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Journal articles on the topic 'Multi Agent Systems'

Author: Grafiati

Published: 4 June 2021

Last updated: 31 August 2024

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1

Dastani, Mehdi. "Programming multi-agent systems." Knowledge Engineering Review 30, no. 4 (September 2015): 394–418. http://dx.doi.org/10.1017/s0269888915000077.

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AbstractWith the significant advances in the area of autonomous agents and multi-agent systems in the last decade, promising technologies for the development and engineering of multi-agent systems have emerged. The result is a variety of agent-oriented programming languages, development frameworks, execution platforms, and tools that facilitate building and engineering of multi-agent systems. This paper provides an overview of the multi-agent programming research field and explains the aim and characteristics of various multi-agent programming languages and development frameworks. This overview is complemented with a discussion on the current trends and challenges in this research community.

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SCHWEITZER, FRANK, and MATTHEW E. TAYLOR. "EDITORIAL: AGENTS AND MULTI-AGENT SYSTEMS." Advances in Complex Systems 14, no. 02 (April 2011): iii—iv. http://dx.doi.org/10.1142/s0219525911003025.

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Julian, Vicente, and Vicente Botti. "Multi-Agent Systems." Applied Sciences 9, no. 7 (April 3, 2019): 1402. http://dx.doi.org/10.3390/app9071402.

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With the current advance of technology, agent-based applications are becoming a standard in a great variety of domains such as e-commerce, logistics, supply chain management, telecommunications, healthcare, and manufacturing. Another reason for the widespread interest in multi-agent systems is that these systems are seen as a technology and a tool that helps in the analysis and development of new models and theories in large-scale distributed systems or in human-centered systems. This last aspect is currently of great interest due to the need for democratization in the use of technology that allows people without technical preparation to interact with the devices in a simple and coherent way. In this Special Issue, different interesting approaches that advance this research discipline have been selected and presented.

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Brafman, R. I., and M. Tennenholtz. "On Partially Controlled Multi-Agent Systems." Journal of Artificial Intelligence Research 4 (June 1, 1996): 477–507. http://dx.doi.org/10.1613/jair.318.

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Motivated by the control theoretic distinction between controllable and uncontrollable events, we distinguish between two types of agents within a multi-agent system: controllable agents, which are directly controlled by the system's designer, and uncontrollable agents, which are not under the designer's direct control. We refer to such systems as partially controlled multi-agent systems, and we investigate how one might influence the behavior of the uncontrolled agents through appropriate design of the controlled agents. In particular, we wish to understand which problems are naturally described in these terms, what methods can be applied to influence the uncontrollable agents, the effectiveness of such methods, and whether similar methods work across different domains. Using a game-theoretic framework, this paper studies the design of partially controlled multi-agent systems in two contexts: in one context, the uncontrollable agents are expected utility maximizers, while in the other they are reinforcement learners. We suggest different techniques for controlling agents' behavior in each domain, assess their success, and examine their relationship.

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BRAZIER, F. M. T., C. M. JONKER, J. TREUR, and N. J. E. WIJNGAARDS. "DELIBERATIVE EVOLUTION IN MULTI-AGENT SYSTEMS." International Journal of Software Engineering and Knowledge Engineering 11, no. 05 (October 2001): 559–81. http://dx.doi.org/10.1142/s0218194001000670.

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Evolution of automated systems, in particular evolution of automated agents based on agent deliberation, is the topic of this paper. Evolution is not a merely material process, it requires interaction within and between individuals, their environments and societies of agents. An architecture for an individual agent capable of (1) deliberation about the creation of new agents, and (2) (run-time) creation of a new agent on the basis of this, is presented. The agent architecture is based on an existing generic agent model, and includes explicit formal conceptual representations of both design structures of agents and (behavioural) properties of agents. The process of deliberation is based on an existing generic reasoning model of design. The architecture has been designed using the compositional development method DESIRE, and has been tested in a prototype implementation.

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Yamada, Kazuaki, Kumiyo Nakakoji, and Kanji Ueda. "A Mutli-agent Systems Approach to Analyze Online Community Activities(Multi-agent and Learning,Session: TP2-A)." Abstracts of the international conference on advanced mechatronics : toward evolutionary fusion of IT and mechatronics : ICAM 2004.4 (2004): 51. http://dx.doi.org/10.1299/jsmeicam.2004.4.51_3.

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ALONSO, EDUARDO, MARK D'INVERNO, DANIEL KUDENKO, MICHAEL LUCK, and JASON NOBLE. "Learning in multi-agent systems." Knowledge Engineering Review 16, no. 3 (September 2001): 277–84. http://dx.doi.org/10.1017/s0269888901000170.

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In recent years, multi-agent systems (MASs) have received increasing attention in the artificial intelligence community. Research in multi-agent systems involves the investigation of autonomous, rational and flexible behaviour of entities such as software programs or robots, and their interaction and coordination in such diverse areas as robotics (Kitano et al., 1997), information retrieval and management (Klusch, 1999), and simulation (Gilbert & Conte, 1995). When designing agent systems, it is impossible to foresee all the potential situations an agent may encounter and specify an agent behaviour optimally in advance. Agents therefore have to learn from, and adapt to, their environment, especially in a multi-agent setting.

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Felcy Judith, P. "Leveraging Multi - Agent Systems for Efficient Grazing Management." International Journal of Science and Research (IJSR) 13, no. 6 (June 5, 2024): 640–42. http://dx.doi.org/10.21275/sr24611203011.

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JONG, STEVEN DE, KARL TUYLS, and KATJA VERBEECK. "Fairness in multi-agent systems." Knowledge Engineering Review 23, no. 2 (June 2008): 153–80. http://dx.doi.org/10.1017/s026988890800132x.

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AbstractMulti-agent systems are complex systems in which multiple autonomous entities, called agents, cooperate in order to achieve a common or personal goal. These entities may be computer software, robots, and also humans. In fact, many multi-agent systems are intended to operate in cooperation with or as a service for humans. Typically, multi-agent systems are designed assuming perfectly rational, self-interested agents, according to the principles of classical game theory. Recently, such strong assumptions have been relaxed in various ways. One such way is explicitly including principles derived from human behavior. For instance, research in the field of behavioral economics shows that humans are not purely self-interested. In addition, they strongly care aboutfairness. Therefore, multi-agent systems that fail to take fairness into account, may not be sufficiently aligned with human expectations and may not reach intended goals. In this paper, we present an overview of work in the area of fairness in multi-agent systems. More precisely, we first look at the classical agent model, that is, rational decision making. We then provide an outline of descriptive models of fairness, that is, models that explain how and why humans reach fair decisions. Then, we look at prescriptive, computational models for achieving fairness in adaptive multi-agent systems. We show that results obtained by these models are compatible with experimental and analytical results obtained in the field of behavioral economics.

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Park, Sankyu, Key-Sun Choi, and K. H. (Kane) Kim. "A Framework for Multi-Agent Systems with Multi-Modal User Interfaces in Distributed Computing Environments." International Journal of Software Engineering and Knowledge Engineering 07, no. 03 (September 1997): 351–69. http://dx.doi.org/10.1142/s0218194097000217.

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In current multi-agent systems, the user is typically interacting with a single agent at a time through relatively inflexible and modestly intelligent interfaces. As a consequence, these systems force the users to submit simplistic requests only and suffer from problems such as the low-level nature of the system services offered to users, the weak reusability of agents, and the weak extensibility of the systems. In this paper, a framework for multi-agent systems called the open agent architecture (OAA) which reduces such problems, is discussed. The OAA is designed to handle complex requests that involve multiple agents. In some cases of complex requests from users, the components of the requests do not directly correspond to the capabilities of various application agents, and therefore, the system is required to translate the user's model of the task into the system's model before apportioning subtasks to the agents. To maximize users' efficiency in generating this type of complex requests, the OAA offers an intelligent multi-modal user interface agent which supports a natural language interface with a mix of spoken language, handwriting, and gesture. The effectiveness of the OAA environment including the intelligent distributed multi-modal interface has been observed in our development of several practical multi-agent systems.

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Olaru, Andrei, and Monica Pricope. "Multi-Modal Decentralized Interaction in Multi-Entity Systems." Sensors 23, no. 6 (March 15, 2023): 3139. http://dx.doi.org/10.3390/s23063139.

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Current multi-agent frameworks usually use centralized, fixed communication infrastructures for the entities that are deployed using them. This decreases the robustness of the system but is less challenging when having to deal with mobile agents that can migrate between nodes. We introduce, in the context of the FLASH-MAS (Fast and Lightweight Agent Shell) multi-entity deployment framework, methods to build decentralized interaction infrastructures which support migrating entities. We discuss the WS-Regions (WebSocket Regions) communication protocol, a proposal for interaction in deployments using multiple communication methods, and a mechanism to facilitate using arbitrary names for entities. The WS-Regions Protocol is compared against Jade (the Java Agent Development Framework), the most popular agent deployment framework, with a favorable trade-off between decentralization and performance.

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Jaleel, Hanan Qassim, Jane Jaleel Stephan, and Sinan A. Naji. "Multi-Agent Systems: A Review Study." Ibn AL- Haitham Journal For Pure and Applied Sciences 33, no. 3 (July 20, 2020): 188. http://dx.doi.org/10.30526/33.3.2483.

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Multi-agent systems are subfield of Artificial Intelligence that has experienced rapid growth because of its flexibility and intelligence in order to solve distributed problems. Multi-agent systems (MAS) have got interest from various researchers in different disciplines for solving sophisticated problems by dividing them into smaller tasks. These tasks can be assigned to agents as autonomous entities with their private database, which act on their environment, perceive, process, retain and recall by using multiple inputs. MAS can be defined as a network of individual agents that share knowledge and communicate with each other in order to solve a problem that is beyond the scope of a single agent. It is imperative to understand the characteristics of the individual agent or computing entity to distinguish a simple distributed system and multi-agent system. To solve each task, the agent can take the appropriate actions, decisions, and can cooperate with other agents to fulfill its goals or objectives. MAS has several applications including cloud computing, web advertisement, e-commerce, computer network, learning abilities, etc. This survey represents a comprehensive discussion of all aspects of MAS, and discusses an overview of MAS definitions, architecture, features, communications, organizations, and finally discusses the some of the real practical applications.

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Dushkin, Roman. "Multi-agent systems for cooperative ITS." Тренды и управление, no. 1 (January 2021): 42–50. http://dx.doi.org/10.7256/2454-0730.2021.1.34169.

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This article presents an original perspective upon the problem of creating intelligent transport systems in the conditions of using highly automated vehicles that freely move on the urban street-road networks. The author explores the issues of organizing a multi-agent system from such vehicles for solving the higher level tasks rather than by an individual agent (in this case – by a vehicle). Attention is also given to different types of interaction between the vehicles or vehicles and other agents. The examples of new tasks, in which the arrangement of such interaction would play a crucial role, are described. The scientific novelty is based on the application of particular methods and technologies of the multi-agent systems theory from the field of artificial intelligence to the creation of intelligent transport systems and organizing free-flow movement of highly automated vehicles. It is demonstrated the multi-agent systems are able to solve more complex tasks than separate agents or a group of non-interacting agents. This allows obtaining the emergent effects of the so-called swarm intelligence of the multiple interacting agents. This article may be valuable to everyone interested in the future of the transport sector.

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Silva, Viviane Torres da, Fernanda Duran, José Guedes, and Carlos J. P. de Lucena. "Governing multi-agent systems." Journal of the Brazilian Computer Society 13, no. 2 (December 2007): 19–34. http://dx.doi.org/10.1590/s0104-65002007000200003.

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Mascardi, Viviana, Danny Weyns, and Alessandro Ricci. "Engineering Multi-Agent Systems." ACM SIGSOFT Software Engineering Notes 44, no. 1 (March 29, 2019): 18–28. http://dx.doi.org/10.1145/3310013.3310035.

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Mascardi, Viviana, Danny Weyns, Alessandro Ricci, Clara Benac Earle, Arthur Casals, Moharram Challenger, Amit Chopra, et al. "Engineering Multi-Agent Systems." ACM SIGSOFT Software Engineering Notes 44, no. 1 (March 29, 2019): 18–28. http://dx.doi.org/10.1145/3310013.3322175.

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Mascardi, Viviana, Danny Weyns, and Alessandro Ricci. "Engineering Multi-Agent Systems." ACM SIGSOFT Software Engineering Notes 44, no. 1 (March 29, 2019): 35. http://dx.doi.org/10.1145/3310013.3322181.

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da Silva, Viviane Torres, and Carlos J. P. de Lucena. "Modeling multi-agent systems." Communications of the ACM 50, no. 5 (May 2007): 103–8. http://dx.doi.org/10.1145/1230819.1241671.

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DASTANI, MEHDI, and JORGE J. GOMEZ-SANZ. "Programming multi-agent systems." Knowledge Engineering Review 20, no. 2 (June 2005): 151–64. http://dx.doi.org/10.1017/s0269888905000482.

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PROMAS (Programming Multi-Agent Systems) is an AgentLink technical forum that aims to bring together the researchers and practitioners from both academia and industry to discuss the problems related to the development of multi-agent systems and to evaluate the existing proposals and results. The specific focus of this technical forum is on agent-oriented programming languages and tools that can effectively and efficiently support the implementation of multi-agent systems. This paper provides an overview of the main contributions and conclusions derived from the first two editions of PROMAS technical forum.

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Byrski, Aleksander, Rafał Dreżewski, Leszek Siwik, and Marek Kisiel-Dorohinicki. "Evolutionary multi-agent systems." Knowledge Engineering Review 30, no. 2 (March 2015): 171–86. http://dx.doi.org/10.1017/s0269888914000289.

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AbstractThe aim of this paper is to give a survey on the development and applications of evolutionary multi-agent systems (EMAS). The paper starts with a general introduction describing the background, structure and behaviour of EMAS. EMAS application to solving global optimisation problems is presented in the next section along with its modification targeted at lowering the computation costs by early removing certain agents based on immunological inspirations. Subsequent sections deal with the elitist variant of EMAS aimed at solving multi-criteria optimisation problems, and the co-evolutionary one aimed at solving multi-modal optimisation problems. Each variation of EMAS is illustrated with selected experimental results.

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Camarinha-Matos, Luis M., Hamideh Afsarmanesh, and Vladimir Mařík. "Multi-agent systems applications." Robotics and Autonomous Systems 27, no. 1-2 (April 1999): 1–2. http://dx.doi.org/10.1016/s0921-8890(99)00006-8.

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da Silva, Viviane Torres, Fernanda Duran, José Guedes, and Carlos J. P. de Lucena. "Governing multi-agent systems." Journal of the Brazilian Computer Society 13, no. 2 (June 2007): 19–34. http://dx.doi.org/10.1007/bf03192407.

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Van Liedekerke, Marc H., and Nicholas M. Avouris. "Debugging multi-agent systems." Information and Software Technology 37, no. 2 (January 1995): 103–12. http://dx.doi.org/10.1016/0950-5849(95)93487-y.

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Flathmann, Christopher, Nathan McNeese, and Lorenzo Barberis Canonico. "Using Human-Agent Teams to Purposefully Design Multi-Agent Systems." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 63, no. 1 (November 2019): 1425–29. http://dx.doi.org/10.1177/1071181319631238.

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With multi-agent teams becoming more of a reality every day, it is important to create a common design model for multi-agent teams. These teams need to be able to function in dynamic environments and still communicate with any humans that may need a problem solved. Existing human-agent research can be used to purposefully create multi-agent teams that are interdependent but can still interact with humans. Rather than creating dynamic agents, the most effective way to overcome the dynamic nature of modern workloads is to create a dynamic team configuration, rather than individual member-agents that can change their roles. Multi-agent teams will require a variety of agents to be designed to cover a diverse subset of problems that need to be solved in the modern workforce. A model based on existing multi-agent teams that satisfies the needs of human-agent teams has been created to serve as a baseline for human-interactive multi-agent teams.

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Lavendelis, Egons, and Janis Grundspenkis. "Design of Multi-Agent Based Intelligent Tutoring Systems." Scientific Journal of Riga Technical University. Computer Sciences 38, no. 38 (January 1, 2009): 48–59. http://dx.doi.org/10.2478/v10143-009-0004-z.

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Design of Multi-Agent Based Intelligent Tutoring SystemsResearch of two fields, namely agent oriented software engineering and intelligent tutoring systems, have to be taken into consideration, during the design of multi-agent based intelligent tutoring systems (ITS). Thus there is a need for specific approaches for agent based ITS design, which take into consideration main ideas from both fields. In this paper we propose a top down design approach for multi-agent based ITSs. The proposed design approach consists of the two main stages: external design and internal design of agents. During the external design phase the behaviour of agents and interactions among them are designed. The following steps are done: task modelling and task allocation to agents, use case map creation, agent interaction design, ontology creation and holon design. During the external design phase agents and holons are defined according to the holonic multi-agent architecture for ITS development. During the internal design stage the internal structure of agents is specified. The internal structure of each agent is represented in the specific diagram, called internal view of the agent, consisting of agent's actions and interactions among them, rules for incoming message and perception processing, incoming and outgoing messages, and beliefs of the agent. The proposed approach is intended to be a part of the full life cycle methodology for multi-agent based ITS development. The approach is developed using the same concepts as JADE agent platform and is suitable for agent code generation from the design diagrams.

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Cardoso, Rafael C., and Angelo Ferrando. "A Review of Agent-Based Programming for Multi-Agent Systems." Computers 10, no. 2 (January 27, 2021): 16. http://dx.doi.org/10.3390/computers10020016.

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Intelligent and autonomous agents is a subarea of symbolic artificial intelligence where these agents decide, either reactively or proactively, upon a course of action by reasoning about the information that is available about the world (including the environment, the agent itself, and other agents). It encompasses a multitude of techniques, such as negotiation protocols, agent simulation, multi-agent argumentation, multi-agent planning, and many others. In this paper, we focus on agent programming and we provide a systematic review of the literature in agent-based programming for multi-agent systems. In particular, we discuss both veteran (still maintained) and novel agent programming languages, their extensions, work on comparing some of these languages, and applications found in the literature that make use of agent programming.

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Moskowitz, Ira S., Pi Rogers, and Stephen Russell. "Mutual Information and Multi-Agent Systems." Entropy 24, no. 12 (November 24, 2022): 1719. http://dx.doi.org/10.3390/e24121719.

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We consider the use of Shannon information theory, and its various entropic terms to aid in reaching optimal decisions that should be made in a multi-agent/Team scenario. The methods that we use are to model how various agents interact, including power allocation. Our metric for agents passing information are classical Shannon channel capacity. Our results are the mathematical theorems showing how combining agents influences the channel capacity.

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Anavankot, Amritha Menon, Stephen Cranefield, and Bastin Tony Roy Savarimuthu. "NEMAS: Norm Entrepreneurship in Multi-Agent Systems." Systems 12, no. 6 (May 25, 2024): 187. http://dx.doi.org/10.3390/systems12060187.

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We propose a framework that integrates norm entrepreneurship from human society into the dynamics of an agent society. Most work in agent coordination in a distributed environment studies norms that are provided to agents as part of their specification or distributed from centralised agents. Exploring an alternate perspective, we focus on peer-to-peer interaction by providing the agents with the freedom to initiate norm creation in demanding situations like a potential interference. This paper explores the concept of norm entrepreneurship through proactive establishment and emergence of norms by agents. A common approach in prior work focuses on coordination problems that are reduced to simple game theory models involving the simultaneous performance of a single action by each agent. Instead, we define the concept of a local coordination plan (LCP), which is a sequence of actions from each agent to cope with an interference in their normal course of action. We identify LCPs across various scenarios and abstract these plans using coordination state machines (CSMs). A coordination state machine contains a separate state machine for each agent where the states encapsulate the potentially constrained and suboptimal movement options agents have at a given time. We also explore how multiple LCPs lead to a coordination state machine of the same format and how a coordination state machine can abstract across multiple scenarios.

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Julian, Vicente, and Vicente Botti. "Special Issue on Multi-Agent Systems." Applied Sciences 13, no. 2 (January 12, 2023): 1021. http://dx.doi.org/10.3390/app13021021.

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SHAKSHUKI, ELHADI, and SAAD ABU-DRAZ. "MULTI-AGENT SYSTEM ARCHITECTURE TO TRADING SYSTEMS." Journal of Interconnection Networks 06, no. 03 (September 2005): 283–302. http://dx.doi.org/10.1142/s0219265905001435.

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Agents for online trading purpose can be seen as a tool that helps computer users to purchase products from distributed resources based on their interests and preferences. One of the major features that determine the success of trading agent is the ability to negotiate with other agents, because most trading tasks involve interaction among agents. This paper presents a peer-to-peer multi-agent system architecture for online trading. The main objective of this system is to address some of the shortcomings that are present in contemporary online trading systems that focused on providing solutions for specific trading issues, such as single attribute-based negotiation, the requirement of an electronic marketplace and variations and status changes within the network. The proposed system architecture is a multi-tier, multi-agent architecture. The system architecture consists of three types of agents that are classified based on their functionality: interface, resource and retrieval agents. The interface agents are the front-end of the system and able to interact with different users to fulfill their needs. At the middle-tier, the resource agents access and capture the contents and the changes of the local information database. The retrieval agents are the back-end of the system and able to travel and interact with other agents at remote host machines. A prototype of this system is implemented using the IBM Aglet SDK.

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Li, Guangyu, Bo Jiang, Hao Zhu, Zhengping Che, and Yan Liu. "Generative Attention Networks for Multi-Agent Behavioral Modeling." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 05 (April 3, 2020): 7195–202. http://dx.doi.org/10.1609/aaai.v34i05.6209.

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Understanding and modeling behavior of multi-agent systems is a central step for artificial intelligence. Here we present a deep generative model which captures behavior generating process of multi-agent systems, supports accurate predictions and inference, infers how agents interact in a complex system, as well as identifies agent groups and interaction types. Built upon advances in deep generative models and a novel attention mechanism, our model can learn interactions in highly heterogeneous systems with linear complexity in the number of agents. We apply this model to three multi-agent systems in different domains and evaluate performance on a diverse set of tasks including behavior prediction, interaction analysis and system identification. Experimental results demonstrate its ability to model multi-agent systems, yielding improved performance over competitive baselines. We also show the model can successfully identify agent groups and interaction types in these systems. Our model offers new opportunities to predict complex multi-agent behaviors and takes a step forward in understanding interactions in multi-agent systems.

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YUAN, SOE-TSYR, and ZENG-LUNG WU. "AN INFRASTRUCTURE FOR ENGINEERING COOPERATIVE AGENTS." International Journal of Software Engineering and Knowledge Engineering 10, no. 06 (December 2000): 681–711. http://dx.doi.org/10.1142/s0218194000000377.

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Currently, systems of cooperative agents (multi-agent systems), possessing the capabilities of autonomy, adaptation, and cooperation, are being used in an increasingly wide variety of application areas, and the conversation-based multi-agent system design is the major design for those multi-agent systems. Supposedly, conversation-based multi-agent systems should have been prevailing enough for tackling dynamic aspects of problems in a variety of domains. However, for industries, multi-agent systems are still found to be in the birth stage where they only show their new values in anticipation for further explorations and improvements in order to attract critical mass of users of information executives or software developers. Nevertheless, what are the success factors that can result in a critical mass of multi-agent system designers? This paper shows one possible success factor — an infrastructure for the bottom-up design of multi-agent systems. The bottom-up design makes it possible for agents to be reassembled into multi-agent systems and reused as needed. However, what do we need to successfully support the bottom-up design? This paper is the first attempt to present a tool that fully supports the bottom-up design of multi-agent systems. The tool has three parts. The first part is a wrapper that wraps each agent so that it exempts the designers from the careful detailed deployment of the inter-relationships between cooperation knowledge and task knowledge inside the agent. This wrapper should be independent of the functions of agents. The second part is an environment that can support the wrapper to automate the cooperation process on behalf of agents. The third part is a graphical assembly panel for developers to visually configure wrapped agents residing at different places of the Internet into a working multi-agent system.

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AHMAD, NAJLA, and ARVIN AGAH. "INTENT RECOGNITION IN MULTI-AGENT SYSTEMS: COW HERDING." International Journal of Pattern Recognition and Artificial Intelligence 28, no. 05 (July 31, 2014): 1459006. http://dx.doi.org/10.1142/s021800141459006x.

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In a multi-agent system, an agent may utilize its idle time to assist other agents in the system. Intent recognition is proposed to accomplish this with minimal communication. An agent performing recognition observes the tasks other agents are performing and, unlike the much studied field of plan recognition, the overall intent of an agent is recognized instead of a specific plan. The observing agent may use capabilities that it has not observed. A conceptual framework is proposed for intent recognition systems. An implementation of the conceptual framework is tested and evaluated. We hypothesize that using intent recognition in a multi-agent system increases utility (where utility is domain specific) and decreases the amount of communication. We test our hypotheses using the domain of Cow Herding, where agents attempt to herd cow agents into team corrals. A set of metrics, including task time and number of communications, is used to compare the performance of plan recognition and intent recognition. In our results, we find that intent recognition agents communicate fewer times than plan recognition agents. In addition, unlike plan recognition, when agents use the novel approach of intent recognition, they select unobserved actions to perform. Intent recognition agents were also able to outperform plan recognition agents by consistently scoring more points in the Cow Herding domain. This research shows that under certain conditions, an intent recognition system is more efficient than a plan recognition system. The advantage of intent recognition over plan recognition becomes more apparent in complex domains.

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D'INVERNO, MARK, MICHAEL FISHER, ALESSIO LOMUSCIO, MICHAEL LUCK, MAARTEN DE RIJKE, MARK RYAN, and MICHAEL WOOLDRIDGE. "Formalisms for multi-agent systems." Knowledge Engineering Review 12, no. 3 (September 1997): 315–21. http://dx.doi.org/10.1017/s0269888997003068.

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As computer scientists, our goals are motivated by the desire to improve computer systems in some way: making them easier to design and implement, more robust and less prone to error, easier to use, faster, cheaper, and so on. In the field of multi-agent systems, our goal is to build systems capable of flexible autonomous decision making, with societies of such systems cooperating with one-another. There is a lot of formal theory in the area but it is often not obvious what such theories should represent and what role the theory is intended to play. Theories of agents are often abstract and obtuse and not related to concrete computational models.

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Kuznetsov, O. P. "Asynchronous Multi-Agent Multisorted Systems." Automation and Remote Control 82, no. 2 (February 2021): 294–307. http://dx.doi.org/10.1134/s0005117921020089.

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Monitor, I&S. "Multi-agent systems: Selected reading." Information & Security: An International Journal 8 (2002): 116–20. http://dx.doi.org/10.11610/isij.0807.

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Minsky, Naftaly. "Law-governed multi-agent systems." ACM SIGSOFT Software Engineering Notes 30, no. 4 (July 2005): 1. http://dx.doi.org/10.1145/1082983.1082965.

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Müller, Jean-Pierre, and H. Van Dyke Parunak. "Multi-Agent Systems and Manufacturing." IFAC Proceedings Volumes 31, no. 15 (June 1998): 545–50. http://dx.doi.org/10.1016/s1474-6670(17)40609-4.

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RAMCHURN, SARVAPALI D., DONG HUYNH, and NICHOLAS R. JENNINGS. "Trust in multi-agent systems." Knowledge Engineering Review 19, no. 1 (March 2004): 1–25. http://dx.doi.org/10.1017/s0269888904000116.

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Trust is a fundamental concern in large-scale open distributed systems. It lies at the core of all interactions between the entities that have to operate in such uncertain and constantly changing environments. Given this complexity, these components, and the ensuing system, are increasingly being conceptualised, designed, and built using agent-based techniques and, to this end, this paper examines the specific role of trust in multi-agent systems. In particular, we survey the state of the art and provide an account of the main directions along which research efforts are being focused. In so doing, we critically evaluate the relative strengths and weaknesses of the main models that have been proposed and show how, fundamentally, they all seek to minimise the uncertainty in interactions. Finally, we outline the areas that require further research in order to develop a comprehensive treatment of trust in complex computational settings.

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BINMORE, KEN, CRISTIANO CASTELFRANCHI, JAMES DORAN, and MICHAEL WOOLDRIDGE. "Rationality in Multi-Agent Systems." Knowledge Engineering Review 13, no. 3 (November 1998): 309–14. http://dx.doi.org/10.1017/s0269888998003026.

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Beer, Martin, Mark d'Inverno, Michael Luck, Nick Jennings, Chris Preist, and Michael Schroeder. "Negotiation in multi-agent systems." Knowledge Engineering Review 14, no. 3 (September 1999): 285–89. http://dx.doi.org/10.1017/s0269888999003021.

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Ghanea-Hercock, Robert, and Ian Gifford. "Top-Secret Multi-Agent Systems." Electronic Notes in Theoretical Computer Science 63 (May 2002): 77–90. http://dx.doi.org/10.1016/s1571-0661(04)80338-4.

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Hedin, Yaqin, and Esmiralda Moradian. "Security in Multi-Agent Systems." Procedia Computer Science 60 (2015): 1604–12. http://dx.doi.org/10.1016/j.procs.2015.08.270.

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Tweedale, J., N. Ichalkaranje, C. Sioutis, B. Jarvis, A. Consoli, and G. Phillips-Wren. "Innovations in multi-agent systems." Journal of Network and Computer Applications 30, no. 3 (August 2007): 1089–115. http://dx.doi.org/10.1016/j.jnca.2006.04.005.

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Dorri, Ali, Salil S. Kanhere, and Raja Jurdak. "Multi-Agent Systems: A Survey." IEEE Access 6 (2018): 28573–93. http://dx.doi.org/10.1109/access.2018.2831228.

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Pitt, Jeremy, Lloyd Kamara, Marek Sergot, and Alexander Artikis. "Voting in Multi-Agent Systems*." Computer Journal 49, no. 2 (January 23, 2006): 156–70. http://dx.doi.org/10.1093/comjnl/bxh164.

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Rohbogner, Gregor, Ulf Hahnel, Pascal Benoit, and Simon Fey. "Multi-agent systems’ asset for smart grid applications." Computer Science and Information Systems 10, no. 4 (2013): 1799–822. http://dx.doi.org/10.2298/csis130224072r.

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Multi-agent systems are a subject of continuously increasing interest in applied technical sciences. Smart grids are one evolving field of application. Numerous smart grid projects with various interpretations of multi-agent systems as new control concept arose in the last decade. Although several theoretical definitions of the term ?agent? exist, there is a lack of practical understanding that might be improved by clearly distinguishing the agent technologies from other state-of-the-art control technologies. In this paper we clarify the differences between controllers, optimizers, learning systems, and agents. Further, we review most recent smart grid projects, and contrast their interpretations with our understanding of agents and multi-agent systems. We point out that multi-agent systems applied in the smart grid can add value when they are understood as fully distributed networks of control entities embedded in dynamic grid environments; able to operate in a cooperative manner and to automatically (re-)configure themselves.

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Claudi, Andrea, Paolo Sernani, and Aldo Franco Dragoni. "Towards Multi-Agent Health Information Systems." International Journal of E-Health and Medical Communications 6, no. 4 (October 2015): 20–38. http://dx.doi.org/10.4018/ijehmc.2015100102.

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One of the key challenges in the healthcare sector is to adapt Health Information Systems to requirements coming from changing societies. In recent years, governments and international healthcare organizations defined a series of requirements for new generation Health Information Systems: they have to preserve past investments on legacy systems, but must also integrate new technologies, include the patient among their users, and ensure that clinical information are available at all times, even in places far from where information are physically stored. This paper proposes a multi agent-oriented architecture for Health Information Systems, which uses international standards for communication and management of clinical documents. The architecture tries to effectively model a generic healthcare organization, and aims at being easily extensible and adaptable to the particularities of specific healthcare systems. The authors present two experimental scenarios to test the proposed multi-agent health information system. In the first, they show how to model a specific use case, a radiology workflow, using agents and well-known standards; in the second one the authors demonstrate how a mobile application can use the services provided by the agents to support the medical staff in an emergency situation.

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Melo, Victor S., Alison R. Panisson, and Rafael H. Bordini. "Meta-Information and Argumentation in Multi-Agent Systems." iSys - Brazilian Journal of Information Systems 10, no. 3 (September 30, 2017): 74–97. http://dx.doi.org/10.5753/isys.2017.347.

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In this work we compile our research regarding meta-information in multi-agent systems. In particular, we describe some agents profiles represent- ing different attitudes which describe how agents consider meta-information in their decisions-making and reasoning processes. Furthermore, we describe how we have combined different meta-information available in multi-agent systems with an argumentation-based reasoning mechanism. In our approach, agents are able to decide more conflicts between information/arguments, given that they are able to use different meta-information (often combined) to decide between such conflicting information. Our framework for meta-information in multi- agent systems was implemented based on a modular architecture, thus other meta-information can be added, as well as different meta-information can be combined in order to create new agents profiles. Therefore, in our approach, different agents profiles can be instantiated for different application domains, allowing flexibility in the choice of how agents will deal with conflicting infor- mation in those particular domains.

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Xu, Lü, Shuanghe Meng, and Liang Chen. "Consensus of Heterogeneous Multi-Agent Systems with Intermittent Communication." Journal of Systems Science and Information 5, no. 4 (September 17, 2017): 328–42. http://dx.doi.org/10.21078/jssi-2017-328-15.

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AbstractThis paper studies consensus of a class of heterogeneous multi-agent systems composed of first-order and second-order agents with intermittent communication. For leaderless multi-agent systems, we propose a distributed consensus algorithm based on the intermittent information of neighboring agents. Some sufficient conditions are obtained to guarantee the consensus of heterogeneous multi-agent systems in terms of bilinear matrix inequalities (BMIs). Meanwhile, the relationship between communication duration and each control period is sought out. Moreover, the designed algorithm is extended to leader-following multi-agent systems without velocity measurements. Finally, the effectiveness of the main results is illustrated by numerical simulations.

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