Incorporating PGMs into a BDI Architecture

  • Yingke Chen
  • Jun Hong
  • Weiru Liu
  • Lluís Godo
  • Carles Sierra
  • Michael Loughlin
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8291)


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.


Bayesian Network Multiagent System Epistemic State Belief Base Probabilistic Graphical Model 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Yingke Chen
    • 1
  • Jun Hong
    • 1
  • Weiru Liu
    • 1
  • Lluís Godo
    • 1
    • 2
  • Carles Sierra
    • 1
    • 2
  • Michael Loughlin
    • 1
  1. 1.Queen’s University BelfastBelfastUK
  2. 2.IIIA, CSICBellaterraSpain

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