Machine Invention of First-order Predicates by Inverting Resolution

Muggleton, Stephen; Buntine, Wray L.

doi:10.1016/b978-0-934613-64-4.50040-2

Cited by 271 publications

(190 citation statements)

References 4 publications

Supporting

Mentioning

182

Contrasting

Unclassified

Order By: Relevance

“…The invention of predicates is an open area of research in ILP [13,16,10,7]. In the case of unconditional functional logic programs it is expected that function invention would be even more necessary than First Order Horn Logic [16].…”

Section: Function Inventionmentioning

confidence: 99%

A Strong Complete Schema for Inductive Functional Logic Programming

Hernández-Orallo¹,

Ramírez-Quintana²

1999

Inductive Logic Programming

View full text Add to dashboard Cite

Abstract.A new IFLP schema is presented as a general framework for the induction of functional logic programs (FLP). Since narrowing (which is the most usual operational semantics of FLP) performs a unification (mgu) followed by a replacement, we introduce two main operators in our IFLP schema: a generalisation and an inverse replacement or intrareplacement, which results in a generic inversion of the transitive property of equality. We prove that this schema is strong complete in the way that, given some evidence, it is possible to induce any program which could have generated that evidence. We outline some possible restrictions in order to improve the tractability of the schema. We also show that inverse narrowing is just a special case of our IFLP schema. Finally, a straightforward extension of the IFLP schema to function invention is illustrated.

show abstract

Section: Function Inventionmentioning

confidence: 99%

A Strong Complete Schema for Inductive Functional Logic Programming

Hernández-Orallo¹,

Ramírez-Quintana²

1999

Inductive Logic Programming

View full text Add to dashboard Cite

show abstract

“…This includes the approaches of Quinlan (1990), Muggleton and Buntine (1988), Muggleton and Feng (1990), and Rouveirol and Puget (1989). There are, however, some important differences between Clint and these other approaches:…”

Section: For Clintmentioning

confidence: 99%

“…The work on Clint is related to the work on versionspaces (Mitchell, 1982), learning apprentices (Kodratoff & Tecuci, 1987;Tecuci & Kodratoff, 1990), explanation based learning (Mitchell, Keller & Kedar-Cabelli, 1986;De Jong & Mooney, 1986), experimentation (Subramanian & Feigenbaum, 1986;Sammut & Banerji, 1986;Krawchuk & Witten, 1988), indirect relevance (Buntine, 1987), identification in the limit and debugging (Shapiro, 1983), shift of bias (Utgoff & Mitchell, 1982;Utgoff, 1986), and inductive logic programming (Muggleton & Buntine, 1988;Muggleton & Feng, 1990;Rouveirol & Puget, 1989;Quinlan, 1990). …”

Section: For Clintmentioning

confidence: 99%

“…The Cia technique is related to model driven learning in Blip (Morik, 1989;Emde, Habel & Rollinger, 1983;Wrobel, 1989), applying analogy on concept-learning (Vrain & Lu, 1988), analogy (Yokomori, 1986), constructive induction (Rendell, 1989;Muggleton & Buntine, 1988;Muggleton, 1987) and discovery (Lenat, 1983).…”

Section: For Ciamentioning

confidence: 99%

“…• The approaches of Quinlan (1990), Muggleton and Buntine (1988), and Muggleton and Feng (1990) aim at inducing rules from large collections of examples. Therefore they focus on one concept and use heuristics to prune the search.…”

Section: For Clintmentioning

confidence: 99%

See 2 more Smart Citations

Interactive concept-learning and constructive induction by analogy

Raedt

Bruynooghe

1992

Mach Learn

View full text Add to dashboard Cite

Abstract. The available concept-learners only partially fulfill the needs imposed by the learning apprentice generation of learners. We present a novel approach to interactive concept-learning and constructive induction that better fits the requirements imposed by the learning apprentice paradigm. The approach is incorporated in the system Clint-Cia, which integrates several user-friendly features into one working whole: it is interactive, generates examples, shifts its bias, identifies concepts in the limit, copes with indirect relevance, recovers from errors, performs constructive induction and invents new concepts by analogy to previously learned ones.

show abstract

The role of assumptions in knowledge engineering

Fensel

Benjamins

1998

Int. J. Intell. Syst.

View full text Add to dashboard Cite

Problem‐solving methods are means of describing the inference process of knowledge‐based systems. In recent years, a number of these problem‐solving methods have been identified that can be reused for building new systems. However, problem‐solving methods require specific types of domain knowledge and introduce specific restrictions on the tasks that can be solved by them. These requirements and restrictions are assumptions that play a key role in the reuse of problem‐solving methods, in the acquisition of domain knowledge, and in the definition of the problem that can be tackled by knowledge‐based systems. In this paper we discuss the different roles assumptions play in the development of knowledge‐based systems and provide a survey of assumptions used in diagnostic problem solving. We show how such assumptions introduce targets and bias for goal‐driven machine learning and knowledge discovery techniques. © 1998 John Wiley & Sons, Inc.

show abstract

Machine Invention of First-order Predicates by Inverting Resolution

Cited by 271 publications

References 4 publications

A Strong Complete Schema for Inductive Functional Logic Programming

A Strong Complete Schema for Inductive Functional Logic Programming

Interactive concept-learning and constructive induction by analogy

The role of assumptions in knowledge engineering

Contact Info

Product

Resources

About