Guarantees for Sound Abstractions for Generalized Planning (Extended Paper)
Blai Bonet, Raquel Fuentetaja, Yolanda E-Martin, Daniel Borrajo

TL;DR
This paper develops a formal framework to analyze and guarantee the correctness of learned abstractions in generalized planning, enabling soundness assurances and invariant synthesis across collections of planning instances.
Contribution
It introduces a method to analyze and provide formal guarantees for inductively learned abstractions in generalized planning, enhancing their reliability and applicability.
Findings
Provides formal guarantees for abstraction correctness.
Enables definition of sound subcollections of instances.
Supports automated synthesis of planning invariants.
Abstract
Generalized planning is about finding plans that solve collections of planning instances, often infinite collections, rather than single instances. Recently it has been shown how to reduce the planning problem for generalized planning to the planning problem for a qualitative numerical problem; the latter being a reformulation that simultaneously captures all the instances in the collection. An important thread of research thus consists in finding such reformulations, or abstractions, automatically. A recent proposal learns the abstractions inductively from a finite and small sample of transitions from instances in the collection. However, as in all inductive processes, the learned abstraction is not guaranteed to be correct for the whole collection. In this work we address this limitation by performing an analysis of the abstraction with respect to the collection, and show how to…
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Taxonomy
TopicsAI-based Problem Solving and Planning · Semantic Web and Ontologies · Constraint Satisfaction and Optimization
