“…The importance of the probability answer set programming frameworks of [Saad and Pontelli, 2006;Saad, 2007a] lies in the fact that the probability answer set programming frameworks of [Saad and Pontelli, 2006;Saad, 2007a] have been shown applicable to a variety of fundamental probabilistic reasoning tasks. These probabilistic reasoning tasks include, but are not limited to, probabilistic planning [Saad, 2007b], probabilistic planning with imperfect sensing actions [Saad, 2009], reinforcement learning in MDP environments [Saad, 2008a], reinforcement learning in POMDP environments , and Bayes reasoning . Moreover, in [Saad, 2008b] it has been proved that stochastic satisfiability (SSAT) can be modularly encoded as probability answer set programs with probability answer set semantics, therefore, the applicability of SSAT to variety of fundamental probabilistic reasoning tasks also carry over to probability answer set programming [Saad and Pontelli, 2006;Saad, 2007a].…”