Generalized Planning as Heuristic Search
This work addresses the problem of scaling planning methods to handle generalized, algorithm-like solutions for researchers in AI planning, representing a novel method rather than an incremental improvement.
The paper tackles the challenge of applying heuristic search to Generalized Planning (GP), which aims to compute algorithm-like plans that generalize across multiple instances, by introducing the first native heuristic search approach to GP, including a novel solution space, evaluation functions, and the Best-First Generalized Planning (BFGP) algorithm.
Although heuristic search is one of the most successful approaches to classical planning, this planning paradigm does not apply straightforwardly to Generalized Planning (GP). Planning as heuristic search traditionally addresses the computation of sequential plans by searching in a grounded state-space. On the other hand GP aims at computing algorithm-like plans, that can branch and loop, and that generalize to a (possibly infinite) set of classical planning instances. This paper adapts the planning as heuristic search paradigm to the particularities of GP, and presents the first native heuristic search approach to GP. First, the paper defines a novel GP solution space that is independent of the number of planning instances in a GP problem, and the size of these instances. Second, the paper defines different evaluation and heuristic functions for guiding a combinatorial search in our GP solution space. Lastly the paper defines a GP algorithm, called Best-First Generalized Planning (BFGP), that implements a best-first search in the solution space guided by our evaluation/heuristic functions.