By Gabriele Valentini

ISBN-10: 3319536087

ISBN-13: 9783319536088

ISBN-10: 3319536095

ISBN-13: 9783319536095

This e-book makes a speciality of the layout and research of collective decision-making thoughts for the best-of-n challenge. After offering a formalization of the constitution of the best-of-n challenge supported via a accomplished survey of the swarm robotics literature, it introduces the functioning of a collective decision-making approach and identifies a suite 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 common requirement of a robotic swarm to decide on one choice from of a finite set while optimizing benefits and prices. The e-book leverages the identification of those mechanisms to increase a modular and model-driven technique to layout collective decision-making thoughts and to research their functionality at assorted point of abstractions. finally, the writer presents a sequence of case experiences within which the proposed method is used to layout diversified techniques, utilizing robotic experiments to teach how the designed suggestions could be ported to diversified program scenarios.

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N}, that we call the agent’s opinion. © Springer International Publishing AG 2017 G. 1007/978-3-319-53609-5_3 33 34 3 Modular Design of Strategies for the Best-of-n Problem Throughout the entire decision-making process, the agents interact with each other and with the environment by executing the control rules prescribed by the collective decision-making strategy. As a result, the agents repeatedly reconsider and change their opinion for the best option eventually converging to a collective decision where the totality or a large majority of them share the same opinion (cf.

The robot controller, being ultimately a black-box model, is difficult to model and analyze mathematically (Francesca et al. 2012). As a consequence, in general the designer cannot maintain and improve the designed solutions (Matari´c and Cliff 1996; Trianni and Nolfi 2011). 2 Automatic Modular Design More recently, Francesca et al. (2014) proposed an automatic design method, called AutoMoDe, that provides a white-box alternative to evolutionary robotics. The robot controllers designed using AutoMoDe are behavior-based and have the form of a probabilistic finite-state machine.

The transition from state Ei to state Di is deterministic (solid arrow) while transitions from state Di to states E j , j ∈ {1, . . , n}, are stochastic (dotted arrows) and depend on the outcome of the individual decision-making mechanism (DR) 38 Fig. 4 Illustration of the control-flow followed by an agent in the exploration state Ei . Routines “Goto” and “Estimation()” are used, respectively, to move in the correct region of the environment and to sample the option quality 3 Modular Design of Strategies for the Best-of-n Problem Exploration state: E1 En Ei [ Goto( i ) ] ρˆ i = Estimate(ρ i ) ∀j < i ∀j > i [ Goto( hub ) ] DR D1 DR DR Di Dn the corresponding option by means of a domain-specific routine.

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Achieving Consensus in Robot Swarms: Design and Analysis of Strategies for the best-of-n Problem by Gabriele Valentini

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