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.
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© 1998 Springer-Verlag Berlin Heidelberg
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Baltes, J. (1998). Planning strategy representation in DoLittle. In: Mercer, R.E., Neufeld, E. (eds) Advances in Artificial Intelligence. Canadian AI 1998. Lecture Notes in Computer Science, vol 1418. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-64575-6_38
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DOI: https://doi.org/10.1007/3-540-64575-6_38
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