By Piero P. Bonissone (auth.), Jing Liu, Cesare Alippi, Bernadette Bouchon-Meunier, Garrison W. Greenwood, Hussein A. Abbass (eds.)
This cutting-edge survey bargains a renewed and fresh concentrate on the growth in evolutionary computation, in neural networks, and in fuzzy structures. The booklet provides the services and stories of best researchers spanning a various spectrum of computational intelligence in those parts. the result's a balanced contribution to the examine sector of computational intelligence that are supposed to serve the group not just as a survey and a reference, but additionally as an notion for the long run development of the state-of-the-art of the sector. The thirteen chosen chapters originate from lectures and shows given on the IEEE international Congress on Computational Intelligence, WCCI 2012, held in Brisbane, Australia, in June 2012.
Read Online or Download Advances in Computational Intelligence: IEEE World Congress on Computational Intelligence, WCCI 2012, Brisbane, Australia, June 10-15, 2012. Plenary/Invited Lectures PDF
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Extra resources for Advances in Computational Intelligence: IEEE World Congress on Computational Intelligence, WCCI 2012, Brisbane, Australia, June 10-15, 2012. Plenary/Invited Lectures
Artificial Intelligence Review 11(1-5), 11–73 (1997) 30. : Is readability compatible with accuracy? From neuro-fuzzy to lazy learning. In: Freksa, C. ) Proceedings in Artificial Intelligence 7, pp. 10–25. Infix/Aka, Berlin (1998) 31. : Multi-objective optimization using evolutionary algorithms. J. Wiley (2001) 32. : Evolutionary Algorithm MOP Approaches, Evolutionary Algorithms for Solving Multi-Objective Problems. Kluwer Academic (2002) 33. : Multi Criteria Decision Making (MCDM): A Framework for Research and Applications.
Pop 130 lolwutamidoing 126 me - Rambo 126 PollusPirata 125 Cyber-trout 124 CirclingBullies 123 SneakySnipers 121 Tut 121 coward1 120 Table 1 shows the top ten teams. Despite the large number of teams, no single competitor emerged that signiﬁcantly outperformed all others. It is interesting to analyze why. In NERO, agents can be “shaped” towards more complex behaviors by progressively changing the environment and the ﬁtness function during training. This process can create teams of agents that perform speciﬁc tasks during a battle.
Evolution of artiﬁcial neural networks, has recently emerged as a powerful approach to constructing complex behaviors for intelligent agents [11,24]. Such networks can take a number of simulated or real sensor values as input, and perform a nonlinear mapping to outputs that represent actions in the world such as moving around, picking up objects, using a tool or ﬁring a weapon. Recurrency in neural networks allow then to integrate information over time, and make decisions robustly even in partially observable domains where traditional value-function based reinforcement learning techniques  have diﬃculty.