The DUCTOR: A Theory Revision System for Propositional Domains

Cain, Timothy Reese

doi:10.1016/b978-1-55860-200-7.50099-4

Cited by 8 publications

(3 citation statements)

References 3 publications

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“…This explains the increasing interest in automatically revising incorrect and incomplete domain theories. Some of the propositional systems are described in (Ginsberg, 1989;Ourston and Mooney, 1990;Craw and Sleeman, 1990;Cain, 1991;Towell, Shavlik, and Noordewier, 1991;Wilkins, 1991), and some of the In'st order revision systems are found in (Shapiro, 1983;Sammut and Banerji, 1986;Pazzani, 1988;Muggleton and Buntine, 1988;Bergadano and Giordana, 1989;Duval, 1991;Wogulis, 1991;Richards and Mooney, 1991;Rouveirol, 1992;DeRaedt, 1992;N~dellec, 1992;N6dellec and Causse, 1992;Esposito et al, 1993;Morik et al, 1993;Feldman and N6dellec, 1994).…”

Section: Some Recent Results -And Problemsmentioning

confidence: 99%

Industrial applications of ML: Illustrations for the KAML dilemma and the CBR dream

Kadratoff

1994

Machine Learning: ECML-94

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Section: Some Recent Results -And Problemsmentioning

confidence: 99%

Industrial applications of ML: Illustrations for the KAML dilemma and the CBR dream

Kadratoff

1994

Machine Learning: ECML-94

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“…RTLS (Ginsberg, 1990), KBANN (Towell & Shavlik, 1993), DUCTOR (Cain, 1991), and KRUST (Craw & Sleeman, 1991) are other theory refinement systems that are restricted to prepositional theories. Of these, KRUST is most closely related to FORTE, since it follows the approach of generating many possible revisions to theories, and then choosing among them based on their performance.…”

Section: Prepositional Theory Refinementmentioning

confidence: 99%

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Richards

Mooney

1995

Machine Learning

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Abstract. Knowledge acquisition is a difficult, error-prone, and time-consuming task. The task of automatically improving an existing knowledge base using learning methods is addressed by the class of systems performing theory refinement. This paper presents a system, FORTE (First-Order Revision of Theories from Examples), which refines first-order Horn-clause theories by integrating a variety of different revision techniques into a coherent whole. FORTE uses these techniques within a hill-climbing framework, guided by a global heuristic. It identifies possible errors in the theory and calls on a library of operators to develop possible revisions. The best revision is implemented, and the process repeats until no further revisions are possible. Operators are drawn from a variety of sources, including prepositional theory refinement, first-order induction, and inverse resolution. FORTE is demonstrated in several domains, including logic programming and qualitative modelling.

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“…They can integrate different reasoning methods and learning strategies. Among these systems we find RTLS (Ginsberg, 1990), DUCE (Muggleton, 1987), DUCTOR (Cain, 1991) and, finally, the system proposed by Matwin & Plante (1991) and EITHER (Mooney & Ourston, 1994), which explicitly use a deductive-inductive method for modifying a given domain theory. Empirical results have shown that revising an imperfect theory results in definitions that are more accurate from fewer examples than pure induction.…”

Section: Introduction and Motivationsmentioning

confidence: 99%

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et al. 2000

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Abstract. This paper presents an integration of induction and abduction in INTHELEX, a prototypical incremental learning system. The refinement operators perform theory revision in a search space whose structure is induced by a quasi-ordering, derived from Plotkin's θ -subsumption, compliant with the principle of Object Identity. A reduced complexity of the refinement is obtained, without a major loss in terms of expressiveness. These inductive operators have been proven ideal for this search space. Abduction supports the inductive operators in the completion of the incoming new observations. Experiments have been run on a standard dataset about family trees as well as in the domain of document classification to prove the effectiveness of such multistrategy incremental learning system with respect to a classical batch algorithm.

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The DUCTOR: A Theory Revision System for Propositional Domains

Cited by 8 publications

References 3 publications

Industrial applications of ML: Illustrations for the KAML dilemma and the CBR dream

Industrial applications of ML: Illustrations for the KAML dilemma and the CBR dream

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