International Journal of Intelligent Games & Simulation 

About: International Journal of Intelligent Games & Simulation is an academic journal. The journal publishes majorly in the area(s): Computer game & Game mechanics. It has an ISSN identifier of 1477-2043. Over the lifetime, 53 publications have been published receiving 1353 citations.

Video Games in Education

Abstract: Computer and video games are a maturing medium and industry and have caught the attention of scholars across a variety of disciplines. By and large, computer and video games have been ignored by educators. When educators have discussed games, they have focused on the social consequences of game play, ignoring important educational potentials of gaming. This paper examines the history of games in educational research, and argues that the cognitive potential of games have been largely ignored by educators. Contemporary developments in gaming, particularly interactive stories, digital authoring tools, and collaborative worlds, suggest powerful new opportunities for educational media.

Sound and immersion in the first-person shooter

Abstract: One of the aims of modern First-Person Shooter (FPS) design is to provide an immersive experience to the player. This paper examines the role of sound in enabling such immersion and argues that, even in 'realism' FPS games, it may be achieved sonically through a focus on caricature rather than realism. The paper utilizes and develops previous work in which both a conceptual framework for the design and analysis of run and gun FPS sound is developed and the notion of the relationship between player and FPS soundscape as an acoustic ecology is put forward (Grimshaw and Schott 2007a; Grimshaw and Schott 2007b). Some problems of sound practice and sound reproduction in the game are highlighted and a conceptual solution is proposed.

On-line Adaptation of Game Opponent AI with Dynamic Scripting.

Abstract: Unsupervised online learning in commercial computer games allows computer-controlled opponents to adapt to the way the game is being played, thereby providing a mechanism to deal with weaknesses in the game AI and to respond to changes in human player tactics. For online learning to work in practice, it must be fast, effective, robust, and efficient. This paper proposes a novel technique called "dynamic scripting" that meets these requirements. In dynamic scripting an adaptive rulebase is used for the generation of intelligent opponents on the fly. The performance of dynamic scripting is evaluated in an experiment in which the adaptive players are pitted against a collective of manually designed tactics in a simulated computer roleplaying game and in a module for the state-of-the-art commercial game NEVERWINTER NIGHTS. The results indicate that dynamic scripting succeeds in endowing computer-controlled opponents with successful adaptive performance. We therefore conclude that dynamic scripting can be successfully applied to the online adaptation of computer game opponent AI.

On-Line Coevolution for Action Games

Abstract: Coevolutionary algorithms (CEAs) have been widely explored in recent years. Cooperative and competitive methods were proposed and evaluated, and many theoretical studies have been made about them and important results have been achieved, however few works have been published about a real-time approach to CEAs, with online agent evolution. The goal of this work is to explore this field of application of CEAs, proposing some methods and strategies for online evolution in an action (real-time) game. In this game, a human player interacts with computer-controlled agents, which begin with very naive or random behaviour and gradually get “smarter”, resulting in improved difficulty levels of gameplay. We present four different methods to do online evolution of the agents: using game specific information; merging offline-evolved data with online evolution; using online data only; and using them together. We will, finally, present some results and a brief discussion of the advantages and disadvantages of each one of the methods proposed, based upon these results.

The execution kernel of RC++: RETE*, a faster RETE with TREAT as a special case

Abstract: Some behaviours of computer game agents can be naturally expressed as collections of rules and knowledge bases General purpose rule-based languages provide high-level constructs for expressing complex conditional behaviour We examine the runtime kernel of RC++, a rule-based language developed for game AI, to explore the costs associated with adopting general-purpose, rule-based approaches for computer game production The kernel of RC++ is the RETE* algorithm, an extension of the RETE algorithm with better time characteristics, but also able to exhibit the beneficial properties of TREAT (a low memory cost alternative to RETE) when required RETE* achieves this functionality and performance by employing (i) asymmetric deletion, (ii) dual tokens, and (iii) a dynamic beta-memory cut mechanism The dynamic beta cut allows the RETE/TREAT trade-off to be exploited by users Theoretical and empirical performance comparisons for RETE, TREAT and RETE* are provided The implications for the utility of rule-based programming for the computer games industry is discussed, and we conclude that there is still some way to go before rule-based programming can be employed in the game-making process