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.
|Title of host publication||PRIMA 2013: Principles and Practice of Multi-Agent Systems|
|Subtitle of host publication||16th International Conference, Dunedin, New Zealand, December 1-6, 2013. Proceedings|
|Editors||Guido Boella, Edith Elkind, Bastin Tony Roy Savarimuthu, Frank Dignum, Martin K. Purvis|
|Number of pages||16|
|Publication status||Published - 18 Nov 2013|
|Name||Lecture Notes in Computer Science|
|Publisher||Springer Berlin Heidelberg|