Uncertainty in Artificial Intelligence
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CAN(PLAN)+: Extending the Operational Semantics of the BDI Architecture to deal with Uncertain Information
Kim Bauters, Weiru Liu, Jun Hong, Carles Sierra, Lluis Godo
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 agentā??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.
Keywords:
Pages: 52-61
PS Link:
PDF Link: /papers/14/p52-bauters.pdf
BibTex:
@INPROCEEDINGS{Bauters14,
AUTHOR = "Kim Bauters and Weiru Liu and Jun Hong and Carles Sierra and Lluis Godo",
TITLE = "CAN(PLAN)+: Extending the Operational Semantics of the BDI Architecture to deal with Uncertain Information",
BOOKTITLE = "Proceedings of the Thirtieth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-14)",
PUBLISHER = "AUAI Press",
ADDRESS = "Corvallis, Oregon",
YEAR = "2014",
PAGES = "52--61"
}


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