Neural-based agents cooperate to survive in the defend and gather computer game

The computer game Defend and Gather was created to evaluate two neural-based agents\u27 ability to learn how to play and win the game. The agents navigate an environment to And resources and defeat enemies. Traditional game agents are often neither challenging enough to human opponents over time, nor scalable to environments not anticipated at the time the agents were originally programmed. We show that neural-based agents have the ability to learn from their human counterparts or from the environment, thus remaining competitive over time. The neural-based agents developed for Defend and Gather have the ability to formulate tactics within Increasingly difficult environments Involving more sophisticated enemies and can win the game over seventy-five percent of the time. © 2007 IEEE

Evolutionary Computation

This book presents several recent advances on Evolutionary Computation, specially evolution-based optimization methods and hybrid algorithms for several applications, from optimization and learning to pattern recognition and bioinformatics. This book also presents new algorithms based on several analogies and metafores, where one of them is based on philosophy, specifically on the philosophy of praxis and dialectics. In this book it is also presented interesting applications on bioinformatics, specially the use of particle swarms to discover gene expression patterns in DNA microarrays. Therefore, this book features representative work on the field of evolutionary computation and applied sciences. The intended audience is graduate, undergraduate, researchers, and anyone who wishes to become familiar with the latest research work on this field