A foundational approach to autonomous knowledge acquisition

A formal, foundational approach to autonomous knowledge acquisition is presented. In particular, "learning from examples" and "learning from being told" and the relation of these approaches to first-order representation systems are investigated. It is assumed initially that the only information available for acquisition is a stream of facts, or ground atomic formulae, describing a domain. On the basis of this information, hypotheses expressed in set-theoretic terms and concerning the application domain may be proposed. As further instances are received, the hypothesized relations may be modified or discarded, and new relations formed. The intent though is to characterize those hypotheses that may potentially be formed, rather than to specify the subset of the hypotheses that, for whatever reason, should be held.
Formal systems are derived by means of which the set of potential hypotheses is precisely specified, and a procedure is 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. Included in this is an investigation of the relation between acquiring knowledge and reasoning deductively. However, the interaction of these approaches leads to immediate difficulties which likely require informal, pragmatic techniques for their resolution. The overall framework is intended both as a foundation for investigating autonomous approaches to learning and as a basis for the development of such autonomous systems.