In the digital age, understanding the dynamics of information spread and opinion formation within networks is paramount. This research introduces an innovative framework that combines the principles of opinion dynamics with the strategic capabilities of Automated Planning. We have developed, to the best of our knowledge, the first-ever numeric PDDL tailored for opinion dynamics. Our tool empowers users to visualize intricate networks, simulate the evolution of opinions, and strategically influence that evolution to achieve specific outcomes. By harnessing Automated Planning techniques, our framework offers a nuanced approach to devise sequences of actions tailored to transition a network from its current opinion landscape to a desired state. This holistic approach provides insights into the intricate interplay of individual nodes within a network and paves the way for targeted interventions. Furthermore, the tool facilitates human-AI collaboration, enabling users to not only understand information spread but also devise practical strategies to mitigate potential harmful outcomes arising from it. Demo Video link - https://tinyurl.com/3k7bp99h

In this study, we investigate the under-explored intervention planning aimed at disseminating accurate information within dynamic opinion networks by leveraging learning strategies. Intervention planning involves identifying key nodes (search) and exerting control (e.g., disseminating accurate/official information through the nodes) to mitigate the influence of misinformation. However, as the network size increases, the problem becomes computationally intractable. To address this, we first introduce a ranking algorithm to identify key nodes for disseminating accurate information, which facilitates the training of neural network (NN) classifiers that provide generalized solutions for the search and planning problems. Second, we mitigate the complexity of label generation—which becomes challenging as the network grows—by developing a reinforcement learning (RL)-based centralized dynamic planning framework. We analyze these NN-based planners for opinion networks governed by two dynamic propagation models. Each model incorporates both binary and continuous opinion and trust representations. Our experimental results demonstrate that the ranking algorithm-based classifiers provide plans that enhance infection rate control, especially with increased action budgets for small networks. Further, we observe that the reward strategies focusing on key metrics, such as the number of susceptible nodes and infection rates, outperform those prioritizing faster blocking strategies. Additionally, our findings reveal that graph convolutional network (GCN)-based planners facilitate scalable centralized plans that achieve lower infection rates (higher control) across various network configurations (e.g., Watts-Strogatz topology, varying action budgets, varying initial infected nodes, and varying degree of infected nodes).

introduction Share on Introduction to the Special Section on Human-centered Security, Privacy, and Trust in the Internet of Things Editors: Mahmoud Barhamgi View Profile , Michael N. Huhns View Profile , Charith Perera View Profile , Pinar Yolum View Profile Authors Info & Claims ACM Transactions on Internet TechnologyVolume 21Issue 1February 2021 Article No.: 16pp 1–3https://doi.org/10.1145/3445790Online:20 January 2021Publication History 1citation157DownloadsMetricsTotal Citations1Total Downloads157Last 12 Months98Last 6 weeks8 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

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.

This paper addresses automatic service composition (ASC) as a means to create new value-added services dynamically and automatically from existing services in service-oriented architecture and cloud computing environments. Manually composing services for relatively static applications has been successful, but automatically composing services requires advances in the semantics of processes and an architectural framework that can capture all stages of an application's lifecycle. A framework for ASC involves four stages: planning an execution workflow, discovering services from a registry, selecting the best candidate services, and executing the selected services. This four-stage architecture is the most widely used to describe ASC, but it is still abstract and incomplete in terms of scalable goal composition, property transformation for seamless automatic composition, and integration architecture. We present a workflow orchestration to enable nested multilevel composition for achieving scalability. We add to the four-stage composition framework a transformation method for abstract composition properties. A general model for the composition architecture is described herein and a complete and detailed composition framework is introduced using our model. Our ASC architecture achieves improved seamlessness and scalability in the integrated framework. The ASC architecture is analyzed and evaluated to show its efficacy.

There are a number of important societal problems that resist conventional, i.e., centralized, solutions. The problems affect the management of our economic systems, climate, energy systems, transportation systems, telecommunication systems, and infrastructure. They are characterized as being distributed and many-faceted, with a large number of interdependent components. For example, the routes of trains and automobile traffic are designed centrally and statically by transportation engineers, rather than designed in real time by the passengers being transported. Allowing the passengers to be responsible for the transportation system constitutes a form of design that is: ldrLarge-scale, because of the size of the system being designed and the number of designers ldrSpatially distributed, in terms of both the system and the designers ldrTemporally distributed, because the design of the system evolves over time and the designers interact with it over a long time period ldrParticipatory, although not necessarily cooperative. There are many similar examples where design is typically done in a centralized manner, but could be done much better in a distributed manner. How can this sort of design be supported? It requires a form of consensus, semantic mediation, and intelligent distributed support. In this talk I will describe how large-scale distributed design can be done with the aid of multiagent systems that produce consensus.

This paper presents a constructive solution to the classic problem of land division. It is the first solution that enables the allocation of higher-dimensional resources without degenerating them first into a series of one-dimensional resource allocation problems. We base our allocation procedure on the topology of overlaps among the regions of interest of different agents. Our result is an algorithm suitable for computer implementation, unlike earlier ones that were only existential in nature. It uses the notion of degree of partial overlap to create a sufficiency condition for the existence of a solution, and proposes a procedure to find the overlaps in such a case. The proposed solution is fair, strategy-proof, non-existential, and does not explicitly need the resource to be measurable. The agents do not have to reveal their private utility functions. We extend our earlier result for one-dimensional resource allocation to this two-dimensional one and explain the distinctive issues involved.

The constraints revealed during a logical composition of services are often too abstract for automatic service composition. The abstract constraints have to be transformed to concrete attributes. This research investigates semi-automatic transformation of intermediate constraints to concrete constraints for automatic service composition. It considers simultaneously a stack of composition attributes for QoS, preferences, and logic constraints.

Automatic service discovery and selection is the key aspect for composing Web services dynamically in service-oriented computing (SOC). Current approaches to automating discovery and selection make use of only structural and functional aspects of the Web services. We believe that behavioral selection of Web services should be used to provide more precise results. Service behavior is difficult to specify prior to service execution and instead is better described based on experience with the service execution. In this paper, we propose a novel approach to service selection and maintenance-inspired by agile software development techniques-that is based on behavioral queries specified as test cases. Behavior is evaluated through the analysis of execution values of functional and non-functional parameters.

A workflow can be represented as a set of Web services and a specification for the control and data flows among these services. It can also be represented as a colored Petri net (CPN), which is a graphical and mathematical modeling tool. In multiagent systems (MAS), a workflow is a dynamic set of tasks performed by a set of agents to reach a shared goal. We show herein that commitments among agents can be used to model a workflow and coordinate their execution of it. This paper presents methodologies to map an OWLS model for a workflow to a CPN, and then to infer commitments and causal relationships from the CPN graph. With our methodologies, agents can collaboratively enact a workflow through commitment-based formalisms

Solving general real-life problems requires a set of appropriate services to be composed via planning, scheduled, and then executed. Web service composition is the most difficult aspect and is our focus. In this paper, we describe a new framework for intelligent semantic Web services that supports the planning and scheduling aspects by a combined HTN planner and CSP. The framework covers all of the procedures needed to deal with a user's request, including domain analysis of the request, task flow decisions and CSP creation by the planner, and solving the CSP by a distributed CSP solver

This paper describes a new approach to the production of robust software. We first motivate the approach by explaining why the two major goals of software engineering — correct software and reusable software — are not being addressed by the current state of software practice. We then describe a methodology based on active, cooperative, and persistent software components, i.e., agents, and show how the methodology produces robust and reusable software. We derive requirements for the structure and behavior of the agents, and report on preliminary experiments on applications based on the methodology. We conclude with a roadmap for development of the methodology and ruminations about uses for the new computational paradigm.

The paper describes how query formulation can be made simple and less complicated by using ontologies. It takes a brief look at several advantages of using ontologies in a distributed, heterogeneous, and dynamic information environment, such as the Internet. It also examines the construction and evaluation of an ontology-based distributed information system developed using the Java language. The paper discusses issues related to software tools that operate in a distributed environment and shows how the client-server architecture and the agent technology used by the Java language can be used effectively in such environments. The software is applied to an information system for healthcare administrators, which spans hospitals, clinics, and governmental health departments.

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.>

The concept of logical consistency of belief among a group of computational agents that are able to reason nonmonotonically is defined. An algorithm for truth maintenance is then provided that guarantees local consistency for each agent and global consistency for data shared by the agents. The algorithm is shown to be complete, in the sense that if a consistent state exists, the algorithm will either find it or report failure. The implications and limitations of this algorithm for cooperating agents are discussed, and several extensions are described. The algorithm has been implemented in the RAD distributed expert system shell.>

MINDS (Multiple Intelligent Node Document Servers) is a distributed system of knowledge-based query engines for efficiently retrieving multimedia documents in an office environment of distributed workstations. By learning document distribution patterns, as well as user interests and preferences during system usage, it customizes document retrievals for each user. A two-layer learning system has been implemented for MINDS. The knowledge base used by the query engine is learned at the lower level with the help of heuristics for assigning credit and recommending adjustments; these heuristics are incrementally refined at the upper level. © 1986 John Wiley & Sons, Inc.

An architecture and implementation for a distributed artificial intelligence (DAI) system are presented, with emphasis given to the control and communication aspects. Problem solving by this system occurs as an iterative refinement of several mechanisms, including problem decomposition, kernel-subproblem solving, and result synthesis. In order for all related nodes to make optimum use of the information obtained from these problem-solving mechanisms, the system dynamically reconfigures itself, thereby improving its performance during operation. This approach offers the possibilities of increased real-time response, improved reliability and flexibility, and lower processing costs. A major component in the node architecture is a database of metaknowledge about the expertise of a node's own expert systems and those of the other processing nodes. This information is gradually accumulated during problem solving. Each node also has a dynamic-planning ability, which guides the problem-solving process in the most promising direction and a focus-control mechanism, which restricts the size of the explored solution space at the task level while reducing the communication bandwidths required. It also has a question-and-answer mechanism, which handles internode communications. Examples in the domain of digital-logic design are given to demonstrate the operation of the system.