Holiday Knight: a Videogame with Skill-based Challenge Generation

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Researchers have identified different components of game fun in the past, but the objective of this contribution is to analyze game challenges and difficulty levels, and their effects on player enjoyment and emotions. This paper presents basic principles for designing game challenges as well as the requirements and qualities required to achieve maximum enjoyment. Special emphasis is given to the accumulated knowledge of Wizards of the Coast (and formerly TSR) in designing role-playing games. Different player types are discussed, the nature of the difficulty curve is examined, and the relationship between player satisfaction and different levels of difficulty is analyzed with a case study.

Cornell University - arXiv, 2018

Dynamic Difficulty Adjustment (DDA) is a mechanism used in video games that automatically tailors the individual gaming experience to match an appropriate difficulty setting. This is generally achieved by removing pre-defined difficulty tiers such as Easy, Medium and Hard; and instead concentrates on balancing the gameplay to match the challenge to the individual's abilities. The work presented in this paper examines the implementation of DDA in a custom survival game developed by the author, namely Colwell's Castle Defence. The premise of this arcade-style game is to defend a castle from hordes of oncoming enemies. The AI system that we developed adjusts the enemy spawn rate based on the current performance of the player. Specifically, we read the Player Health and Gate Health at the end of each level and then assign the player with an appropriate difficulty tier for the proceeding level. We tested the impact of our technique on thirty human players and concluded, based on questionnaire feedback, that enabling the technique led to more enjoyable gameplay.

2010

In this paper, we present a novel methodology to improve gaming experiences by automatically adjusting the game difficulty throughout the game play using a Profile-based Adaptive Difficulty System (PADS). We utilize a player's gaming experience and objective to create a player profile. Utilizing this profile and a performancebased algorithm, the PADS customizes the game's difficulty levels to accommodate each individual. Our experimental results successfully demonstrate improvements in both perceptual and actual gaming experiences. With our approach, traditional program-centered video games can be transformed to provide personalized, playercentered gaming experiences.

International Journal of Human–Computer Interaction, 2018

In this article, three approaches are proposed for measuring difficulty that can be useful in developing Dynamic Difficulty Adjustment (DDA) systems in different game genres. Our analysis of the existing DDA systems shows that there are three ways to measure the difficulty of the game: using the formal model of gameplay, using the features of the game, and direct examination of the player. These approaches are described in this article and supplemented by appropriate examples of DDA implementations. In addition, the article describes the distinction between task complexity and task difficulty in DDA systems. Separating task complexity (especially the structural one) is suggested, which is an objective characteristic of the task, and task difficulty, which is related to the interaction between the task and the task performer.

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.

Entertainment Computing, 2011

Advances in Human-Computer Interaction, 2018

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.

Proceedings of The 8th Australasian Conference on Interactive Entertainment Playing the System - IE '12, 2012

This article focuses on personalised games, which we define as games that utilise player models for the purpose of tailoring the game experience to the individual player. The main contribution of the article is a motivation for personalised gaming, supported by an extensive overview of scientific literature. The motivatin concerns (a) the psychological foundation, (b) the effect on player satisfaction, (c) the contribution to game development, and (d) the requirement for achieving ambitions. The provided overview of scientific literature goes into the subject of player modelling, as well as eight adaptive components: (1) space adaptation, (2) mission / task adaptation, (3) character adaptation, (4) game mechanics adaptation, (5) narrative adaptation, (6) music / sound adaptation, (7) player matching (multiplayer), and (8) difficulty scaling. In the concluding sections, the relationship to procedural content generation is discussed, as well as the generalisation to other domains.

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.

Springer International Publishing eBooks, 2022

Exergames can motivate the elderly to exercise as long as they derive pleasure from them. Therefore, the different needs and expectations of older people must be first understood while taking into account their different characteristics both in exergame design phase and actual playing time. In this paper, the GAME2AWE platform is introduced that combines hardware and intelligent software to create personalized gaming experiences aimed at improving the motor and cognitive functionality of the elderly. This work focuses, in particular, on dynamic adaptation mechanisms specified to adjust the difficulty or challenge of a game, according to the players’ performance. Dynamic difficulty adjustment provides a solution to configuring the variables that define the difficull ty of a game in order to compensate for the abilities of a particular player during a game session. Capturing game and player related information during the gameplay creates datasets which are then used to develop models for estimating the satisfaction of the player while playing. These models are then used to adjust the difficulty variables in order to achieve optimal user satisfaction. The main proposition regarding the model construction is that the prediction of fun can be defined as a function of different attributes which a machine learning method can learn.