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Recently, sketches have been introduced as a general language forrepresenting the subgoal structure of instances drawn from the same domain.Sketches are collections of rules of the form C -> E over a given set offeatures where C expresses Boolean conditions and E expresses qualitativechanges. Each sketch rule defines a subproblem: going from a state thatsatisfies C to a state that achieves the change expressed by E or a goal state.Sketches can encode simple goal serializations, general policies, ordecompositions of bounded width that can be solved greedily, in polynomialtime, by the SIW_R variant of the SIW algorithm. Previous work has shown thecomputational value of sketches over benchmark domains that, while tractable,are challenging for domain-independent planners. In this work, we address theproblem of learning sketches automatically given a planning domain, someinstances of the target class of problems, and the desired bound on the sketchwidth. We present a logical formulation of the problem, an implementation usingthe ASP solver Clingo, and experimental results. The sketch learner and theSIW_R planner yield a domain-independent planner that learns and exploitsdomain structure in a crisp and explicit form.

Learning Sketches for Decomposing Planning Problems into Subproblems of Bounded Width: Extended Version