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

Additional URL

http://www.scopus.com/inward/record.url?eid=2-s2.0-62949085640&partnerID=MN8TOARS

Share

COinS