Titlebook: Applications of Evolutionary Computation; EvoApplications 2011 Cecilia Chio,Stefano Cagnoni,Georgios N. Yannakaki Conference proceedings 20

Fillet,Filet

Learning Chasing Behaviours of Non-Player Characters in Games Using SARSAexplored by game AI researchers. The application of machine learning in games could enhance game playing experience. In this report, we investigate the design and implementation of reinforcement learning to learn the chasing behaviours of NPCs. The design and the simulation results are discussed and further work in this area is suggested.

誓言

Dana Jalobeanu,Charles T. Wolfelayers’ average fighting time. For that purpose, four different map representations are tested and compared. Results obtained showcase the clear advantage of some representations in generating interesting FPS maps and demonstrate the promise of the approach followed for automatic level design in that game genre.

FICE

Artificial Offshore Seismic Sources,g and Neural Network function approximation we train agents that learn a game position evaluation function for these games. We show that the resulting agents significantly outperform the open-source program Tavli3D.

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Conference proceedings 2011held in Torino, Italy, in April 2011 colocated with the Evo* 2011 events. Thanks to the large number of submissions received, the proceedings for EvoApplications 2011 are divided across two volumes (LNCS 6624 and 6625). The present volume contains contributions for EvoCOMPLEX, EvoGAMES, EvoIASP, Evo

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Alchemy, Chemistry, and Metallurgyaving the same size, the evolved instances required a computation time which was 30,000 times greater than what was needed to solve the original instance that seeded the search. The success of this case study shows the potential of Evolutionary Search to provide new test-case scenarios for algorithms and their software implementations.

Synchronism

Absolute Space in Natural Philosophy a base-function for the dynamic problems. An experimental setup is proposed for investigating these issues. The results show that, at least under the proposed framework, a GA with the sandpile mutation self-adapts the mutation rates to the dynamics of the problem and to the characteristics of the base-function.

Vital-Signs

OWL

GRILL