By Gabriele Valentini
This ebook specializes in the layout and research of collective decision-making ideas for the best-of-n challenge. After offering a formalization of the constitution of the best-of-n challenge supported by means of a accomplished survey of the swarm robotics literature, it introduces the functioning of a collective decision-making process and identiﬁes a collection of mechanisms which are crucial for a method to resolve the best-of-n challenge. The best-of-n challenge is an abstraction that captures the widespread requirement of a robotic swarm to decide on one choice from of a ﬁnite set whilst optimizing beneﬁts and prices. The ebook leverages the identiﬁcation of those mechanisms to improve a modular and model-driven technique to layout collective decision-making recommendations and to investigate their functionality at assorted point of abstractions. finally, the writer presents a chain of case reports during which the proposed technique is used to layout diversified options, utilizing robotic experiments to teach how the designed innovations should be ported to diversified software scenarios.
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Extra info for Achieving Consensus in Robot Swarms: Design and Analysis of Strategies for the best-of-n Problem
At the swarm level, information gathering and information pooling are highly coupled processes simultaneously performed by different agents of the swarm. • Information gathering is the process through which the agents explore the environment, discover alternative options of the decision-making problem, and individually collect information about the quality of these options. • Information pooling is the process through which the agents spread throughout the swarm the information gathered about each option of the decision-making problem, sample the information of other members of the swarm and use it to reconsider their opinion.
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Achieving Consensus in Robot Swarms: Design and Analysis of Strategies for the best-of-n Problem by Gabriele Valentini