Relational Partially Observable MDPs

Relational Markov Decision Processes (MDP) are a useraction for stochastic planning problems since one can develop abstract solutions for them that are independent of domain size or instantiation. While there has been an increased interest in developing relational fully observable MDPs, there has been very little work on relational partially observable MDPs (POMDP), which deal with uncertainty in problem states in addition to stochastic action effects. This paper provides a concrete formalization of relational POMDPs making several technical contributions toward their solution. First, we show that to maintain correctness one must distinguish between quantification over states and quantification over belief states; this implies that solutions based on value iteration are inherently limited to the finite horizon case. Second, we provide a symbolic dynamic programing algorithm for finite horizon relational POMDPs, them at an abstract level, by lifting the propositional incremental prunin...

• Type: Conference
• Year: 2010
• Where: AAAI
• Authors: Chenggang Wang, Roni Khardon
• Keywords: AAAI 2010, Intelligent Agents, Observable Mdps, Relational Markov Decision, Relational Pomdps