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            <title xml:lang="en">$\delta$-logit : Dynamic Difficulty Adjustment Using Few Data Points</title>
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            <idno type="halRefHtml">&lt;i&gt;1st Joint International Conference on Entertainment Computing and Serious Games (ICEC-JCSG)&lt;/i&gt;, Nov 2019, Arequipa, Peru. pp.158-171, &lt;a target="_blank" href="https://dx.doi.org/10.1007/978-3-030-34644-7_13"&gt;&amp;#x27E8;10.1007/978-3-030-34644-7_13&amp;#x27E9;&lt;/a&gt;</idno>
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                <term xml:lang="en">Video Games</term>
                <term xml:lang="en">Difficulty</term>
                <term xml:lang="en">Player Profiling</term>
                <term xml:lang="en">Accessibility</term>
                <term xml:lang="en">Dynamic Difficulty Adaptation</term>
                <term xml:lang="fr">Difficulté</term>
                <term xml:lang="fr">Jeu</term>
                <term xml:lang="fr">Accessibilité</term>
                <term xml:lang="fr">Equilibrage</term>
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              <p>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 δ-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 , δ-logit drives a single metavariable to adjust the game's difficulty. It starts with a simple +/-δ 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 δ-logit and estimate its accuracy and convergence speed with a study on 37 participants playing a tank shooter game.</p>
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              <p>La difficulté est un facteur fondamental de plaisir et de motivation dans les jeux vidéo. Ainsi, de nombreux jeux utilisent des systèmes d'ajustement automatique de la difficulté pour offrir au joueur un niveau de challenge optimal. Cependant, la plupart de ces systèmes sont spécifiques à un jeu donné, limités à un certain niveau de difficulté, ou nécessitent plus de données que peut en être récoltée au cours d'une courte session de jeu.. Dans cet article, nous proposons l'algorithme δ-logit. Cet algorithme peut être utilisé sur de nombreux types de jeux, permet au développeur de choisir précisément son niveau de difficulté avec, au cours de notre expérience, un taux d'erreur inférieur à 20% en moins de deux minutes de jeu. Pour estimer la difficulté le plus rapidement possible, δ-logit s'appuie sur une seule métavariable pour ajuster la difficulté. Il début par un simple simple équilibrage +/-δ pour observer le comportement du joueur, puis utilise un régression logistique pour estimer la probabilité d'échec du joueur dès que la plus petite quantité de donnée nécessaire a été enregistrée. Le but de cet article est de décrire δ-logit et d'estimer sa précision et sa vitesse de convergence pour une étude regroupant 37 participants pour un jeu de combat de tanks.</p>
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