any decentralized system. We build on recent efforts to use argumentation to reason about trust. Specifically, we provide a set of schemes, abstract patterns of reasoning that apply in multiple situations, geared toward trust. We describe, in the form of a set of critical questions, the situations in which the schemes may default.

This paper is concerned with the combination of argumentation with the Dempster-Shafer theory of evidence. In particular, we show how logical elements of evidence, associated with numerical degrees of belief, can be combined into arguments.

Service selection, where some of the services are accessed indirectly as constituents of composite services, is difficult for the following reasons: (1) the interpretation of service qualities is subjective; (2) evidence must be combined from multiple sources; (3) service profiles change dynamically; and (4) constituent services may be only partially observable behind composite services. We propose an approach where we map service qualities to a common probabilistic trust metric. Whereas current trust approaches estimate the trustworthiness of a composite service based on a fully observable and static setting, we propose a statistical approach built on expectation maximized over a finite mixture model. Our experiments show that our approach can dynamically punish or reward the constituents of composite services while making only partial observations.

Nodes that are part of a multihop wireless network, typically deployed in mission critical settings, are expected to perform specific functions. Establishing a notion of reliability of the nodes with respect to each function (referred to as functional reliability or FR) is essential for efficient operations and management of the network. This is typically assessed based on evidence collected by nodes with regards to other nodes in the network. However, such evidence is often affected by factors such as channel induced effects and interference. In multihop contexts, unreliable intermediary relays may also influence evidence. We design a framework for collaborative assessment of the FR of nodes, with respect to different types of functions; our framework accounts for the above factors that influence evidence collection. Each node (say Chloe) in the network derives the FR of other nodes (say Jack) based on two types of evidence: (i) direct evidence, based on her direct transactions with each such node and (ii) indirect evidence, based on feedback received regarding Jack from others. Our framework is generic and is applicable in a variety of contexts. We also design a module that drastically reduces the overhead incurred in the propagation of indirect evidence at the expense of slightly increased uncertainty in the assessed FR values. We implement our framework on an indoor/outdoor wireless testbed. We show that with our framework, each node is able to determine the FR for every other node in the network with high accuracy. Our indirect evidence propagation module decreases the overhead by 37% compared to a simple flooding based evidence propagation, while the accuracy of the FR computations is decreased only by 8%. Finally, we examine the effect of different routing protocols on the accuracy of the assessed values.

A contract is a legally binding agreement between real-world business entities whom we treat as providing services to one another. We focus on business rather than technical services. We think of a business contract as specifying the functional and nonfunctional behaviors of and interactions among the services. In current practice, contracts are produced as text documents. Thus the relevant service capabilities, requirements, qualities, and risks are hidden and difficult to access and reason about. We describe a simple but effective unsupervised information extraction approach and tool, Contract Miner, for discovering service exceptions at the phrase level from a large contract repository. Our approach involves preprocessing followed by an application of linguistic patterns and parsing to extract the service exception phrases. Identifying such (noun) phrases can help build service exception vocabularies that support the development of a taxonomy of business terms, and also facilitate modeling and analyzing service engagements. A lightweight online tool that comes with Contract Miner highlights the relevant text in service contracts and thereby assists users in reviewing contracts. Contract Miner produces promising results in terms of precision and recall when evaluated over a corpus of manually annotated contracts.

Cross-organizational business processes are routine in today's economy. Of necessity, enterprises conduct their business in cooperation to create products and services for the marketplace. Thus business processes inherently involve autonomous partners with heterogeneous software designs and implementations. Therefore, it would be natural to model such processes via high-level abstractions that reflect the contractual relationships among the business partners. Yet, in today's IT practice, cross-organizational processes are modeled at a low level of abstraction in terms of the control and data flow among the participants. This paper makes the following contributions. First, it proposes a simple, yet expressive, declarative way to specify business models at a high level based on the notion of commitments. Second, it shows how such a high-level model maps to a conventional operational model. Third, it provides a basis for verifying the correctness of the operational representations with respect to the declarative business model using existing temporal model checking tools. This paper validates the above claims using the well-known Quote To Cash business process, e.g., as supported by vendors such as SAP and applied in large enterprises. In this manner, this paper helps bridge the gap between high-level business models and their IT realizations.

Local State Transfer (LoST) is a simple, declarative approach for enacting communication protocols. LoST is perfectly distributed and relies only upon the local knowledge of each business partner. It involves a novel treatment of the information bases of protocols, especially in terms of how their parameters are specified. As a result, LoST can capture subtle patterns of interaction that more complex approaches cannot handle well. Further, LoST lends itself to implementations that are robust against unordered and lossy message transmission.

Leading agent-based trust models address two important needs. First, they show how an agent may estimate the trustworthiness of another agent based on prior interactions. Second, they show how agents may share their knowledge in order to cooperatively assess the trustworthiness of others. However, in real-life settings, information relevant to trust is usually obtained piecemeal, not all at once. Unfortunately, the problem of maintaining trust has drawn little attention. Existing approaches handle trust updates in a heuristic, not a principled, manner. This paper builds on a formal model that considers probability and certainty as two dimensions of trust. It proposes a mechanism using which an agent can update the amount of trust it places in other agents on an ongoing basis. This paper shows via simulation that the proposed approach (a) provides accurate estimates of the trustworthiness of agents that change behavior frequently; and (b) captures the dynamic behavior of the agents. This paper includes an evaluation based on a real dataset drawn from Amazon Marketplace, a leading e-commerce site.

We consider Service-Oriented Computing (SOC) environments. Such environments are populated with services that stand proxy for a variety of information resources. A fundamental challenge in SOC is to select and compose services, to support specified user needs directly or by providing additional services. Existing approaches for service selection either fail to capture the dynamic relationships between services or assume that the environment is fully observable. In practical situations, however, consumers are often not aware of how the services are implemented. We propose two distributed trust-aware service selection approaches: one based on Bayesian networks and the other on a beta-mixture model. We experimentally validate our approach through a simulation study. Our results show that both approaches accurately punish and reward services in terms of the qualities they offer, and further that the approaches are effective despite incomplete observations regarding the services under consideration.

In multiagent interactions, such as e-commerce and file sharing, being able to accurately assess the trustworthiness of others is important for agents to protect themselves from losing utility. Focusing on rational agents in e-commerce, we prove that an agent's discount factor (time preference of utility) is a direct measure of the agent's trustworthiness for a set of reasonably general assumptions and definitions. We propose a general list of desiderata for trust systems and discuss how discount factors as trustworthiness meet these desiderata. We discuss how discount factors are a robust measure when entering commitments that exhibit moral hazards. Using an online market as a motivating example, we derive some analytical methods both for measuring discount factors and for aggregating the measurements.

An evidence-based account of trust is essential for an appropriate treatment of application-level interactions among autonomous and adaptive parties. Key examples include social networks and service-oriented computing. Existing approaches either ignore evidence or only partially address the twin challenges of mapping evidence to trustworthiness and combining trust reports from imperfectly trusted sources. This article develops a mathematically well-formulated approach that naturally supports discounting and combining evidence-based trust reports. This article understands an agent Alice's trust in an agent Bob in terms of Alice's certainty in her belief that Bob is trustworthy. Unlike previous approaches, this article formulates certainty in terms of evidence based on a statistical measure defined over a probability distribution of the probability of positive outcomes. This definition supports important mathematical properties ensuring correct results despite conflicting evidence: (1) for a fixed amount of evidence, certainty increases as conflict in the evidence decreases and (2) for a fixed level of conflict, certainty increases as the amount of evidence increases. Moreover, despite a subtle definition of certainty, this work (3) establishes a bijection between evidence and trust spaces, enabling robust combination of trust reports and (4) provides an efficient algorithm for computing this bijection.

Business service engagements involve processes that extend across two or more autonomous organizations. Because of regulatory and competitive reasons, requirements for cross-organizational business processes often evolve in subtle ways. The changes may concern the business transactions supported by a process, the organizational structure of the parties participating in the process, or the contextual policies that apply to the process. Current business process modeling approaches handle such changes in an ad hoc manner, and lack a principled means for determining what needs to be changed and where. Cross-organizational settings exacerbate the shortcomings of traditional approaches because changes in one organization can potentially affect the workings of another. This article describes Amoeba, a methodology for business processes that is based on business protocols . Protocols capture the business meaning of interactions among autonomous parties via commitments. Amoeba includes guidelines for (1) specifying cross-organizational processes using business protocols, and (2) handling the evolution of requirements via a novel application of protocol composition. This article evaluates Amoeba using enhancements of a real-life business scenario of auto-insurance claim processing, and an aerospace case study.

Business process modeling and enactment are notoriously complex, especially in open settings, where business partners are autonomous, requirements must be continually finessed, and exceptions frequently arise because of real-world or organizational problems. Traditional approaches, which attempt to capture processes as monolithic flows, have proven inadequate in addressing these challenges. We propose (business) protocols as components for developing business processes. A protocol is an abstract, modular, publishable specification of an interaction among different roles to be played by different participants. When instantiated with the participants' internal policies, protocols yield concrete business processes. Protocols are reusable and refinable, thus simplifying business process design. We show how protocols and their composition are theoretically founded in the phi;-calculus.

Developing, maintaining, and disseminating trust in open, dynamic environments is crucial. We propose self-organizing referral networks as a means for establishing trust in such environments. A referral network consists of autonomous agents that model others in terms of their trustworthiness and disseminate information on others' trustworthiness. An agent may request a service from another; a requested agent may provide the requested service or give a referral to someone else. Possibly with its user's help, each agent can judge the quality of service obtained. Importantly, the agents autonomously and adaptively decide with whom to interact and choose what referrals to issue, if any. The choices of the agents lead to the evolution of the referral network, whereby the agents move closer to those that they trust. This paper studies the guidelines for engineering self-organizing referral networks. To do so, it investigates properties of referral networks via simulation. By controlling the actions of the agents appropriately, different referral networks can be generated. This paper first shows how the exchange of referrals affects service selection. It identifies interesting network topologies and shows under which conditions these topologies emerge. Based on the link structure of the network, some agents can be identified as authorities. Finally, the paper shows how and when such authorities emerge. The observations of these simulations are then formulated into design recommendations that can be used to develop robust, self-organizing referral networks.

Emerging Web services standards enable the development of large-scale applications in open environments. In particular, they enable services to be dynamically bound. However, current techniques fail to address the critical problem of selecting the right service instances. Service selection should be determined based on user preferences and business policies, and consider the trustworthiness of service instances.

The Web is moving toward a collection of interoperating Web services. Achieving this interoperability requires dynamic discovery of Web services on the basis of their capabilities. The capability of a service can be properly determined by using not only its functional description (or service interface), but also its quality attributes as judged by previous users of the service. We develop a service repository that extends UDDI registries. This repository combines an ontology of attributes with evaluation data. We base our repository on a new query and manipulation language based on DAML. Our language includes support for a rich set of operations, which are needed to maintain an attribute ontology, publish services, rate services, and select services based on their functional attributes as well as evaluations by others. We have implemented our approach and evaluated its practical completeness via a number of key query and manipulation templates.

Business processes are conventionally modeled as monolithic flows that capture the desired business logic. However, developing process flows is challenging. Because a flow specifies what its participants should do, it restricts the autonomy of its participants, thus limiting their ability to exploit opportunities or accommodate exceptions according to their business preferences. We take a dual perspective wherein business processes are modeled as compositions of (instantiated) business protocols. Each business protocol specifies interactions among its partners; each protocol serves a unique business purpose, e.g., processing a payment or shipping an item. Thus, modularizing a monolithic business process via business protocols allows clear separation of concerns for modeling and enacting the process. We develop an approach in which protocols are compiled into local skeletal flows for each participant that can be fleshed out with local business logic as needed. Such flows are naturally distributed but can be enacted using commercial business flow engines. Thus, our protocol-based approach combines the benefit of improved modeling with simplified implementations.

Business processes involve interactions among autonomous partners. We propose that these interactions be specified modularly as protocols. Protocols can be published, enabling implementors to independently develop components that respect published protocols and yet serve diverse interests. A variety of business protocols would be needed to capture subtle business needs. We propose that the same kinds of conceptual abstractions be developed for protocols as for information models. Specifically, we consider (1) refinement: a subprotocol may satisfy the requirements of a superprotocol, but support additional properties; and (2) aggregation: a protocol may combine existing protocols. In support of the above, this paper develops a semantics of protocols and an operational characterization of them. This supports judgments about the potential subclass-superclass relations between protocols, which are a result of protocol refinement. It also enables protocol aggregation by splicing a protocol into another protocol.

Traditional approaches to knowledge management are essentially limited to document management. However, much knowledge in organizations or communities resides in an informal social network and may be accessed only by asking the right people. This paper describes MARS, a multiagent referral system for knowledge management. MARS assigns a software agent to each user. The agents facilitate their users' interactions and help manage their personal social networks. Moreover, the agents cooperate with one another by giving and taking referrals to help their users find the right parties to contact for a specific knowledge need.

One of the major challenges for electronic commerce is how to establish a relationship of trust between different parties. Establishing trust is nontrivial, because the traditional physical or social means of trust cannot apply directly in virtual settings. In many cases, the parties involved may not ever have interacted before. Reputation systems seek to address the development of trust by recording the reputations of different parties. However, most existing reputation systems are restricted to individual market websites. Further, relevant information about a party may come from several websites and from interactions that were not mediated by any website. This paper considers the problem of automatically collecting ratings about a given party from others. Our approach involves a distributed agent architecture and adapts the mathematical theory of evidence to represent and propagate the ratings that participants give to each other. When evaluating the trustworthiness of a given party, a peer combines its local evidence (based on direct prior interactions with the party) with the testimonies of others regarding the same party. This approach satisfies certain important properties of distributed reputation management and is experimentally evaluated through simulations.

Successful e-commerce presupposes techniques by which autonomous trading entities can interoperate. Although much progress has been made on data exchange and payment protocols, interoperation in the face of autonomy is still inadequately understood. Current techniques, designed for closed environments, support only the simplest interactions. We develop a multiagent approach for interoperation in e-commerce. This approach consists of: (1) a behavioral model to specify autonomous, heterogeneous agents representing different trading entities (businesses, consumers, brokers), (2) a metamodel that provides a language (based on XML) for specifying a variety of service agreements and accommodating exceptions and revisions, and (3) an execution architecture that supports persistent and dynamic (re)execution.

Article Free Access Share on E-commerce over communicators: challenges and solutions for user interfaces Authors: Mona Singh Department of Computer Science, North Carolina State University, EGRC 446, 1010 Main Campus Drive, Raleigh, NC Department of Computer Science, North Carolina State University, EGRC 446, 1010 Main Campus Drive, Raleigh, NCView Profile , Anuj K. Jain View Profile , Munindar P. Singh View Profile Authors Info & Claims EC '99: Proceedings of the 1st ACM conference on Electronic commerceNovember 1999 Pages 177–186https://doi.org/10.1145/336992.337040Published:01 November 1999Publication History 3citation899DownloadsMetricsTotal Citations3Total Downloads899Last 12 Months29Last 6 weeks7 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteeReaderPDF

A clear understanding and formalization of actions is essential to computing, and especially so to reasoning about and constructing intelligent agents. Several approaches have been proposed over the years. However, most approaches concentrate on the causes and effects of actions, but do not give general characterizations of actions themselves. A useful formalization of actions would be based on a general, possibly nondiscrete, model of time that allows branching (to capture agents’ choices). A desirable formalization would also allow actions to be of arbitrary duration and would permit multiple agents to act concurrently. We develop a branching‐time framework that allows great flexibility in how time and action are modeled. We motivate and formalize several coherence constraints on our models, which capture some nice intuitions and validate some useful inferences relating actions with time.

The BDI paradigm is a powerful means for constructing intelligent agents in terms of their beliefs, desires, and intentions. For this paradigm to bear its full potential, it must incorporate considerations from rationality. This paper develops a set of postulates for intelligent agents who deliberate about their intentions and actions. However, even simple postulates can lead to paradoxical results when formalized naively. We propose an approach based on temporal possibility and action that avoids those problems. This approach incorporates a formal model based on branching time in which a probabilistic analysis of choice can be captured. In this manner, the intuitions of the BDI paradigm can be reconciled with those of rational agency.

Planning algorithms have traditionally been geared towards achievement goals in single-agent environments. Such algorithms essentially produce plans to reach one of a specified set of states. More general approaches for planning based on temporal logic (TL) are emerging. Current approaches tend to use linear TL, and can handle sets of sequences of states. However, they assume deterministic actions with all changes effected solely by one agent. By contrast, we use a branching model of time that can express concurrent actions by multiple agents and the environment, leading to nondeterministic effects of an agent's actions. For this reason, we view plans not as sequences of actions, but as decision graphs describing the agent's actions in different situations. Thus, although we consider single-agent decision graphs, our approach is better suited to multiagent systems. We also consider an expressive formalism, which allows a wider variety of goals, including achievement and maintenance goals. Achievement corresponds to traditional planning, but maintenance is more powerful than traditional maintenance goals, and may require nonterminating plans. To formalize decision graphs requires a means to ‘alternate’ the agent's and the environment's choices. From logics of program, we introduce the prepositional mu-calculus;, which has operators for least and greatest fixpoints. We give a semantics, a fixpoint characterization, and an algorithm to compute decision graphs.

Workflows are the semantically appropriate composite activities in heterogeneous computing environments. Such environments typically comprise a great diversity of locally autonomous databases, applications and interfaces. Much good research has focused on the semantics of workflows and how to capture them in different extended transaction models. We address the complementary issues pertaining to how workflows may be declaratively specified and how distributed constraints may be derived from those specifications to enable local control, thus obviating a centralized scheduler. Previous approaches to this problem were limited and often lacked a formal semantics.

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The Carnot Project at MCC is addressing the problem of logically unifying physically-distributed, enterprise-wide, heterogeneous information. Carnot will provide a user with the means to navigate information efficiently and transparently, to update that information consistently, and to write applications easily for large, heterogeneous, distributed information systems. A prototype has been implemented which provides services for (a) enterprise modeling and model integration to create an enterprise-wide view, (b) semantic expansion of queries on the view to queries on individual resources, and (c) inter-resource consistency management. This paper describes the Carnot approach to transaction processing in environments where heterogeneous, distributed, and autonomous systems are required to coordinate the update of the local information under their control. In this approach, subtransactions are represented as a set of tasks and a set of intertask dependencies that capture the semantics of a particular relaxed transaction model. A scheduler has been implemented which schedules the execution of these tasks in the Carnot environment so that all intertask dependencies are satisfied.

This paper explores the specification and semantics of multiagent problem-solving systems, focusing on the representations that agents have of each other. It provides a declarative representation for such systems. Several procedural solutions to a well-known test-bed problem are considered, and the requirements they impose on different agents are identified. A study of these requirements yields a representational scheme based on temporal logic for specifying the acting, perceiving, communicating, and reasoning abilities of computational agents. A formal semantics is provided for this scheme. The resulting representation is highly declarative, and useful for describing systems of agents solving problems reactively.>