While context-awareness has been found to be effective for decision support in complex domains, most of such decision support systems are hard-coded, incurring significant development efforts. To ease the knowledge acquisition bottleneck, this paper presents a class of cognitive agents based on self-organizing neural model known as TD-FALCON that integrates rules and learning for supporting context-aware decision making. Besides the ability to incorporate a priori knowledge in the form of symbolic propositional rules, TD-FALCON performs reinforcement learning(RL), enabling knowledge refinement and expansion through the interaction with its environment. The efficacy of the developed Context-Aware Decision Support(CaDS) system is demonstrated through a case study of command and control in a virtual environment. © 2008 IEEE.
CITATION STYLE
Teng, T. H., & Tan, A. H. (2008). Cognitive agents integrating rules and reinforcement learning for context-aware decision support. In Proceedings - 2008 IEEE/WIC/ACM International Conference on Intelligent Agent Technology, IAT 2008 (pp. 318–321). https://doi.org/10.1109/WIIAT.2008.163
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