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.

文章介绍一种基于 Answer Set Programming (ASP) 的不完整信息规划的通用方法，提出使用量化 Answer Set Programming (QASP) 来解决符合和条件型规划问题，并介绍了一款基于翻译的 QASP 求解器用于将量化逻辑程序转换为量化布尔公式 (QBF) 进而求解。

不完全信息的定量答案集编程规划

Planning with Incomplete Information in Quantified Answer Set  Programming

Conformant planning is the problem of finding a sequence of actions for
achieving a goal in the presence of uncertainty in the initial state or action
effects. The problem has been approached as a path-finding problem in belief
space where good belief representations and heuristics are critical for scaling
up. In this work, a different formulation is introduced for conformant problems
with deterministic actions where they are automatically converted into
classical ones and solved by an off-the-shelf classical planner. The
translation maps literals L and sets of assumptions t about the initial
situation, into new literals KL/t that represent that L must be true if t is
initially true. We lay out a general translation scheme that is sound and
establish the conditions under which the translation is also complete. We show
that the complexity of the complete translation is exponential in a parameter
of the problem called the conformant width, which for most benchmarks is
bounded. The planner based on this translation exhibits good performance in
comparison with existing planners, and is the basis for T0, the best performing
planner in the Conformant Track of the 2006 International Planning Competition.

本文介绍一种新的确定性行动的一致性问题的公式化方法，通过将此类问题转化为经典问题并使用经典规划器求解，作者提出的翻译映射方案被证明是可行且完整的，该方案的复杂度受到问题的一致宽度参数的影响，但大部分问题的参数均有界，基于此翻译的规划器表现优异。