Dynamic Difficulty Adjustment Impact on Players' Confidence

This paper introduces a model of Dynamic Difficulty Adjustment (DDA) in First person shooter (FPS) games. First, 'difficulty' is explored from neuroscience perspective through research on event-related potentials (ERP). Second, two approaches to modeling and managing difficulty in computer games are discussed: a) game-centered game design; b) Dynamical Difficulty Adjustment (DDA). Third, ways of measuring and modifying difficulty in FPS are introduced. Difficulty can be measured through: performance, emotional response or physiological measures indicating arousal. In FPS it can be modified through: alteration of sensors', actuators' or defense system's efficiency; change in the resources availability (weapons, knowledge, artifacts etc.) among others. Finally, an FPS game-level AI, based on Marr's levels of analysis, is introduced and characterized through a PEAS description.

HCI International 2021 - Late Breaking Papers: Design and User Experience, 2021

The Dynamic Difficulty Adjustment (DDA) of games can play an important role in increasing the player engagement and fun. Gameplay difficulty can be adapted according to the player’s perfor- mance, its affective state or by using a hybrid model that combines both approaches. This work investigates a hybrid DDA mechanism for a plat- form game to appropriately adapt its difficulty level and keep the player in a state of flow. The three approaches are compared to verify the ef- ficiency of each model. An open source platform game was adapted to support the hybrid DDA algorithms. Game telemetry was introduced to acquire performance data and the affective state of the player is esti- mated through physiological data obtained from the Electrodermal Ac- tivity (EDA) of the skin. A method that estimates game difficulty when varying platform size and jump height was developed to support the DDA process. Besides playing with the different DDA models, each par- ticipant answered questionnaires and had their data collected for inquiry purposes. The results indicate that the DDA models were able to adjust the gameplay difficulty to the players, increasing the number of com- pleted levels and reducing the variation in the playing time.

HCI in Games. Lecture Notes in Computer Science, 2022

With the increasing reach of digital games, it is beyond doubt that the gaming experience should be pleasurable while at the same time appropriately challenging. In this context, the Dynamic Difficulty Adjustment (DDA) technique is used to adapt the difficulty level as a function of the player's ability. In this work, electrodermal activity (EDA) data were used to infer the arousal levels and affective states of each player in order to use them as input in the comparison of two DDA algorithms: Data Subset Analysis (DSA) and Real-Time Arousal Set (RTA). A blind experiment was conducted with 60 participants, implementing these algorithms within the game Asteroids: in the 2nd and 1/2th Dimension and collecting data through game metrics, algorithm adjustments, and through questionnaires regarding the participants' perception of the experiments, such as game difficulty and their joy while playing the game. Our findings indicated that the DSA algorithm could detect the player's excitement level more adequately when compared to the RTA algorithm. This allows for finer adjustments to the game's difficulty, creating a more enjoyable experience for players.

The goal of this thesis is to investigate whether dynamic difficulty adjustment (DDA) based on emotions is better than DDA based on a player’s performance. DDA is a technique in computer games to change the difficulty of the game so that it meets a certain criteria. For the emotion-based DDA the goal is to keep the player in a state of Flow which was coined by Csikzentmihalyi. For the performance-based DDA the goal is to keep the player performing well without dying. To accomplish this two DDA systems have to be built. Tetris was chosen as the game for the two DDA systems as difficulty could easily be controlled in Tetris. The emotion-based DDA system uses physiological signals to recognize emotions. Based on the literature study the physiological signals chosen are heart rate (HR), temperature and galvanic skin response (GSR). Due to equipment availability HR (based on blood volume pulse(BVP)), GSR and electromyography (EMG) of the facial muscles were used instead. These signals were measured while 5 test participants played Tetris and self-rated their valence and arousal which could be translated into emotions later. Another test was made where test participants had to look at images and self-rate their valence and arousal. This test, or validation study, was made to ensure that the Tetris results were correct. Using the data from these tests a classifier was trained using a total of 44 features. The results were not good as they ranged from 22% to 46% with a chance level of 33%. Therefore the emotion-based DDA was changed to only using heart rate as it correlated with arousal. The new DDA tried to keep the player’s heart rate slightly elevated above the resting heart rate. The performance-based DDA uses the game state to judge whether a change in difficulty is needed. The goal of the performance-based DDA is to keep the block height in Tetris in a certain level. If the block height is below the levelthen difficulty is increased and vice versa. 9 participants played both DDA versions and self-rated their arousal, valence and sense of challenge. The results of this experiment showed no statistically difference between the two DDA versions although a manual inspection of the results indicated that some participants might have preferred the performance-based DDA.

Frontiers in Psychology

Why do we seek out and enjoy uncertain success in playing games? Game designers and researchers suggest that games whose challenges match player skills afford engaging experiences of achievement, competence, or effectance—of doing well. Yet, current models struggle to explain why such balanced challenges best afford these experiences and do not straightforwardly account for the appeal of high- and low-challenge game genres like Idle and Soulslike games. In this article, we show that Predictive Processing (PP) provides a coherent formal cognitive framework which can explain the fun in tackling game challenges with uncertain success as the dynamic process of reducing uncertainty surprisingly efficiently. In gameplay as elsewhere, people enjoy doing better than expected, which can track learning progress. In different forms, balanced, Idle, and Soulslike games alike afford regular accelerations of uncertainty reduction. We argue that this model also aligns with a popular practitioner m...

PRESENCE: Virtual and Augmented Reality, 2021

In the current study we examine whether, in a virtual environment, self-controlling the difficulty level of a game will result in worse performance compared to a fixed difficulty level. Our hypothesis is evaluated using a novel VR game. Forty-five participants were randomly assigned to two between-participants groups: the Experimental group, in which the participant determined the desired level of difficulty in all five game sets by themselves, and the Controlled group, in which the level of difficulty was set to medium for all participants in all game sets. The results demonstrate that the mean difficulty level selected by the Experimental group was lower than medium in the first two sets, and higher than medium in the last three sets. In the last two sets, an earlier termination of the game and lower scores were demonstrated for the Experimental group compared to the Controlled group. The conclusions are that the participants in an unfamiliar virtual environment might not be able to select their difficulty levels to match the task at hand and might not optimize their learning zones.

The Video Game Theory Reader, 2009

International Simulation and Gaming …, 2009

Maximizing player enjoyment in computer games is one of the most popular issues in game design. Different components of game fun have been determined by researchers in the past, but this article will focus on game challenges, difficulty levels, and their effects on player enjoyment. Various different genres exist in the computer gaming world, but it is important to note that roleplaying games provide diverse gaming challenges uniformly in their structure and require special attention in terms of player enjoyment. This study will focus on lessons learned from computer roleplaying games (CRPGs), giving examples from the best practices of the industry.

2012

Dynamic balancing of game difficulty can help cater for different levels of ability in players. However, performance in some game tasks depends on not only the player's ability but also their desire to take risk. Taking or avoiding risk can offer players its own reward in a game situation. Furthermore, a game designer may want to adjust the mechanics differently for a risky, high ability player, as opposed to a risky, low ability player. In this work, we describe a novel modelling technique known as particle filtering which can be used to model various levels of player ability while also considering the player's risk profile. We demonstrate this technique by developing a game challenge where players are required to make a decision between a number of possible alternatives where only a single alternative is correct. Risky players respond faster but with more likelihood of failure. Cautious players wait longer for more evidence, increasing their likelihood of success, but at the expense of game time. By gathering empirical data for the player's response time and accuracy, we develop particle filter models. These models can then be used in real-time to categorise players into different ability and risk-taking levels.

2010

Automatic generation of game levels improves replayability and also allows content adaptation. One important aspect to take into account in the creation of any videogame is difficulty, in particular when it is possible to adapt content. However, defining difficulty is not a straight subject. In this paper we propose a metric for game difficulty in platform levels, mostly based on users’ losing probability for each obstacle. This metric can be further used in automated processes that generate levels for this type of games, helping the process to recognize whether a level is suitable or not for a certain player. We also present some examples of the usage of this metric in commercial games.