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

Standard

Challenging AI: Evaluating the Effect of MCTS-Driven Dynamic Difficulty Adjustment on Player Enjoyment. / Demediuk, Simon Peter; Tamassia, Marco; Li, Xiaodong; Raffe, William.

Proceedings of ACSW. 2019.

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

Harvard

Demediuk, SP, Tamassia, M, Li, X & Raffe, W 2019, Challenging AI: Evaluating the Effect of MCTS-Driven Dynamic Difficulty Adjustment on Player Enjoyment. in Proceedings of ACSW.

APA

Demediuk, S. P., Tamassia, M., Li, X., & Raffe, W. (2019). Challenging AI: Evaluating the Effect of MCTS-Driven Dynamic Difficulty Adjustment on Player Enjoyment. In Proceedings of ACSW

Vancouver

Demediuk SP, Tamassia M, Li X, Raffe W. Challenging AI: Evaluating the Effect of MCTS-Driven Dynamic Difficulty Adjustment on Player Enjoyment. In Proceedings of ACSW. 2019

Author

Demediuk, Simon Peter ; Tamassia, Marco ; Li, Xiaodong ; Raffe, William. / Challenging AI: Evaluating the Effect of MCTS-Driven Dynamic Difficulty Adjustment on Player Enjoyment. Proceedings of ACSW. 2019.

Bibtex - Download

@inproceedings{fc523797117d4d41920329b60ae8c556,
title = "Challenging AI: Evaluating the Effect of MCTS-Driven Dynamic Difficulty Adjustment on Player Enjoyment",
abstract = "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.",
author = "Demediuk, {Simon Peter} and Marco Tamassia and Xiaodong Li and William Raffe",
year = "2019",
month = jan,
day = "29",
language = "English",
booktitle = "Proceedings of ACSW",

}

RIS (suitable for import to EndNote) - Download

TY - GEN

T1 - Challenging AI: Evaluating the Effect of MCTS-Driven Dynamic Difficulty Adjustment on Player Enjoyment

AU - Demediuk, Simon Peter

AU - Tamassia, Marco

AU - Li, Xiaodong

AU - Raffe, William

PY - 2019/1/29

Y1 - 2019/1/29

N2 - 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.

AB - 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.

M3 - Conference contribution

BT - Proceedings of ACSW

ER -