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<a target="_blank" rel="noopener" href="https://fatcat.wiki/container/weoplee4x5anpi62cco5v4higa" style="color: black;">Artificial Intelligence</a>
Automatic intel~gent agents i~abiting a shared environment must coordinate their activities. Cooperation-not merely coordination-may improve the performance of the individual agents or the overall behavior of the system they form. Research in Distributed Artificial Intelligence (DAI) addresses the problem of designing automated intelligent systems which interact effectively. DA1 is not the only field to take on the challenge of understanding cooperation and coordination. There are a variety of<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1016/s0004-3702(97)00025-8">doi:10.1016/s0004-3702(97)00025-8</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/d7ilfacq6rcdvnzkvky2cej4yu">fatcat:d7ilfacq6rcdvnzkvky2cej4yu</a> </span>
more »... ther multi-entity environments in which the entities coordinate their activity and cooperate. Among them are groups of people, animals, particles, and computers. We argue that in order to address the challenge of building coo~nated and collabom~d intelligent agents, it is beneficial to combine AI techniques with methods and techniques from a range of muIti-entity fields, such as game theory, operations research, physics and philosophy. To support this claim, we describe some of our projects, where we have successfully taken an interdisciplinary approach. We demomtrate the benefits in applying multi-entity methodologies and show the adaptations, modifications and extensions necessary for solving the DA1 problems. @ 1997 Elsevier Science B.V.
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