Bridging Commonsense Reasoning and Probabilistic Planning via a
  Probabilistic Action Language
  To be responsive to dynamically changing real-world environments, an
intelligent agent needs to perform complex sequential decision-making tasks
that are often guided by commonsense knowledge. The previous work on this line
of research led to the framework called "interleaved commonsense reasoning and
probabilistic planning" (icorpp), which used P-log for representing
commmonsense knowledge and Markov Decision Processes (MDPs) or Partially
Observable MDPs (POMDPs) for planning under uncertainty. A main limitation of
icorpp is that its implementation requires non-trivial engineering efforts to
bridge the commonsense reasoning and probabilistic planning formalisms. In this
paper, we present a unified framework to integrate icorpp's reasoning and
planning components. In particular, we extend probabilistic action language
pBC+ to express utility, belief states, and observation as in POMDP models.
Inheriting the advantages of action languages, the new action language provides
an elaboration tolerant representation of POMDP that reflects commonsense
knowledge. The idea led to the design of the system pbcplus2pomdp, which
compiles a pBC+ action description into a POMDP model that can be directly
processed by off-the-shelf POMDP solvers to compute an optimal policy of the
pBC+ action description. Our experiments show that it retains the advantages of
icorpp while avoiding the manual efforts in bridging the commonsense reasoner
and the probabilistic planner.
