  We present a probabilistic logic programming framework to reinforcement
learning, by integrating reinforce-ment learning, in POMDP environments, with
normal hybrid probabilistic logic programs with probabilistic answer set
seman-tics, that is capable of representing domain-specific knowledge. We
formally prove the correctness of our approach. We show that the complexity of
finding a policy for a reinforcement learning problem in our approach is
NP-complete. In addition, we show that any reinforcement learning problem can
be encoded as a classical logic program with answer set semantics. We also show
that a reinforcement learning problem can be encoded as a SAT problem. We
present a new high level action description language that allows the factored
representation of POMDP. Moreover, we modify the original model of POMDP so
that it be able to distinguish between knowledge producing actions and actions
that change the environment.
