Planning strategy representation in DoLittle

Abstract

This paper introduces multi-strategy planning and describes its implementation in the DoLlittle 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. Planning 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 work is to develop a representation weak enough to represent a wide variety of different strategies, 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.