Learning Action Strategies for Planning Domains

This paper reports on experiments where techniques of supervised machine learning are applied to the problem of planning. The input to the learning algorithm is composed of a description of a planning domain, planning problems in this domain, and solutions for them. The output is an eecient algorithm | a strategy | for solving problems in that domain. We test the strategy on an independent set of planning problems from the same domain, so that success is measured by its ability to solve complete problems. A system, L2Act, has been developed in order to perform these experiments. We have experimented with the blocks world domain and the logistics transportation domain, using strategies in the form of a generalisation of decision lists. The condition of a rule in the decision list is an existentially quantiied rst order expression, and each such rule indicates which action to take when the condition is satissed. The learning algorithm is a variant of Rivest's 33] algorithm, improved with several techniques that reduce its time complexity. The experiments demonstrate that the approach is feasible, and generalisation is achieved so that unseen problems can be solved by the learned strategies. Moreover, the learned strategies are eecient, the solutions found by them are competitive with those of known heuristics for the domains, and transfer from small planning problems in the examples to larger ones in the test set is exhibited.