Multi-agent systems: an introduction to distributed artificial intelligence
Shelving Category: Artificial IntelligenceMulti-Agent SystemsAn Introduction to Distributed Artificial IntelligenceJacques FerberQuote 1: Quote 2: Quote 3: What are multi-agent systems?How do they work?What do they do?If you are looking for the answers to these questions, read on; for Jacques Ferber's authoritative book is the first to provide a single, coherent overview of multi-agent systems.no prior knowledge required7 introduces and defines key concepts throughout the text 7 provides numerous examples to illustrate core principles 7 clear, accessible writing style transcends traditional subject area boundaries to make this complex subject understandable to allthe whole picture7 draws on contributions from different disciplines to present a holistic, comprehensive picture of state-of-the art agent technology. future trends7 describes all the latest developments in the field and encourages the reader to reflect on possibilities for the futureAbout the Author: Jacques Ferber is Professor of Computer Science and Artificial Intelligence at the University of Montpellier, France and head of one of the foremost research groups in Europe investigating the applications of distributed artificial intelligence and multi-agent systems. Translated and updated from the original French, this book was winner of the 1996 French Association of Engineering and Information Systems award to the most outstanding technical book of the year.
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Interactions and Cooperation
Action and Behaviour
7 other sections not shown
activity agent x allocation analysis application architecture automata automaton basis behaviour beliefs BRIC capable capacities carry cellular automata centralised Chapter characteristic Clifford cognitive agents cognitons commitment communication complex components computing concept conflicts considered constraints contract cooperation coordination of actions corresponding cubes defined depends described dimension Distributed Artificial Intelligence Drogoul dynamic entities environment ethology example execution Ferber Figure function genetic algorithms goals hysteretic implementation individual intention interaction internal kenetics KQML language large number linked logic manager mechanisms memorising modal logics modify module multi-agent planning multi-agent systems n-puzzle number of agents objects operators organisation perception performance Petri nets plans possible predators prey animals problem production reactive agents relation relationships represent representation request for bids response signal simple Simulating Societies simulation situations skills social solving specialised speech acts structure subsumption architecture synchronisation take account tasks techniques theory totipotent transition
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