Theory Revision and Related Problems in Learning Theory

理论修正是对给定的、大致正确的规则的修正，也称为概念或理论。这个问题经常出现在机器学习中，例如，当专家系统的初始输出不正确时，当机器学习问题太大或太复杂而无法从头开始解决时，需要一个大致正确的规则来启动学习过程。已经有相当多的临时建立的理论修改系统，但对该理论知之甚少。这项研究考察了有效的理论修正的基本数学可能性和局限性。作为这项研究的结果，计算学习理论的理论修正将成为研究学习情境的一般框架，在学习情境中，有大量的初始信息是可用的或必要的。特别是，PI研究了以下领域：扩展了他们之前关于命题逻辑理论的修改与查询的工作，与证书复杂性和属性高效学习的关系，谓词逻辑表示的修改问题，以及范畴语法的学习和修改。

Theory revision is the correcting of a given, roughly correct rule,also known as a concept or theory. This problem arises frequently inmachine learning, for instance, when the initial output of an expert system is not correct, and when the machine learningproblem is too large or too complex to solve from scratch, and anapproximately correct rule is needed to jump-start the learningprocess. There has been considerable ad hoc building of theory revision systems, but the theory is poorly understood. This researchinvestigates fundamental mathematical possibilities and limitationsof efficient theory revision. It is hoped that as a result ofthis research, theory revision in computational learning theory willemerge as a general framework for the study of learning situationswhere a large amount of initial information is available ornecessary. In particular, the PIs investigate the following areas: extensionsof their previous work on propositional logic theory revisionwith queries, relations to certificate complexity andattribute-efficient learning, revision problems for predicate logicrepresentations, and both the learning and revising of categorial grammars.