Quantitative modelling and analysis of BDI agents

Archibald, B. , Calder, M. , Sevegnani, M. and Xu, M. (2023) Quantitative modelling and analysis of BDI agents. Software and Systems Modeling, (doi: 10.1007/s10270-023-01121-5) (Early Online Publication)

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Belief–desire–intention (BDI) agents are a popular agent architecture. We extend conceptual agent notation (Can)—a BDI programming language with advanced features such as failure recovery and declarative goals—to include probabilistic action outcomes, e.g. to reflect failed actuators, and probabilistic policies, e.g. for probabilistic plan and intention selection. The extension is encoded in Milner’s bigraphs. Through application of our BigraphER tool and the PRISM model checker, the probability of success (intention completion) under different probabilistic outcomes and plan/event/intention selection strategies can be investigated and compared. We present a smart manufacturing use case. A significant result is that plan selection has limited effect compared with intention selection. We also see that the impact of action failures can be marginal—even when failure probabilities are large—due to the agent making smarter choices.

Item Type:Articles
Status:Early Online Publication
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
Journal Name:Software and Systems Modeling
ISSN (Online):1619-1374
Published Online:28 August 2023
Copyright Holders:Copyright © 2023 The Authors
First Published:First published in Software and Systems Modeling 2023
Publisher Policy:Reproduced under a Creative Commons licence

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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