Reasoning and Planning with Sensing Actions, Incomplete Information, and
  Static Causal Laws using Answer Set Programming
  We extend the 0-approximation of sensing actions and incomplete information
in [Son and Baral 2000] to action theories with static causal laws and prove
its soundness with respect to the possible world semantics. We also show that
the conditional planning problem with respect to this approximation is
NP-complete. We then present an answer set programming based conditional
planner, called ASCP, that is capable of generating both conformant plans and
conditional plans in the presence of sensing actions, incomplete information
about the initial state, and static causal laws. We prove the correctness of
our implementation and argue that our planner is sound and complete with
respect to the proposed approximation. Finally, we present experimental results
comparing ASCP to other planners.
