Any theory aimed at understanding common sense reasoning, the process that humans use to cope with the mundane but complex aspects of the world in evaluating everyday situations, should account for its flexibility, its adaptability, and the speed with which it is performed. Current theories of reasoning, however, do not satisfy these requirements, a fact we attribute, at least partly, to their seperation from learning.

While the central role of learning in conition is widely acknowledged, most lines of research nevertheless study the phenomenon of "learning" separately from that of "reasoning". The work presented here is motivated by the belief that learning is at the core of any attempt at understanding high level cognitive tasks. A formal model for the study of reasoning is developed in which a learning component has a principal role, and its advantages oer traditional formalism for the study of reasoning are shown.

This papers presents an integrated theory of learning, knowledge representation and reasoning within a unified framework called Learning to Reason. The Learning to Reason framework combines the interfaces to the world use by known learning models with a reasoning task and a performance criterion suitable for it. It is shown that the framework efficiently supports "more reasoning" than traditional approaches and at the same time matches our expectations of plausible parrterns of reasoning. Several results are presented to substantiate this claim, presenting cases where learning to reason about the world is feasible but either reasoning from a given representation of the world of learning representations of the world do not have efficient solutions.

The papers presents work originally introduced by Khardon and Roth (Khardon and Roth 1994n) and surveys further developments made within this framework more recently.