Incorporating PGMs into a BDI Architecture

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

Research output: Chapter in Book/Report/Conference proceedingConference contribution

12 Citations (Scopus)
159 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 publicationProceedings of PRIMA 2013: Principles and Practice of Multi-Agent Systems
EditorsGuido Boella, Edith Elkind, Bastin Tony Roy Savarimuthu, Frank Dignum, Martin K. Purvis
PublisherSpringer
Pages54-69
Number of pages16
ISBN (Electronic)978-3-642-44927-7
ISBN (Print)978-3-642-44926-0
DOIs
Publication statusPublished - 2013
Event16th International Conference on Principles and Practice of Multi-Agent Systems (PRIMA 2013) - Dunedin, New Zealand
Duration: 01 Dec 201306 Dec 2013

Publication series

NameLecture Notes in Computer Science
PublisherSpringer
Volume8921
ISSN (Print)0302-9743

Conference

Conference16th International Conference on Principles and Practice of Multi-Agent Systems (PRIMA 2013)
CountryNew Zealand
CityDunedin
Period01/12/201306/12/2013

Fingerprint

Scalability
SCADA systems
Bayesian networks
Decision making
Uncertainty

Cite this

Chen, Y., Hong, J., Liu, W., Godo, L., Sierra, C., & Loughlin, M. (2013). Incorporating PGMs into a BDI Architecture. In G. Boella, E. Elkind, B. T. R. Savarimuthu, F. Dignum, & M. K. Purvis (Eds.), Proceedings of PRIMA 2013: Principles and Practice of Multi-Agent Systems (pp. 54-69). (Lecture Notes in Computer Science; Vol. 8921). Springer. https://doi.org/10.1007/978-3-642-44927-7_5
Chen, Yingke ; Hong, Jun ; Liu, Weiru ; Godo, Luis ; Sierra, Carles ; Loughlin, Michael. / Incorporating PGMs into a BDI Architecture. Proceedings of PRIMA 2013: Principles and Practice of Multi-Agent Systems. editor / Guido Boella ; Edith Elkind ; Bastin Tony Roy Savarimuthu ; Frank Dignum ; Martin K. Purvis. Springer, 2013. pp. 54-69 (Lecture Notes in Computer Science).
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Chen, Y, Hong, J, Liu, W, Godo, L, Sierra, C & Loughlin, M 2013, Incorporating PGMs into a BDI Architecture. in G Boella, E Elkind, BTR Savarimuthu, F Dignum & MK Purvis (eds), Proceedings of PRIMA 2013: Principles and Practice of Multi-Agent Systems. Lecture Notes in Computer Science, vol. 8921, Springer, pp. 54-69, 16th International Conference on Principles and Practice of Multi-Agent Systems (PRIMA 2013), Dunedin, New Zealand, 01/12/2013. https://doi.org/10.1007/978-3-642-44927-7_5

Incorporating PGMs into a BDI Architecture. / Chen, Yingke; Hong, Jun; Liu, Weiru; Godo, Luis; Sierra, Carles; Loughlin, Michael.

Proceedings of PRIMA 2013: Principles and Practice of Multi-Agent Systems. ed. / Guido Boella; Edith Elkind; Bastin Tony Roy Savarimuthu; Frank Dignum; Martin K. Purvis. Springer, 2013. p. 54-69 (Lecture Notes in Computer Science; Vol. 8921).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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T1 - Incorporating PGMs into a BDI Architecture

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AU - Sierra, Carles

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AB - 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.

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Chen Y, Hong J, Liu W, Godo L, Sierra C, Loughlin M. Incorporating PGMs into a BDI Architecture. In Boella G, Elkind E, Savarimuthu BTR, Dignum F, Purvis MK, editors, Proceedings of PRIMA 2013: Principles and Practice of Multi-Agent Systems. Springer. 2013. p. 54-69. (Lecture Notes in Computer Science). https://doi.org/10.1007/978-3-642-44927-7_5