A Theory and Methodology of Multistrategy Learning

在过去的几年里，我们看到了一个巨大的扩张， 机械研究方向和方法的多样化 学习，同时对开发系统感兴趣， 整合各种学习策略。 这种情况造成 需要分析和澄清 不同的战略和方法，并建立一个理论 多策略学习系统的实施基础。 本研究试图建立一个理论框架， 描述不同的学习过程，基于一个一般概念， 推理理论（Inference-based Theory） 根据该 理论上，系统通过尝试从输入信息中学习， 理解它，即，将其与背景知识联系起来 （BK）。 基于这一理论，研究人员计划开发一种 多策略任务自适应学习系统， 协同整合不同的学习策略。 给定 输入和学习目标，MTL学习者应用 最合适的策略）， 输入和学习者的背景知识之间的关系 学习者的目标。 MTL方法旨在整合 最终，这些学习策略，如经验学习， 建构归纳法，基于推理的学习，溯因推理， 通过类比和抽象来学习。

The last several years have seen a great expansion and diversification of research directions and approaches in machine learning, and a simultaneous interest in developing systems that integrate various learning strategies. Such a situation creates a need for analyzing and clarifying the relationships among different strategies and approaches, and building a theoretical basis for the implementation of multistrategy learning systems. This research attempts to develop a theoretical framework for describing diverse learning processes, based on a general notion of inference (hence, inference-based theory). According to this theory, a system learns from input information by trying to understand it i.e., to relate it to its background knowledge (BK). Based on this theory the researcher plans to develop a multistrategy task-adaptive learning (MTL) system, that synergistically integrates different learning strategies. Given an input and a goal of learning, an MTL learner applies the strategies) that are most appropriate according to the relationship between the input and learner's BK in the context of the learner's goal. The MTL methodology is intended to integrate ultimately such learning strategies as empirical learning, constructive induction, explanation-based learning, abduction, learning by analogy and abstraction.