Environments for Multi-Agent Systems

1. Front Matter

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2. Survey

   1. Environments for Multiagent Systems State-of-the-Art and Research Challenges

      • Danny Weyns, H. Van Dyke Parunak, Fabien Michel, Tom Holvoet, Jacques Ferber

      Pages 1-47

3. Conceptual Models

   1. AGRE: Integrating Environments with Organizations

      • J. Ferber, F. Michel, J. Baez

      Pages 48-56

   2. From Reality to Mind: A Cognitive Middle Layer of Environment Concepts for Believable Agents

      • Paul Hsueh-Min Chang, Kuang-Tai Chen, Yu-Hung Chien, Edward Kao, Von-Wun Soo

      Pages 57-73

   3. A Spatially Dependent Communication Model for Ubiquitous Systems

      • Stefania Bandini, Sara Manzoni, Giuseppe Vizzari

      Pages 74-90

4. Languages for Design and Specification

   1. ELMS: An Environment Description Language for Multi-agent Simulation

      • Fabio Y. Okuyama, Rafael H. Bordini, Antônio Carlos da Rocha Costa

      Pages 91-108

   2. MIC*: A Deployment Environment for Autonomous Agents

      • Abdelkader Gouaïch, Fabien Michel, Yves Guiraud

      Pages 109-126

5. Simulation and Environments

   1. About the Role of the Environment in Multi-agent Simulations

      • Franziska Klügl, Manuel Fehler, Rainer Herrler

      Pages 127-149

   2. Modelling Environments for Distributed Simulation

      • Michael Lees, Brian Logan, Rob Minson, Ton Oguara, Georgios Theodoropoulos

      Pages 150-167

6. Mediated Coordination

   1. Supporting Context-Aware Interaction in Dynamic Multi-agent Systems

      • Christine Julien, Gruia-Catalin Roman

      Pages 168-189

   2. Environment-Based Coordination Through Coordination Artifacts

      • Alessandro Ricci, Mirko Viroli, Andrea Omicini

      Pages 190-214

   3. “Exhibitionists” and “Voyeurs” Do It Better: A Shared Environment for Flexible Coordination with Tacit Messages

      • Luca Tummolini, Cristiano Castelfranchi, Alessandro Ricci, Mirko Viroli, Andrea Omicini

      Pages 215-231

7. Applications

   1. Swarming Distributed Pattern Detection and Classification

      • Sven A. Brueckner, H. Van Dyke Parunak

      Pages 232-245

   2. Digital Pheromones for Coordination of Unmanned Vehicles

      • H. Van Dyke Parunak, Sven A. Brueckner, John Sauter

      Pages 246-263

   3. Motion Coordination in the Quake 3 Arena Environment: A Field-Based Approach

      • Marco Mamei, Franco Zambonelli

      Pages 264-278

8. Back Matter

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The modern ?eld of multiagent systems has developed from two main lines of earlier research. Its practitioners generally regard it as a form of arti?cial intelligence (AI). Some of its earliest work was reported in a series of workshops in the US dating from1980,revealinglyentitled,“DistributedArti?cialIntelligence,”andpioneers often quoted a statement attributed to Nils Nilsson that “all AI is distributed. ” The locus of classical AI was what happens in the head of a single agent, and much MAS research re?ects this heritage with its emphasis on detailed modeling of the mental state and processes of individual agents. From this perspective, intelligenceisultimatelythepurviewofasinglemind,thoughitcanbeampli?ed by appropriate interactions with other minds. These interactions are typically mediated by structured protocols of various sorts, modeled on human conver- tional behavior. But the modern ?eld of MAS was not born of a single parent. A few - searchershavepersistentlyadvocatedideasfromthe?eldofarti?ciallife(ALife). These scientists were impressed by the complex adaptive behaviors of commu- ties of animals (often extremely simple animals, such as insects or even micro- ganisms). The computational models on which they drew were often created by biologists who used them not to solve practical engineering problems but to test their hypotheses about the mechanisms used by natural systems. In the ar- ?cial life model, intelligence need not reside in a single agent, but emerges at the level of the community from the nonlinear interactions among agents. - cause the individual agents are often subcognitive, their interactions cannot be modeled by protocols that presume linguistic competence.

• AI agents
• ALife
• ALife agents
• adaptive agents
• agent communication
• agent environments
• artificial intelligence
• autonomous agents
• classification
• distributed artificial intelligence
• environment-based coordination
• intelligence
• multi-agent simulation
• multi-agent system
• multiagent systems

• DistriNet Labs, K.U. Leuven, Belgium

  Danny Weyns

• NewVectors, Ann Arbor, USA

  H. Dyke Parunak

• MODECO-CReSTIC, Université de Reims, Reims, France

  Fabien Michel

Policies and ethics