Cautious induction: An alternative to clause-at-a-time hypothesis construction in inductive logic programming |
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Authors: | Simon Anthony Alan M Frisch |
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Affiliation: | (1) Intelligent Systems Group Department of Computer Science, University of York, YO10 5DD York, UK |
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Abstract: | Hypotheses constructed by inductive logic programming (ILP) systems are finite sets of definite clauses. Top-down ILP systems
usually adopt the following greedy clause-at-a-time strategy to construct such a hypothesis: start with the empty set of clauses
and repeatedly add the clause that most improves the quality of the set. This paper formulates and analyses an alternative
method for constructing hypotheses. The method, calledcautious induction, consists of a first stage, which finds a finite set of candidate clauses, and a second stage, which selects a finite subset
of these clauses to form a hypothesis. By using a less greedy method in the second stage, cautious induction can find hypotheses
of higher quality than can be found with a clause-at-a-time algorithm. We have implemented a top-down, cautious ILP system
called CILS. This paper presents CILS and compares it to Progol, a top-down clause-at-a-time ILP system. The sizes of the
search spaces confronted by the two systems are analysed and an experiment examines their performance on a series of mutagenesis
learning problems.
Simon Anthony, BEng.: Simon, perhaps better known as “Mr. Cautious” in Inductive Logic Programming (ILP) circles, completed a BEng in Information
Engineering at the University of York in 1995. He remained at York as a research student in the Intelligent Systems Group.
Concentrating on ILP, his research interests are Cautious Induction and developing number handling techniques using Constraint
Logic Programming.
Alan M. Frisch, Ph.D.: He is the Reader in Intelligent Systems at the University of York (UK), and he heads the Intelligent Systems Group in the
Department of Computer Science. He was awarded a Ph. D. in Computer Science from the University of Rochester (USA) in 1986
and has held faculty positions at the University of Sussex (UK) and the University of Illinois at Urbana-Champaign (USA).
For over 15 years Dr. Frisch has been conducting research on a wide range of topics in the area of automated reasoning, including
knowledge retrieval, probabilistic inference, constraint solving, parsing as deduction, inductive logic programming and the
integration of constraint solvers into automated deduction systems. |
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Keywords: | Machine Learning Inductive Learning Inductive Logic Programming Cautious Induction |
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