The Universal PDDL Domain
This work addresses a foundational issue in AI planning by enabling generalized planning across diverse domains, though it appears incremental as it builds on existing PDDL frameworks.
The paper tackles the challenge of creating a single PDDL domain that can represent any propositional planning problem, and it constructs multiple formulations of this universal domain to analyze its impact on planning complexity.
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.