Relational Markov decision processes: promise and prospects
Saket Joshi, Roni Khardon, Prasad V. Tadepalli, Alan Fern, Aswin Raghavan · 2013
an elegant formalism that combines probabilistic and relational knowledge representations with the decisiontheoretic 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.