Norio Baba Libri

The evolution of technologies has significantly transformed our industry and daily lives, leading to the emergence and growth of industries that were previously nonexistent, notably the computer game industry. This book compiles recent research on the application of computational intelligence techniques and internet technology in gaming, structured into eight chapters. The first chapter, authored by N. Baba and H. Handa, explores the use of evolutionary algorithms to enhance the excitement of the COMMONS GAME, demonstrating how these algorithms have revitalized this popular environmental game. The second chapter, by H. Barber and D. Kudenko, discusses the adaptive generation of dilemma-based interactive narratives, presenting a narrative generator capable of creating storylines infused with dilemmas to heighten dramatic tension, and suggesting its potential as a tool for enriching game experiences. The third chapter, contributed by J. Tongelius, S. M. Lucas, and R. D. Nardi, focuses on the role of computational intelligence in racing games, proposing that CI techniques can serve various functions, including controller evolution and track design tailored for skilled players.

The world's most powerful computers are utilized not only for scientific research, defense, and business but also in the thriving multi-billion dollar gaming industry. Recent advancements in computational intelligence have sparked significant interest in the theory and implementation of games. Game theory, a branch of operational research, focuses on decision-making in competitive scenarios, employing mathematical calculations and heuristics to optimize gameplay. This book showcases recent research on applying computational intelligence techniques in gaming, comprising seven chapters. The first chapter, authored by Chen, Fanelli, Castellano, and Jain, introduces computational intelligence paradigms, covering key components such as knowledge representation, probability-based approaches, fuzzy logic, neural networks, genetic algorithms, and rough sets. In the second chapter, Chellapilla and Fogel explore the evolution of a neural network designed to play checkers autonomously. They detail a population of neural networks, each acting as an evaluation function for board positions. Remarkably, after just over 800 generations, this evolutionary process produced a neural network capable of playing checkers at an expert level, as recognized by the U.S. Chess Federation rating system. The authors' program has also demonstrated competitive performance against commercial software.