论文标题
符合表达扩展的象征性搜索最佳计划
Symbolic Search for Optimal Planning with Expressive Extensions
论文作者
论文摘要
在古典计划中，目标是得出一项行动，使智能代理人可以从发现自己的任何情况转移到满足其目标的情况下。经典计划被认为是独立于域的，即，它不限于特定应用程序，可以用于解决不同类型的推理问题。但是，实际上，手头计划问题的某些属性需要对标准古典规划形式主义的表达性扩展，以捕获和建模它们。尽管许多这些扩展的重要性是众所周知的，但大多数计划者，尤其是最佳计划者，都不支持这些扩展的计划形式主义。缺乏支持不仅限制了这些计划者在某些问题上的使用，而且即使在没有这些扩展的情况下进行对问题进行建模，它通常会导致在建模方面增加努力或使建模实际上是不可能的，因为所需的问题编码规模呈指数增长。 在本论文中，我们建议将符号搜索用于成本优势计划，以实现经典计划的不同表达性扩展，所有这些都捕获了问题的不同方面。特别是，我们使用公理，以国家依赖的行动成本，超额检查计划和TOP-K计划来研究计划。对于所有形式主义，我们呈现出复杂性和汇编结果，强调了它是理想的，甚至是本性支持相应特征所必需的。我们分析符号启发式搜索，并表明搜索性能并不总是从使用启发式中受益，即使在最佳情况下，即使是完美的启发式启发式，搜索性能也可能会成倍地恶化。这加剧了象征性盲目搜索是如今的主要符号搜索策略，与其他最先进的成本优势计划策略相提并论...
In classical planning, the goal is to derive a course of actions that allows an intelligent agent to move from any situation it finds itself in to one that satisfies its goals. Classical planning is considered domain-independent, i.e., it is not limited to a particular application and can be used to solve different types of reasoning problems. In practice, however, some properties of a planning problem at hand require an expressive extension of the standard classical planning formalism to capture and model them. Although the importance of many of these extensions is well known, most planners, especially optimal planners, do not support these extended planning formalisms. The lack of support not only limits the use of these planners for certain problems, but even if it is possible to model the problems without these extensions, it often leads to increased effort in modeling or makes modeling practically impossible as the required problem encoding size increases exponentially. In this thesis, we propose to use symbolic search for cost-optimal planning for different expressive extensions of classical planning, all capturing different aspects of the problem. In particular, we study planning with axioms, planning with state-dependent action costs, oversubscription planning, and top-k planning. For all formalisms, we present complexity and compilability results, highlighting that it is desirable and even necessary to natively support the corresponding features. We analyze symbolic heuristic search and show that the search performance does not always benefit from the use of a heuristic and that the search performance can exponentially deteriorate even under the best possible circumstances, namely the perfect heuristic. This reinforces that symbolic blind search is the dominant symbolic search strategy nowadays, on par with other state-of-the-art cost-optimal planning strategies...
