Benchmarking Collective Perception: New Task Difficulty Metrics for Collective Decision-Making

Bartashevich, Palina; Mostaghim, Sanaz

doi:10.1007/978-3-030-30241-2_58

Cited by 20 publications

(24 citation statements)

References 13 publications

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“…4.2.3 and 4.2.4 concentrate on the study of the effects of the group size and the increased number of the options, respectively. The implementation of the presented framework and the computations below are done in Matlab, extending our previous multi-agent simulation from (Bartashevich and Mostaghim 2019a).…”

Section: Experiments and Resultsmentioning

confidence: 99%

“…In this paper, we extend the binary environment generator (Bartashevich and Mostaghim 2019a) to a multi-featured case with n > 2 colours and study the performance of the proposed framework on seven environmental patterns: "Random", "Stripe", "Star", "Band", "Band-Stripe", "Bandwidth" and "Rectangle". Figure 2a shows an example for n = 3 .…”

Section: Multi-featured Benchmark Generatormentioning

confidence: 99%

“…The objectives of the current study are (1) to extend previously introduced benchmark problems for a collective perception scenario (Bartashevich and Mostaghim 2019a) to n > 2 possibilities (i.e. multi-featured case) and (2) to examine the fusion of the information received from the environment and other agents in regard to the robustness against possible spatial correlations in a priory unknown environment.…”

Section: Introductionmentioning

confidence: 99%

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

Bartashevich

Mostaghim

2021

Swarm Intell

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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 .

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Section: Experiments and Resultsmentioning

confidence: 99%

Section: Multi-featured Benchmark Generatormentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Bartashevich

Mostaghim

2021

Swarm Intell

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“…In [36], the authors use a robot swarm to collectively decide which of two colors is the most represented in a pattern drawn on arena ground. The algorithm developed in [36] was tested for benchmarking and generalization in [4] across a larger number of patterns (nine). Contrary to [36], the authors in [4] find that the difficulty of the collective perception process doesn't depend mainly on the ratio of one color to the other, but on the distribution of each color in the environment.…”

Section: Related Workmentioning

confidence: 99%

“…The algorithm developed in [36] was tested for benchmarking and generalization in [4] across a larger number of patterns (nine). Contrary to [36], the authors in [4] find that the difficulty of the collective perception process doesn't depend mainly on the ratio of one color to the other, but on the distribution of each color in the environment. The authors in [7] proposed a distributed Bayesian algorithm to solve the collective perception task of a similar two-color environment.…”

Section: Related Workmentioning

confidence: 99%

Construction Task Allocation Through the Collective Perception of a Dynamic Environment

Khaluf

Allwright

Rausch

et al. 2020

Lecture Notes in Computer Science

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Building structures is a remarkable collective process but its automation remains an open challenge. Robot swarms provide a promising solution to this challenge. However, collective construction involves a number of difficulties regarding efficient robots allocation to the different activities, particularly if the goal is to reach an optimal construction rate. In this paper, we study an abstract construction scenario, where a swarm of robots is engaged in a collective perception process to estimate the density of building blocks around a construction site. The goal of this perception process is to maintain a minimum density of blocks available to the robots for construction. To maintain this density, the allocation of robots to the foraging task needs to be adjusted such that enough blocks are retrieved. Our results show a robust collective perception that enables the swarm to maintain a minimum block density under different rates of construction and foraging. Our approach leads the system to stabilize around a state in which the robots allocation allows the swarm to maintain a tile density that is close to or above the target minimum.

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Collective Decision Making in Swarm Robotics with Distributed Bayesian Hypothesis Testing

Shan

Mostaghim

2020

Lecture Notes in Computer Science

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Benchmarking Collective Perception: New Task Difficulty Metrics for Collective Decision-Making

Cited by 20 publications

References 13 publications

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

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

Construction Task Allocation Through the Collective Perception of a Dynamic Environment

Collective Decision Making in Swarm Robotics with Distributed Bayesian Hypothesis Testing

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