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<p>Systems that induce first-order logic programs have drawn considerable interest recently within the artificial intelligence community. Inductive logic programming, for example, has very impressive applications in knowledge discovery in databases. Genetic programming, a promising alternative that builds on genetic algorithm search strategies, demonstrates equally impressive results across a wide range of uses.</p> <p>Both these strategies, however, have serious limitations. Despite its strong theoretical foundation from logic programming and computational learning theory, ILP does not handle concept learning well, nor can it achieve other learning paradigms such as reinforcement learning and strategy learning. GP has a much weaker theoretical foundation, as well as a laundry list of practical shortcomings.</p> <p>To alleviate or eliminate these shortcomings, we have devised a novel framework, called the Genetic Logic Programming Structure or GLPS, that integrates these two better known approaches. To demonstrate the viability of our GLPS approach, we have tested a preliminary implementation on a battery of learning tasks: Winston's arch problem, the modified Quinlan network reachability problem, the factorial problem, and the chess endgame problem.</p>

M. L. Wong and K. S. Leung, "Inducing Logic Programs With Genetic Algorithms: The Genetic Logic Programming System," in IEEE Intelligent Systems, vol. 10, no. , pp. 68-76, 1995.
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