Rational Machine Learning for AI Planning

本研究试图建立一个关于何时学习的理论框架。机器学习研究新概念的自动习得，主要集中在开发学习机制。诸如“人工智能计算机系统如何根据相对较少的几个观察到的训练实例形成一个一般概念？”以及“需要什么先验知识才能支持这种概念习得？”在这一领域占据了主导地位。然而，很明显，肆无忌惮地获取概念(即使是正确的概念)可能会降低它们旨在帮助的人工智能系统的性能。在复杂的应用程序中，比如人工智能规划中出现的情况，学习的性能损失可能是规则，而不是例外。因此，除非受到适当的约束，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。一个新概念的益处取决于绩效系统以及机器学习机制的特点。它还可能受到提供给业绩系统的预期任务概况和先前已获得的概念的强烈影响。这项拟议的研究旨在产生一种理性学习的拳头理论。如果人工智能系统的行为平均而言能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理性学习者。这样的理论必须为机器学习系统、使用所获得的概念的性能系统和估计的任务分布的特征之间的交互提供一个总体框架。本研究将探索估计预期概念效用的经验方法和分析方法。规划领域将作为工作的载体，但其结果很可能适用于其他业绩系统。基于解释的方法构成了学习方法的核心，因为它比归纳法更容易出现有害学习的现象。这项研究的好处将包括对理性学习类型的列举和分类。它将阐明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，并代表着朝着使机器学习成为人工智能其余领域的服务领域迈出的一些第一步。

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.