提出一个通用PDDL领域，可涵盖任意命题规划问题。
The Universal PDDL Domain
- 构建一个能容纳任意规划问题的统一PDDL领域
- 证明该通用领域在提升规划复杂度上的理论意义
- 适合研究通用规划与领域抽象的学者参考
在人工智能规划中，通常区分规划领域与问题实例：领域是一组相关实例的集合。这一区分对泛化规划尤为重要，即寻找一个通用解法以应对同一领域下的所有实例。在PDDL中，领域定义类型、谓词符号和动作模式，而问题实例则指定具体对象、初始状态和目标条件。本文表明，可以轻松构造一个PDDL领域，使得任意命题规划问题（来自任何领域）均可作为该‘通用’领域的实例。我们提出了多种通用领域的形式化方案，并讨论其对提升型领域依赖或泛化规划复杂性的启示。
原文摘要 · Abstract (English)
In AI planning, it is common to distinguish between planning domains and problem instances, where a "domain" is generally understood as a set of related problem instances. This distinction is important, for example, in generalised planning, which aims to find a single, general plan or policy that solves all instances of a given domain. In PDDL, domains and problem instances are clearly separated: the domain defines the types, predicate symbols, and action schemata, while the problem instance specifies the concrete set of (typed) objects, the initial state, and the goal condition. In this paper, we show that it is quite easy to define a PDDL domain such that any propositional planning problem instance, from any domain, becomes an instance of this (lifted) "universal" domain. We construct different formulations of the universal domain, and discuss their implications for the complexity of lifted domain-dependent or generalised planning.
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