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Inductive Logic Programming Beyond Logical Implication
 Proceedings of the 7th International Workshop on Arithmetic Learning Theory, 1996 Lecture Notes in Artificial Intelligence 1160
, 1996
"... This paper discusses the generalization of definite Horn programs beyond the ordering of logical implication. Since the seminal paper on generalization of clauses based on ` subsumption, there are various extensions in this area. Especially in inductive logic programming(ILP), people are using vario ..."
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This paper discusses the generalization of definite Horn programs beyond the ordering of logical implication. Since the seminal paper on generalization of clauses based on ` subsumption, there are various extensions in this area. Especially in inductive logic programming(ILP), people are using various methods that approximate logical implication, such as inverse resolution(IR), relative least general generalization(RLGG), and inverse implication(II), to generalize clauses. However, the logical implication is not the most desirable form of generalization. A program is more general than another program does not necessarily mean that the former should logically imply the latter. Instead, a more natural notion of generalization is the set inclusion ordering on the success set of logic programs. We observe that this kind of generalization relation is especially useful for inductive synthesis of logic programs. In this paper, we first define an ordering between logic programs which is strict...
A Learning Mechanism for Logic Programs Using Dynamically Shared Substructures
 In Machine Intelligence 15
, 1995
"... : A reasoning method that proves a predicate logic formula by reducing its graph representation is proposed. Since the method directly reduces a logic formula represented by a graph, it can be understood to selfoptimize a graph representation, meaning that it automatically transforms a logic formu ..."
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: A reasoning method that proves a predicate logic formula by reducing its graph representation is proposed. Since the method directly reduces a logic formula represented by a graph, it can be understood to selfoptimize a graph representation, meaning that it automatically transforms a logic formula into an efficient form equivalent to that acquired by ExplanationBased Learning. By sharing the original subgraphs between the learned formulae, reasoning efficiency does not deteriorate even after learning several examples. Therefore, the utility problem is overcome in the sense that no extra search is necessary for macros. The present paper demonstrates these facts in simple list manipulation problems and by proving geometric theories. 1 Introduction Neural networks are superior to knowledge representation due to their natural learning ability. However, in contrast to pattern recognition or voice synthesis, AI applications require structured descriptions with variables, which are not ...