کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
394180 | 665782 | 2010 | 19 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Empirical analysis of an on-line adaptive system using a mixture of Bayesian networks
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موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
هوش مصنوعی
پیش نمایش صفحه اول مقاله
![عکس صفحه اول مقاله: Empirical analysis of an on-line adaptive system using a mixture of Bayesian networks Empirical analysis of an on-line adaptive system using a mixture of Bayesian networks](/preview/png/394180.png)
چکیده انگلیسی
An on-line reinforcement learning system that adapts to environmental changes using a mixture of Bayesian networks is described. Building intelligent systems able to adapt to dynamic environments is important for deploying real-world applications. Machine learning approaches, such as those using reinforcement learning methods and stochastic models, have been used to acquire behavior appropriate to environments characterized by uncertainty. However, efficient hybrid architectures based on these approaches have not yet been developed. The results of several experiments demonstrated that an agent using the proposed system can flexibly adapt to various kinds of environmental changes.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Information Sciences - Volume 180, Issue 15, 1 August 2010, Pages 2856–2874
Journal: Information Sciences - Volume 180, Issue 15, 1 August 2010, Pages 2856–2874
نویسندگان
Daisuke Kitakoshi, Hiroyuki Shioya, Ryohei Nakano,