Artificial Intelligence for Computer Games

Abdennour El Rhalibi,Kok Wai Wong,Marc Price

doi:10.1155/2009/251652

Abstract

Artificial intelligence (AI) in computer games covers the behaviour and decision-making process of game-playing opponents (also known as nonplayer character or NPC). Current generations of computer and video games offer an amazingly interesting testbed for AI research and new ideas. Such games combine rich and complex environments with expertly developed, stable, physics-based simulation. They are real-time and very dynamic, encouraging fast and intelligent decisions. Computer games are also often multiagents, making teamwork, competition, and NPC modelling key elements to success. In commercial games, such as action games, role-playing games, and strategy games, the behaviour of the NPC is usually implemented as a variation of simple rule-based systems. With a few exceptions, machine-learning techniques are hardly ever applied to state-of-the-art computer games. Machine-learning techniques may enable the NPCs with the capability to improve their performance by learning from mistakes and successes, to automatically adapt to the strengths and weaknesses of a player, or to learn from their opponents by imitating their tactics.

Highlights

This special issue starts with the first paper “Performance simulations of moving target search algorithms” by Peter Kok Keong Loh et al In this paper, the authors focused on the design of moving target search (MTS) algorithms for computer generated bots
We introduce a number of interesting papers contributing to a wide range of these topics and reflecting the current state of Artificial intelligence (AI) for Computer Game in academia
A total of 20 papers have been submitted to this special issue, of which 9 high-quality papers have been accepted after the peer review process

Summary

Introduction

This special issue starts with the first paper “Performance simulations of moving target search algorithms” by Peter Kok Keong Loh et al In this paper, the authors focused on the design of moving target search (MTS) algorithms for computer generated bots. The authors investigate the performance and behaviour of existing moving target search algorithms when applied to search-and-capture gaming scenarios. They conduct performance simulations with a game bot and moving target within randomly generated mazes of increasing sizes and reveal that abstraction MTS exhibits competitive performance even with large problem spaces.

Results

Conclusion