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.

This paper outlines basic assumptions and a theoretical basis for multistrategy task.adaptive learning (MTL) methodology, which aims at ultimately integrating a spectrum of learning strategies, such as empirical teaming, constructive induction, abduction, analytic learning. learning by analogy, and reinforcement learning. In MTL, in response to an input, a learner deternines the su'ategy, or a combination of su'ategies, that is mo. st appropriate for the learning task. This detemination is based on the relationship between the input, the leamegs background knowledge and the leamer's task. By means of a simple example we show how an MTL learner can employ, depending on the above relationship, emprical learning, constructive inductive generaiiz. afion, abduction, explanation-based learning and abstraction.

Any learning process can be viewed as a self-modification of the leamer's current knowledge through an interaction with some information source. Such knowledge modification s graded by the learner s destre to achieve a certain outcome, and can engage any kind of inference. The typ0 of inference involved depends on the input information, the current (background) knowledge and the learne's task,.at h, and: Based on such a view of learning, several fundamental concepts are ananzeu ano clarified, in particular, analytic and synthetic learning, derivational and hypothetical explanation, constructive 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 are two related forms of constructive deduction. Using this conceptual framework, a methodology for multistrategy task-adaptive learning (MTL) is outlined, in which learning strategies are combined dynamically, depending on the current learning situation. Specifically, an MTL learner anali?es a "triad" 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 leamer can employ, depending on the above triad relationship, emprical learning, constructive inductive generalization, abduction, explanation-based learning and abstraction.