The Universal PDDL Domain
Patrik Haslum, Augusto B. Corr\^ea

TL;DR
This paper introduces a universal PDDL domain capable of representing any propositional planning problem, facilitating generalized planning by unifying diverse problem instances under a single domain schema.
Contribution
It presents the construction of a universal PDDL domain that can encode any propositional planning problem, advancing the understanding of domain generality in AI planning.
Findings
Universal domain can encode any propositional planning problem
Different formulations impact complexity of generalized planning
Facilitates development of domain-independent planning algorithms
Abstract
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…
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Taxonomy
TopicsAI-based Problem Solving and Planning · Logic, Reasoning, and Knowledge · Robotic Path Planning Algorithms
MethodsSparse Evolutionary Training
