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Title: Joint intentions as a model of multi-agent cooperation in complex dynamic environments
Author: Jennings, Nick R.
ISNI:       0000 0001 3590 2213
Awarding Body: University of London
Current Institution: Queen Mary, University of London
Date of Award: 1992
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Computer-based systems are being used to tackle increasingly complex problems in ever more demanding domains. The size and amount of knowledge needed by such systems means they are becoming unwieldy and difficult to engineer into reliable, consistent products. One paradigm for overcoming this barrier is to decompose the problem into smaller more manageable components which can communicate and cooperate at the level of sharing processing responsibilities and information. Until recently, research in multi-agent systems has been based on ad hoc models of action and interaction; however, the notion of intentions is beginning to emerge as a prime candidate upon which a sound theory could be based. This research develops a new model of joint intentions as a means of describing the activities of groups of agents working collaboratively. The model stresses the role of intentions in controlling agents� current and future actions; defining preconditions which must be satisfied before joint problem solving can commence and prescribing how individual agents should behave once it has been established. Such a model becomes especially important in dynamic environments in which agents may possess neither complete nor correct beliefs about their world or other agents, have changeable goals and fallible actions and be subject to interruption from external events. The theory has been implemented in a general purpose cooperation framework, called GRATE*, and applied to the real-world problem of electricity transportation management. In this application, individual problem solvers have to take decisions using partial, imprecise information and respond to an ever changing external world. This fertile environment enabled the quantitative benefits of the theory to be assessed and comparisons with other models of collaborative problem solving to be undertaken. These experiments highlighted the high degree of coherence attained by GRATE* problem solving groups, even in the most dynamic and unpredictable application contexts.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available
Keywords: Distributed AI