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- W2149231823 abstract "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." @default.
- W2149231823 created "2016-06-24" @default.
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- W2149231823 date "2008-12-01" @default.
- W2149231823 modified "2023-09-25" @default.
- W2149231823 title "Cognitive Agents Integrating Rules and Reinforcement Learning for Context-Aware Decision Support" @default.
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- W2149231823 doi "https://doi.org/10.1109/wiiat.2008.163" @default.
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