A Polynomial All Outcome Determinization for Probabilistic Planning

Most predominant approaches in probabilistic planning utilize techniques from the more thoroughly investigated field of classical planning by determinizing the problem at hand. In this paper, we present a method to map probabilistic operators to an equivalent set of probabilistic operators in a novel normal form, requiring polynomial time and space. From this, we directly derive a determinization which can be used for, e. g., replanning strategies incorporating a classical planning system. Unlike previously described all outcome determinizations, the number of deterministic operators is not exponentially but polynomially bounded in the number of parallel probabilistic effects, enabling the use of more sophisticated determinization-based techniques in the future.

• Type: Journal
• Year: 2011
• Where: AIPS
• Authors: Thomas Keller, Patrick Eyerich
• Keywords: AIPS 2011, Artificial Intelligence, Planning System, Polynomial Time, Probabilistic Effects