Cognitive Agents Integrating Rules and Reinforcement Learning for Context-Aware Decision Support

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.

• Type: Conference
• Year: 2008
• Where: IAT
• Authors: Teck-Hou Teng, Ah-Hwee Tan
• Keywords: Context-aware Decision, Context-aware Decision Making, IAT 2008, Intelligent Agents, Knowledge Acquisition Bottleneck