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

Abstract

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.

An intelligent quality-based approach to fusing multi-source possibilistic information

“…The book [18] provides a formal foundation and implementation strategies allowing to incorporate information quality into the information fusion processes to various decision support applications for reallife scenarios such as remote sensing, medicine, automated driving, environmental protection, crime analysis, intelligence, defense and security. In [19], the authors describe the whole process of modelling uncertain sensor information. The Dempster-Shafer (DS) theory was chosen to model uncertain sensor information.…”

“…Step 8: Selection of the best subsets for the final fusion To verify the quality of a subset, it is possible to calculate a score associated with a fusion that is a weighted sum of its credibility and its information quantity ( ). More formally the score of the fusion based on the subset is defined as: (19) Normalizing the weights so that 1 + 2 = 1 , we get…”

A Dempster‐Shafer theory and uninorm‐based framework of reasoning and multiattribute decision‐making for surveillance system

“…So their work is quite similar to ours, but their framework cannot help security teams figure out which event is the most dangerous. Also, Calderwood et al study how to fuse the uncertain and incomplete information from heterogeneous sources which may be unreliable or conflicting. They use D‐S theory of evidence to model the sensor information, but they employ a different method, called context‐dependent combination, to fuse the information.…”

Modelling and Reasoning with Uncertain Event-observations for Event Inference

“…The original and most common method of combining mass functions is using Dempster's combination rule. However, other combination operators such as the context-dependent combination rule from (Calderwood et al, 2016) or the disjunctive combination rule from (Dubois and Prade, 1992) may be more suitable given the information obtained from the sources.…”