Incorporating PGMs into a BDI Architecture

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

doi:10.1007/978-3-642-44927-7_5

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 proceeding › Conference contribution

17 Citations (Scopus)

315 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 language	English
Title of host publication	Proceedings of PRIMA 2013: Principles and Practice of Multi-Agent Systems
Editors	Guido Boella, Edith Elkind, Bastin Tony Roy Savarimuthu, Frank Dignum, Martin K. Purvis
Publisher	Springer
Pages	54-69
Number of pages	16
ISBN (Electronic)	978-3-642-44927-7
ISBN (Print)	978-3-642-44926-0
DOIs	https://doi.org/10.1007/978-3-642-44927-7_5
Publication status	Published - 2013
Event	16th International Conference on Principles and Practice of Multi-Agent Systems (PRIMA 2013) - Dunedin, New Zealand Duration: 01 Dec 2013 → 06 Dec 2013

Publication series

Name	Lecture Notes in Computer Science
Publisher	Springer
Volume	8921
ISSN (Print)	0302-9743

Conference

Conference	16th International Conference on Principles and Practice of Multi-Agent Systems (PRIMA 2013)
Country/Territory	New Zealand
City	Dunedin
Period	01/12/2013 → 06/12/2013

Access to Document

10.1007/978-3-642-44927-7_5

Incorporating PGMs into a BDI Architecture
© Springer International Publishing AG, The final publication is available at Springer via http://link.springer.com/chapter/10.1007%2F978-3-642-44927-7_5
Accepted author manuscript, 649 KB

http://link.springer.com/chapter/10.1007%2F978-3-642-44927-7_5

R1070CSC: Providing Autonomous Capabilities for Evolving SCADA (PACES)
Liu, W. (PI), Hong, J. (CoI) & Sezer, S. (CoI)
01/08/2011 → 31/03/2016
Project: Research

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

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title = "Incorporating PGMs into a BDI Architecture",

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{\textquoteright}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.",

author = "Yingke Chen and Jun Hong and Weiru Liu and Luis Godo and Carles Sierra and Michael Loughlin",

year = "2013",

doi = "10.1007/978-3-642-44927-7_5",

language = "English",

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editor = "Guido Boella and Elkind, {Edith } and Savarimuthu, {Bastin Tony Roy} and Frank Dignum and Purvis, {Martin K.}",

booktitle = "Proceedings of PRIMA 2013: Principles and Practice of Multi-Agent Systems",

note = "16th International Conference on Principles and Practice of Multi-Agent Systems (PRIMA 2013) ; Conference date: 01-12-2013 Through 06-12-2013",

}

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 et al.
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 proceeding › Conference contribution

TY - GEN

T1 - Incorporating PGMs into a BDI Architecture

AU - Chen, Yingke

AU - Hong, Jun

AU - Liu, Weiru

AU - Godo, Luis

AU - Sierra, Carles

AU - Loughlin, Michael

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N2 - 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.

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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BT - Proceedings of PRIMA 2013: Principles and Practice of Multi-Agent Systems

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A2 - Savarimuthu, Bastin Tony Roy

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T2 - 16th International Conference on Principles and Practice of Multi-Agent Systems (PRIMA 2013)

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Incorporating PGMs into a BDI Architecture

Abstract

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Projects

R1070CSC: Providing Autonomous Capabilities for Evolving SCADA (PACES)

Cite this