Seminar on Computational Learning and
Adaptation
Logic for Learning: Achievements and Challenges
Stephen Muggleton
Department of Computer Science
University of York, UK
stephen@cs.york.ac.uk
In this seminar we will discuss the use of Logic, in the form of
first-order predicate calculus, as a representation language for
Machine Learning and describe ongoing developments in this area. Both
Turing (1950) and McCarthy (1959) viewed a combination of Logic and
Machine Learning as central to the development of Artificial
Intelligence research. Inductive Logic Programming (ILP) employs a
first-order Horn clause representation which provides not only high
expressivity but also a natural representation for examples,
hypotheses and background knowledge. ILP has had especially successful
within prediction problems in Bioinformatics and Natural Language.
The notion of "generality" in Machine Learning coincides closely with
logical entailment between formulae. The semi-decidability of
entailment led Plotkin (1970) to investigate subsumption as a
restricted, decidable replacement. Conducting effective search is one
of the central difficulties in employing highly expressive
representations. Approaches to search include lattice operators
(Plotkin), minimal refinement operators (Shapiro), inverse resolution
(Muggleton and Buntine) and inverse entailment (Muggleton and
Yamamoto). Within Inductive Logic Programming, one of the key
challenges presently is the sound incorporation of probabilistic
information into both 1) the inductive search and 2) the
representation of examples, hypotheses and background knowledge. For
1) Bayesian priors have been used to provide effective preference
biases. Stochastic logic programs (Muggleton, 1987) have been used for
purpose 2). Initial approaches to inductive construction of stochastic
logic programs (Muggleton, 2000) will be described.
Date: Thurs., July 6
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Time: 4:15-5:30PM
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Place: Gates Building, room 104
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