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Additional resources for Achieving Consensus in Robot Swarms
Garnier, M. Beekman, T. Latty, Information integration and multiattribute decision making in non-neuronal organisms. Anim. Behav. 100, 44–50 (2015) A. Reina, M. Dorigo, V. Trianni, Towards a cognitive design pattern for collective decision-making, ed. By M. Dorigo, M. Birattari, S. Garnier, H. A. Montes de Oca, C. Solnon, T. Stützle. Swarm Intelligence, vol. 8667. LNCS (Springer, 2014), pp. 194–205 A. Reina, R. Miletitch, M. Dorigo, V. Trianni, A quantitative micro-macro link for collective decisions: the shortest path discovery/selection example.
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. After collecting a sample estimate ρˆi , the agent transits to the dissemination state Di .
B. Mouret, S. Doncieux, The transferability approach: crossing the reality gap in evolutionary robotics. IEEE Trans. Evol. Comput. 17(1), 122–145 (2013) S. Kornienko, O. Kornienko, C. Constantinescu, M. Pradier, P. Levi, Cognitive micro-agents: individual and collective perception in microrobotic swarm, in Proceedings of the IJCAI-05 Workshop on Agents in real-time and dynamic environments, Edinburgh, UK, pp. 33–42 (2005a) S. Kornienko, O. Kornienko, P. Levi, Minimalistic approach towards communication and perception in microrobotic swarms, in 2005 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp.