Dynamic difficulty adjustment on MOBA games

Silva, Mirna Paula; Silva, Victor do Nascimento; Chaimowicz, Luiz

doi:10.1016/j.entcom.2016.10.002

Cited by 53 publications

(24 citation statements)

References 25 publications

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“…Generally, difficulty adaptation is perceived as beneficial to one's gaming experience (Chen, 2007), as it leads to a more balanced gameplay. Recent work in the area of games user research has provided evidence that suggests that adaptive features in games also have a positive effect on the gaming experiences in shooting games (Bateman et al, 2011;Vicencio-Moreira et al, 2015), MOBA games (Silva et al, 2017), racing games (Cechanowicz et al, 2014), and casual games (Smeddinck et al, 2016): resulting in higher levels of enjoyment and fun (Newheiser, 2009), experiences of autonomy (Smeddinck et al, 2016;Klarkowski et al, 2016), and competence (Vicencio-Moreira et al, 2015). Though the effect of adaptive technologies on more generic player experiences, such as immersion and engagement, has not been explored to the same extent.…”

Section: Perception Of Adaptive Technology In Digital Gamesmentioning

confidence: 99%

Player experience and deceptive expectations of difficulty adaptation in digital games

Denisova

Cairns

2019

Entertainment Computing

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Increasingly, digital games are including adaptive features that adjust the level of difficulty to match the skills of individual players. The intention is to improve and prolong the player experience by allowing the player to have the feeling of challenge without it being overwhelming and leading to repeated failure and frustration. Previous work has shown that player experience is indeed improved by such adaptations but also that the player experience can be improved also by simply claiming such an adaptation is present even when it is not. It is therefore possible that claims about adaptations and the actual adaptations could interact and not lead to the intended outcomes for the players or worse disappoint players. This paper reports on two studies that were conducted to experimentally investigate the interaction between game adaptations and player information about adaptations on the player experience, specifically their sense of immersion in the game. For this, two games were developed using two different kinds of adaptations to adjust difficulty based on players' performance in the game. Participants were provided with information about game adaptations independently of whether the adaptations were present. The results suggest that players felt more immersed in the game when told that the game adapts to them, regardless of whether the adaptation was present in the game or not. This effect was observed in both games despite their different adaptations and it remained prominent even during longer gaming sessions. These findings demonstrate that players' knowledge of adaptations influences their experience independently of adaptations. In this particular context, the knowledge reinforced the experience of the adaptations. This suggests that, at least in some circumstances, developers do not need to be concerned about negative effects of telling players about in-game adaptations.

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Section: Perception Of Adaptive Technology In Digital Gamesmentioning

confidence: 99%

Player experience and deceptive expectations of difficulty adaptation in digital games

Denisova

Cairns

2019

Entertainment Computing

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“…Many studies propose automated game testing methods to replace manual testing. Several works [3][4][5] apply Petri-net based methodology that offers a graphical analysis for stepwise processes including choice, iteration, and concurrent execution. On the other hand, Dynamic Difficulty Adjustment (DDA) based on genetic algorithm [6] is proposed to heuristically search possible gameplay model and evaluate the overall leveling difficulty.…”

Section: Background: Game Development Life Cyclementioning

confidence: 99%

A Long/Short-Term Memory Based Automated Testing Model to Quantitatively Evaluate Game Design

Chen

Chang

et al. 2020

Applied Sciences

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The mobile casual game application lifespan is getting shorter. A company has to shorten the game testing procedure to avoid being squeezed out of the game market share. There is no sufficient testing indicator to objectively evaluate the operability of different game designs. Many automated testing methodologies are proposed, but they adopt rule-based approaches and cannot provide quantitative analysis to statistically evaluate gameplay experience. This study suggests applying “Learning Time” as a testing indicator and using the learning curve to identify the operability of different game designs. This study also proposes a Long/Short-Term Memory based automated testing model (called LSTM-Testing) to statistically testing game experience through end-to-end functionality (Input: game image; Output: game action) without any manual intervention. The experiment results demonstrate LSTM-Testing can provide quantitative testing data by using learning time as the control variable, game design as the independent variable, and time to complete game as the dependent variable. This study also demonstrates how LSTM-Testing evaluates the effectiveness of different gameplay learning strategies, e.g., reviewing the newest decisions, reviewing the correct decision, or reviewing the wrong decisions. The contributions of LSTM-Testing are (1) providing an objective and quantitative analytical game-testing framework, (2) reducing the labor cost of inefficient and subjective manual game testing, and (3) allowing game company boosts software development by focusing on game intellectual property and leaves game testing to artificial intelligence (AI).

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“…The simplest and most popular DDA methods are based only on performance measures (eg, in-game score), which provide an easily measurable and interpretable indicator of perceived game difficulty. However, although many studies have shown the positive effects of performance-based DDA on user experience [ 7 ], performance alone does not necessarily provide insight into the player’s psychological state, for example, a frustrated player can get a high score in a game without enjoying it. This has led to the development of affective games, an emerging type of videogame that adapts difficulty based on a combination of the player's performance and their psychological (cognitive and affective) state.…”

Section: Introductionmentioning

confidence: 99%

User Experience With Dynamic Difficulty Adjustment Methods for an Affective Exergame: Comparative Laboratory-Based Study

Darzi¹,

McCrea²,

Novak³

2021

JMIR Serious Games

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Background In affective exergames, game difficulty is dynamically adjusted to match the user’s physical and psychological state. Such an adjustment is commonly made based on a combination of performance measures (eg, in-game scores) and physiological measurements, which provide insight into the player’s psychological state. However, although many prototypes of affective games have been presented and many studies have shown that physiological measurements allow more accurate classification of the player’s psychological state than performance measures, few studies have examined whether dynamic difficulty adjustment (DDA) based on physiological measurements (which requires additional sensors) results in a better user experience than performance-based DDA or manual difficulty adjustment. Objective This study aims to compare five DDA methods in an affective exergame: manual (player-controlled), random, performance-based, personality-performance–based, and physiology-personality-performance–based (all-data). Methods A total of 50 participants (N=50) were divided into five groups, corresponding to the five DDA methods. They played an exergame version of Pong for 18 minutes, starting at a medium difficulty; every 2 minutes, two game difficulty parameters (ball speed and paddle size) were adjusted using the participant’s assigned DDA method. The DDA rules for the performance-based, personality-performance–based, and all-data groups were developed based on data from a previous open-loop study. Seven physiological responses were recorded throughout the sessions, and participants self-reported their preferred changes to difficulty every 2 minutes. After playing the game, participants reported their in-game experience using two questionnaires: the Intrinsic Motivation Inventory and the Flow Experience Measure. Results Although the all-data method resulted in the most accurate changes to ball speed and paddle size (defined as the percentage match between DDA choice and participants’ preference), no significant differences between DDA methods were found on the Intrinsic Motivation Inventory and Flow Experience Measure. When the data from all four automated DDA methods were pooled together, the accuracy of changes in ball speed was significantly correlated with players’ enjoyment (r=0.38) and pressure (r=0.43). Conclusions Although our study is limited by the use of a between-subjects design and may not generalize to other exergame designs, the results do not currently support the inclusion of physiological measurements in affective exergames, as they did not result in an improved user experience. As the accuracy of difficulty changes is correlated with user experience, the results support the development of more effective DDA methods. However, they show that the inclusion of physiological measurements does not guarantee a better user experience even if it yields promising results in offline cross-validation.

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Dynamic difficulty adjustment on MOBA games

Cited by 53 publications

References 25 publications

Player experience and deceptive expectations of difficulty adaptation in digital games

Player experience and deceptive expectations of difficulty adaptation in digital games

A Long/Short-Term Memory Based Automated Testing Model to Quantitatively Evaluate Game Design

User Experience With Dynamic Difficulty Adjustment Methods for an Affective Exergame: Comparative Laboratory-Based Study

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