caa a22 4500 467946124 CHVBK 20180323165700.0 cr unu---uuuuu 170328e19890301xx s 000 0 eng 10.1007/BF00116836 doi (NATIONALLICENCE)springer-10.1007/BF00116836 (NATIONALLICENCE)springer-10.1023/A:1022693717366 Iba Glenn GTE Laboratories, Incorporated, 40 Sylvan Road, 02254, Waltham, MA, U.S.A. aut A heuristic approach to the discovery of macro-operators [Elektronische Daten] [Glenn Iba] This paper describes a heuristic approach to the discovery of useful macro-operators (macros) in problem solving. The approach has been implemented in a program,Maclearn, that has three parts: macro-proposer, static filter, and dynamic filter. Learning occurs during problem solving, so that performance unproves in the course of a single problem trial. Primitive operators and macros are both represented within a uniform representational framework that is closed under composition. This means that new macros can be defined in terms of others, which leads to a definitional hierarchy. The representation also supports the transfer of macros to related problems.Maclearn is embedded in a supporting system that carries out best-first search. Experiments in macro learning were conducted for two classes of problems: peg solitaire (generalized "Hi-Q puzzle”), and tile sliding (generalized "Fiteen puzzle”). The results indicate thatMaclearn's filtering heuristics all improve search performance, sometimes dramatically. When the system was given practice on simpler training problems, it learned a set of macros that led to successful solutions of several much harder problems. Kluwer Academic Publishers, 1989 search, problem solving nationallicence Macro-operators nationallicence composition nationallicence empirical learning nationallicence search nationallicence problem solving nationallicence Machine Learning Kluwer Academic Publishers 3/4(1989-03-01), 285-317 0885-6125 3:4<285 1989 3 10994 https://doi.org/10.1007/BF00116836 text/html Onlinezugriff via DOI 1 research-article jats NATIONALLICENCE 856 40 https://doi.org/10.1007/BF00116836 text/html Onlinezugriff via DOI NATIONALLICENCE 100 1- Iba Glenn GTE Laboratories, Incorporated, 40 Sylvan Road, 02254, Waltham, MA, U.S.A aut NATIONALLICENCE 773 0- Machine Learning Kluwer Academic Publishers 3/4(1989-03-01), 285-317 0885-6125 3:4<285 1989 3 10994 NATIONALLICENCE 856 40 https://doi.org/10.1023/A:1022693717366 text/html Onlinezugriff via DOI NATIONALLICENCE 100 1- Iba Glenn E-mail: GI01@GTE.COM aut NATIONALLICENCE 773 0- Machine Learning Kluwer Academic Publishers-Plenum Publishers 3/4(1989-03-01), 285-317 0885-6125 3:4<285 1989 3 10994 Metadata rights reserved Springer special CC-BY-NC licence nationallicence BK010053 XK010053 XK010000 NATIONALLICENCE NATIONALLICENCE NL-springer NATIONALLICENCE NATIONALLICENCE NL-springer