An analytic learning system for specializing heuristics

Authors:
Steven Minton
Affiliations:
Sterling Software, NASA Ames Research Center, Moffett Field, CA
Venue:
IJCAI'93 Proceedings of the 13th international joint conference on Artifical intelligence - Volume 2
Year:
1993

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Abstract

This paper describes how meta-level theories are used for analytic learning in MULTI-TAC. MULTI-TAC operationalizes generic heuristics for constraint-satisfaction problems, in order to create programs that are tailored to specific problems. For each of its generic heuristics, MULTI-TAC has a meta-theory specifically designed for operationalising that heuristic. We present examples of the specialisation process and discuss how the theories influence the tractability of the learning process. We also describe an empirical study showing that the specialised programs produced by MULTITAC compare favorably to hand-coded programs.