Planning with Incomplete Information in Quantified Answer Set
  Programming
  We present a general approach to planning with incomplete information in
Answer Set Programming (ASP). More precisely, we consider the problems of
conformant and conditional planning with sensing actions and assumptions. We
represent planning problems using a simple formalism where logic programs
describe the transition function between states, the initial states and the
goal states. For solving planning problems, we use Quantified Answer Set
Programming (QASP), an extension of ASP with existential and universal
quantifiers over atoms that is analogous to Quantified Boolean Formulas (QBFs).
We define the language of quantified logic programs and use it to represent the
solutions to different variants of conformant and conditional planning. On the
practical side, we present a translation-based QASP solver that converts
quantified logic programs into QBFs and then executes a QBF solver, and we
evaluate experimentally the approach on conformant and conditional planning
benchmarks. Under consideration for acceptance in TPLP.
