By the same authors

Challenging AI: Evaluating the Effect of MCTS-Driven Dynamic Difficulty Adjustment on Player Enjoyment

Research output: Chapter in Book/Report/Conference proceedingConference contribution



Publication details

Title of host publicationProceedings of ACSW
DateAccepted/In press - 21 Oct 2018
DatePublished (current) - 29 Jan 2019
Original languageEnglish


Providing a challenging Artificial Intelligent opponent is an important aspect of making video games enjoyable and immersive. A game that is too easy, or conversely too hard may frustrate or bore players. Dynamic Difficulty Adjustment is a method that aims at improving the traditional methods of difficulty selection, by providing an opponent that tailors the challenge it presents to players such that it is at an optimal level for them. This research presents a player evaluation of three different Dynamic Difficulty Adjustment approaches using Monte Carlo Tree Search and measures their impact on player enjoyment, realism and perceived level of difficulty. In particular, it investigates the effect that different win/loss ratios, employed by Dynamic Difficulty Adjustment, have on player enjoyment.

Discover related content

Find related publications, people, projects, datasets and more using interactive charts.

View graph of relations