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\(\delta \)-logit : Dynamic Difficulty Adjustment Using Few Data Points

  • William Rao FernandesEmail author
  • Guillaume LevieuxEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11863)

Abstract

Difficulty is a fundamental factor of enjoyment and motivation in video games. Thus, many video games use Dynamic Difficulty Adjustment systems to provide players with an optimal level of challenge. However, many of these systems are either game specific, limited to a specific range of difficulties, or require much more data than one can track during a short play session. In this paper, we introduce the \(\delta \)-logit algorithm. It can be used on many game types, allows a developer to set the game’s difficulty to any level, with, in our experiment, a player failure error prediction rate lower than 20% in less than two minutes of playtime. In order to roughly estimate the difficulty as quickly as possible, \(\delta \)-logit drives a single metavariable to adjust the game’s difficulty. It starts with a simple +/\(-\) \(\delta \) algorithm to gather a few data points and then uses logistic regression to estimate the players failure probability when the smallest required amount of data has been collected. The goal of this paper is to describe \(\delta \)-logit and estimate its accuracy and convergence speed with a study on 37 participants playing a tank shooter game.

Keywords

Difficulty Dynamic difficulty adjustment Game balancing Player modeling Motivation Video games 

Notes

Acknowledgement

This research is part of the Programme d’investissement d’avenir E-FRAN project DysApp, conducted with Caisse des Dépôts and supported by the French Government.

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Copyright information

© IFIP International Federation for Information Processing 2019

Authors and Affiliations

  1. 1.CNAM CEDRICParisFrance

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