This paper studies the use of eye tracking in a First-Person Shooter (FPS) game as a mechanism to: (1) control the attention of the player’s avatar according to the attention deployed by the player; and (2) guide the gameplay and game’s procedural content generation, accordingly. This results in a more natural use of eye tracking in comparison to a use in which the eye tracker directly substitutes control input devices, such as gamepads. The study was conducted on a custom endless runner FPS, Zombie Runner, using an affordable eye tracker. Evaluation sessions showed that the proposed use of eye tracking provides a more challenging and immersive experience to the player, when compared to its absence. However, a strong correlation between eye tracker calibration problems and player’s overall experience was found. This means that eye tracking technology still needs to evolve but also means that once technology gets mature enough players are expected to benefit greatly from the inclusion of eye tracking in their gaming experience.
Within the EDPCG framework, it is possible to generate levels that are adapted to the strengths and limitations of the player, in an attempt to maximize the fun factor. Many studies propose that the fun and challenge factors are directly linked @cite_29 @cite_18 @cite_9 @cite_10 @cite_0 @cite_25 @cite_8 , which means that, to tune the fun factor, one often has to tune the challenge factor. In this line, in this paper, we propose the integration of PGC with gaze tracking so as to adapt the challenge the player as to face according to his her form of playing and, as a result , to improve the fun factor.
@cite_18 @cite_8 @cite_9 @cite_29 @cite_0 @cite_10 @cite_25
1605527696 2398446582 2168304578 2036493203 2121045458
This paper presents a generative system for the automatic creation of video game levels. Our approach is novel in that it allows high-level design goals to be expressed in a top-down manner, while existing bottom-up techniques do not. We use the FI-2Pop genetic algorithm as a natural way to express both constraints and optimization goals for potential level designs. We develop a genetic encoding technique specific to level design, which proves to be extremely flexible. Example levels are generated for two different genres of game, demonstrating the system’s broad applicability. In this paper we investigate the evolutionary changes of chess variants. For this purpose we propose a measure of the game’s entertaining impact. The measure is derived from grandmaster games and is applicable to chess variants independent of the game under consideration. For the case where no grandmaster games are available we perform computer self-play experiments to estimate the measure. For several old chess variants the measure as well as other measures, such as the search-space complexity and draw rate, are calculated. Some relationships between the measures investigated are found. Based on these relationships the evolutionary changes of chess variants are discussed. This paper explores using the NEAT and rtNEAT neuro-evolution methodologies to generate intelligent opponents in real-time strategy (RTS) games. The main objective is to adapt the challenge generated by the game opponents to match the skill of a player in real-time, ultimately leading to a higher entertainment value perceived by a human player of the game. Results indicate the effectiveness of NEAT and rtNEAT but demonstrate their limitations for use in real-time strategy games. Mainly motivated by the current lack of a qualitative and quantitative entertainment formulation of computer games and the procedures to generate it, this article covers the following issues: It presents the features-extracted primarily from the opponent behavior-that make a predator prey game appealing; provides the qualitative and quantitative means for measuring player entertainment in real time, and introduces a successful methodology for obtaining games of high satisfaction. This methodology is based on online (during play) learning opponents who demonstrate cooperative action. By testing the game against humans, we confirm our hypothesis that the proposed entertainment measure is consistent with the judgment of human players. As far as learning in real time against human players is concerned, results suggest that longer games are required for humans to notice some sort of change in their entertainment. Commercial games possess various methods of game balancing. Each of them modifies the game's entertainment value for players of different skill levels. This paper deals with one of them, viz. a way of automatically adapting a game's balance which is based on the theory of incongruity. We tested our approach on a group of subjects, who played a game with three difficulty settings. The idea is to maintain a specific difference in incongruity automatically. We tested our idea extensively and may report that the results coincide with the theory of incongruity as far as positive incongruity is concerned. The main conclusion is that, owing to our automatically maintained balanced difficulty setting, we can avoid that a game becomes boring or frustrating.