Mixing non-monotonic logical reasoning and probabilistic planning for robots

Abstract

This paper describes an architecture that com-bines the complementary strengths of probabilistic graphical models and declarative programming to represent and reason with qualitative and quantita-tive descriptions of domain knowledge and uncer-tainty. An action language is used for the architec-ture’s low-level (LL) and high-level (HL) system descriptions, and the HL definition of recorded his-tory is expanded to allow prioritized defaults. For any given objective, each action in the plan created in the HL using non-monotonic logical reasoning is executed probabilistically in the LL, refining the HL description to identify the relevant sorts, fluents and actions, and adding the corresponding action outcomes to the HL history. The HL and LL do-main representations are translated into an Answer Set Prolog (ASP) program and a partially observ-able Markov decision process (POMDP) respec-tively. ASP-based inference provides a multino-mial prior for POMDP state estimation, and pop-ulates a Beta density of priors for metareasoning and early termination. Robots equipped with this architecture reason with violation of defaults, noisy observations and unreliable actions in complex do-mains. The architecture is evaluated in simulation and on a mobile robot moving target objects to de-sired locations in an office domain.