ARTIFICIAL INTELLIGENCE

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.

In this special issue, we introduce a number of interesting papers contributing to a wide range of these topics and reflecting the current state of 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.

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. MTS algorithms pose important challenges as they have to satisfy rigorous requirements which involve combinatorial computation and performance. In this paper, the authors investigate the performance and behaviour of existing moving target search algorithms when applied to search-and-capture gaming scenarios. As part of the investigation, they also introduce a novel algorithm known as abstraction MTS. 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.

The second paper is proposed by Julio Clempner and is entitled “A shortest-path lyapunov approach for forward decision processes.” In this paper, the author presents a formal framework for shortest-path decision process problem representation. Dynamic systems governed by ordinary difference equations described by Petri nets are considered. The trajectory over the net is calculated forward using a discrete Lyapunov-like function. Natural generalizations of the standard outcomes are proved for the deterministic shortest-path problem. In this context, the authors are changing the traditional cost function by a trajectory-tracking function which is also an optimal cost-to-target function for tracking the net. This makes an important contribution in the conceptualization of the problem domain. The Lyapunov method introduces a new equilibrium and stability concept in decision process for shortest path.

The third paper is entitled “Fractal analysis of stealthy pathfinding aesthetics” authored by Coleman Ron. In this paper, the author uses a fractal model to analyze aesthetic values of a new class of obstacle prone or “stealthy” pathfinding which seeks to avoid detection, exposure, and openness in videogames. This study is interesting since in general the artificial intelligence literature has given relatively little attention to aesthetic outcomes in pathfinding. The data reported, according to the fractal model, suggests that stealthy paths are statistically significantly unique in relative aesthetic value when compared to control paths. The author also shows that paths generated with different stealth regimes are also statistically significantly unique. These conclusions are supported by statistical analysis of model results on experimental trials involving pathfinding in randomly generated, multiroom virtual worlds.

The next paper is proposed by Frank Dignum et al. and discusses “Games and agents: designing intelligent Gameplay.” Multiagent system research offers a promising technology to implement cognitive intelligent NPC’s. However, the technologies used in game engines and multiagent platforms are not readily compatible due to some inherent differences of concerns. Where game engines focus on real-time aspects and thus propagate efficiency and central control, multiagent platforms assume autonomy of the agents. Increased autonomy and intelligence may offer benefits for a more compelling gameplay and may even be necessary for serious games. However, it raises problems when current game design techniques are used to incorporate state of the art multiagent system technology. In this paper, the authors focused on three specific problem areas that arise from this difference of view: synchronization, information representation, and communication. They argue that current attempts for integration still fall short on some of these aspects. They show that to fully integrate intelligent agents in games, one should not only use a technical solution, but also a design methodology such as OperA, that is amenable to agents.

The fifth paper presents “A multiagent potential fields based bot for real-time strategy games” authored by Johan

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