Abstract
The basic belief-desire-intention (BDI) agent model appears to be inappropriate for building complex system that that must learn and adapt their behaviour dynamically. The contribution of the paper is the introduction of a new “intelligent-Deliberation” process in the hybrid BDI (h-BD[I]) architecture that enables an improved decision making features in a dynamic, and complex environment. Shared learning vector quantization (SLVQ) based neural network is proposed for the intelligent deliberation of the agent model. Paper discusses the benefits of incorporating knowledge based techniques in the deliberation process of the extended h-BD[I] model.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Wooldridge, M., Jennings, N.R.: Intelligent agents: Theory to practice, the knowledge engineering review 10(12), 115–152 (1995)
Georgeff, M., Pell, B., Pollack, M., Tambe, M., Wooldridge, M.: The Belief-Desire- Intention Model of Agency. Springer Publishers, Heidelberg (1998)
Lokuge, D.P.S., Alahakoon, L.D.: Hybrid BDI Agents with Improved learning capabilities for Adaptive Planning in a Container Terminal Application. In: Proceeding of IEEE/WIC/ACM International conference on Intelligent Agent Technology (IAT), pp. 120–127. IEEE computer society, China (2004)
Lokuge, D.P.S., Alahakoon, L.D.: Motivation based behavior in Hybrid Intelligent Agents for Intention Reconsideration Process in Vessel Berthing applications. In: Proceeding of the 4th International conference on Hybrid Intelligent Systems (HIS 2004), IEEE computer Society, Japan (2004)
Brown, G.G., Lawphongpanich, S., Thurman, K.P.: Optimizing Ship Berthing: Naval research logistics 41, 1–15 (1994)
Gambardeella, L.M., Rizzoli, A.E., Zaffalon, M.: Simulation and planning of an intermodal container terminal. Special Issue simulation on Harbour and Maritime Simulation (1998)
Wooldridge, M.: Reasoning about rational agents. The MIT Press, Cambridge (2000)
Kinny, D., Georgeff, M.: Commitment and effectiveness of situated agents. In: Proceeding of the Twelfth International Joint Conference on Artificial Intelligence (IJCAI 1991), Sydney, Australia, pp. 82–88 (1991)
Bloodsworth, P., Greenwood, S., Nealon, J.: A Generic Model for Distributed Real-time Scheduling Based on Dynamic Heterogeneous Data. In: Lee, J., et al. (eds.) Intelligent and Multi-agent Systems, Springer, Heidelberg (2004)
Wooldridge, M.: An Introduction to Multi-Agent Systems. John Wiley & Sons Ltd, Chichester (2002)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2005 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Lokuge, P., Alahakoon, D. (2005). Shared Learning Vector Quantization in a New Agent Architecture for Intelligent Deliberation. In: Khosla, R., Howlett, R.J., Jain, L.C. (eds) Knowledge-Based Intelligent Information and Engineering Systems. KES 2005. Lecture Notes in Computer Science(), vol 3682. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11552451_110
Download citation
DOI: https://doi.org/10.1007/11552451_110
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-28895-4
Online ISBN: 978-3-540-31986-3
eBook Packages: Computer ScienceComputer Science (R0)