Rational Machine Learning for AI Planning

这项研究旨在建立一个理论框架， 要学习. 机器学习，研究自动 新概念的获得，主要集中在开发 学习的机制。 例如“人工智能计算机如何 系统形成一个一般概念是为了回应一个相对 几个观察到的训练实例？和“什么是先验知识 需要支持这种概念收购？“已经主宰了 领域 然而，很明显， 概念（即使是正确的概念）的性能会降低 他们想要帮助的人工智能系统。 在复杂 应用程序，例如在人工智能规划中出现的应用程序， 因为学习可以成为规则而不是例外。 因此，在本发明中， 机器学习机制不可能成功，除非 被限制住了 不幸的是，何时学习的判断是 本身复杂。 新概念的好处取决于功能 性能系统以及机器学习 机制 它也会受到预期的强烈影响。 要交给业绩制度的任务和 这些概念是以前学过的。 这项拟议中的研究旨在提出一个第一理论， 理性学习 AI系统的学习组件是一个 理性学习者，如果AI系统的行为得到保证， 平均水平，通过获得任何新概念来改善。 这样的理论必须为相互作用提供一个总体框架 在机器学习系统和性能系统之间， 使用所获得的概念和估计的特征， 分配任务。 本研究将探讨实证和 和分析方法来估计预期的概念效用。 规划领域将作为工作的载体，但 结果可能适用于其他业绩制度。 基于推理的方法是学习方法的核心 因为它，比归纳法，似乎更容易 有害学习的现象。 研究的好处 将包括一个枚举和分类的类型的理性 学习 它将揭示哪些机器学习机制 可能对哪种类型的人工智能系统最有用，并代表了一些 使机器学习成为服务领域的第一步 其余的AI。

This research seeks to build a theoretical framework for when to learn. Machine learning, which studies the automatic acquisition of new concepts, has largely focussed on developing mechanisms for learning. Questions such as "How can an AI computer system come to form a general concept in response to a relatively few observed training examples?" and "What prior knowledge is required to support such concept acquisition?" have dominated the field. However, it has become clear that the unbridled acquisition of concepts (even correct concepts) can degrade the performance of the AI system that they are intended to help. In sophisticated applications, such as arise in AI planning, a performance penalty for learning can be the rule rather than the exception. Thus, machine learning mechanisms cannot be successful unless suitably restrained. Unfortunately, the judgement of when to learn is itself complex. The benefit of a new concept depends on features of the performance system as well as the machine learning mechanism. It can also be strongly influenced by the expected profile of tasks to be given to the performance system and by the concepts that have previously been acquired. The proposed research is intended to produce a fist theory of rational learning. The learning component of an AI system is a rational learner if the AI system's behavior is guaranteed, on average, to be improved by the acquisition of any new concepts. Such a theory must provide a general framework for the interactions between a machine learning system, the performance system that employs the acquired concepts, and characteristics of the estimated distribution of tasks. The research will explore both empirical and analytic approaches to estimating expected concept utility. The area of planning will serve as a vehicle for the work, but the results will likely be applicable to other performance systems. Explanation-based methods form the nucleus of learning methods since it, more than the inductive approach, seems liable to the phenomenon of detrimental learning. The benefits of the research will include an enumeration and taxonomy of the types of rational learning. It will shed light on which machine learning mechanisms may be most useful to which type of AI systems, and represents some first steps towards making machine learning a service area for the rest of AI.