Modular Design of Strategies for the Best-of-n Problem

Multi-featured collective perception with Evidence Theory: tackling spatial correlations

Collective perception allows sparsely distributed agents to form a global view on a common spatially distributed problem without any direct access to global knowledge and only based on a combination of locally perceived information. However, the evidence gathered from the environment is often subject to spatial correlations and depends on the movements of the agents. The latter is not always easy to control and the main question is how to share and to combine the estimated information to achieve the most precise global estimate in the least possible time. The current article aims at answering this question with the help of evidence theory, also known as Dempster–Shafer theory, applied to the collective perception scenario as a collective decision-making problem. We study eight most common belief combination operators to address the arising conflict between different sources of evidence in a highly dynamic multi-agent setting, driven by modulation of positive feedback. In comparison with existing approaches, such as voter models, the presented framework operates on quantitative belief assignments of the agents based on the observation time of the options according to the agents’ opinions. The evaluated results on an extended benchmark set for multiple options ($$n>2$$ n > 2 ) indicate that the proportional conflict redistribution (PCR) principle allows a collective of small size ($$N=20$$ N = 20 ), occupying $$3.5\%$$ 3.5 % of the surface, to successfully resolve the conflict between clustered areas of features and reach a consensus with almost $$100\%$$ 100 % certainty up to $$n=5$$ n = 5 .