In this paper, an algorithm is presented for learning concept classification rules. It is a hybrid between evolutionary computing and inductive logic programming (ILP). Given input of positive and nega-tive examples, the algorithm constructs a logic program to classify these examples. The algorithm has several attractive features including the ability to explicitly use background (user-supplied) knowledge and to produce comprehensible output. We present results of using the algo-rithm to tackle the chess-endgame problem (KRK). The results show that using fitness proportionate selection to bias the population of ILP learners does not significantly increase classification accuracy. However, when rules are exchanged at intermediate stages in learning, in a manner similar to crossover in Genetic Programming, the predictive accuracy is frequently improved.
CITATION STYLE
Reiser, P. G. K., & Riddle, P. J. (1999). Evolving logic programs to classify chess-endgame positions. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 1585, pp. 138–145). Springer Verlag. https://doi.org/10.1007/3-540-48873-1_19
Mendeley helps you to discover research relevant for your work.