By Gabriele Valentini
This publication specializes in the layout and research of collective decision-making thoughts for the best-of-n problem. After supplying a formalization of the constitution of the best-of-n problem supported via a complete survey of the swarm robotics literature, it introduces the functioning of a collective decision-making procedure and identiﬁes a collection of mechanisms which are crucial for a method to unravel the best-of-n problem. The best-of-n problem is an abstraction that captures the common requirement of a robotic swarm to decide on one choice from of a ﬁnite set while optimizing beneﬁts and prices. The publication leverages the identiﬁcation of those mechanisms to strengthen a modular and model-driven technique to layout collective decision-making options and to investigate their functionality at assorted point of abstractions. finally, the writer presents a sequence of case stories within which the proposed technique is used to layout various ideas, utilizing robotic experiments to teach how the designed recommendations may be ported to varied software scenarios.
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Achieving Consensus in Robot Swarms: Design and Analysis of Strategies for the best-of-n Problem (Studies in Computational Intelligence) by Gabriele Valentini