Learning Abductive Programs

LAP (Learning Abductive Programs) is a system that learns abductive logic programs from examples and from a background abductive theory. Abductive derivability is used as the coverage relation.

LAP is based on the basic top-down ILP algorithm that has been suitably extended. In particular, coverage of examples is tested by using the abductive proof procedure defined by Kakas and Mancarella. Assumptions can be made in order to cover positive examples and to avoid the coverage of negative ones, and these assumptions can be used as new training data. LAP can be applied for learning in presence of incomplete knowledge and for learning exceptions to classification rules.

See the following paper for a detailed description of the system:

The system has been implemented in Sicstus Prolog 3#5. To install it, download the following files:

Please send a mail to Fabrizio Riguzzi if you download the code. Contact him also to report comments, bugs and new experiments.
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