A Markovian Model for Dynamic Constraint Processing
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چکیده
Dynamic constraint satisfaction is a useful tool for representing and solving sequential decision problems with complete knowledge in dynamic world. We propose an extension of the dynamic constraint satisfaction framework so as to represent stochastic events in sequential decision making. The basis of this extension consists in modeling evolution of the dynamic constraint satisfaction problem by a Markov decision process. We first study complexity of the problem of finding an optimal policy for this model and then we will see that this model can address multiagent planning problems under environmental constraints. Finally application examples are given.
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