Formal limits on the automatic generation and maintenance of integrity constraints
PODS '87 Proceedings of the sixth ACM SIGACT-SIGMOD-SIGART symposium on Principles of database systems
Inductive Inference: Theory and Methods
ACM Computing Surveys (CSUR)
A formal treatment of incomplete knowledge bases
A formal treatment of incomplete knowledge bases
A foundational approach to conjecture and knowledge in knowledge bases
A foundational approach to conjecture and knowledge in knowledge bases
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This paper presents a formal, foundational approach to learning from examples in machine learning. It is assumed that a learning system is presented with a stream of facts describing a domain of application. The task of the system is to form and modify hypotheses characterising the relations in the domain, based on this information. Presumably the set of hypotheses that may be so formed will require continual revision as further information is received. The emphasis in this paper is to characterise those hypotheses that may potentially be formed, rather than to specify the subset of the hypotheses that, for whatever reason, should be held. To this end. formal systems are derived from which the set of potential hypotheses that may be formed is precisely specified. A procedure is also derived for restoring the consistency of a set of hypotheses after conflicting evidence is encountered. In addition, this work is extended to where a learning system may be "told" arbitrary sentences concerning a domain The approach is intended to provide a basic framework lor the development of systems that learn from examples, as well as a neutral point from which such systems may be viewed and compared.