Multiagent learning using a variable learning rate

Learning to act in a multiagent environment is a difficult problem since the normal definition of an optimal policy no longer applies. The optimal policy at any moment depends on the policies of the other agents. This creates a situation of learning a moving target. Previous learning algorithms have one of two shortcomings depending on their approach. They either converge to a policy that may not be optimal against the specific opponents' policies, or they may not converge at all. In this article we examine this learning problem in the framework of stochastic games. We look at a number of previous learning algorithms showing how they fail at one of the above criteria. We then contribute a new reinforcement learning technique using a variable learning rate to overcome these shortcomings. Specifically, we introduce the WoLF principle, “Win or Learn Fast”, for varying the learning rate. We examine this technique theoretically, proving convergence in self-play on a restricted class of iterated matrix games. We also present empirical results on a variety of more general stochastic games, in situations of self-play and otherwise, demonstrating the wide applicability of this method.     

基于约束优化的联想记忆模型学习算法

本文提出了一种对称互连神经元网络的学习策略,利用全局约束优化方法确定连接权。优化过程采用了梯度下降技术。这种学习算法可以保证训练样本成为系统的稳定吸引子,并且具有优化意义上的最大吸引域。本文讨论了网络的存储容量,训练样本的渐近稳定性和吸引域大小。计算机实验结果说明了学习算法的优越性。     

Hierarchical intelligent control with flexible AC transmission systems application

A novel hierarchical intelligent controller configuration is proposed using an artificial neural network as a control-mode classifier in the supervisory level and a set of pre-designed controllers in the lower level. Controller outputs are modified nonlinearly by the classifying signals in a structure resembling one artificial neuron with adaptively changed weights. The lower-level local controllers are implemented using neural networks. An illustrative example of this approach is based on the transient stabilization of a single-machine infinite-bus system studied in Flexible AC Transmission Systems (FACTS) research.     

Knowledge extraction for production management

A procedure and underlying algorithm for extracting knowledge from production and inventory databases to support engineering management activities is described. The process searches for, detects and isolates behaviour patterns inherent in the data. It relates these patterns to production irregularities, suggests connections with specific causes and helps propose possible corrective or preventive actions. The approach is based on a four-phase procedure: (1) the decision-maker focuses on the subject or difficulty at issue, represented by a target concept; (2) the KEDB algorithm, based on a machine learning approach, processes the relevant database and provides knowledge characterizing and classifying the target concept; (3) the output is interpreted in Pareto fashion as a series of possible circumstances explaining the target concept behaviour; and (4) based on these causes, the decision-maker decides on possible corrective actions to improve the situation, or preventive actions to forestall unfavourable conditions. A case study based on an actual quality control database is detailed.     

关于最优等效替代法的实质及其应用条件的研究

本文研究了经济分析基本方法之一最优等效替代法的实质,系统地导出了被评价方案的绝对经济指标与相对经济指标的关系,并结合典型案例分析指出了该方法的应用条件,对正确进行建设项目的经济评价具有现实意义。     

光纤制导技术研究

介绍了光纤制导系统及作为该系统的关键技术：光纤传输系统，制导光缆，光纤的缠绕与释放，并作了理论分析和试验测试。     

A guided wave inspection technique for nuclear steam generator tubing

The utility of guided ultrasonic waves for increasing the efficiency and sensitivity of nuclear steam generator tubing evaluation by nondestructive means is explored. The basic theoretical background of guided waves in tubes is presented, with major emphasis placed on those results with direct relevance to NDE applications. The possibility of increasing the inspection range of the guided wave modes as well as their sensitivity to defects by proper choice of mode and frequency is also discussed.     

Distinguished Scientific Awards for an Early Career Contribution to Psychology.

The Early Career Awards recognize the large number of excellent young psychologists. Recipients of this award may not have held a PhD for more than nine years. The areas considered in 1991 were animal learning and behavior, developmental, and personality. The winners for 1991 were Kent C. Berridge, Susan A. Gelman, and Randy J. Larsen. For each winner a citation, biography, and selected bibliography of their works are presented. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Neural Network Feedback Control: Work at UTA’s Automation and Robotics Research Institute

This is an outline of research in neural networks for feedback control done since the mid 1990s at the Automation and Robotics Research Institute (ARRI) of The University of Texas at Arlington (UTA). It shows how the developments of Intelligent Control Systems based on neural networks have followed three main generations. This statement provides a short, broad-brush perspective on the development of intelligent neural feedback controllers.     

Feedback Specificity, Learning Opportunities, and Learning.

Although increasing feedback specificity is generally beneficial for immediate performance, it can undermine certain aspects of the learning needed for later, more independent performance. The results of the present transfer experiment demonstrate that the effects of increasing feedback specificity on learning depended on what was to be learned, and these effects were partially mediated through the opportunities to learn how to respond to different task conditions during practice. More specific feedback was beneficial for learning how to respond to good performance and detrimental for learning how to respond to poor performance. The former relationship was partially mediated by feedback specificity's effect on learning opportunities during practice. The results have implications for designing feedback interventions and training to maximize the learning of various aspects of a task. (PsycINFO Database Record (c) 2010 APA, all rights reserved)