摘要:

This paper presents an approach to learning to control a dynamic physical system. The approach has been implemented in a program named CART, and applied to a simple physical system studied previously by several researchers. Experiments illustrate that a control method is learned in about 16 trials, an improvement over previous learning programs.

ISSN:0824-7935    

DOI:10.1111/j.1467-8640.1987.tb00219.x

出版商:Blackwell Publishing Ltd    

年代:1987

数据来源: WILEY

摘要:

In this paper we present a computational theory of human motor performance and learning. The theory is implemented as a running AI system called MAGGIE. Given a description of a desired movement as input, the system generates simulated motor behavior as output. The theory states mat skills are encoded asmotor schemas,which specify the positions and velocities of a limb at selected points in time. Moreover, there exist two natural representations for such knowledge;viewer‐centeredschemas describe visually perceived behavior, aridjoint‐centeredschemas are used to generate behavior. When the model acts upon these two representational formats, they exhibit quite different behavioral characteristics. MAGGIE performs the desired movement within a feedback control paradigm, monitoring for errors and correcting them when it detects them. Learning involves improving the joint‐centered schema over many practice trials; this reduces the need for monitoring. The model accounts for a number of well‐documented motor phenomena, including the speed‐accuracy trade‐off and the gradual improvement in performance with practice. It also makes several testable predictions. We close with a discussion of the theory's strengths and weaknesses, along with directions for futu

ISSN:0824-7935    

DOI:10.1111/j.1467-8640.1987.tb00220.x

出版商:Blackwell Publishing Ltd    

年代:1987

数据来源: WILEY

摘要:

This paper describes a novel approach to machine learning, based on the principle of learning byreasoning.Current learning systems have significant limitations such asbrittleness,i.e., the deterioration of performance on a different domain or problem and lack of power required for handling real‐world learning problems. The goal of our research was to develop an approach in which many of these limitations are overcome in a unified, coherent and general framework. Our learning approach is based on principles of reasoning, such as the discovery of theunderlying principleand the recognition of the deeper basis of similarity, which is somewhat akin to human learning. In this paper, we argue the importance of these principles and tie the limitations of current systems to the lack of application of these principles. We then present the technique developed and illustrate it on a learning problem not directly solvable by previous approache

ISSN:0824-7935    

DOI:10.1111/j.1467-8640.1987.tb00221.x

出版商:Blackwell Publishing Ltd    

年代:1987

数据来源: WILEY