MPRL: Multiple-Periodic Reinforcement Learning for difficulty adjustment in rehabilitation games

Sekhavat, Yoones A.

doi:10.1109/segah.2017.7939260

Cited by 27 publications

(11 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…By incorporating the player's performance into the action selection [12] or the reward function [13], RL can be used to train AI opponents that play on the same level as the player. Instead of training non-player characters, other approaches use RL to fine-tune specific in-game parameters such as speed and size of objects that directly influence the difficulty of the player's task [14].…”

Section: Related Workmentioning

confidence: 99%

“…Besides increasing physical activity, rehabilitation is one of the main applications for exergames [2]. Many rehabilitation exergames focus on post-stroke rehabilitation [14], [21]- [24]. Here, regular exercise can help to improve mobility in affected body parts.…”

Section: Related Workmentioning

confidence: 99%

“…the speed of in-game objects) according to heuristics based on the player's performance [21]- [24], [26]. Other rehabilitation exergames adapted similar in-game parameters through fuzzy systems [26], evolutionary algorithms [27] and RL agents [14].…”

Section: Related Workmentioning

confidence: 99%

See 2 more Smart Citations

Dynamic Difficulty Adjustment in Virtual Reality Exergames through Experience-driven Procedural Content Generation

Huber,

Mertes,

Rangelova

et al. 2021

Preprint

View full text Add to dashboard Cite

Virtual Reality (VR) games that feature physical activities have been shown to increase players' motivation to do physical exercise. However, for such exercises to have a positive healthcare effect, they have to be repeated several times a week. To maintain player motivation over longer periods of time, games often employ Dynamic Difficulty Adjustment (DDA) to adapt the game's challenge according to the player's capabilities. For exercise games, this is mostly done by tuning specific in-game parameters like the speed of objects. In this work, we propose to use experience-driven Procedural Content Generation for DDA in VR exercise games by procedurally generating levels that match the player's current capabilities. Not only finetuning specific parameters but creating completely new levels has the potential to decrease repetition over longer time periods and allows for the simultaneous adaptation of the cognitive and physical challenge of the exergame.As a proof-of-concept, we implement an initial prototype in which the player must traverse a maze that includes several exercise rooms, whereby the generation of the maze is realized by a neural network. Passing those exercise rooms requires the player to perform physical activities. To match the player's capabilities, we use Deep Reinforcement Learning to adjust the structure of the maze and to decide which exercise rooms to include in the maze. We evaluate our prototype in an exploratory user study utilizing both biodata and subjective questionnaires.

show abstract

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Dynamic Difficulty Adjustment in Virtual Reality Exergames through Experience-driven Procedural Content Generation

Huber,

Mertes,

Rangelova

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…When the difficulty is right, some of the games end quickly and some last long. Xue et al [10] optimize the expected number of rounds in the game, while Sekhavat [11] employs a similar approach, by optimizing the difference between the number of losses and the number of wins of a player in multiple periods. Hamlet system embedded in the Half-Life game engine [12], [13] assumes that the player should move between states according to the flow model.…”

Section: Related Workmentioning

confidence: 99%

DL-DDA -- Deep Learning based Dynamic Difficulty Adjustment with UX and Gameplay constraints

Or¹,

Kolomenkin²,

Shabat³

2021

Preprint

View full text Add to dashboard Cite

Dynamic difficulty adjustment (DDA) is a process of automatically changing a game difficulty for the optimization of user experience. It is a vital part of almost any modern game. Most existing DDA approaches concentrate on the experience of a player without looking at the rest of the players. We propose a method that automatically optimizes user experience while taking into consideration other players and macro constraints imposed by the game. The method is based on deep neural network architecture that involves a count loss constraint that has zero gradients in most of its support. We suggest a method to optimize this loss function and provide theoretical analysis for its performance. Finally, we provide empirical results of an internal experiment that was done on 200, 000 players and was found to outperform the corresponding manual heuristics crafted by game design experts.

show abstract

“…Sekhavat [35] suggested a personalized DDA method for a rehab game that manages difficulty settings automatically, based on a real-time patient's skills. Concepts of reinforcement learning were used as a DDA technique.…”

Section: Reinforcement Learningmentioning

confidence: 99%

Dynamic Difficulty Adjustment (DDA) in Computer Games: A Review

Zohaib

2018

Advances in Human-Computer Interaction

141

View full text Add to dashboard Cite

Dynamic difficulty adjustment (DDA) is a method of automatically modifying a game’s features, behaviors, and scenarios in real-time, depending on the player’s skill, so that the player, when the game is very simple, does not feel bored or frustrated, when it is very difficult. The intent of the DDA is to keep the player engrossed till the end and to provide him/her with a challenging experience. In traditional games, difficulty levels increase linearly or stepwise during the course of the game. The features such as frequency, starting levels, or rates can be set only at the beginning of the game by choosing a level of difficulty. This can, however, result in a negative experience for players as they try to map a predecided learning curve. DDA attempts to solve this problem by presenting a customized solution for the gamers. This paper provides a review of the current approaches to DDA.

show abstract

MPRL: Multiple-Periodic Reinforcement Learning for difficulty adjustment in rehabilitation games

Cited by 27 publications

References 20 publications

Dynamic Difficulty Adjustment in Virtual Reality Exergames through Experience-driven Procedural Content Generation

Dynamic Difficulty Adjustment in Virtual Reality Exergames through Experience-driven Procedural Content Generation

DL-DDA -- Deep Learning based Dynamic Difficulty Adjustment with UX and Gameplay constraints

Dynamic Difficulty Adjustment (DDA) in Computer Games: A Review

Contact Info

Product

Resources

About