Publication Type
Conference Proceeding Article
Version
acceptedVersion
Publication Date
12-2008
Abstract
This paper presents iFALCON, a model of BDI (beliefdesire-intention) agents that is fully realized as a selforganizing neural network architecture. Based on multichannel network model called fusion ART, iFALCON is developed to bridge the gap between a self-organizing neural network that autonomously adapts its knowledge and the BDI agent model that follows explicit descriptions. Novel techniques called gradient encoding are introduced for representing sequences and hierarchical structures to realize plans and the intention structure. This paper shows that a simplified plan representation can be encoded as weighted connections in the neural network through a process of supervised learning. A case study using the blocks world domain shows that an iFALCON agent can also do planning to solve problems when the knowledge is incomplete.
Discipline
Databases and Information Systems | OS and Networks
Research Areas
Data Science and Engineering
Publication
Proceedings of 2008 IEEE/WIC/ACM International Conference on Intelligent Agent Technology, IAT 2008, Sydney, Australia, December 9-12
First Page
231
Last Page
237
ISBN
9780769534961
Identifier
10.1109/WIIAT.2008.29
Publisher
IEEE Computer Society
City or Country
Washington
Citation
1
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.