Inferential Theory of Learning as a Conceptual Basis for Multistrategy Learning [chapter]

Ryszard S. Michalski
1993 Multistrategy Learning  
The development of multistrategy learning systems requires a clear understanding of the roles and the applicability conditions of different learning strategies. To this end, this chapter introduces the Inferential Theory of Learning that provides a conceptual framework for explaining logical capabilities of learning strategies, i.e., their competence. V iewing learning as a process of modifying the learner's knowledge by exploring the learner's experience, the theory postulates that any such
more » ... cess can be described as a search in a knowledge space, which involves the learner's experience, pior knowledge and the learning goal . The search operators are instantiations of knowledge transmutations, which are generic patterns of knowledge change. Transmutations may employ any basic type of inference deduction, induction or analogy. Several fundamental knowledg e transmutations are described in a novel and general way, such as generalization, abstraction, explanation and similization, and their counterparts, specialization, concretion, prediction and dissimilization, respectively. Generalization enlarges the reference set of a description (the set of entities that are being described). Abstraction reduces the amount of the detail about the reference set. Explanation generates premises that explain (or imply) the given properties of the reference set. Similization transfers knowledge from one reference set to a similar reference set. Using concepts of the theory, a multistrategy task -adaptive learning (MTL) methodology is outlined, and illustrated by an example. MTL dynamically adapts strategies to the learning task , defined by the input information, learner's background knowledge, and the learning goal. The goal of MTL research is to synergistically integrate a wide range of inferential learning strategies, such as empirical generalization, constructive induction, deductive generalization, explanation, prediction, abstraction, and similization.
doi:10.1007/978-1-4615-3202-6_2 fatcat:3z4yxkxg3ndunpwth5h4lqmpam