Integrating uncertainty handling formalisms in distributed artificial intelligence
In distributed artificial intelligence systems it is important that the constituent intelligent systems communicate. This may be a problem if the systems use different methods to represent uncertain information. This paper presents a method that enables systems that use different uncertainty handling formalisms to qualitatively integrate their uncertain information, and argues that this makes it possible for distributed intelligent systems to achieve tasks that would otherwise be beyond them.
KeywordsBelief Function Uncertain Information Monotonicity Assumption Artificial Intelligence System Conditional Belief
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