用目标意图推导启发式，让规划更高效。
Planning as Goal Recognition: Deriving Heuristics from Intention Models -- Extended Version
- 基于意图分歧构建新启发式框架
- 两类新启发式提升顶尖规划器性能
- 适合研究规划与意图建模的学者
经典规划的目标是找到一个动作序列，将起始状态映射到目标状态之一。当某条轨迹看似通向目标时，是否应优先探索？早期目标识别（GR）工作将GR定义为经典规划问题，采用经典求解器与启发式进行计划识别。本文反其道而行之，研究由目标识别导出的启发式在经典规划中的应用与性质。我们提出一种基于差异的新框架来评估目标意图，并据此生成一类可高效计算的启发式。作为概念验证，我们推导出两种此类启发式，并证明它们已能显著提升当前顶尖经典规划器的表现。本工作为理解与设计基于概率意图的规划启发式提供了基础性知识。
原文摘要 · Abstract (English)
Classical planning aims to find a sequence of actions, a plan, that maps a starting state into one of the goal states. If a trajectory appears to be leading to the goal, should we prioritise exploring it? Seminal work in goal recognition (GR) has defined GR in terms of a classical planning problem, adopting classical solvers and heuristics to recognise plans. We come full circle, and study the adoption and properties of GR-derived heuristics for seeking solutions to classical planning problems. We propose a new divergence-based framework for assessing goal intention, which informs a new class of efficiently-computable heuristics. As a proof of concept, we derive two such heuristics, and show that they can already yield improvements for top-scoring classical planners. Our work provides foundational knowledge for understanding and deriving probabilistic intention-based heuristics for planning.
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