Reasoning by analogy with applications to heuristic problem-solving: a case study
Reasoning by analogy with applications to heuristic problem-solving: a case study
Steps toward automatic theory formation
IJCAI'73 Proceedings of the 3rd international joint conference on Artificial intelligence
How to Upgrade Propositional Learners to First Order Logic: A Case Study
Machine Learning and Its Applications, Advanced Lectures
Learning of Class Descriptions from Class Discriminations: A Hybrid Approach for Relational Objects
KI '02 Proceedings of the 25th Annual German Conference on AI: Advances in Artificial Intelligence
Learning conjunctive concepts in structural domains
AAAI'87 Proceedings of the sixth National conference on Artificial intelligence - Volume 2
Learning conjunctive concepts in structural domains
AAAI'87 Proceedings of the sixth National conference on Artificial intelligence - Volume 2
Automatic generation of object class descriptions using symbolic learning techniques
AAAI'91 Proceedings of the ninth National conference on Artificial intelligence - Volume 2
BIMBO: a system which learns its expertise
PKWBS-W'84 Proceedings of the 1984 IEEE conference on Principles of knowledge-based systems
Adaptive reasoning for cooperative agents
INAP'09 Proceedings of the 18th international conference on Applications of declarative programming and knowledge management
ON SETS OF TERMS: A STUDY OF A GENERALISATION RELATION AND OF ITS ALGORITHMIC PROPERTIES
Fundamenta Informaticae
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Positive and negative instances of a concept are assumed to be described by a conjunction of literals in the predicate calculus, with terms limited to constants and universally quantified variables. A graph representation of a conjunction of literals, called a "product graph", is introduced. It is desirable to merge positive instances by generalization, while maintaining discrimination against negative instances. This is accomplished by an induction procedure which operates on the product graph form of these positive and negative instances. The correctness of the procedure is proven, together with several related results of direct practical significance. This work is directed to the goal of providing a formal model for the inductive processes which are observed in artificial intelligence studies in specialized areas.