H. Van Dyke Parunak – författare

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.

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.

This book constitutes the thoroughly refereed post-proceedings of the Second International Workshop on Environments for Multiagent Systems, E4MAS 2005, held in July 2005. The 16 revised papers presented were carefully reviewed and selected from the lectures given at the workshop. The papers are organized in topical sections on models, architecture, and design, mediated coordination, as well as applications.

This book constitutes the thoroughly refereed post-proceedings of the Third International Workshop on Environments for Multiagent Systems, E4MAS 2006, held in Hakodate, Japan in May 2006. The 15 revised papers in this volume are organized in topical sections on models, architecture, and design, mediated interaction and stigmery, governing environment, and applications.

This volume contains a selection of the papers presented at the 10th Inter- tional Workshop on Multi-Agent-Based Simulation (MABS 2009), a workshop co-located with the 8th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2009),which was held during May 10-15,2009, in Budapest, Hungary. The MABS Workshops Series continues to representan important event that brings together researchers from the multi-agent systems community and the social simulation one. There were 43 submissions for the 2009 edition, each one reviewed by at least three Programme Committee members, and 16 were - cepted for presentation during the workshop. All of them underwent a second review phase, and 12 papers were selected and are collected here, grouped into four general thematic areas. We would like to thank all the authors who considered MABS as a venue to share their work and all those who attended the workshop for their active participation and lively discussion. March 2010 Gennaro Di Tosto H.Van Dyke Parunak Organization The 10th International Workshop on Multi-Agent-Based Simulation (MABS 2009) was organized by the Institute of Cognitive Sciences and Technologies - CNR,Italy,and NewVectors division of TechTeam Government Solutions Inc., MI, USA. Gennaro Di Tosto enjoyed funding from the European Science Fo- dationunderthe EUROCORESinitiative"TECT:the EvolutionofCooperation and Trade".

This book constitutes the thoroughly refereed post-conference proceedings of the 14th International Workshop on Multi-Agent-Based Simulation, MABS 2013, held in Saint Paul, Minnesota, USA, in May 2013. The workshop was help in conjunction with Twelfth International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2013. The 11 revised full papers included in this volume were carefully selected from 29 submissions. The papers are organized in topical sections on MABS for real-time and online data, formal approaches in MABS: design and validation, MABS in environmental modeling, simulating social phenomena.