|
Inductive logic programming (ILP) is a subfield of machine learning which uses logic programming as a uniform representation for examples, background knowledge and hypotheses. Given an encoding of the known background knowledge and a set of examples represented as a logical database of facts, an ILP system will derive a hypothesised logic program which entails all the positive and none of the negative examples. Schema: ''positive examples'' + ''negative examples'' + ''background knowledge'' => ''hypothesis''. Inductive logic programming is particularly useful in bioinformatics and natural language processing. Ehud Shapiro laid the theoretical foundation for inductive logic programming〔Shapiro, Ehud Y. Inductive inference of theories from facts, Research Report 192, Yale University, Department of Computer Science, 1981. Reprinted in J.-L. Lassez, G. Plotkin (Eds.), Computational Logic, The MIT Press, Cambridge, MA, 1991, pp. 199–254.〕〔Shapiro, Ehud Y. (1983). ''Algorithmic program debugging''. Cambridge, Mass: MIT Press. ISBN 0-262-19218-7〕 and built its first implementation (Model Inference System) in 1981:〔Shapiro, Ehud Y. "The model inference system." Proceedings of the 7th international joint conference on Artificial intelligence-Volume 2. Morgan Kaufmann Publishers Inc., 1981.〕 a Prolog program that inductively inferred logic programs from positive and negative examples. The term ''Inductive Logic Programming'' was first introduced〔Luc De Raedt. A Perspective on Inductive Logic Programming. The Workshop on Current and Future Trends in Logic Programming, Shakertown, to appear in Springer LNCS, 1999. CiteSeerX: 〕 in a paper by Stephen Muggleton in 1991. The term "''inductive''" here refers to philosophical (i.e. suggesting a theory to explain observed facts) rather than mathematical (i.e. proving a property for all members of a well-ordered set) induction. ==Formal definition== The ''background knowledge'' is given as a logic theory , commonly in the form of Horn clauses used in logic programming. The ''positive'' and ''negative'' examples are given as a conjunction and of unnegated and negated ground literals, respectively. A ''correct hypothesis'' is a logic proposition satisfying the following requirements.〔; here: Sect.2.1〕 |} "''Necessity''" does not impose a restriction on , but forbids any generation of a hypothesis as long as the positive facts are explainable without it. "''Sufficiency''" requires any generated hypothesis to explain all positive examples . "''Weak consistency''" forbids generation of any hypothesis that contradicts the background knowledge . "''Strong consistency''" also forbids generation of any hypothesis that is inconsistent with the negative examples , given the background knowledge ; it implies "''Weak consistency''"; if no negative examples are given, both requirements coincide. Džeroski 〔; here: Sect.5.2.4〕 requires only "''Sufficiency''" (called "Completeness" there) and "''Strong consistency''". 抄文引用元・出典: フリー百科事典『 ウィキペディア（Wikipedia）』 ■ウィキペディアで「inductive logic programming」の詳細全文を読む スポンサード リンク
|