This special day event on Responsible Artificial Intelligence (AI) brings together researchers, practitioners, and policymakers to explore how data mining and machine learning systems can be designed to align with ethical principles, societal values, and human well-being. As AI technologies increasingly influence decisions in healthcare, finance, governance, and social systems, there is a critical need to develop frameworks that embed fairness, accountability, and privacy directly into the foundations of knowledge discovery. This full-day event will feature a mix of invited talks, interactive debates, expert panels, and peer-reviewed research presentations, all focused on the practical integration of ethical design into data-driven systems. The Responsible AI Day builds on the success of Canada's NSERC CREATE Program on Responsible AI, an interdisciplinary initiative training the next generation of AI researchers across computer science, law, bioethics, public health, and media studies. Topics will span scalable AI governance, privacy-preserving computation, algorithmic bias mitigation, and the socio-legal tensions emerging in generative AI. By positioning responsible AI as a sociotechnical challenge, this special day aligns with KDD's mission of advancing data science that is not only technically robust but also socially conscious.

Cardiopulmonary resuscitation (CPR) is a critical, life-saving intervention aimed at restoring blood circulation and breathing in individuals experiencing cardiac arrest or respiratory failure. Accurate and real-time analysis of biomedical signals during CPR is essential for monitoring and decision-making, from the pre-hospital stage to the intensive care unit (ICU). However, CPR signals are often corrupted by noise and artifacts, making precise interpretation challenging. Traditional denoising methods, such as filters, struggle to adapt to the varying and complex noise patterns present in CPR signals. Given the high-stakes nature of CPR, where rapid and accurate responses can determine survival, there is a pressing need for more robust and adaptive denoising techniques. In this context, an unsupervised machine learning (ML) methodology is particularly valuable, as it removes the dependence on labeled data, which can be scarce or impractical in emergency scenarios. This paper introduces a novel unsupervised ML approach for denoising CPR signals using a multi-modality framework, which leverages multiple signal sources to enhance the denoising process. The proposed approach not only improves noise reduction and signal fidelity but also preserves critical inter-signal correlations (0.9993) which is crucial for downstream tasks. Furthermore, it outperforms existing methods in an unsupervised context in terms of signal-to-noise ratio (SNR) and peak signal-to-noise ratio (PSNR), making it highly effective for real-time applications. The integration of multi-modality further enhances the system's adaptability to various biomedical signals beyond CPR, improving both automated CPR systems and clinical decision-making. • Unsupervised multi-modal ML approach for robust CPR signal denoising. • Independently processes signals while preserving inter-signal correlations. • Outperform existing methods without labels, enhance reliability in practice. • Improves adaptability and explainability for real-time healthcare use cases. • Adaptable for various biomedical signals beyond CPR in clinical settings.

We present the Multi-Modal Discussion Transformer (mDT), a novel method for detecting hate speech on online social networks such as Reddit discussions. In contrast to traditional comment-only methods, our approach to labelling a comment as hate speech involves a holistic analysis of text and images grounded in the discussion context. This is done by leveraging graph transformers to capture the contextual relationships in the discussion surrounding a comment and grounding the interwoven fusion layers that combine text and image embeddings instead of processing modalities separately. To evaluate our work, we present a new dataset, HatefulDiscussions, comprising complete multi-modal discussions from multiple online communities on Reddit. We compare the performance of our model to baselines that only process individual comments and conduct extensive ablation studies.

ABSTRACT In this paper, we present a model for supporting user‐specific explanations of AI systems. We then discuss a user study that was conducted to gauge whether the decisions for adjusting output to users with certain characteristics was confirmed to be of value to participants. We focus on the merit of having explanations attuned to particular psychological profiles of users, and the value of having different options for the level of explanation that is offered (including allowing for no explanation, as one possibility). Following the description of the study, we present an approach for mining data from user participant responses in order to determine whether the model that was developed for varying the output to users was well‐founded. While our results in this respect are preliminary, we explain how using varied machine learning methods is of value as a concrete step toward validation of specific approaches for AI explanation. We conclude with a discussion of related work and some ideas for new directions with the research, in the future.

In this article, we present a novel approach to quantitative evaluation of a model for parsing web pages as visual images, intended to provide improvements for users with assistive needs (cognitive or visual deficits, enabling decluttering or zooming and supporting more effective screen reader output). This segmentation-classification pipeline is tested in stages: We first discuss the validation of the segmentation algorithm, showing that our approach produces automated segmentations that are very similar to those produced by real users when making use of a drawing interface to designate edges and regions. We also examine the properties of these ground truth segmentations produced under different conditions. We then describe our Hidden Markov tree approach for classification and present results which serve provide important validation for this model. The analysis is set against effective choices for dataset and pruning options, measured with respect to manual ground truth labelling of regions. In all, we offer a detailed quantitative validation (focused on complex news pages) of a fully pipelined approach for interpreting web pages as visual images, an approach which enables important advances for users with assistive needs.

The “black-box” nature of artificial intelligence (AI) models has been the source of many concerns in their use for critical applications. Explainable Artificial Intelligence (XAI) is a rapidly growing research field that aims to create machine learning models that can provide clear and interpretable explanations for their decisions and actions. In the field of cybersecurity, XAI has the potential to revolutionize the way we approach network and system security by enabling us to better understand the behavior of cyber threats and to design more effective defenses. In this survey, we review the state of the art in XAI for cybersecurity and explore the various approaches that have been proposed to address this important problem. The review follows a systematic classification of cybersecurity threats and issues in networks and digital systems. We discuss the challenges and limitations of current XAI methods in the context of cybersecurity and outline promising directions for future research.

In this paper we show how integrating both domain specific and generic trust indicators into a prediction of trust links between users in social networks can improve upon methods for recommending content to users and how clustering of users to deliver personalized solutions offers even greater advantages.

Autonomous Vehicles (AV) will transform transportation, but also the interaction between vehicles and pedestrians. In the absence of a driver, it is not clear how an AV can communicate its intention to pedestrians. One option is to use visual signals. To advance their design, we conduct four human-participant experiments and evaluate six representative AV visual signals for visibility, intuitiveness, persuasiveness, and usability at pedestrian crossings. Based on the results, we distill twelve practical design recommendations for AV visual signals, with focus on signal pattern design and placement. Moreover, the paper advances the methodology for experimental evaluation of visual signals, including lab, closed-course, and public road tests using an autonomous vehicle. In addition, the paper also reports insights on pedestrian crosswalk behaviours and the impacts of pedestrian trust towards AVs on the behaviors. We hope that this work will constitute valuable input to the ongoing development of international standards for AV lamps, and thus help mature automated driving in general.

Inter-vehicle communication for autonomous vehicles (AVs) stands to provide significant benefits in terms of perception robustness. We propose a novel approach for AVs to communicate perceptual observations, tempered by trust modelling of peers providing reports. Based on the accuracy of reported object detections as verified locally, communicated messages can be fused to augment perception performance beyond line of sight and at great distance from the ego vehicle. Also presented is a new synthetic dataset which can be used to test cooperative perception. The TruPercept dataset includes unreliable and malicious behaviour scenarios to experiment with some challenges cooperative perception introduces. The TruPercept runtime and evaluation framework allows modular component replacement to facilitate ablation studies as well as the creation of new trust scenarios we are able to show.

The rapid growth of online social networks and the recognition of their potency as a medium for the spread of misinformation has provoked a growing interest in modelling reputation and trust in multi agent networks. Intended as a novel approach towards modelling the effects a user is having on the well-being of an online community, this paper presents a method for extracting features from tree-shaped discussions and evaluates a large set of linguistic and metadata based features for their predictive ability in a data set of Reddit comments. We show that some qualities of discussion-starting comments are predictable based solely on an analysis of the discussion that follows, and outline a road-map for how learning associations between community reactions and detectable anti-social behaviour could be used to model the reputation of users.

We examine moral machine decision making as inspired by a central question posed by Rossi with respect to moral preferences: can AI systems based on statistical machine learning (which do not provide a natural way to explain or justify their decisions) be used for embedding morality into a machine in a way that allows us to prove that nothing morally wrong will happen? We argue for an evaluation which is held to the same standards as a human agent, removing the demand that ethical behaviour is always achieved. We introduce four key meta-qualities desired for our moral standards, and then proceed to clarify how we can prove that an agent will correctly learn to perform moral actions given a set of samples within certain error bounds. Our group-dynamic approach enables us to demonstrate that the learned models converge to a common function to achieve stability. We further explain a valuable intrinsic consistency check made possible through the derivation of logical statements from the machine learning model. In all, this work proposes an approach for building ethical AI systems, coming from the perspective of artificial intelligence research, and sheds important light on understanding how much learning is required in order for an intelligent agent to behave morally with negligible error.

In this paper, we introduce a framework for modeling the trustworthiness of peers in the setting of online social networks. In these contexts, it may be important to be filtering the wealth of messages that have been sent, which form the ongoing communication within a large community of users. This is achieved by constructing an intelligent agent that reasons about the message and each peer rater of the message, learning over time to properly gage whether a message is good or bad to show a user, based on message ratings, rater similarity, and rater credibility. Our approach employs a partially observable Markov decision process for trust modeling, moving beyond the more traditional adoption of probabilistic reasoning using beta reputation functions. In addition to outlining the technique in full, we present empirical results to demonstrate the effectiveness of our methods, both in simulations featuring head to head comparisons with competitors, and in the context of some existing online social networks where ground truth data are available.

In this paper we present an overview of our proposed algorithms for classifying regions of web pages based on content and visual properties. We show how hidden Markov trees may be effective for the classification and how this may end up offering improved experiences to users who are trying to view webpages.

In this article, we present a distributed algorithm for allocating resources to tasks in multiagent systems, one that adapts well to dynamic task arrivals where new work arises at short notice. Our algorithm is designed to leverage preemption if it is available, revoking resource allocations to tasks in progress if new opportunities arise that those resources are better suited to handle. Our multiagent model assigns a task agent to each task that must be completed and a proxy agent to each resource that is available. Preemption occurs when a task agent approaches a proxy agent with a sufficiently compelling need that the proxy agent determines the newcomer derives more benefit from the proxy agent’s resource than the task agent currently using that resource. Task agents reason about which resources to request based on a learning of churn and congestion. We compare to a well-established multiagent resource allocation framework that permits preemption under more conservative assumptions and show through simulation that our model allows for improved allocations through more permissive preemption. In all, we offer a novel approach for multiagent resource allocation that is able to cope well with dynamic task arrivals.

Deciding the outcome of an election when voters have provided only partial orderings over their preferences requires voting rules that accommodate missing data. While existing techniques, including considerable recent work, address missingness through circumvention, we propose the novel application of conventional machine learning techniques to predict the missing components of ballots via latent patterns in the information that voters are able to provide. We show that suitable predictive features can be extracted from the data, and demonstrate the high performance of our new framework on the ballots from many real world elections, including comparisons with existing techniques for voting with partial orderings. Our technique offers a new and interesting conceptualization of the problem, with stronger connections to machine learning than conventional social choice techniques.

We consider the problem of repeatedly matching a set of alternatives to a set of agents with dynamic ordinal preferences. Despite a recent focus on designing one-shot matching mechanisms in the absence of monetary transfers, little study has been done on strategic behavior of agents in sequential assignment problems. We formulate a generic dynamic matching problem via a sequential stochastic matching process. We design a mechanism based on random serial dictatorship (RSD) that, given any history of preferences and matching decisions, guarantees global stochastic strategyproofness while satisfying desirable local properties. We further investigate the notion of envyfreeness in such sequential settings.

We consider the problem of repeatedly matching a set of alternatives to a set of agents in the absence of monetary transfer. We propose a generic framework for evaluating sequential matching mechanisms with dynamic preferences, and show that unlike single-shot settings, the random serial dictatorship mechanism is manipulable.

In this article, we introduce a framework for selecting web objects (texts, videos, simulations) from a large online repository to present to patients and caregivers, in order to assist in their healthcare. Motivated by the paradigm of peer-based intelligent tutoring, we model the learning gains achieved by users when exposed to specific web objects in order to recommend those objects most likely to deliver benefit to new users. We are able to show that this streamlined presentation leads to effective knowledge gains, both through a process of simulated learning and through a user study, for the specific application of caring for children with autism. The value of our framework for peer-driven content selection of health information is emphasized through two additional roles for peers: attaching commentary to web objects and proposing subdivided objects for presentation, both of which are demonstrated to deliver effective learning gains, in simulations. In all, we are offering an opportunity for patients to navigate the deep waters of excessive online information towards effective management of healthcare, through content selection influenced by previous peer experiences.

In order to validate a particular approach to trust modeling, researchers have typically designed simulations in which various multliagent conditions are modeled and tested. Graphs have tracked different measures to demonstrate success of the proposed trust model, including satisfaction of buying agents, profit of selling agents (in e-marketplaces) or the extent to which the simulation matched some ground truth for the user. In this paper we report on an effort to locate and employ existing datasets with information about real users, in order to validate a trust model. We describe how Reddit and Epinions datasets can be put to good use, towards this end. In addition to describing what we did for the validation of our own trust model, we reflect on how other trust modeling researchers may perform a similar process, of benefit for their own empirical studies.

This paper presents a validation of CredTrust, a multiagent trust model aimed at addressing the problem of folklore in online social networks: strong similarity among mis-guided peers should be ignored, at the expense of valuable advice provided by less similar credible peers. A series of simulations provides measured validation of the value of our approach in coping with a wide variety of messages, recommending those with high predicted benefit and avoiding those with low predicted benefit; we quantify our improvements in comparison with an established trust-based system (which fails to incorporate credibility).

In this paper we produce an algorithm for presenting messages to users in social networks that integrates reasoning about the message, the author, the recipient and the social network. Our proposed model was derived on the basis of immersion within three different existing social networking environments, that of Courser a, Reddit, and medical self-help groups such as PatientsLikeMe. We first present three models, each of which is designed to perform well within the context of one specific social network. From here we derive a generalized model which can be effective regardless of social network context. We conclude with a discussion of possible directions for future research, with an emphasis on promoting the use of trust modeling and user modeling, in a view to exploring additional networks and include as well a comparison to competing models within the artificial intelligence literature. Our aim is to offer insights into coping with the massive amount of information that currently resides within our social networks.

During a mass casualty incident, there will be many victims who need to be driven in an ambulance to a hospital. Reasoning about which patients to assign to which hospitals can be viewed as a multiagent resource allocation issue. The approach taken in this paper is to view this as a constraint satisfaction problem that should also be sensitive to a chosen social welfare metric. Our proposed algorithm employing local search is presented and then implemented in a series of simulations which experiment with different social welfare functions. The initial state used in the search is identified as a factor in the results. Moreover, a global view of the scenario helps to decide the appropriate strategies. We conclude with a discussion of next steps for multiagent resource allocation problems during mass casualty incidents. In short, we offer a more reasoned approach for ambulance allocation that may provide guidance for effective healthcare delivery.

ABSTRACT In this paper, we present a trust‐modeling framework for message propagation and evaluation in vehicular ad hoc networks. In the framework, peers share information regarding road condition or safety, and others provide opinions about whether the information can be trusted. More specifically, our trust‐based message propagation model collects and propagates peers' opinions in an efficient, secure, and scalable way by dynamically controlling information dissemination. The trust‐based message evaluation model allows peers to derive a local action decision about whether to follow the information by evaluating the information in a distributed and collaborative fashion while taking into account others' opinions. Experimental results demonstrate that our proposed trust‐modeling framework promotes network scalability and system effectiveness, which are the two essentially important factors for the popularization of vehicular ad hoc networks, in information propagation and evaluation under the pervasive presence of false information. In particular, we clarify how our relay control serves to decrease the number of inappropriate actions taken on the basis of malicious information and enables honest peers to produce a greater number of deliveries within the network. Copyright © 2012 John Wiley & Sons, Ltd.

In this article, we begin by presenting OMeD, a medical decision support system, and argue for its value over purely probabilistic approaches that reason about patients for time-critical decision scenarios. We then progress to present Holmes, a Hybrid Ontological and Learning MEdical System which supports decision making about patient treatment. This system is introduced in order to cope with the case of missing data. We demonstrate its effectiveness by operating on an extensive set of real-world patient health data from the CDC, applied to the decision-making scenario of administering sleeping pills. In particular, we clarify how the combination of semantic, ontological representations, and probabilistic reasoning together enable the proposal of effective patient treatments. Our focus is thus on presenting an approach for interpreting medical data in the context of real-time decision making. This constitutes a comprehensive framework for the design of medical recommendation systems for potential use by medical professionals and patients both, with the end result being personalized patient treatment. We conclude with a discussion of the value of our particular approach for such diverse considerations as coping with misinformation provided by patients, performing effectively in time-critical environments where real-time decisions are necessary, and potential applications facilitating patient information gathering.

In this article, we present a framework of use in electronic marketplaces that allows buying agents to model the trustworthiness of selling agents in an effective way, making use of seller ratings provided by other buying agents known as advisors. The trustworthiness of the advisors is also modeled, using an approach that combines both personal and public knowledge and allows the relative weighting to be adjusted over time. Through a series of experiments that simulate e-marketplaces, including ones where sellers may vary their behavior over time, we are able to demonstrate that our proposed framework delivers effective seller recommendations to buyers, resulting in important buyer profit. We also propose limiting seller bids as a method for promoting seller honesty, thus facilitating successful selection of sellers by buyers, and demonstrate the value of this approach through experimental results. Overall, this research is focused on the technological aspects of electronic commerce and specifically on technology that would be used to manage trust.

Service selection is one of the important problems in applications of multi-agent systems. A qualitative way is desirable for service selection according to agents' preferences on non-functional Quality of Service (QoS) attributes of services. Howeve

Multiagent Resource Allocation (MARA) distributes a set of resources among a set of intelligent agents in order to respect the preferences of the agents and to maximize some measure of global utility, which may include minimizing total costs or maximizing total return. We are interested in MARA solutions that provide optimal or close-to-optimal allocation of resources in terms of maximizing a global welfare function with low communication and computation cost, with respect to the priority of agents, and temporal dependencies between resources. We propose an MDP approach for resource planning in multiagent environments. Our approach formulates internal preference modeling and success of each individual agent as a single MDP and then to optimize global utility, we apply a market-based solution to coordinate these decentralized MDPs.

In this paper, we present a framework which enables medical decision making in the presence of partial information. At its core is ontology-based automated reasoning, machine learning techniques are integrated to enhance existing patient datasets in order to address the issue of missing data. Our approach supports interoperability between different health information systems. This is clarified in a sample implementation that combines three separate datasets (patient data, drug-drug interactions and drug prescription rules) to demonstrate the effectiveness of our algorithms in producing effective medical decisions. In short, we demonstrate the potential for machine learning to support a task where there is a critical need from medical professionals by coping with missing or noisy patient data and enabling the use of multiple medical datasets.

In this paper, we propose a novel incentive mechanism for promoting honesty in electronic marketplaces that is based on trust modeling. In our mechanism, buyers model other buyers and select the most trustworthy ones as their neighbors to form a social network which can be used to ask advice about sellers. In addition, however, sellers model the reputation of buyers based on the social network. Reputable buyers provide truthful ratings for sellers, and are likely to be neighbors of many other buyers. Sellers will provide more attractive products to reputable buyer to build their own reputation. We theoretically prove that a marketplace operating with our mechanism leads to greater profit both for honest buyers and honest sellers. We emphasize the value of our approach through a series of illustrative examples and in direct contrast to other frameworks for addressing agent trustworthiness. In all, we offer an effective approach for the design of e‐marketplaces that is attractive to users, through its promotion of honesty.

In this paper, we promote a model for peer-based intelligent tutoring that leverages the past learning experiences of students with a repository of learning objects. Consistent with McCalla’s ecological approach, we determine appropriate peers and appropriate learning objects to direct a new student's learning. In particular, we focus on allowing peers to provide annotations of learning objects. We revisit a procedure developed to select which annotations to present to students in order to improve their learning: one that combines a modeling of the reputation of the annotation (based on its approval or disapproval by previous students), the reputability of the annotator (based on the reputation of all annotations left by the student) and the similarity of the raters with the new student. Our focus is on developing effective validation of the procedure’s benefit, using an approach of simulated student learning. This is achieved by developing algorithms in greater detail and then making particular design decisions for the simulation in order to manage the reputability of the annotators and annotations in a way that enables the best learning objects to be employed for the tutoring. We are able to demonstrate the value of our proposed approach using distinct measures of rater similarity. We conclude with a comparison to related work and a view to future directions for the research. As a result, we present an approach for interpreting data from interactions with previous students in order to influence how to interact with current and future students, to enable effective learning.

This paper provides an approach for improving the trust modelling of users when a social network of advisors is employed (for example when advisors are recommending the most trustworthy service providers). We present three important improvements to trust modelling, two directly relating to the size of the network (through either the use of a threshold or by setting a maximum network size) and a third (advisor referrals) which focuses on ensuring that the advisors have attained an appropriate level of expertise, for the advice that they provide. Experimental results confirm the value of our methods when choosing parameters in a principled manner, leading to improvements in the accuracy of trust modelling (shown in the context of electronic marketplaces). In all, this research provides insights into how to set the size and composition of social networks, in order to effectively integrate the advice of peers when modelling the trust of users.

An increasingly large number of cars are being equipped with global positioning system and Wi-Fi devices, enabling vehicle-to-vehicle (V2V) communication with the goal of providing increased passenger and road safety. This technology actuates the need for agents that assist users by intelligently processing the received information. Some of these agents might become self-interested and try to maximize car owners' utility by sending out false information. Given the dire consequences of acting on false information in this context, there is a serious need to establish trust among agents. The main goal of this paper is then to develop a framework that models the trustworthiness of the agents of other vehicles, in order to receive the most effective information. We develop a multifaceted trust modeling approach that incorporates role-, experience-, priority-, and majority-based trust and this is able to restrict the number of reports that are received. We include an algorithm that proposes how to integrate these various dimensions of trust, along with experimentation to validate the benefit of our approach, emphasizing the importance of each of the different facets that are included. The result is an important methodology to enable effective V2V communication via intelligent agents.

In this paper, we propose a trust opinion aggregation scheme in vehicular ad-hoc networks, to support trust models used to evaluate the quality of information shared among peers in the environment. Our scheme extends an existing identity-based aggregate signature algorithm to correctly combine signatures for multiple messages into one aggregate signature and eliminate signature redundancy. As a result, our proposed scheme is secure and archives both space efficiency and time efficiency, as confirmed by our comparative analysis.

A qualitative way is desirable for web service selection according to agents' preferences on non-functional Quality of Service (QoS) attributes of services. However, it is challenging when the decision has to be made for multiple agents with preferences on attributes that may be incomplete. In this paper, we first use a qualitative graphical representation tool called CP-nets to describe preference relations in a relatively compact, intuitive and structured manner. We then propose algorithms based on this representation to select web services for multiple agents despite the presence of incompleteness in their preference orderings. Our experimental results indicate that this method can always obtain some optimal outcomes which closely satisfy all agents.

In large electronic marketplaces populated by buying and selling agents, it is difficult to judge trustworthiness. A variety of systems have been proposed to help traders to find trustworthy partners by learning to discount or disregard disreputable parties. In this article, we present a novel model for providing safe electronic marketplaces: Commodity Trunits, a system that considers trust as a tradable commodity. In this system, sellers require units of trust (trunits) to participate in transactions, and risk losing trunits if they act dishonestly. Sellers can purchase trunits when needed, and sell excess quantities. We demonstrate that under Commodity Trunits, rational sellers will choose to be honest, since this is the profit maximizing strategy. We also show that Commodity Trunits provides protection from a number of vulnerabilities common in existing trust and reputation systems, e.g., the important exit problem, where sellers can cheat without fear of repercussions if they intend to leave the market. We then present a simulation that validates the system by demonstrating that a market operator can manage the trunit marketplace to ensure sustainability. We conclude with a discussion of the value of Commodity Trunits as a method for promoting trust in electronic marketplaces.

Assigning bug reports to individual developers is typically a manual, time-consuming, and tedious task. In this paper, we present a framework for automated assignment of bug-fixing tasks. Our approach employs preference elicitation to learn developer predilections in fixing bugs within a given system. This approach infers knowledge about a developer's expertise by analyzing the history of bugs previously resolved by the developer. We apply a vector space model to recommend experts for resolving bugs. When a new bug report arrives, the system automatically assigns it to the appropriate developer considering his or her expertise, current workload, and preferences. We address the task allocation problem by proposing a set of heuristics that support accurate assignment of bug reports to the developers.

The increasing number of service providers on the Web makes it challenging to select a provider for a specific service demand. Each service consumer has different expectations for a given service in different contexts, so the selection process should be consumer‐oriented and context‐dependent. Current approaches for service selection typically have consumers receive ratings of providers from other consumers, where the ratings reflect the consumers' overall subjective opinions. This may be misleading if consumers have different contexts and satisfaction criteria. In this paper, we propose that consumers objectively record their experiences, using an ontology to capture subtle details. This can then be interpreted by consumers according to their own criteria and contexts. We then integrate a method for addressing consumers who lie about their experiences, filtering them out during service selection. We demonstrate the value of our approach through experiments comparing our model with three recent rating‐based service selection models. Our experiments show that using the proposed approach, service consumers can select the service providers for their needs more accurately even if the consumers have different criteria, they change the contexts of their service demands over time, or a significant portion of them are liars.

The unfair rating problem exists when a buying agent models the trustworthiness of selling agents by also relying on ratings of the sellers from other buyers. Different probabilistic approaches have been proposed to cope with this issue. In this paper, we first summarize these approaches and provide a detailed categorization of them. This includes our own "personalized" approach for addressing this problem. Based on the implication of such analysis, we then focus on experimental comparison of our approach with two key models in a framework that simulates a dynamic electronic marketplace environment. We specifically examine different scenarios, including ones where the majority of buyers are dishonest, buyers lack personal experience with sellers, sellers may vary their behavior, and buyers may provide a large number of ratings. Our study provides the basis for deciding which approach is most appropriate to employ, in which scenario.

In the context of the Semantic Web, it may be beneficial for a user (consumer) to receive ratings from other users (advisors) regarding the reliability of an information source (provider). We offer a method for building more effective social networks of trust by critiquing the ratings provided by the advisors. Our approach models the consumer's private reputations of advisors based on ratings for providers whom the consumer has had experience with. It models public reputations of the advisors according to all ratings from these advisors for providers, including those who are unknown to the consumer. We then combine private and public reputations by assigning weights for each of them. Experimental results demonstrate that our approach is robust even when there are large numbers of advisors providing large numbers of unfair ratings. We show that we can effectively model the trustworthiness of advisors even when the population of providers grows increasingly large and discuss how our approach is beneficial in modeling providers. As such, we present a framework for sharing ratings of possibly unreliable sources, of value as users on the Semantic Web attempt to critique the trustworthiness of the information they seek.

In electronic marketplaces populated by self-interested agents, buyer agents would benefit by modeling the reputation of seller agents, in order to make effective decisions about which agents to trust. One method for representing reputation is to ask other agents in the system (called advisor agents) to provide ratings of the seller agents. The problem of unfair ratings exists in almost every reputation system, including both unfairly high and unfairly low ratings. We begin by surveying some existing approaches to this problem, characterizing their capabilities and categorizing them in terms of two main dimensions: public-private and global-local. The impact of reputation system architectures on approach selection is also discussed. Based on the study, we propose a novel personalized approach for effectively handling unfair ratings in an enhanced centralized reputation system. Experimental results demonstrate that the approach effectively adjusts the trustworthiness of advisor agents according to the percentages of unfair ratings provided by them. We then argue for the merits of our model as the basis for designing social networks to share reputation ratings of sellers in electronic marketplaces.

In large electronic marketplaces populated by buying and selling agents, repeated transactions between traders may be rare. This makes it difficult for buying agents to judge the reliability of selling agents, discouraging participation in the market. A variety of systems have been proposed to help traders to find trustworthy partners; however, most proposed systems suffer from multiple vulnerabilities that might be exploited by unscrupulous parties. In this paper, we propose a new model, wherein abstract units are used to represent trust in much the same way that units of money represent value. In a manner similar to money, 'trunits' flow during transactions. A trader's trunit balance determines if they are trustworthy for a given transaction. Faithful execution of a transaction results in a larger trunit balance, permitting the trader to engage in more transactions in the future---a built-in economic incentive for honesty. We demonstrate that for a wide range of realistic market parameters, the Trunits mechanism ensures that honest sellers profit more than dishonest sellers. We also discuss how intrinsic properties of our model make it secure from many of the attacks to which other systems are vulnerable. In summary, we present our Trunits model as the basis for modeling trust in electronic marketplaces, useful as buying agents develop algorithms to intelligently choose trustworthy sellers for their business partners.

Familiarity between agents is often considered to be an important factor in determining the level of trust. In electronic marketplaces, trust is modeled, for instance, in order to allow buying agents to make effective selection of selling agents. In previous research, familiarity between two agents has been simply assumed to be the similarity between them, which is fixed for the two agents. We propose an improved familiarity measurement based on the exploration of factors that affect a human's feelings of familiarity and the mapping from those factors to the properties of agent societies. We examine the trust model in the context of a multiagent system within an e-commerce framework. We also carry out experiments to compare the stability of the system using the trust model with the improved familiarity measurement and that with the fixed familiarity values. Experimental results show that the stability of the system is increased by 33.47% through the improved familiarity measurement.

Adaptive business agents operate in electronic marketplaces, learning from past experiences to make effective decisions on behalf of their users. How best to design these agents is an open question. In this article, we present an approach for the design of adaptive business agents that uses a combination of reinforcement learning and reputation modeling. In particular, we take into account the fact that multiple selling agents may offer the same good with different qualities, and that selling agents may alter the quality of their goods. We also consider the possibility of dishonest agents in the marketplace. Our buying agents exploit the reputation of selling agents to avoid interaction with the disreputable ones, and therefore to reduce the risk of purchasing low value goods. We then experimentally compare the performance of our agents with those designed using a recursive modeling approach. We are able to show that agents designed according to our algorithms achieve better performance in terms of satisfaction and computational time and as such are well suited for the design of electronic marketplaces.

In this paper, we propose a reputation–oriented reinforcement learning algorithm for buying and selling agents in electronic market environments. We take into account the fact that multiple selling agents may offer the same good with different qualities. In our approach, buying agents learn to avoid the risk of purchasing low–quality goods and to maximize their expected value of goods by dynamically maintaining sets of reputable sellers. Selling agents learn to maximize their expected profits by adjusting product prices and by optionally altering the quality of their goods. Modeling the reputation of sellers allows buying agents to focus on those sellers with whom a certain degree of trust has been established. We also include the ability for buying agents to optionally explore the marketplace in order to discover new reputable sellers. As detailed in the paper, we believe that our proposed strategy leads to improved satisfaction for buyers and sellers, reduced communication load, and robust systems. In addition, we present preliminary experimental results that confirm some potential advantages of the proposed algorithm, and outline planned future experimentation to continue the evaluation of the model.

Allen's interval algebra has been shown to be useful for representing plans. We present a strengthened algorithm for temporal reasoning about plans, which improves on straightforward applications of the existing reasoning algorithms for the algebra. This is made possible by viewing plans as both temporal networks and hierarchical structures. The temporal network view allows us to check for inconsistencies as well as propagate the effects of new temporal constraints, whereas the hierarchical view helps us to get the strengthened results by taking into account the dependency relationships between actions. We further apply our algorithm to the process of plan recognition through the analysis of natural language input. We show that such an application has two useful effects: the temporal relations derived from the natural language input can be used as constraints to reduce the number of candidate plans, and the derived constraints can be made more specific by combining them with the prestored constraints in the plans being recognized.

Traditionally, plan recognition is defined as the process of matching a set of observations from a user to a library of possible plans in a domain, indicating the possible candidate plans of the user. We have developed a system which allows updates to the plan library during plan recognition and which amends the list of candidate plans, without having to restart the plan recognition process. This work builds on H. Kautz's model (1987) of plan recognition and applies truth maintenance. We describe a graphic-oriented tool which we have developed for users of our system. We comment on the insights which we have gained, about the kinds of users and the kinds of modifications which we might want to accommodate. We emphasize the significance of the system, allowing incomplete or incorrect libraries to be amended. We summarize the challenges which remain, both for interface design and plan recognition. Finally, we elaborate on the potential use of our system for a variety of artificial intelligence applications including knowledge based systems, cooperative AI and natural language understanding.>

Recognizing the plan underlying a query aids in the generation of an appropriate response. In this paper, we address the problem of how to generate cooperative responses when the user's plan is ambiguous. We show that it is not always necessary to resolve the ambiguity, and provide a procedure that estimates whether the ambiguity matters to the task of formulating a response. The procedure makes use of the critiquing of possible plans and identifies plans with the same fault. We illustrate the process of critiquing with examples. If the ambiguity does matter, we propose to resolve the ambiguity by entering into a clarification dialogue with the user and provide a procedure that performs this task. Together, these procedures allow a question‐answering system to take advantage of the interactive and collaborative nature of dialogue in order to recognize plans and resolve ambiguity. This work therefore presents a view of generation in advice‐giving contexts which is different from the straightforward model of a passive selection of responses to questions asked by users. We also report on a trial implementation in a course‐advising domain, which provides insights on the practicality of the procedures and directions for future research.

When trying to understand a speaker's argument, it is necessary to determine what her claim is and what evidence she provides for it. It is necessary, therefore, to be able to recognize evidence relations in terms of the speaker's beliefs. This paper describes an implementation of anevidence oracle, which tests for evidence between statements and builds a model of the speaker based on the evidence relations found. This implementation is intended to be an advance in the development of practical discourse analysis systems, proposing a basis for verifying certain relationships between utterances. Another contribution of the work is a stratified speaker model which allows for varying levels of acceptance of beliefs attributed to the speaker. Integration of the implemented evidence oracle into a full discourse analyser is presented, together with output illustrating the analysis for several sample arguments. Some extensions of this approach for plan inference are also discussed. Lorsque l'on essaie de comprendre l'argument d'un locuteur, il importe de déterminer la nature de sa prétention et le type d'évidence qui l'accompagne. Par conséquent, il est nécessaire de pouvoir distinguer des relations d'évidence les croyances du locuteur. Cet article décrit la mise en oeuvre d'un oracle qui recherche l'évidence entre des énoncés et construit un modèle du locuteur en fonction des relations d'évidence constatées. Cette mise en oeuvre propose une base pour vérifier certaines relations entre des énoncés; elle se veut une contribution au développement d'un système pratique d'analyse du discours. Une autre contribution de cette recherche est l'élaboration d'un modèle de locuteur stratifyé qui tient compte de niveaux variables d'acceptation des croyances attributeés au locuteur. l'intégration de l'oracle d'évidence sous forme d'analyseur de discours est présentée, ainsi que des illustrations de l' analyse de plusieurs arguments types. Une extension de cette approche à l'inférence de plans est également discutée.

This article describes the design decisions taken in implementing a processing model for understanding goal-oriented discourse. This model analyzes a restricted form of discourse known as arguments. Two main contributions are: (i) an integrated processing algorithm, which combines basic processing constraints with an interpretation of clue words–words and phrases which serve to indicate the structure of the discourse; (ii) a working version of the “evidence oracle,” which establishes connections between utterances in the discourse. This oracle determines if an “evidence” relation is intended between two utterances, and builds a model of the speaker based on the evidence relations found. This article thus emphasizes the general insights gained from the implementation exercise, both for the specification of a discourse analysis model, and for the general problem of recognizing a speaker's intentions and plans.

Allen gives an algebra for representing qualitative temporal information about the relationships between pairs of intervals. In this paper, we address a fundamental reasoning task that arises in applications of the algebra: Given (possibly indefinite) knowledge about the relationships between intervals, find all feasible relationships between two intervals. We call this the minimal labels problem. Finding the minimal labels can be viewed as computing the deductive consequences of our knowledge. Determining exact solutions to this problem has been shown to be (almost assuredly) intractable. Allen gives an approximation algorithm based on constraint propagation. We present new approximation algorithms; determine analytically under what conditions the algorithms are exact; and examine, through some computational experiments, the quality of the approximate solutions produced by the algorithms. We also give a simple test for predicting when the approximation algorithms will and will not produce good quality approximations. Finally, we survey three example applications of the interval algebra chosen from the literature to show where the results of this paper could be useful.

R. Cohen (Computational Linguistics, vol.13, no.1-2, p.11-24, 1987) has described a model for the analysis of arguments that includes: (1) a theory of expected coherent structure, which is used to limit analysis to the reconstruction of particular transmission forms; (2) a theory of linguistic clues which assigns a functional interpretation to special words and phrases used by the speaker to indicate the structure of the argument; and (3) a theory of evidence relationships which includes the demand for pragmatic analysis to accommodate beliefs not currently held. The author summarizes the prescriptions for coherent analysis, with a view to their application in the translation of technical material.>

This paper examines the use of clue words in argument dialogues. These are special words and phrases directly indicating the structure of the argument to the hearer. Two main conclusions are drawn: 1) clue words can occur in conjunction with coherent transmissions, to reduce processing of the hearer 2) clue words must occur with more complex forms of transmission, to facilitate recognition of the argument structure. Interpretation rules to process clues are proposed. In addition, a relationship between use of clues and complexity of processing is suggested for the case of exceptional transmission strategies.

This paper outlines research on processing strategies being developed for a language understanding system, designed to interpret the structure of arguments. For the system, arguments are viewed as trees, with claims as fathers to their evidence. Then understanding becomes a problem of developing a representative argtmlent tree, by locating each proposition of the argument at its appropriate place. The processing strategies we develop for the hearer are based on expectations that the speaker will use particular coherent transmission strategies and are designed to be fairly efficient (work in linear time). We also comment on the use by the speaker of linguistic clues to indicate structure, illustrating how the hearer can interpret the clues to limit his processing search and thus improve the comlexity of the understanding process.