On the Relationship Between Satisfiability and Markov Decision Processes
Proceedings of The 35th Uncertainty in Artificial Intelligence Conference, PMLR 115:1105-1115, 2020.
Stochastic satisfiability (SSAT) and decision-theoretic planning in finite horizon partially observable Markov decision processes (POMDPs) are both PSPACE-Complete. We describe constructive reductions between SSAT and flat POMDPs that open the door to comparisons and future cross-fertilization between the solution techniques of those problems. We also propose a new SSAT solver called Prime that incorporates recent advances from the SAT and #SAT literature. Using our reduction from POMDP to SSAT, we demonstrate the competitiveness of Prime on finite horizon POMDP problems.