In many domains (such as dialogue participation and multi-agent cooperative planning) it is often necessary that the system maintains complex models of the beliefs of agents with whom it is interacting. In particular, it is normally the case that models of the beliefs of agents about another agent's beliefs must be modeled. While in limited domains it is possible to have such nested belief models pregenerated, in general it is more reasonable to have a mechanism for generating the nested models on demand. Two methods for such generation are discussed, one based on triggering stereotypes, and the other based on perturbation of the system's beliefs. Both of these approaches have limitations. An alternative is proposed that merges the two approaches, thus gaining the benefits of each and using those benefits to avoid the problems of either of the individual methods.
Interacting with agents in an intelligent manner means that the computer program is able to adapt itself to the specific requirements of agents. The dissertation is concerned with an important feature necessary for this ability to adapt: the use of models of the beliefs and knowledge of the interacting agents.The objective of this dissertation is to detail a theory of belief, by which is meant a theory of how the contents of nested belief models are formed.The work is motivated by (i) the aspects of representation, formation, and revision of nested belief models that have been neglected, and (ii) the lack of a unifying framework for all of these features of nested beliefs.
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