By Marco Dorigo, Mauro Birattari, Simon Garnier, Heiko Hamann, Marco Montes de Oca, Christine Solnon, Thomas Stützle
This e-book constitutes the court cases of the ninth foreign convention on Swarm Intelligence, held in Brussels, Belgium, in September 2014. This quantity includes 17 complete papers, nine brief papers, and seven prolonged abstracts conscientiously chosen out of fifty five submissions. The papers conceal empirical and theoretical examine in swarm intelligence resembling: behavioral types of social bugs or different animal societies, ant colony optimization, particle swarm optimization, swarm robotics systems.
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This publication constitutes the court cases of the ninth foreign convention on Swarm Intelligence, held in Brussels, Belgium, in September 2014. This quantity comprises 17 complete papers, nine brief papers, and seven prolonged abstracts conscientiously chosen out of fifty five submissions. The papers hide empirical and theoretical examine in swarm intelligence akin to: behavioral versions of social bugs or different animal societies, ant colony optimization, particle swarm optimization, swarm robotics platforms.
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Additional info for Swarm Intelligence: 9th International Conference, ANTS 2014, Brussels, Belgium, September 10-12, 2014. Proceedings
5] proposed a novel approach: AutoMoDe, automatic modular design. AutoMoDe synthesizes control software in the form of a probabilistic ﬁnite state machine by selecting, assembling, and ﬁne tuning pre-existing parametric modules. The rationale behind AutoMoDe lies in the machine learning concept of bias–variance tradeoﬀ : Francesca et al.  M. Dorigo et al. ): ANTS 2014, LNCS 8667, pp. 25–37, 2014. c Springer International Publishing Switzerland 2014 26 G. Francesca et al. conjectured that the diﬃculty experienced by evolutionary robotics in overcoming the reality gap bears a resemblance to the generalization problem faced by function approximators in supervised learning.
They highlight the strengths of Vanilla and also a weakness, for which we suggest a possible solution. More generally, these results provide a new insight into the design of robot swarms. They show that, at least under our experimental protocol, manual design suﬀers from the reality gap to an extent comparable to that of automatic design. To the best of our knowledge, this has never been discussed in the literature and has never been observed in a controlled empirical study. Finally, contrary to An Experiment in Automatic Design of Robot Swarms 27 Table 1.
Evolutionary Robotics: The Biology, Intelligence, and Technology of Self-organizing Machines. MIT Press, Cambridge (2000) 2. : Swarm robotics: A review from the swarm engineering perspective. Swarm Intelligence 7(1), 1–41 (2013) 3. : Swarm robotics. Scholarpedia 9(1), 1463 (2014) 4. : Engineering the evolution of self-organizing behaviors in swarm robotics: A case study. Artiﬁcial Life 17(3), 183–202 (2011) 5. : AutoMoDe: A novel approach to the automatic design of control software for robot swarms.
Swarm Intelligence: 9th International Conference, ANTS 2014, Brussels, Belgium, September 10-12, 2014. Proceedings by Marco Dorigo, Mauro Birattari, Simon Garnier, Heiko Hamann, Marco Montes de Oca, Christine Solnon, Thomas Stützle