Real-Time Game Adaptation for Optimizing Player Satisfaction

TLDR

Artificial neural network user models map individual playing characteristics to entertainment preferences in augmented‑reality games. The paper demonstrates a methodology to optimize player satisfaction on the Playware interactive platform. The adaptive mechanism applies gradient ascent on the user model to adjust game parameters in real time, using derivative‑based rules and frequent updates, and is evaluated with a game survey experiment. The mechanism proved effective and robust in tailoring games to individual players and improving gameplay, though limitations were noted.

Abstract

A methodology for optimizing player satisfaction in games on the "playware" physical interactive platform is demonstrated in this paper. Previously constructed artificial neural network user models, reported in the literature, map individual playing characteristics to reported entertainment preferences for augmented-reality game players. An adaptive mechanism then adjusts controllable game parameters in real time in order to improve the entertainment value of the game for the player. The basic approach presented here applies gradient ascent to the user model to suggest the direction of parameter adjustment that leads toward games of higher entertainment value. A simple rule set exploits the derivative information to adjust specific game parameters to augment the entertainment value. Those adjustments take place frequently during the game with interadjustment intervals that maintain the user model's accuracy. Performance of the adaptation mechanism is evaluated using a game survey experiment. Results indicate the efficacy and robustness of the mechanism in adapting the game according to a user's individual playing features and enhancing the gameplay experience. The limitations and the use of the methodology as an effective adaptive mechanism for entertainment capture and augmentation are discussed.