Shared learning vector quantization in a new agent architecture for intelligent deliberation

Authors:
Prasanna Lokuge;Damminda Alahakoon
Affiliations:
School of Business Systems, Monash University, Australia;School of Business Systems, Monash University, Australia
Venue:
KES'05 Proceedings of the 9th international conference on Knowledge-Based Intelligent Information and Engineering Systems - Volume Part II
Year:
2005

Citing 4
Cited 0

Introduction to Multiagent Systems

Introduction to Multiagent Systems
Hybrid BDI Agents with Improved Learning Capabilities for Adaptive Planning in a Container Terminal Application

IAT '04 Proceedings of the IEEE/WIC/ACM International Conference on Intelligent Agent Technology
A Motivation Based Behavior in Hybrid Intelligent Agents for Intention Reconsideration Process in Vessel Berthing Applications

HIS '04 Proceedings of the Fourth International Conference on Hybrid Intelligent Systems
Commitment and effectiveness of situated agents

IJCAI'91 Proceedings of the 12th international joint conference on Artificial intelligence - Volume 1

Quantified Score

Hi-index	0.00

Visualization

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.