An Experimental Comparison of Human and Machine Learning Formalisms

In this paper we describe the results of a set of experiments in which we compared the learning performance of human and machine learning agents. The problem involved the learning of a concept description for deciding on the legality of positions within the chess endgame King and Rook against King. Various amounts of background knowledge were made available to each learning agent. We concluded that the ability to produce high performance in this domain was almost entirely dependent on the ability to express first-order predicate relationships. 1 Introduction It is a commonly held belief that the use of a restricted hypothesis language simplifies the task of learning. In this paper we investigate a simple problem in which this is not the case. We describe a set of experiments in which a number of different inductive learning agents, with various hypothesis languages, were provided with the same training and test material. In all the experiments described the training and test instances..