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Hindawi Publishing CorporationInternational Journal of Computer Games TechnologyVolume 2009, Article ID 251652, 3 pagesdoi:10.1155/2009/251652

Editorial

Artificial Intelligence for Computer Games

Abdennour El Rhalibi,1 Kok Wai Wong,2 and Marc Price3

1 School of Computing and Mathematical Sciences, Liverpool John Moores University, Byrom Street, Liverpool L3 3AF, UK2 School of Information Technology, Murdoch University, 90 South Street, Murdoch WA 6150, Australia3 BBC Research, Kingswood Warren, Tadworth, Surrey KT20 6NP, UK

Correspondence should be addressed to Abdennour El Rhalibi, [email protected]

Received 15 December 2008; Accepted 15 December 2008

Copyright © 2009 Abdennour El Rhalibi et al. This is an open access article distributed under the Creative Commons AttributionLicense, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properlycited.

Artificial intelligence (AI) in computer games covers thebehaviour and decision-making process of game-playingopponents (also known as nonplayer character or NPC).Current generations of computer and video games offeran amazingly interesting testbed for AI research and newideas. Such games combine rich and complex environmentswith expertly developed, stable, physics-based simulation.They are real-time and very dynamic, encouraging fast andintelligent decisions. Computer games are also often multia-gents, making teamwork, competition, and NPC modellingkey elements to success. In commercial games, such asaction games, role-playing games, and strategy games, thebehaviour of the NPC is usually implemented as a varia-tion 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 techniquesmay enable the NPCs with the capability to improve theirperformance by learning from mistakes and successes, toautomatically adapt to the strengths and weaknesses of aplayer, or to learn from their opponents by imitating theirtactics.

In this special issue, we introduce a number of interestingpapers contributing to a wide range of these topics andreflecting the current state of AI for Computer Game inacademia. A total of 20 papers have been submitted tothis special issue, of which 9 high-quality papers have beenaccepted after the peer review process.

This special issue starts with the first paper “Performancesimulations of moving target search algorithms” by PeterKok Keong Loh et al. In this paper, the authors focused onthe design of moving target search (MTS) algorithms forcomputer generated bots. MTS algorithms pose important

challenges as they have to satisfy rigorous requirementswhich involve combinatorial computation and performance.In this paper, the authors investigate the performance andbehaviour of existing moving target search algorithms whenapplied to search-and-capture gaming scenarios. As partof the investigation, they also introduce a novel algorithmknown as abstraction MTS. They conduct performancesimulations with a game bot and moving target withinrandomly generated mazes of increasing sizes and reveal thatabstraction MTS exhibits competitive performance even withlarge problem spaces.

The second paper is proposed by Julio Clempner andis entitled “A shortest-path lyapunov approach for forwarddecision processes.” In this paper, the author presents aformal framework for shortest-path decision process prob-lem representation. Dynamic systems governed by ordinarydifference equations described by Petri nets are considered.The trajectory over the net is calculated forward using a dis-crete Lyapunov-like function. Natural generalizations of thestandard outcomes are proved for the deterministic shortest-path problem. In this context, the authors are changing thetraditional cost function by a trajectory-tracking functionwhich is also an optimal cost-to-target function for trackingthe net. This makes an important contribution in theconceptualization of the problem domain. The Lyapunovmethod introduces a new equilibrium and stability conceptin decision process for shortest path.

The third paper is entitled “Fractal analysis of stealthypathfinding aesthetics” authored by Coleman Ron. In thispaper, the author uses a fractal model to analyze aestheticvalues of a new class of obstacle prone or “stealthy”pathfinding which seeks to avoid detection, exposure, and

2 International Journal of Computer Games Technology

openness in videogames. This study is interesting since ingeneral the artificial intelligence literature has given relativelylittle attention to aesthetic outcomes in pathfinding. Thedata reported, according to the fractal model, suggeststhat stealthy paths are statistically significantly unique inrelative aesthetic value when compared to control paths.The author also shows that paths generated with differentstealth regimes are also statistically significantly unique.These conclusions are supported by statistical analysis ofmodel results on experimental trials involving pathfinding inrandomly generated, multiroom virtual worlds.

The next paper is proposed by Frank Dignum et al.and discusses “Games and agents: designing intelligentGameplay.” Multiagent system research offers a promisingtechnology to implement cognitive intelligent NPC’s. How-ever, the technologies used in game engines and multiagentplatforms are not readily compatible due to some inherentdifferences of concerns. Where game engines focus on real-time aspects and thus propagate efficiency and central con-trol, multiagent platforms assume autonomy of the agents.Increased autonomy and intelligence may offer benefits for amore compelling gameplay and may even be necessary forserious games. However, it raises problems when currentgame design techniques are used to incorporate state ofthe art multiagent system technology. In this paper, theauthors focused on three specific problem areas that arisefrom this difference of view: synchronization, informationrepresentation, and communication. They argue that currentattempts for integration still fall short on some of theseaspects. They show that to fully integrate intelligent agentsin games, one should not only use a technical solution, butalso a design methodology such as OperA, that is amenableto agents.

The fifth paper presents “A multiagent potential fieldsbased bot for real-time strategy games” authored by JohanHagelback et al. The paper discusses bots for real-timestrategy (RTS). A bot controls a number of units that willhave to navigate in a partially unknown environment, whileat the same time avoid each other, search for enemies, andcoordinate attacks to fight them down. “Potential fields” isa technique originating from the area of robotics where itis used in controlling the navigation of robots in dynamicenvironments. The authors present a multiagent potentialfield based bot architecture which is evaluated in twodifferent real-time strategy game settings and compare it, interms of performance, and configurability, to other state-of-the-art solutions. The authors show that the solution is ahighly configurable bot which can match the performanceof traditional RTS bots. They also show that a multiagentpotential field-based bot is highly competitive in a resourcegathering scenario.

The next paper is “Combining artificial intelligencemethods for learning bots in a real time strategy game”authored by Robin Baumgarten et al. The authors describean approach to simulate human game-play in strategygames using a variety of AI techniques, including simulatedannealing, decision tree learning, and case-based reasoning.They have implemented an AI-bot that uses these techniquesto form a novel approach to plan fleet movements and attacks

in DEFCON, a nuclear war simulation strategy game releasedin 2006 by Introversion Software Ltd, Surrey, UK. Theydescribe how the AI-bot operates, and the experimentationthey have performed in order to determine an optimalconfiguration for it. With this configuration, the proposedAI-bot beats Introversion’s finite state machine automatedplayer in 76.7% of 150 matches played.

The seventh paper is “Enhancing artificial intelligence ona real mobile game” by Fabio Aiolli et al. Mobile gamingrepresents a killer application that is attracting millions ofsubscribers worldwide; yet, several technical issues in thiscontext remain unsolved. One of the aspects crucial to thecommercial success of a game is ensuring an appropriatelychallenging artificial intelligence (AI) algorithm againstwhich to play. However, creating this component is partic-ularly complex as classic search AI algorithms cannot beemployed by limited devices such as mobile phones or, evenon more powerful computers, when considering imperfectinformation games (i.e., games in which participants havenot a complete knowledge of the game state at any moment).In this paper, the authors propose to solve the imperfectinformation game issue by resorting to a machine learningalgorithm which uses profiling functionalities in order toinfer the missing information, and making the AI able toefficiently adapt its strategies to the human opponent. Theystudied a very simple and computationally light machinelearning method that can be employed with success, enablingAI improvements for imperfect information games even onmobile phones. They present results on a simple game calledGhosts which show the ability of their algorithm to quicklyimprove its own predictive performance as the number ofgames against the same human opponent increases. A mobilephone-based version of the game has been also created whichcan be played either against another player or against the AIalgorithm.

The eighth paper is “Breeding terrains with geneticterrain programming—the evolution of terrain generators”by Miguel Frade et al. Although a number of terraingeneration techniques have been proposed during the lastfew years, all of them have some key constraints. Modellingtechniques depend highly upon designer’s skills, time, andeffort to obtain acceptable results, and cannot be used toautomatically generate terrains. The simpler methods allowonly a narrow variety of terrain types and offer little controlon the outcome terrain. The Genetic Terrain Programmingtechnique, proposed, based on evolutionary design withgenetic programming, allows designers to evolve terrainsaccording to their aesthetic intentions or desired features.This technique evolves terrain programmes (TPs) that arecapable of generating a family of terrains - different terrainsthat consistently present the same morphological charac-teristics. This paper presents a study about the persistenceof morphological characteristics of terrains generated withdifferent resolutions by a given TP. Results show that itis possible to use low resolutions during the evolutionaryphase without compromising the outcome and that terrainmacrofeatures are scale invariant.

Finally, the last paper is “Fine-tuning parameters foremergent environments in games using artificial intelligence”

International Journal of Computer Games Technology 3

authored by Vishnu Kotrajaras et al. This paper presents thedesign, development, and test results of a tool for adjustingproperties of emergent environment maps automaticallyaccording to a given scenario. Adjusting properties for ascenario allows a specific scene to take place while stillenables players to meddle with emergent maps. The tool usesgenetic algorithm and steepest ascent hill-climbing to learnand adjust map properties. The authors shows that usingthe proposed tool, the need for time consuming and labour-intensive parameter adjustments when setting up scenariosin emergent environment maps, is greatly reduced. The toolworks by converting the paths of events created by usersfor a map to the properties of the map that plays out thescenario set by the given paths of events. Test results showgood properties preservation.

Abdennour El RhalibiKok Wai Wong

Marc Price

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