Probabilistic BDI Agents: Actions, Plans, and Intentions

Archibald, B. , Calder, M. , Sevegnani, M. and Xu, M. (2021) Probabilistic BDI Agents: Actions, Plans, and Intentions. In: SEFM 2021, 6-10 Dec 2021, pp. 262-281. ISBN 9783030921231 (doi: 10.1007/978-3-030-92124-8_15)

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Abstract

The Belief-Desire-Intention (BDI) architecture is a popular framework for rational agents, yet most verification approaches are limited to analysing qualitative properties, for example whether an intention completes. BDI-based systems, however, operate in uncertain environments with dynamic behaviours: we may need quantitative analysis to establish properties such as the probability of eventually completing an intention. We define a probabilistic extension to the Conceptual Agent Notation (CAN) for BDI agents that supports probabilistic action outcomes, and probabilistic plan and intention selection. The semantics is executable via an encoding in Milner’s bigraphs and the BigraphER tool. Quantitative analysis is conducted using PRISM. While the new semantics can be applied to any CAN program, we demonstrate the extension by comparing with standard plan and intention selection strategies (e.g. ordered or fixed schedules) and evaluating probabilistic action executions in a smart manufacturing scenario. The results show we can improve significantly the probability of intention completion, with appropriate probabilistic distribution. We also show the impact of probabilistic action outcomes can be marginal, even when the failure probabilities are large, due to the agent making smarter intention selection choices.

Item Type:Conference Proceedings
Additional Information:This work is supported by the Engineering and Physical Sciences Research Council, under PETRAS SRF grant MAGIC (EP/S035362/1) and S4: Science of Sensor Systems Software. (EP/N007565/1)
Status:Published
Refereed:Yes
Glasgow Author(s) Enlighten ID:Calder, Professor Muffy and Xu, Dr Mengwei and Sevegnani, Dr Michele and Archibald, Dr Blair
Authors: Archibald, B., Calder, M., Sevegnani, M., and Xu, M.
College/School:College of Science and Engineering > School of Computing Science
ISSN:0302-9743
ISBN:9783030921231
Copyright Holders:Copyright © Springer Nature Switzerland AG 2021
First Published:First published in Calinescu R., Păsăreanu C.S. (eds) Software Engineering and Formal Methods. SEFM 2021. Lecture Notes in Computer Science, vol 13085
Publisher Policy:Reproduced in accordance with the publisher copyright policy
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Project CodeAward NoProject NamePrincipal InvestigatorFunder's NameFunder RefLead Dept
172422Science of Sensor System Software (SSSS)Muffy CalderEngineering and Physical Sciences Research Council (EPSRC)EP/N007565/1Computing Science