Publication Type
Conference Proceeding Article
Version
acceptedVersion
Publication Date
12-2008
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.
Discipline
Databases and Information Systems | Graphics and Human Computer Interfaces
Research Areas
Data Science and Engineering
Publication
Proceedings, IEEE/WIC/ACM International Conference on Intelligent Agent Technology (IAT'08), Sydney, NSW, Australia, Dec 9-12
First Page
318
Last Page
321
Identifier
10.1109/WIIAT.2008.163
Publisher
IEEE
City or Country
New York
Citation
TENG, Teck-Hou and TAN, Ah-hwee.
Cognitive agents integrating rules and reinforcement learning for context-aware decision support. (2008). Proceedings, IEEE/WIC/ACM International Conference on Intelligent Agent Technology (IAT'08), Sydney, NSW, Australia, Dec 9-12. 318-321.
Available at: https://ink.library.smu.edu.sg/sis_research/6664
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
http://www.scopus.com/inward/record.url?eid=2-s2.0-62949085640&partnerID=MN8TOARS
Included in
Databases and Information Systems Commons, Graphics and Human Computer Interfaces Commons