Knowledge Representation for Stochastic Decision Processes

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T h e independent choice logic for modelling multiple agents under uncertainty. ilrtificial Intelligence, 94( 1-2):7-56, 1997. 49. David Poole. Probabilistic partial evaluation: Exploiting rule structure in probabilistic inference. In Proceedings of the Fifteenth International J o i n t Conference o n Artificial Intelligence, pages 1284-1291, Nagoya, 1997. 50. Martin L. Puterman. M a r k o ~Decision Processes: Discrete Stochastic D y n a m i c Programming. Wiley, New 'fork, 1994. 51. Rabiner. A tutorial on hidden Markov models and selected applications in speech recognition.

Within DBNs we can solve the frame problem in both senses of the term “solution”-we can relieve the user from the burden of explicitly specifying persistence relationships, and we can encode (automatically generated) frame “axioms” rather efficiently. This comparison has laid bare some issues that deserve further research. First, we have not discussed nondeterministic actions in great detail. Several proposals for dealing with nondeterministic action effects have been proposed, with the key difficulty arising through the interaction of nondeterniinism with persistence [32, 231.

44. Edwin Pednault. -4DL: Exploring the middle ground between S T R I P S and the situation calculus. In Proceedings of the First International Conference on Principles of linowledge Representation and Reasoning, pages 324-332, Toronto, 1989. 45, Mark A. Peot and David E. Smith. Conditional nonlinear planning. In Proceedings of the First International Conference on A 1 Planning Systems, pages 189-197, College Park, MD, 1992. 46. David Poole. Probabilistic Horn abduction and Bayesian networks. ilrtificial Intelligence, 64(1):81-129, 1993.

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