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Investigation of automatic construction of reactive controllers

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  • In real-time control systems, the value of a control decision depends not only on the correctness of the decision but also on the time when that decision is available. Recent work in real-time decision making has used machine learning techniques to automatically construct reactive controllers, that is, controllers with little or no internal state and low time complexity pathways between sensors and effectors. This paper presents research on 1) how a problem representation affects the trade-offs between space and performance, and 2) off -line versus on-line approaches for collecting training examples when using machine learning techniques to construct reactive controllers. Empirical results show that for a partially observable problem both the inclusion of history information in the problem representation and the use of on-line rather than off -line learning can improve the performance of the reactive controller.
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