FOIL is a first-order learning system that uses information in a collection of relations to construct theories expressed in a dialect of Prolog. This paper provides an overview of the principal ideas and methods used in the current version of the system, including two recent additions. We present examples of tasks tackled by FOIL and of systems that adapt and extend its approach. 1. Introduction All symbolic machine learning leads to the formulation or modification of theories, so the language in which theories are expressed is an important consideration. Firstorder theory languages have been used for at least thirty years, as documented by Sammut [1993]. Explanation-based generalisation systems [Mitchell, Keller and Kedar-Cabelli, 1986; DeJong and Mooney, 1986] have always required them, but the early and influential work of Shapiro [1983] and Sammut and Banerji [1986] also employed them in an inductive learning context. Nevertheless, first-order empirical learning, including...

Ten years ago the ICCA Journal published an overview of machine learning approaches to computer chess (Skiena, 1986). The author's results were rather pessimistic. In particular he concludes that ``... with the exception of rote learning in the opening book few results have trickled into competitive programs.'' and that ``There appear no research projects on the horizon which offer reason for optimism.'' In this paper we will update Skiena's work with research that has been conducted in this area since the publication of his paper. By doing so we hope to show that at least Skiena's second conclusion is no longer valid.

This paper provides a survey of previously published work on machine learning in game playing. The material is organized around a variety of problems that typically arise in game playing and that can be solved with machine learning methods. This approach, we believe, allows both, researchers in game playing to find appropriate learning techniques for helping to solve their problems as well as machine learning researchers to identify rewarding topics for further research in game-playing domains. The paper covers learning techniques that range from neural networks to decision tree learning in games that range from poker to chess.

Abstract. We apply genetic programming to the evolution of strategies for playing chess endgames. Our evolved programs are able to draw or win against an expert human-based strategy, and draw against CRAFTY—a world-class chess program, which finished second in the 2004 Computer Chess Championship. 1

Abstract Kriegspiel is a chess variant invented to make chess more similar to real warfare. In a Kriegspiel game the players have to deal with incomplete information because they are not informed of their opponent’s moves. Each player tries to guess the position of the opponent’s pieces as the game progresses by trying moves that can be either legal or illegal with respect to the real situation: a referee accepts the legal moves and rejects the illegal ones. However the latter are most useful to gain insight into the opponent’s position. While in the past this game has been popular in research centres such as the RAND Institute, currently it is played mostly over the Internet Chess Club. The paper describes the rationale and design of a Kriegspiel program to play the ending for King and Rook versus King. Such a kind of ending has been theoretically shown to be won for White, however no programs exist that play the related positions perfectly. We introduce an evaluation function to play these simple Kriegspiel positions, and evaluate it.

In this paper we argue that chess databases have a significant potential as a test-bed for techniques in the area of Knowledge Discovery in Databases (KDD). Conversely, we think that research in Artificial Intelligence has not yet come up with reasonable solutions for the knowledge representation and reasoning problems that are posed by knowledgebased computer chess programs, and consequently argue that KDD techniques could be useful for the advancement of various types of knowledgebased computer chess systems. Although we cannot present any concrete results, we hope to outline some fruitful directions for further research and exchange of ideas between the KDD and computer chess communities. 1 Introduction Knowledge Discovery in Databases (KDD) or Data Mining is a rapidly growing research area which focuses on the discovery of useful and understandable pieces of knowledge from databases [Frawley et al., 1992; Fayyad et al., 1996] . On the other hand, the rapid increase in computing p...

In this paper, I describe genetic programming as a machine learning paradigm and evaluate its results in attempting to learn basic chess rules. Genetic programming exploits a simulation of Darwinian evolution to construct programs. When applied to the King-Rook-King (KRK) chess endgame problem, genetic programming shows promising results in spite of a lack of significant chess knowledge.

In the past decade, there has been a lot of interest in phase transitions within artificial intelligence, and more recently, in machine learning and inductive logic programming. We investigate phase transitions in learning k-term DNF boolean formulae, a practically relevant class of concepts. We do not only show that there exist phase transitions, but also characterize and locate these phase transitions using the parameters k, the number of positive and negative examples, and the number of boolean variables. Subsequently, we investigate stochastic local search (SLS) for k-term DNF learning. We compare several variants that first reduce k-term DNF to SAT and then apply well-known SLS algorithms, such as GSAT and WalkSAT. Our experiments indicate that WalkSAT is able to solve the largest fraction of hard problem instances.

In this paper the classical learning systems C4.5 and FOIL are compared with the genetic based learning method GEA and GeLog. The latter two systems have been developed by the authors. The former one is a general implementation of evolutionary algorithms, while the latter one combines the learning approaches of inductive logic programming and genetic algorithms.