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The Best-of-n Problem in Robot Swarms

par Valentini, Gabriele
Président du jury Stützle, Thomas
Promoteur Dorigo, Marco
Co-Promoteur Hamann, Heiko
Publication Non publié, 2016-07-04

Thèse de doctorat

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Résumé :

Collective decision making can be seen as a means of designing and understanding swarm robotics systems. While decision-making is generally conceived as the cognitive ability of individual agents to select a belief based only on their preferences and available information, collective decision making is a decentralized cognitive process, whereby an ensemble of agents gathers, shares, and processes information as a single organism and makes a choice that is not attributable to any of its individuals. A principled selection of the rules governing this cognitive process allows the designer to define, shape, and foresee the dynamics of the swarm.We begin this monograph by introducing the reader to the topic of collective decision making. We focus on artificial systems for discrete consensus achievement and review the literature of swarm robotics. In this endeavor, we formalize the best-of-n problem—a generalization of the logic underlying several cognitive problems—and define a taxonomy of its possible variants that are of interest for the design of robot swarms. By leveraging on this understanding, we identify the building-blocks that are essential to achieve a collective decision addressing the best-of-n problem: option exploration, opinion dissemination, modulation of positive feedback, and individual decision-making mechanism. We show how a modular perspective of a collective decision-making strategy allows for the systematic modeling of the resulting swarm performance. In doing so, we put forward a modular and model-driven design methodology that allows the designer to study the dynamics of a swarm at different level of abstractions. Successively, we employ the proposed design methodology to derive and to study different collective decision-making strategies for the best-of-n problem. We show how the designed strategies can be readily applied to different real-world scenarios by performing two series of robot experiments. In the first series, we use a swarm of 100 robots to tackle a site-selection scenario; in the second series, we show instead how the same strategies apply to a collective perception scenario. We conclude with a discussion of our research contributions and provide futuredirection of research.