AAAI Publications, Workshops at the Twenty-Seventh AAAI Conference on Artificial Intelligence

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Relational Markov Decision Processes: Promise and Prospects
Saket Joshi, Roni Khardon, Prasad Tadepalli, Alan Fern, Aswin Raghavan

Last modified: 2013-06-29


Relational Markov Decision Processes (RMDPs) offer an elegant formalism that combines probabilistic and relational knowledge representations with the decision-theoretic notions of action and utility. In this paper we motivate RMDPs to address a variety of problems in AI, including open world planning, transfer learning, and relational inference. We describe a symbolic dynamic programming approach via the "template method" which addresses the problem of reasoning about exogenous events. We end with a discussion of the challenges involved and some promising future research directions.


Relationall MDPs; Symbolic Dynamic Programming; Decision-Theoretic Planning; First Order Decision Diagrams

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