TY - GEN
T1 - Planning strategy representation in dolittle
AU - Baltes, Jacky
N1 - Publisher Copyright:
© Springer-Verlag Berlin Heidelberg 1998.
PY - 1998
Y1 - 1998
N2 - This paper introduces multi-strategy planning and describes its implementation 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 casebased planning on a single problem. Planning strategies are defined as methods to reduce the search space by exploiting some assumptions (socalled 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.
AB - This paper introduces multi-strategy planning and describes its implementation 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 casebased planning on a single problem. Planning strategies are defined as methods to reduce the search space by exploiting some assumptions (socalled 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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U2 - 10.1007/3-540-64575-6_38
DO - 10.1007/3-540-64575-6_38
M3 - Conference contribution
AN - SCOPUS:84958625097
SN - 3540645756
SN - 9783540645757
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 30
EP - 44
BT - Advances in Artificial Intelligence - 12th Biennial Conference of the Canadian Society for Computational Studies of Intelligence, AI 1998, Proceedings
A2 - Mercer, Robert E.
A2 - Neufeld , Eric
PB - Springer Verlag
T2 - 12th Biennial Conference on Artificial Intelligence, AI 1998
Y2 - 18 June 1998 through 20 June 1998
ER -