Learning with Abduction

A.C. Kakas1, F. Riguzzi2

1 Department of Computer Sdence, University of Cyprus
75 Kallipoleos str., CY-1678 Nicosia, Cyprus
antonis@turing.cs.ucy.ac.cy
2 DEIS, Universit di Bologna
Viale Risorgimento 2, 40136 Bologna, Italy
friguzzi@deis.unibo.it


Abstract. We investigate how abduction and induction can be integrated 
into a common learning framework through the notion of Abductive 
Concept Learning (ACL). ACL is an extension of Inductive Logic
Programming (ILP) to the case in which both the background and the
target theory are abductive logic programs and where an abductive notion 
of entailment is used as the coverage relation. In this framework, it
is then possible to learn with incomplete information about the examples
by exploiting the hypothetical reasoning of abduction.
The paper presents the basic framework of ACL with its main characteristics. 
An algorithm for an intermediate version of AOL is developed by
suitably extending the top-down ILP method and integrating this with
an abductive proof procedure for Abductive Logic Programming (ALP).
A prototype system has been developed and applied to learning problems
with incomplete information.
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