Measuring player experience on runtime dynamic difficulty scaling in an RTS game

Hagelbäck, Johan; Johansson, Stefan J.

doi:10.1109/cig.2009.5286494

Cited by 20 publications

(13 citation statements)

References 6 publications

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“…RTS game players get clear and direct confrontation experience when controlling resources and destroying opponent forces. Hagelback and Johansson (2009) found that RTS game players derive more enjoyment when playing against an opponent that adapts to the performance of the player (human), than when playing against an opponent with static difficulty (AI) (Hagelback and Johansson 2009). With human opponents, as well as with AI opponents, being adequately challenged is one of the most important factors in a player's overall enjoyment of a particular game.…”

Section: Related Workmentioning

confidence: 98%

Cognitive Load in Real-Time Strategy Gaming: Human Opponent Versus AI Opponent

Yang

Whittinghill

2015

Comput Game J

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This paper examines the cognitive load in real-time strategy gaming subjects along with physiological and subjective measurements. The subjects were college students from a northeastern university in China. This paper is a study into players' cognitive load measurement when playing against human opponents and game artificial intelligences (game AIs). Using eye trackers, action per minute recorders and questionnaires, we collected qualitative data from participants about their cognitive load through playing against human opponents and game AI opponents. The results indicate that players have more cognitive load when playing against human opponents than against game AI opponents. Secondly, as the players' gaming skill increased, their cognitive load did not differ substantially when they switched from playing against human players to playing against game AI opponents. A deeper understanding of players' cognitive load can enable a developer to design a more appropriate game experience.

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Section: Related Workmentioning

confidence: 98%

Cognitive Load in Real-Time Strategy Gaming: Human Opponent Versus AI Opponent

Yang

Whittinghill

2015

Comput Game J

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“…A similar system was implemented in the first-person shooter Max Payne, 21 where the health of enemies and the amount of auto-aim help are adjusted, based on the player's performance. Hagelbäck and Johansson (2009) aim at specific score difference in an RTS game. According to questionnaires, their method significantly raised enjoyment and perceived variability compared to static opponents.…”

Section: Difficulty Scalingmentioning

confidence: 99%

Reinforcement Learning in Games

Szita

2012

Adaptation, Learning, and Optimization

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Abstract. Reinforcement learning and games have a long and mutually beneficial common history. From one side, games are rich and challenging domains for testing reinforcement learning algorithms. From the other side, in several games the best computer players use reinforcement learning. The chapter begins with a selection of games and notable reinforcement learning implementations. Without any modifications, the basic reinforcement learning algorithms are rarely sufficient for high-level gameplay, so it is essential to discuss the additional ideas, ways of inserting domain knowledge, implementation decisions that are necessary for scaling up. These are reviewed in sufficient detail to understand their potentials and their limitations. The second part of the chapter lists challenges for reinforcement learning in games, together with a review of proposed solution methods. While this listing has a game-centric viewpoint, and some of the items are specific to games (like opponent modelling), a large portion of this overview can provide insight for other kinds of applications, too. In the third part we review how reinforcement learning can be useful in game development and find its way into commercial computer games. Finally, we provide pointers for more in-depth reviews of specific games and solution approaches.

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“…Hagelback and Johansson [33] in a study observed that players enjoy playing an evenly matched game against opponents who adapt to their styles. To this end, Tan, Tan, and Tay [34] developed an adaptive AI for games that promotes even play rather than beating opponents.…”

Section: Reinforcement Learningmentioning

confidence: 99%

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

Zohaib

2018

Advances in Human-Computer Interaction

141

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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.

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Measuring player experience on runtime dynamic difficulty scaling in an RTS game

Cited by 20 publications

References 6 publications

Cognitive Load in Real-Time Strategy Gaming: Human Opponent Versus AI Opponent

Cognitive Load in Real-Time Strategy Gaming: Human Opponent Versus AI Opponent

Reinforcement Learning in Games

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

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