Evolutionary patterns of agent organizations

Problems approached by multi-agent systems are typically complex. It is usually difficult to know at system design stage how many agents need to be in the system, what each agent's role is, and how the agents should interact to get optimal performance out of the group. The aim of the testbed presented here is to investigate which kinds of multi-agent systems could be developed to solve ranges of problems, avoiding the need to reorganize the agents from scratch for each task. The agent organization process explored here is based on the agents' knowledge, and not on their tasks. This opens up a new approach for distributed artificial intelligence designers to have their domain organized before the allocation of tasks among agents. These kinds of organizations should be more robust for solving different problems related to the same knowledge. We define information oriented domains for that purpose. An evolutionary approach to the design of a multi-agent system is suggested. Our model is based on a cellular automaton whose rules of dynamics induce the formation of an organization of agents. Patterns of organization obtained empirically are presented. Our knowledge-based organization approach is analyzed both from theoretical and practical perspectives.

[1]  Teuvo Kohonen,et al.  Self-Organization and Associative Memory , 1988 .

[2]  Kendall Preston,et al.  Modern Cellular Automata , 1984, Advanced Applications in Pattern Recognition.

[3]  Sarit Kraus,et al.  Collaborative Plans for Group Activities , 1993, IJCAI.

[4]  M Dresden,et al.  Life games and statistical models. , 1975, Proceedings of the National Academy of Sciences of the United States of America.

[5]  Eithan Ephrati,et al.  Reaching Agreement through Partial Revelation of Preferences , 1992, ECAI.

[6]  W. Calvin.  The Six Essentials? Minimal Requirements for the Darwinian Bootstrapping of Quality , 1997 .

[7]  Melanie Mitchell,et al.  An introduction to genetic algorithms , 1996 .

[8]  Claudia V. Goldman,et al.  Musag an Agent That Learns What You Mean , 1997, Appl. Artif. Intell..

[9]  Victor Lesser,et al.  Learning Organizational Roles in a Heterogeneous Multi-agent System , 1995 .

[10]  Frédéric Guichard,et al.  Logical Reorganization of DAI Systems , 1994, ECAI Workshop on Agent Theories, Architectures, and Languages.

[11]  Tommaso Toffoli,et al.  Cellular automata machines - a new environment for modeling , 1987, MIT Press series in scientific computation.

[12]  Jeffrey S. Rosenschein and Gilad Zlotkin.  Rules of Encounter , 1994 .

[13]  E. Berlekamp,et al.  Winning Ways for Your Mathematical Plays , 1983 .

[14]  L. Schulman,et al.  Statistical mechanics of a dynamical system based on Conway's game of Life , 1978 .

[15]  Claudia V. Goldman,et al.  Partitioned multiagent systems in information oriented domains , 1999, AGENTS '99.

[16]  Janet L. Kolodner,et al.  Case-Based Reasoning , 1989, IJCAI 1989.

[17]  Richard O. Duda,et al.  Pattern classification and scene analysis , 1974, A Wiley-Interscience publication.

[18]  Tad Hogg,et al.  Communities of practice: Performance and evolution , 1994, Comput. Math. Organ. Theory.

[19]  Bernardo A. Huberman,et al.  Social dilemmas and fluid organizations , 1994 .

[20]  Filippo Menczer,et al.  Adaptive information agents in distributed textual environments , 1998, AGENTS '98.

[21]  Oren Etzioni,et al.  The World-Wide Web: quagmire or gold mine? , 1996, CACM.

[22]  Claudia V. Goldman,et al.  Emergent Coordination through the Use of Cooperative State-Changing Rules , 1994, AAAI.

[23]  Victor R. Lesser,et al.  Designing a Family of Coordination Algorithms , 1997, ICMAS.

[24]  The origin of biological information , 1997 .

[25]  Gary Marchionini,et al.  A self-organizing semantic map for information retrieval , 1991, SIGIR '91.

[26]  Eithan Ephrati,et al.  The Clarke Tax as a Consensus Mechanism Among Automated Agents , 1991, AAAI.

[27]  Pattie Maes,et al.  Amalthaea: An Evolving Multi-Agent Information Filtering and Discovery System for the WWW , 2004, Autonomous Agents and Multi-Agent Systems.

[28]  John von Neumann,et al.  Theory Of Self Reproducing Automata , 1967 .

[29]  Michael McGill,et al.  Introduction to Modern Information Retrieval , 1983 .