What is failure?: an approach to constructive negation
Acta Informatica
Automated Refinement of First-Order Horn-Clause Domain Theories
Machine Learning
Machine Learning - Special issue on inductive transfer
Theory Completion Using Inverse Entailment
ILP '00 Proceedings of the 10th International Conference on Inductive Logic Programming
Revising first-order logic theories from examples through stochastic local search
ILP'07 Proceedings of the 17th international conference on Inductive logic programming
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The game of chess has been a major testbed for research in artificial intelligence, since it requires focus on intelligent reasoning. Particularly, several challenges arise to machine learning systems when inducing a model describing legal moves of the chess, including the collection of the examples, the learning of a model correctly representing the official rules of the game, covering all the branches and restrictions of the correct moves, and the comprehensibility of such a model. Besides, the game of chess has inspired the creation of numerous variants, ranging from faster to more challenging or to regional versions of the game. The question arises if it is possible to take advantage of an initial classifier of chess as a starting point to obtain classifiers for the different variants. We approach this problem as an instance of theory revision from examples. The initial classifier of chess is inspired by a FOL theory approved by a chess expert and the examples are defined as sequences of moves within a game. Starting from a standard revision system, we argue that abduction and negation are also required to best address this problem. Experimental results show the effectiveness of our approach.