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Incorporating PGMs into a BDI Architecture

AutorChen, Yingke; Hong, Jun; Liu, Weiru; Godo, Lluis ; Sierra, Carles ; Loughlin, Michael
Palabras claveStochastic environment
Influence diagram
Supervisory control and data acquisition
Transit scenarios
Uncertainty reasoning
Probabilistic graphical models
BDI (belief-desire-intention)
Decision making process
Fecha de publicación1-dic-2013
EditorSpringer
CitaciónLecture Notes in Computer Science 8291. PRIMA 2013: Principles and Practice of Multi-Agent Systems, 16th International Conference, Dunedin, New Zealand, December 1-6, 2013. Proceedings, pp. 54-69.
ResumenIn 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. © 2013 Springer-Verlag.
URIhttp://hdl.handle.net/10261/133446
DOI10.1007/978-3-642-44927-7_5
Identificadoresdoi: 10.1007/978-3-642-44927-7_5
isbn: 978-3-642-44926-0
Aparece en las colecciones: (IIIA) Comunicaciones congresos
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