This paper introduces multi-strategy planning and describes its implemtnation in the DoLittle system, which can combine many different planning strategies, including means-ends analysis, macro-based planning, abstraction-based planning (reduced and relaxed), and case-based planning on a single problem. Plannign strategies are defined as methods to reduce the search space by exploiting some assumptions (so-called planning biases) about the problem domain. General operators are generalizations of standard Strips operators that conveniently represent many different planning strategies. The focus of this wrok is to develop a representation weak enough to represent a wide variety of different stratetgies, but still strong enough to emulate them. The search control method applies different general operators based on a strongest first principle; planning biases that are expected to lead to small search spaces are tried first. An empirical evaluation in three domains showed that multi-strategy planning performed significantly better than the best single strategy planners in these domains.
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