The requires an integrative biological systems analysis as the quantitative description at the hierarchical level of molecular, cellular and phenotypic functions including their interaction with the environment is very complex. The growing awareness of the complex interplay between the genome and physiological functions of the cell needs a new holistic and full integrative view. In this paper we present a logical formalization of breast cancer diagnostic and treatment.
[Show abstract][Hide abstract] ABSTRACT: This paper presents a general procedure for inverse entailment which constructs inductive hypotheses in inductive logic programming. Based on inverse entailment, not only unit clauses but also characteristic clauses are deduced from a background theory together with the negation of positive examples. Such clauses can be computed by a resolution method for consequence finding. Unlike previous work on inverse entailment, our proposed method called CF-induction is sound and complete for finding hypotheses from full clausal theories, and can be used for inducing not only definite clauses but also non-Horn clauses and integrity constraints. We also show that CF-induction can be used to compute abductive explanations, and then compare induction and abduction from the viewpoint of inverse entailment and consequence finding.
[Show abstract][Hide abstract] ABSTRACT: This paper firstly provides a re-appraisal of the development of techniques for inverting deduction, secondly introduces Mode-Directed Inverse Entailment (MDIE) as a generalisation and enhancement of previous approaches and thirdly describes an implementation of MDIE in the Progol system. Progol is implemented in C and available by anonymous ftp. The re-assessment of previous techniques in terms of inverse entailment leads to new results for learning from positive data and inverting implication between pairs of clauses. Keywords: Learning, logic programming, induction, predicate invention, inverse resolution, inverse entailment, information compression. 1 Introduction Since its inception in this journal  Inductive Logic Programming (ILP) has grown to become a substantial sub-area of both Machine Learning and Logic Programming (see ). The success of the subject lies partly in the choice of the core representation language of logic programs. Least Herbrand models of log...
New Generation Computing 09/1999; 13(3). DOI:10.1007/BF03037227 · 0.82 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: In this paper, we re-evaluate the consequence finding problem within first-order logic. Firstly, consequence finding is generalized to the problem in which only interesting clauses having a certain property (called characteristic clauses) should be found. The use of characteristic clauses enables characterization of various reasoning problems of interest to AI, including abduction, nonmonotonic reasoning, prime implicates and truth maintenance systems. Secondly, an extension of the Model Elimination theorem proving procedure (SOL-resolution) is presented, providing an effective mechanism complete for finding the characteristic clauses. An important feature of SOL-resolution is that it constructs such a subset of consequences directly without testing each generated clause for the required property. We also discuss efficient but incomplete variations of SOL-resolution and their properties, which address finding the most specific and the least specific abductive explanations.
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