Using IDA* for Multi-Agent Systems Planning

D. Stefanoiu (Germany/Canada), D. Norrie (Canada), and F. Ionescu (Germany)


iterative deepening search, multiagent systems, planning, fuzzy uncertainty measures.


IDA* – an Iterative Deepening Algorithm – is one of the most efficient AI algorithms devoted to rapid search within tree structures. A model of Multi-Agent Systems (MAS) dynamics could be constructed around such a structure. Since the interactions between agents are affected by uncertainty, a suitable cost function to be used in conjunction with IDA* is the fuzzy measure of ambiguity. A model based on ambiguity minimization is thus obtained and its performance is described in this paper. The model could assist the user to select a least uncertain MAS plan, aiming to reach a preset goal.

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