Any learning process can be viewed as a self-modification of the leaxnefs current knowledge tArough an. interaction with some information source. Such knowledge modification is guided by the learner's deshe to achieve a certain outcome, and can engage any kind of inference. The type of inference involved depends on he input information, the current (background) knowledge and the learneFs task ax hand. Based on such a view of learning, several fundamental concepts are analized and clarified, in paxticular, analytic and synthetic learning, derivm:ional and hypothetical explanation, constnictive induction, abduction, abstraction and deductive generalization. It is shown that inductive generalization and abduction can be viewed as two basic forms of general induction, and that abstraction and deductive generalization axe two related forms of constructive deduction. Using this conceptual framework, a methodology for multistrategy task-adaptive learning (MTL) is outlined, in which learning strategies axe combined dynamically, depending on the current learning situation. Speccally, an MTL learner anaLizes a "wiad" relationship among the input information, the background knowledge and the learning task, and on that basis determines which strategy, or. a combination thereof, is most appropriate at a given learning step. To implement the MTL methodology, a new knowledge representation is proposed, based on the parametric association rules (PARs). Basic ideas of MTL are illustrated by means of the well-known "cup" example, through which is shown how an MTL learner can employ, depending the above mad relationship, emprical learning, constructive inductive generalization, abduction, explanation-based learning and absuaction.

Initial results toward developing a unifying conceptual framework for characterizing diverse learning strategies and paradigms are presented. We outline the inferential theory of learning that aims at understanding competence aspects of learning processes, in contrast to computational theory that is concerned with complexity aspects. The theory views learning as a goal-oriented process of creating or modifying knowledge representations. Such a process may involve any type of inference (deduction, analogy or induction) or information transmutation (e.g., reformulation, abstraction or copying). Any type of learning can therefore be characterized in terms of the types of such knowledge transformations that occur in a learning process. Several concepts fundamental to understanding learning are analyzed in a novel way and compared, such as analytic vs. synthetic learning, deduction, induction, abduction, abstraction and generalization. It is shown, for example, that inductive generalization, inductive specialization and abduction can be viewed as various forms of general induction, and that abstraction is a form of constructive deduction. Based on these concepts, a general multicriterion classification of learning processes is proposed. The presented ideas have a special significance for the development of a new generation of learning systems, called multistrategy systems, that integrate diverse learning strategies in a goal-oriented fashion. 1.

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