Objective Coordination In Multi Agent System Engineering
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Objective Coordination in Multi-Agent System Engineering
Based on a suitably defined coordination model distinguishing between objective (inter-agent) coordination and subjective (intra-agent) coordination, this book addresses the engineering of multi-agent systems and thus contributes to closing the gap between research and applications in agent technology. After reviewing the state of the art, the author introduces the general coordination model ECM and the corresponding object-oriented coordination language STL++. The practicability of ECM/STL++ is illustrated by the simulation of a particular collective robotics application and the automation of an e-commerce trading system. Situated at the intersection of behavior-based artificial intelligence and concurrent and distributed systems, this monograph is of relevance to the agent R&D community approaching agent technology from the distributed artificial intelligence point of view as well as for the distributed systems community.
Objective Coordination in Multi-Agent System Engineering
Author: Michael Schumacher
language: en
Publisher: Springer Science & Business Media
Release Date: 2001-04-25
Based on a suitably defined coordination model distinguishing between objective (inter-agent) coordination and subjective (intra-agent) coordination, this book addresses the engineering of multi-agent systems and thus contributes to closing the gap between research and applications in agent technology. After reviewing the state of the art, the author introduces the general coordination model ECM and the corresponding object-oriented coordination language STL++. The practicability of ECM/STL++ is illustrated by the simulation of a particular collective robotics application and the automation of an e-commerce trading system. Situated at the intersection of behavior-based artificial intelligence and concurrent and distributed systems, this monograph is of relevance to the agent R&D community approaching agent technology from the distributed artificial intelligence point of view as well as for the distributed systems community.
Environments for Multi-Agent Systems
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.