面向交互式决策的情感计算与学习方法及其在过程控制工程中的应用

宿翀; 李宏光

doi:10.3724/SP.J.1004.2013.00617

面向交互式决策的情感计算与学习方法及其在过程控制工程中的应用

doi: 10.3724/SP.J.1004.2013.00617

宿翀,
李宏光

1.
北京化工大学信息科学与技术学院北京 100029

详细信息

通讯作者:
李宏光

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出版历程
- 收稿日期: 2012-05-14
- 修回日期: 2013-03-27
- 刊出日期: 2013-05-20

Approaches to Affective Computing and Learning towards Interactive Decision Making in Process Control Engineering

1.
School of Information Science and Technology, Beijing University of Chemical Technology, Beijing 100029, China

摘要

摘要: 当今大量的工业过程控制工程问题, 如控制性能评价等的多目标决策问题通过人机交互得到解决. 然而, 在使用传统的交互式进化计算方法求解多目标决策问题时, 表现为局部搜索能力不强和过度依赖决策人员的主观意图. 本文提出一种新的情感计算方法和人机交互学习机制的解决方案. 提出一类基于刺激响应的情感计算模型(STAM), 并给出了情感空间和人的主观偏好之间的定量关系. 此外, 基于遗传算法框架, 建立情感交互学习策略, 旨在决策过程中逐步掌握人员的主观偏好, 降低人的疲劳程度, 使决策更加客观和科学. 附录A和B分别展示了情感学习算法的复杂度和收敛性分析. 为验证所提方法的正确性, 以测试函数及单回路反馈控制的PID参数整定问题进行研究, 得到了满意的结果, 验证了所提方法的可靠性与有效性.
- 情感计算, 情感学习, 交互式进化计算(IEC), 决策, 过程控制
Abstract: Numerous multi-objective decision-making problems related to industrial process control engineering such as control and operation performance evaluation are being resolved through human-computer interactions. With regard to the problems that traditional interactive evolutionary computing approaches suffer i.e., limited searching ability and human's strong subjectivity in multi-objective-attribute decision-making, a novel affective computing and learning solution adapted to human-computer interaction mechanism is explicitly proposed. Therein, a kind of stimulating response based affective computing model (STAM) is constructed, along with quantitative relations between affective space and human's subjective preferences. Thereafter, affective learning strategies based on genetic algorithms are introduced which are responsible for gradually grasping essentials in human's subjective judgments in decision-making, reducing human's subjective fatigue as well as making the decisions more objective and scientific. Affective learning algorithm's complexity and convergence analysis are shown in Appendices A and B. To exemplify applications of the proposed methods, ad-hoc test functions and PID parameter tuning are suggested as case studies, giving rise to satisfying results and showing validity of the contributions.
- Affective computing, affective learning, interactive evolutionary computing (IEC), decision-making, process control

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