%0 Conference Paper %1 icml98-ricardo %A Aler, Ricardo %A Borrajo, Daniel %A Isasi, Pedro %B Proceedings of the Fifteenth International Conference on Machine Learning, ICML'98 %C Madison, Wisconsin, USA %D 1998 %E Shavlik, Jude %I Morgan Kaufmann %K Learning Multistrategy Planning, algorithms, genetic in learning programming, %P 10--18 %T Genetic Programming and Deductive-Inductive Learning: A Multistrategy Approach %U http://scalab.uc3m.es/~dborrajo/papers/icml98.ps.gz %X Genetic Programming (GP) is a machine learning technique that was not conceived to use domain knowledge for generating new candidate solutions. It has been shown that GP can benefit from domain knowledge obtained by other machine learning methods with more powerful heuristics. However, it is not obvious that a combination of GP and a knowledge intensive machine learning method can work better than the knowledge intensive method alone. In this paper we present a multistrategy approach where an already multistrategy approach (hamlet combines analytical and inductive learning) and an evolutionary technique based on GP (EvoCK) are combined for the task of learning control rules for problem solving in planning. Results show that both methods complement each other, supplying to the other method what the other method lacks and obtaining better results than using each method alone. %@ 1-55860-556-8

@inproceedings{icml98-ricardo, abstract = {Genetic Programming (GP) is a machine learning technique that was not conceived to use domain knowledge for generating new candidate solutions. It has been shown that GP can benefit from domain knowledge obtained by other machine learning methods with more powerful heuristics. However, it is not obvious that a combination of GP and a knowledge intensive machine learning method can work better than the knowledge intensive method alone. In this paper we present a multistrategy approach where an already multistrategy approach ({\sc hamlet} combines analytical and inductive learning) and an evolutionary technique based on GP (EvoCK) are combined for the task of learning control rules for problem solving in planning. Results show that both methods complement each other, supplying to the other method what the other method lacks and obtaining better results than using each method alone.}, added-at = {2008-06-19T17:35:00.000+0200}, address = {Madison, Wisconsin, USA}, author = {Aler, Ricardo and Borrajo, Daniel and Isasi, Pedro}, biburl = {https://www.bibsonomy.org/bibtex/2e61a2f28143d247d1bb6b6e12c3870b3/brazovayeye}, booktitle = {Proceedings of the Fifteenth International Conference on Machine Learning, ICML'98}, editor = {Shavlik, Jude}, interhash = {68984521695e21d7e5bcc510fd2c5447}, intrahash = {e61a2f28143d247d1bb6b6e12c3870b3}, isbn = {1-55860-556-8}, keywords = {Learning Multistrategy Planning, algorithms, genetic in learning programming,}, month = {July}, notes = {ICML'98 http://www.cs.wisc.edu/icml98/ blocksworld many random problems generated in order to train system. No crossover, steady state population size = 2, tournament size = 2}, pages = {10--18}, publisher = {Morgan Kaufmann}, size = {9 pages}, timestamp = {2008-06-19T17:35:28.000+0200}, title = {Genetic Programming and Deductive-Inductive Learning: {A} Multistrategy Approach}, url = {http://scalab.uc3m.es/~dborrajo/papers/icml98.ps.gz}, year = 1998 }