A Prolog technology theorem prover: a new exposition and implementation in Prolog
Theoretical Computer Science - Selected papers on theoretical issues of design and implementation of symbolic computation systems
Linear resolution for consequence finding
Artificial Intelligence
Minimal Answer Computation and SOL
JELIA '02 Proceedings of the European Conference on Logics in Artificial Intelligence
Which Hypotheses Can Be Found with Inverse Entailment?
ILP '97 Proceedings of the 7th International Workshop on Inductive Logic Programming
Hypothesis finding based on upward refinement of residue hypotheses
Theoretical Computer Science - Selected papers in honour of Setsuo Arikawa
Induction as Consequence Finding
Machine Learning
Automated theorem proving: A logical basis (Fundamental studies in computer science)
Automated theorem proving: A logical basis (Fundamental studies in computer science)
Guest editorial: special issue on Inductive Logic Programming
Machine Learning
A consequence finding approach for full clausal abduction
DS'07 Proceedings of the 10th international conference on Discovery science
A consequence finding approach for full clausal abduction
DS'07 Proceedings of the 10th international conference on Discovery science
Towards a logical reconstruction of CF-induction
JSAI'07 Proceedings of the 2007 conference on New frontiers in artificial intelligence
Comparison of upward and downward generalizations in CF-Induction
ILP'11 Proceedings of the 21st international conference on Inductive Logic Programming
Completing causal networks by meta-level abduction
Machine Learning
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Mode declarations are a successful form of language bias in explanatory ILP. But, while they are heavily used in Horn systems, they have yet to be similarly exploited in more expressive clausal settings. This paper presents a mode-directed ILP procedure for full clausal logic. It employs a first-order inference engine to abductively and inductively explain a set of examples with respect to a background theory. Each stage of hypothesis formation is guided by mode declarations using a generalisation of efficient Horn clause techniques for inverting entailment. Our approach exploits language bias more effectively than previous non-Horn ILP methods and avoids the need for interactive user assistance.