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Efficient decision-making in a self-organizing robot swarm: On the speed versus accuracy trade-off
- in Proc. 14th Int. Conf. Auton. Agents Multiagent Syst. (AAMAS
, 2015
"... We study a self-organized collective decision-making strategy to solve the best-of-n decision problem in a swarm of robots. We define a dis-tributed and iterative decision-making strategy. Using this strategy, robots explore the available options, determine the options ’ qualities, decide au-tonomou ..."
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Cited by 3 (3 self)
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We study a self-organized collective decision-making strategy to solve the best-of-n decision problem in a swarm of robots. We define a dis-tributed and iterative decision-making strategy. Using this strategy, robots explore the available options, determine the options ’ qualities, decide au-tonomously which option to take, and communicate their decision to neighboring robots. We study the effectiveness and robustness of the pro-posed strategy using a swarm of 100 Kilobots. We study the well-known speed versus accuracy trade-off analytically by developing a mean-field model. Compared to a previously published simpler method, our decision-making strategy shows a considerable speed-up but has lower accuracy. We analyze our decision-making strategy with particular focus on how the spatial density of robots impacts the dynamics of decisions. The num-ber of neighboring robots is found to influence the speed and accuracy of the decision-making process. Larger neighborhoods speed up the decision but lower its accuracy. We observe that the parity of the neighborhood cardinality determines whether the system will over- or under-perform. 1
Time-Variant Feedback Processes in Collective Decision-Making Systems 29
- Neural Computing & Applications
, 2012
"... Self-organizing systems rely on positive feedback (amplification of per-turbations). Especially in swarm systems, positive feedback builds up in a transient phase until maximal positive feedback is reached and the sys-tem converges temporarily on a state close to consensus. We investigate two exampl ..."
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Self-organizing systems rely on positive feedback (amplification of per-turbations). Especially in swarm systems, positive feedback builds up in a transient phase until maximal positive feedback is reached and the sys-tem converges temporarily on a state close to consensus. We investigate two examples of swarm systems showing time-variant positive feedback: alignment in locust swarms and adaptive aggregation of swarms. We iden-tify an influencing bias in the spatial distribution of agents compared to a well-mixed distribution and two features, percentage of aligned swarm members and neighborhood size, that allow to model the time variance of feedbacks. We report an urn model that is capable of qualitatively repre-senting all these relevant features. The increase of neighborhood sizes over 1 time enables the swarm to lock in a highly aligned state but also allows for infrequent switching between lock-in states. We report similar occur-rences of time-variant feedback in a second collective system to indicate the potential for generality of this phenomenon. Our study is concluded by applications of methods from renormalization group theory that allow to focus on the neighborhood dynamics as scale transformations. Corre-lation lengths and critical exponents are determined empirically. 1
Derivation of a Micro-Macro Link for Collective Decision-Making Systems: Uncover Network Features Based on Drift Measurements
, 2014
"... Relating microscopic features (individual level) to macroscopic fea-tures (swarm level) of self-organizing collective systems is challenging. In this paper, we report the mathematical derivation of a macroscopic model starting from a microscopic one for the example of collective decision-making. The ..."
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Relating microscopic features (individual level) to macroscopic fea-tures (swarm level) of self-organizing collective systems is challenging. In this paper, we report the mathematical derivation of a macroscopic model starting from a microscopic one for the example of collective decision-making. The collective system is based on the application of a majority rule over groups of variable size which is modeled by chemical reactions (micro-model). From an approximated master equation we derive the drift term of a stochastic differential equation (macro-model) which is applied to predict the expected swarm behavior. We give a recursive definition of the polynomials defining this drift term. Our results are validated by Gillespie simulations and simulations of the locust alignment. 1
the shortest path discovery/selection
"... A quantitative micro–macro link for collective decisions: ..."
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Self-Organized Collective Decision-Making in Swarms of Autonomous Robots (Doctoral Consortium)
"... ABSTRACT In my Ph.D. thesis, I focus on the design and mathematical modeling of collective decision-making mechanisms for swarm of autonomous robots. In particular, most of the attention of my studies concerns collective decision-making problems that are characterized by a discrete and finite numbe ..."
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ABSTRACT In my Ph.D. thesis, I focus on the design and mathematical modeling of collective decision-making mechanisms for swarm of autonomous robots. In particular, most of the attention of my studies concerns collective decision-making problems that are characterized by a discrete and finite number of possible alternatives (e.g., the best-of-n decision problem discussed in Section 2). The aim of my studies is to develop mathematical models that enable designers to study collective decision-making mechanisms across different levels of abstraction: from mean field approximations that permit the study of asymptotic properties, to stochastic mathematical models that account for finite-size effects, and therefore, allow designer to predict the performance of actual systems.
Influence and Effect of Dynamic Neighborhood Sizes
, 2015
"... The information provided is the sole responsibility of the authors and does not necessarily reflect the opinion of the members of IRIDIA. The authors take full responsibility for any copyright breaches that may result from publication of this paper in the IRIDIA – Technical Report Series. IRIDIA is ..."
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The information provided is the sole responsibility of the authors and does not necessarily reflect the opinion of the members of IRIDIA. The authors take full responsibility for any copyright breaches that may result from publication of this paper in the IRIDIA – Technical Report Series. IRIDIA is not responsible for any use that might be made of data appearing in this publication.
Published by: IRIDIA, Institut de Recherches Interdisciplinaires et de Développements en Intelligence Artificielle
, 2014
"... The information provided is the sole responsibility of the authors and does not necessarily reflect the opinion of the members of IRIDIA. The authors take full responsibility for any copyright breaches that may result from publication of this paper in the IRIDIA – Technical Report Series. IRIDIA is ..."
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The information provided is the sole responsibility of the authors and does not necessarily reflect the opinion of the members of IRIDIA. The authors take full responsibility for any copyright breaches that may result from publication of this paper in the IRIDIA – Technical Report Series. IRIDIA is not responsible for any use that might be made of data appearing in this publication. Efficient Decision-Making in a Self-Organizing Robot
Collective Decision with 100 Kilobots Speed vs Accuracy in Binary Discrimination Problems
, 2015
"... The information provided is the sole responsibility of the authors and does not necessarily reflect the opinion of the members of IRIDIA. The authors take full responsibility for any copyright breaches that may result from publication of this paper in the IRIDIA – Technical Report Series. IRIDIA is ..."
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The information provided is the sole responsibility of the authors and does not necessarily reflect the opinion of the members of IRIDIA. The authors take full responsibility for any copyright breaches that may result from publication of this paper in the IRIDIA – Technical Report Series. IRIDIA is not responsible for any use that might be made of data appearing in this publication. Noname manuscript No. (will be inserted by the editor)
Swarm of Simple Robots: On the Speed Versus Accuracy Trade-Off
, 2014
"... The information provided is the sole responsibility of the authors and does not necessarily reflect the opinion of the members of IRIDIA. The authors take full responsibility for any copyright breaches that may result from publication of this paper in the IRIDIA – Technical Report Series. IRIDIA is ..."
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The information provided is the sole responsibility of the authors and does not necessarily reflect the opinion of the members of IRIDIA. The authors take full responsibility for any copyright breaches that may result from publication of this paper in the IRIDIA – Technical Report Series. IRIDIA is not responsible for any use that might be made of data appearing in this publication. Efficient Decision-Making in a Self-Organizing
