Probabilistic Planning with Imperfect Sensing Actions Using Hybrid Probabilistic Logic Programs

Abstract

Effective planning in uncertain environment is important to agents and multi-agents systems. In this paper, we introduce a new logic based approach to probabilistic contingent planning (probabilistic planning with imperfect sensing actions), by relating probabilistic contingent planning to normal hybrid probabilistic logic programs with probabilistic answer set semantics [24]. We show that any probabilistic contingent planning problem can be encoded as a normal hybrid probabilistic logic program. We formally prove the correctness of our approach. Moreover, we show that the complexity of finding a probabilistic contingent plan in our approach is NP-complete. In addition, we show that any probabilistic contingent planning problem, \(\cal PP\), can be encoded as a classical normal logic program with answer set semantics, whose answer sets corresponds to valid trajectories in \(\cal PP\). We show that probabilistic contingent planning problems can be encoded as SAT problems. We present a new high level probabilistic action description language that allows the representation of sensing actions with probabilistic outcomes.KeywordsLogic ProgramTime MomentProbabilistic ActionContingent PlanningProbabilistic PlanningThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.