Rational Machine Learning for AI Planning

本研究旨在建立一个何时学习的理论框架。机器学习研究新概念的自动获取，主要集中在学习机制的开发上。诸如“人工智能计算机系统如何根据相对较少的观察到的训练示例形成一般概念？”和“需要哪些先验知识来支持这种概念获取？”等问题主导了该领域。然而，毫无节制地获取概念（甚至是正确的概念）会降低AI系统的性能，这一点已经变得很明显。在复杂的应用程序中，例如人工智能规划，学习的性能损失可能是规则而不是例外。因此，除非受到适当的限制，否则机器学习机制不可能成功。不幸的是，何时学习的判断本身就很复杂。新概念的好处取决于性能系统的特征以及机器学习机制。它也可能受到预期分配给绩效系统的任务概况和先前获得的概念的强烈影响。本研究旨在提出理性学习的第一个理论。如果人工智能系统的行为平均而言可以通过获得任何新概念而得到改善，那么人工智能系统的学习组件就是一个理性学习者。这样的理论必须为机器学习系统、使用获得的概念的性能系统和任务估计分布的特征之间的交互提供一个一般框架。本研究将探讨评估预期概念效用的实证与分析方法。规划领域将作为工作的工具，但其结果可能适用于其他业绩制度。基于解释的方法形成了学习方法的核心，因为它比归纳方法更容易产生有害的学习现象。这项研究的好处将包括列举和分类理性学习的类型。它将阐明哪种机器学习机制可能对哪种类型的人工智能系统最有用，并代表着使机器学习成为其他人工智能服务领域的一些初步步骤。

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.