Inferential Theory of Learning as a Conceptual Basis for Multistrategy Learning

Abstract

In view of a great proliferation of machine learning methods and paradigms, there is a need for a general conceptual framework that would explain their interrelationships and provide a basis for their integration into multistrategy learning systems. This article presents initial results on the Inferential Theory of Learning that aims at developing such a framework, with the primary emphasis on explaining logical capabilities of learning systems, i.e., their competence . The theory views learning as a goal-oriented process of modifying the learner's knowledge by exploring the learner's experience. Such a process is described as a search through a knowledge space , conducted by applying knowledge transformation operators, called knowledge transmutations . Transmutations can be performed using any type of inference—deduction, induction, or analogy. Several fundamental pairs of transmutations are presented in a novel and very general way. These include generalization and specialization, explanation and prediction, abstraction and concretion, and similization and dissimilization. Generalization and specialization transmutations change the reference set of a description (the set of entities being described). Explanations and predictions derive additional knowledge about the reference set (explanatory or predictive). Abstractions and concretions change the level of detail in describing a reference set. Similizations and dissimilizations hypothesize knowledge about a reference set based on its similarity or dissimilarity with another reference set. The theory provides a basis for multistrategy task-adaptive learning (MTL), which is outlined and illustrated by an example. MTL dynamically adapts strategies to the learning task , defined by the input information, the learner's background knowledge, and the learning goal. It aims at synergistically integrating a wide range of inferential learning strategies, such as empirical and constructive inductive generalization, deductive generalization, abductive derivation, abstraction, similization, and others.