Incorporating PGMs into a BDI Architecture

Yingke Chen, Jun Hong, Weiru Liu, Lluis Godo, Carles Sierra, Michael Loughlin

Research output: Chapter in Book/Report/Conference proceedingConference Contribution (Conference Proceeding)

12 Citations (Scopus)
198 Downloads (Pure)

Abstract

In this paper, we present a hybrid BDI-PGM framework, in which PGMs (Probabilistic Graphical Models) are incorporated into a BDI (belief-desire-intention) architecture. This work is motivated by the need to address the scalability and noisy sensing issues in SCADA (Supervisory Control And Data Acquisition) systems. Our approach uses the incorporated PGMs to model the uncertainty reasoning and decision making processes of agents situated in a stochastic environment. In particular, we use Bayesian networks to reason about an agent’s beliefs about the environment based on its sensory observations, and select optimal plans according to the utilities of actions defined in influence diagrams. This approach takes the advantage of the scalability of the BDI architecture and the uncertainty reasoning capability of PGMs. We present a prototype of the proposed approach using a transit scenario to validate its effectiveness.
Original languageEnglish
Title of host publicationPRIMA 2013: Principles and Practice of Multi-Agent Systems
Subtitle of host publication16th International Conference, Dunedin, New Zealand, December 1-6, 2013. Proceedings
EditorsGuido Boella, Edith Elkind, Bastin Tony Roy Savarimuthu, Frank Dignum, Martin K. Purvis
PublisherSpringer
Pages54-69
Number of pages16
ISBN (Electronic)9783642449277
ISBN (Print)9783642449260
DOIs
Publication statusPublished - 18 Nov 2013

Publication series

NameLecture Notes in Computer Science
PublisherSpringer Berlin Heidelberg
Volume8291
ISSN (Print)0302-9743

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