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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.
This modern field of multi-agent systems has developed from two main lines of earlier research: its practitioners generally regard it as a form of distributed artificial intelligence, whereas some researchers have persistently advocated ideas from the field of artificial life. AI agents (and their designers) usually take the environment for agent interaction as granted. From the ALife perspective and for ALife agents, the environment for interaction is an active participant in agent dynamics, a first class member of the overall systems. This book originates from the First International Workshop on Environments for Multi-Agent Systems, E4MAS 2004, held in New York, NY, USA in July 2004 as a satellite workshop of AAMAS 2004. The 13 carefully selected reviewed and revised papers presented together with an introductory survey article of close to 50 pages are organized in topical sections on conceptual models, language for design and specification, simulation and environments, mediated coordination, and applications.
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