Yen Gary G, Acay Daghan
Oklahoma State University, School of Electrical and Computer Engineering, Stillwater, OK 74078, USA.
ISA Trans. 2009 Apr;48(2):196-205. doi: 10.1016/j.isatra.2008.11.002. Epub 2008 Dec 11.
In this paper we propose a novel idea for adaptation of the user interface in complex supervisory tasks. Under the assumption that the user behavior is stationary and that the user has limited cognitive and motor abilities, we have shown that a combination of genetic algorithm for constrained optimization and probabilistic modeling of the user may evolve the adaptive interface to the level of personalization. The non-parametric statistics has been employed in order to evaluate the feasibility of the ranking approach. The method proposed is flexible and easy to use in various problem domains. We have tested the method with an automated user and a group of real users in an air traffic control environment. The automated user, implemented for initial tests, is built first under the same assumptions as a real user. In the second step, we have exploited the adaptive interface through a group of real users and collected subjective ratings using questionnaires. We have shown that the proposed method can effectively improve human-computer interaction and our approach is pragmatically a valid approach for the interface adaptation in complex environments.
在本文中，我们提出了一种在复杂监督任务中适配用户界面的新想法。在用户行为稳定且用户认知和运动能力有限的假设下，我们表明，用于约束优化的遗传算法与用户概率建模相结合，可以将自适应界面发展到个性化水平。采用非参数统计来评估排序方法的可行性。所提出的方法灵活且易于在各种问题领域中使用。我们在空管环境中对该方法进行了自动化用户和一组真实用户的测试。为初始测试而实现的自动化用户首先是在与真实用户相同的假设下构建的。第二步，我们通过一组真实用户利用了自适应界面，并使用问卷收集主观评分。我们已经表明，所提出的方法可以有效地改善人机交互，并且我们的方法实际上是复杂环境中界面适配的有效方法。
