Can(Plan)+: Extending the Operational Semantics for the BDI architecture to deal with Uncertain Information

Kim Bauters Queen’s University Belfast, Weiru Liu Queen’s University Belfast, Jun Hong Queen’s University Belfast, Carles Sierra IIIA CSIC, Lluis Godo Artificial Intelligence Research Institute
Proceedings of the 30th Conference on Uncertainty in Artificial Intelligence, PMLR R12:451-460, 2014.

Abstract

The BDI architecture, where agents are modelled based on their beliefs, desires and intentions, pro- vides a practical approach to develop large scale systems. However, it is not well suited to model complex Supervisory Control And Data Acquisi- tion (SCADA) systems pervaded by uncertainty. In this paper we address this issue by extending the operational semantics of CAN(PLAN) into CAN(PLAN)+. We start by modelling the beliefs of an agent as a set of epistemic states where each state, possibly using a different representation, models part of the agent’s beliefs. These epis- temic states are stratified to make them commen- surable and to reason about the uncertain beliefs of the agent. The syntax and semantics of a BDI agent are extended accordingly and we identify fragments with computationally efficient seman- tics. Finally, we examine how primitive actions are affected by uncertainty and we define an ap- propriate form of lookahead planning.

Cite this Paper

BibTeX
@InProceedings{pmlr-vR12-belfast14a, title = {Can(Plan)+: Extending the Operational Semantics for the {BDI} architecture to deal with Uncertain Information}, author = {Belfast, Kim Bauters Queen's University and Belfast, Weiru Liu Queen's University and Belfast, Jun Hong Queen's University and CSIC, Carles Sierra IIIA and Institute, Lluis Godo Artificial Intelligence Research}, booktitle = {Proceedings of the 30th Conference on Uncertainty in Artificial Intelligence}, pages = {451--460}, year = {2014}, editor = {Zhang, Nevin L. and Tian, Jin}, volume = {R12}, series = {Proceedings of Machine Learning Research}, month = {23--27 Jul}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/r12/main/assets/belfast14a/belfast14a.pdf}, url = {https://proceedings.mlr.press/r12/belfast14a.html}, abstract = {The BDI architecture, where agents are modelled based on their beliefs, desires and intentions, pro- vides a practical approach to develop large scale systems. However, it is not well suited to model complex Supervisory Control And Data Acquisi- tion (SCADA) systems pervaded by uncertainty. In this paper we address this issue by extending the operational semantics of CAN(PLAN) into CAN(PLAN)+. We start by modelling the beliefs of an agent as a set of epistemic states where each state, possibly using a different representation, models part of the agent’s beliefs. These epis- temic states are stratified to make them commen- surable and to reason about the uncertain beliefs of the agent. The syntax and semantics of a BDI agent are extended accordingly and we identify fragments with computationally efficient seman- tics. Finally, we examine how primitive actions are affected by uncertainty and we define an ap- propriate form of lookahead planning.}, note = {Reissued by PMLR on 04 October 2026.} }
Endnote
%0 Conference Paper %T Can(Plan)+: Extending the Operational Semantics for the BDI architecture to deal with Uncertain Information %A Kim Bauters Queen’s University Belfast %A Weiru Liu Queen’s University Belfast %A Jun Hong Queen’s University Belfast %A Carles Sierra IIIA CSIC %A Lluis Godo Artificial Intelligence Research Institute %B Proceedings of the 30th Conference on Uncertainty in Artificial Intelligence %C Proceedings of Machine Learning Research %D 2014 %E Nevin L. Zhang %E Jin Tian %F pmlr-vR12-belfast14a %I PMLR %P 451--460 %U https://proceedings.mlr.press/r12/belfast14a.html %V R12 %X The BDI architecture, where agents are modelled based on their beliefs, desires and intentions, pro- vides a practical approach to develop large scale systems. However, it is not well suited to model complex Supervisory Control And Data Acquisi- tion (SCADA) systems pervaded by uncertainty. In this paper we address this issue by extending the operational semantics of CAN(PLAN) into CAN(PLAN)+. We start by modelling the beliefs of an agent as a set of epistemic states where each state, possibly using a different representation, models part of the agent’s beliefs. These epis- temic states are stratified to make them commen- surable and to reason about the uncertain beliefs of the agent. The syntax and semantics of a BDI agent are extended accordingly and we identify fragments with computationally efficient seman- tics. Finally, we examine how primitive actions are affected by uncertainty and we define an ap- propriate form of lookahead planning. %Z Reissued by PMLR on 04 October 2026.
APA
Belfast, K.B.Q.U., Belfast, W.L.Q.U., Belfast, J.H.Q.U., CSIC, C.S.I. & Institute, L.G.A.I.R.. (2014). Can(Plan)+: Extending the Operational Semantics for the BDI architecture to deal with Uncertain Information. Proceedings of the 30th Conference on Uncertainty in Artificial Intelligence, in Proceedings of Machine Learning Research R12:451-460 Available from https://proceedings.mlr.press/r12/belfast14a.html. Reissued by PMLR on 04 October 2026.

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