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An Introduction to MultiAgent Systems.

01 Jan 2003-Vol. 17, pp 58
About: The article was published on 2003-01-01 and is currently open access. It has received 3093 citations till now.
Citations
More filters
Journal ArticleDOI
TL;DR: In this article, a wide list of topics ranging from opinion and cultural and language dynamics to crowd behavior, hierarchy formation, human dynamics, and social spreading are reviewed and connections between these problems and other, more traditional, topics of statistical physics are highlighted.
Abstract: Statistical physics has proven to be a fruitful framework to describe phenomena outside the realm of traditional physics. Recent years have witnessed an attempt by physicists to study collective phenomena emerging from the interactions of individuals as elementary units in social structures. A wide list of topics are reviewed ranging from opinion and cultural and language dynamics to crowd behavior, hierarchy formation, human dynamics, and social spreading. The connections between these problems and other, more traditional, topics of statistical physics are highlighted. Comparison of model results with empirical data from social systems are also emphasized.

3,840 citations


Cites background from "An Introduction to MultiAgent Syste..."

  • ...On short time scales, coexisting ordered domains of small size (both positive and negative) are formed....

    [...]

  • ...…Steels, 1995; Varela and Bourgine, 1992; Weiss, 1999), but since then agent-based simulations have become an important tool in other scientific fields and in particular in the study of social systems (Axelrod, 2006; Conte et al., 1997; Macy and Willer, 2002; Schweitzer, 2003; Wooldridge, 2002)....

    [...]

Journal ArticleDOI
TL;DR: It is argued that a multiagent system can naturally be viewed and architected as a computational organization, and the appropriate organizational abstractions that are central to the analysis and design of such systems are identified.
Abstract: Systems composed of interacting autonomous agents offer a promising software engineering approach for developing applications in complex domains. However, this multiagent system paradigm introduces a number of new abstractions and design/development issues when compared with more traditional approaches to software development. Accordingly, new analysis and design methodologies, as well as new tools, are needed to effectively engineer such systems. Against this background, the contribution of this article is twofold. First, we synthesize and clarify the key abstractions of agent-based computing as they pertain to agent-oriented software engineering. In particular, we argue that a multiagent system can naturally be viewed and architected as a computational organization, and we identify the appropriate organizational abstractions that are central to the analysis and design of such systems. Second, we detail and extend the Gaia methodology for the analysis and design of multiagent systems. Gaia exploits the aforementioned organizational abstractions to provide clear guidelines for the analysis and design of complex and open software systems. Two representative case studies are introduced to exemplify Gaia's concepts and to show its use and effectiveness in different types of multiagent system.

1,432 citations


Cites methods from "An Introduction to MultiAgent Syste..."

  • ...Given this new landscape, we advocate the use of multiagent systems (MASs) as a software engineering paradigm for designing and developing complex software systems [58, 27, 57]....

    [...]

Book
01 Jul 2007
TL;DR: The Jason Agent Programming Language as discussed by the authors is a programming language based on the BDI Agent Model that allows to define simulated environments and communicate with multiple agents in a BDI agent language.
Abstract: Preface. 1 Introduction. 1.1 Autonomous Agents. 1.2 Characteristics of Agents. 1.3 Multi-Agent Systems. 1.4 Hello World! 2 The BDI Agent Model. 2.1 Agent-Oriented Programming. 2.2 Practical Reasoning. 2.3 A Computational Model of BDI Practical Reasoning. 2.4 The Procedural Reasoning System. 2.5 Agent Communication. 3 The Jason Agent Programming Language. 3.1 Beliefs. 3.2 Goals. 3.3 Plans. 3.4 Example: A Complete Agent Program. 3.5 Exercises. 4 Jason Interpreter. 4.1 The Reasoning Cycle. 4.2 Plan Failure. 4.3 Interpreter Configuration and Execution Modes. 4.4 Pre-Defined Plan Annotations. 4.5 Exercises. 5 Environments. 5.1 Support for Defining Simulated Environments. 5.2 Example: Running a System of Multiple Situated Agents. 5.3 Exercises. 6 Communication and Interaction. 6.1 Available Performatives. 6.2 Informal Semantics of Receiving Messages. 6.3 Example: Contract Net Protocol. 6.4 Exercises. 7 User-Defined Components. 7.1 Defining New Internal Actions. 7.2 Customising the Agent Class. 7.3 Customising the Overall Architecture. 7.4 Customising the Belief Base. 7.5 Pre-Processing Directives. 7.6 Exercises. 8 Advanced Goal-Based Programming. 8.1 BDI Programming. 8.2 Declarative (Achievement) Goal Patterns. 8.3 Commitment Strategy Patterns. 8.4 Other Useful Patterns. 8.5 Pre-Processing Directives for Plan Patterns. 9 Case Studies. 9.1 Case Study I: Gold Miners. 9.2 Case Study II: Electronic Bookstore. 10 Formal Semantics. 10.1 Semantic Rules. 10.2 Semantics of Message Exchange in a Multi-Agent System. 10.3 Semantic Rules for Receiving Messages. 10.4 Semantics of the BDI Modalities for AgentSpeak. 11 Conclusions. 11.1 Jason and Agent-Oriented Programming. 11.2 Ongoing Work and Related Research. 11.3 General Advice on Programming Style and Practice. A Reference Guide. A.1 EBNF for the Agent Language. A.2 EBNF for the Multi-Agent Systems Language. A.3 Standard Internal Actions. A.4 Pre-Defined Annotations. A.5 Pre-Processing Directives. A.6 Interpreter Configuration. Bibliography.

1,173 citations

Book ChapterDOI
01 Jan 2008
TL;DR: The general public becomes rapidly jaded with such ‘bold predictions’ that fail to live up to their original hype, and which ultimately render the zealots’ promises as counter-productive.
Abstract: The Artificial Intelligence field continues to be plagued by what can only be described as ‘bold promises for the future syndrome’, often perpetrated by researchers who should know better. While impartial assessment can point to concrete contributions over the past 50 years (such as automated theorem proving, games strategies, the LISP and Prolog high-level computer languages, Automatic Speech Recognition, Natural Language Processing, mobile robot path planning, unmanned vehicles, humanoid robots, data mining, and more), the more cynical argue that AI has witnessed more than its fair share of ‘unmitigated disasters’ during this time – see, for example [3,58,107,125,186]. The general public becomes rapidly jaded with such ‘bold predictions’ that fail to live up to their original hype, and which ultimately render the zealots’ promises as counter-productive.

846 citations


Cites background from "An Introduction to MultiAgent Syste..."

  • ...Agents are touted as being better able to interact with complex, real-world situations than objects, since they are network-centric, adaptive and self-modifying (indeed, self-repairing) – unlike objects, which are fixed and unable to modify their behavior over time, being constrained to obey the Boolean logic rules which underpin them [19,183,197,210,317]....

    [...]

Journal ArticleDOI
TL;DR: The evolution of agent technologies and manufacturing will probably proceed hand in hand and the former can receive real challenges from the latter, which will have more and more benefits in applying agent technologies, presumably together with well-established or emerging approaches of other disciplines.
Abstract: The emerging paradigm of agent-based computation has revolutionized the building of intelligent and decentralized systems. The new technologies met well the requirements in all domains of manufacturing where problems of uncertainty and temporal dynamics, information sharing and distributed operation, or coordination and cooperation of autonomous entities had to be tackled. In the paper software agents and multi-agent systems are introduced and through a comprehensive survey, their potential manufacturing applications are outlined. Special emphasis is laid on methodological issues and deployed industrial systems. After discussing open issues and strategic research directions, we conclude that the evolution of agent technologies and manufacturing will probably proceed hand in hand. The former can receive real challenges from the latter, which, in turn, will have more and more benefits in applying agent technologies, presumably together with well-established or emerging approaches of other disciplines.

668 citations


Cites background from "An Introduction to MultiAgent Syste..."

  • ...The most important common properties of computational agents are as follows: • Agents act on behalf of their designer or the user they represent in order to meet a particular purpose....

    [...]

  • ...With all of its merits, integration resulted in rigid, hierarchical control architectures whose structural complexity grew rapidly with the size and the scope of the systems....

    [...]

  • ...Typically, one way to take care of global objectives and system-wide constraints is to channel all/some interactions through a central coordinator (manager) agent....

    [...]

References
More filters
Journal ArticleDOI
TL;DR: In this article, a wide list of topics ranging from opinion and cultural and language dynamics to crowd behavior, hierarchy formation, human dynamics, and social spreading are reviewed and connections between these problems and other, more traditional, topics of statistical physics are highlighted.
Abstract: Statistical physics has proven to be a fruitful framework to describe phenomena outside the realm of traditional physics. Recent years have witnessed an attempt by physicists to study collective phenomena emerging from the interactions of individuals as elementary units in social structures. A wide list of topics are reviewed ranging from opinion and cultural and language dynamics to crowd behavior, hierarchy formation, human dynamics, and social spreading. The connections between these problems and other, more traditional, topics of statistical physics are highlighted. Comparison of model results with empirical data from social systems are also emphasized.

3,840 citations

Journal ArticleDOI
TL;DR: It is argued that a multiagent system can naturally be viewed and architected as a computational organization, and the appropriate organizational abstractions that are central to the analysis and design of such systems are identified.
Abstract: Systems composed of interacting autonomous agents offer a promising software engineering approach for developing applications in complex domains. However, this multiagent system paradigm introduces a number of new abstractions and design/development issues when compared with more traditional approaches to software development. Accordingly, new analysis and design methodologies, as well as new tools, are needed to effectively engineer such systems. Against this background, the contribution of this article is twofold. First, we synthesize and clarify the key abstractions of agent-based computing as they pertain to agent-oriented software engineering. In particular, we argue that a multiagent system can naturally be viewed and architected as a computational organization, and we identify the appropriate organizational abstractions that are central to the analysis and design of such systems. Second, we detail and extend the Gaia methodology for the analysis and design of multiagent systems. Gaia exploits the aforementioned organizational abstractions to provide clear guidelines for the analysis and design of complex and open software systems. Two representative case studies are introduced to exemplify Gaia's concepts and to show its use and effectiveness in different types of multiagent system.

1,432 citations

Book
01 Jul 2007
TL;DR: The Jason Agent Programming Language as discussed by the authors is a programming language based on the BDI Agent Model that allows to define simulated environments and communicate with multiple agents in a BDI agent language.
Abstract: Preface. 1 Introduction. 1.1 Autonomous Agents. 1.2 Characteristics of Agents. 1.3 Multi-Agent Systems. 1.4 Hello World! 2 The BDI Agent Model. 2.1 Agent-Oriented Programming. 2.2 Practical Reasoning. 2.3 A Computational Model of BDI Practical Reasoning. 2.4 The Procedural Reasoning System. 2.5 Agent Communication. 3 The Jason Agent Programming Language. 3.1 Beliefs. 3.2 Goals. 3.3 Plans. 3.4 Example: A Complete Agent Program. 3.5 Exercises. 4 Jason Interpreter. 4.1 The Reasoning Cycle. 4.2 Plan Failure. 4.3 Interpreter Configuration and Execution Modes. 4.4 Pre-Defined Plan Annotations. 4.5 Exercises. 5 Environments. 5.1 Support for Defining Simulated Environments. 5.2 Example: Running a System of Multiple Situated Agents. 5.3 Exercises. 6 Communication and Interaction. 6.1 Available Performatives. 6.2 Informal Semantics of Receiving Messages. 6.3 Example: Contract Net Protocol. 6.4 Exercises. 7 User-Defined Components. 7.1 Defining New Internal Actions. 7.2 Customising the Agent Class. 7.3 Customising the Overall Architecture. 7.4 Customising the Belief Base. 7.5 Pre-Processing Directives. 7.6 Exercises. 8 Advanced Goal-Based Programming. 8.1 BDI Programming. 8.2 Declarative (Achievement) Goal Patterns. 8.3 Commitment Strategy Patterns. 8.4 Other Useful Patterns. 8.5 Pre-Processing Directives for Plan Patterns. 9 Case Studies. 9.1 Case Study I: Gold Miners. 9.2 Case Study II: Electronic Bookstore. 10 Formal Semantics. 10.1 Semantic Rules. 10.2 Semantics of Message Exchange in a Multi-Agent System. 10.3 Semantic Rules for Receiving Messages. 10.4 Semantics of the BDI Modalities for AgentSpeak. 11 Conclusions. 11.1 Jason and Agent-Oriented Programming. 11.2 Ongoing Work and Related Research. 11.3 General Advice on Programming Style and Practice. A Reference Guide. A.1 EBNF for the Agent Language. A.2 EBNF for the Multi-Agent Systems Language. A.3 Standard Internal Actions. A.4 Pre-Defined Annotations. A.5 Pre-Processing Directives. A.6 Interpreter Configuration. Bibliography.

1,173 citations

Book ChapterDOI
01 Jan 2008
TL;DR: The general public becomes rapidly jaded with such ‘bold predictions’ that fail to live up to their original hype, and which ultimately render the zealots’ promises as counter-productive.
Abstract: The Artificial Intelligence field continues to be plagued by what can only be described as ‘bold promises for the future syndrome’, often perpetrated by researchers who should know better. While impartial assessment can point to concrete contributions over the past 50 years (such as automated theorem proving, games strategies, the LISP and Prolog high-level computer languages, Automatic Speech Recognition, Natural Language Processing, mobile robot path planning, unmanned vehicles, humanoid robots, data mining, and more), the more cynical argue that AI has witnessed more than its fair share of ‘unmitigated disasters’ during this time – see, for example [3,58,107,125,186]. The general public becomes rapidly jaded with such ‘bold predictions’ that fail to live up to their original hype, and which ultimately render the zealots’ promises as counter-productive.

846 citations

Journal ArticleDOI
TL;DR: The evolution of agent technologies and manufacturing will probably proceed hand in hand and the former can receive real challenges from the latter, which will have more and more benefits in applying agent technologies, presumably together with well-established or emerging approaches of other disciplines.
Abstract: The emerging paradigm of agent-based computation has revolutionized the building of intelligent and decentralized systems. The new technologies met well the requirements in all domains of manufacturing where problems of uncertainty and temporal dynamics, information sharing and distributed operation, or coordination and cooperation of autonomous entities had to be tackled. In the paper software agents and multi-agent systems are introduced and through a comprehensive survey, their potential manufacturing applications are outlined. Special emphasis is laid on methodological issues and deployed industrial systems. After discussing open issues and strategic research directions, we conclude that the evolution of agent technologies and manufacturing will probably proceed hand in hand. The former can receive real challenges from the latter, which, in turn, will have more and more benefits in applying agent technologies, presumably together with well-established or emerging approaches of other disciplines.

668 citations