By Piero P. Bonissone (auth.), Jing Liu, Cesare Alippi, Bernadette Bouchon-Meunier, Garrison W. Greenwood, Hussein A. Abbass (eds.)

This cutting-edge survey deals a renewed and fresh concentrate on the development in evolutionary computation, in neural networks, and in fuzzy structures. The publication offers the services and reviews of prime researchers spanning a various spectrum of computational intelligence in those components. the result's a balanced contribution to the learn region of computational intelligence that are supposed to serve the group not just as a survey and a reference, but in addition as an notion for the longer term development of the cutting-edge of the sphere. The thirteen chosen chapters originate from lectures and displays given on the IEEE international Congress on Computational Intelligence, WCCI 2012, held in Brisbane, Australia, in June 2012.

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Additional info for Advances in Computational Intelligence: IEEE World Congress on Computational Intelligence, WCCI 2012, Brisbane, Australia, June 10-15, 2012. Plenary/Invited Lectures

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For the battle task, two teams—each consisting of 50 NERO agents—occupy a continuous, two-dimensional, virtual playing field of fixed size (see Figure 8). 38 R. Miikkulainen et al. The playing field contains one central obstacle (a wall), four peripheral obstacles (trees), and four walls around the perimeter to contain all agents in the same general area. Each NERO agent starts a battle with 20 hit-points. At each time slice of the simulation, each agent has the opportunity to fire a virtual laser at the closest target on the opponent’s team that is within two degrees of the agent’s current orientation.

503–521. Addison Wesley, Harlow (1992) 29. : Locally Weighted Learning. 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.

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 [42] have difficulty. g. video games, board games, mobile robots, and autonomous vehicles [40,13,45,12,21,16,23]. Much of the work so far has focused on developing intelligent behaviors for single agents in a complex environment. As such behaviors have become more successful, a need for principled multiagent interactions has also risen.

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