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Context-dependent combination of sensor information in Dempster–Shafer theory for BDI

Calderwood, Sarah; McAreavey, Kevin; Liu, Weiru; Hong, Jun


Sarah Calderwood

Kevin McAreavey

Weiru Liu

Jun Hong
Professor in Artificial Intelligence


© 2016, The Author(s). There has been much interest in the belief–desire–intention (BDI) agent-based model for developing scalable intelligent systems, e.g. using the AgentSpeak framework. However, reasoning from sensor information in these large-scale systems remains a significant challenge. For example, agents may be faced with information from heterogeneous sources which is uncertain and incomplete, while the sources themselves may be unreliable or conflicting. In order to derive meaningful conclusions, it is important that such information be correctly modelled and combined. In this paper, we choose to model uncertain sensor information in Dempster–Shafer (DS) theory. Unfortunately, as in other uncertainty theories, simple combination strategies in DS theory are often too restrictive (losing valuable information) or too permissive (resulting in ignorance). For this reason, we investigate how a context-dependent strategy originally defined for possibility theory can be adapted to DS theory. In particular, we use the notion of largely partially maximal consistent subsets (LPMCSes) to characterise the context for when to use Dempster’s original rule of combination and for when to resort to an alternative. To guide this process, we identify existing measures of similarity and conflict for finding LPMCSes along with quality of information heuristics to ensure that LPMCSes are formed around high-quality information. We then propose an intelligent sensor model for integrating this information into the AgentSpeak framework which is responsible for applying evidence propagation to construct compatible information, for performing context-dependent combination and for deriving beliefs for revising an agent’s belief base. Finally, we present a power grid scenario inspired by a real-world case study to demonstrate our work.


Calderwood, S., McAreavey, K., Liu, W., & Hong, J. (2017). Context-dependent combination of sensor information in Dempster–Shafer theory for BDI. Knowledge and Information Systems, 51(1), 259-285.

Journal Article Type Article
Acceptance Date Jul 26, 2016
Online Publication Date Aug 4, 2016
Publication Date Apr 1, 2017
Journal Knowledge and Information Systems
Print ISSN 0219-1377
Electronic ISSN 0219-3116
Publisher Springer Verlag
Peer Reviewed Peer Reviewed
Volume 51
Issue 1
Pages 259-285
Keywords Dempster–Shafer theory, information fusion, context-dependent combination, BDI, AgentSpeak, uncertain beliefs
Public URL
Publisher URL
Additional Information Additional Information : The final publication is available at Springer via


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