Dynamic Difficulty Adjustment System Research Articles

Measuring Control to Dynamically Induce Flow in Tetris

Dynamic difficulty adjustment (DDA) is a set of techniques that aim to automatically adapt the difficulty of a video game based on the player’s performance. This article presents a methodology for DDA using ideas from the theory of flow and case-based reasoning (CBR). In essence, we are looking to generate game sessions with a similar difficulty evolution to previous game sessions that have produced flow in players with a similar skill level. We propose a CBR approach to dynamically assess the player’s skill level and adapt the difficulty of the game based on the relative complexity of the last game states. We develop a DDA system for <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Tetris</i> using this methodology and show, in an experiment with 40 participants, that the DDA version has a measurable impact on the perceived flow using validated questionnaires.

Customizable Serious Speech Therapy Games with Dynamic Difficulty Adjustment for Children with Sigmatism.

Children with speech sound disorders should attend speech and language therapy and should practice the speech exercises regularly to surpass their speech difficulties. Since doing the speech exercises often may be tedious, there is the need to motivate children to practice them. During the COVID-19 pandemic, speech and language pathologists had the need to adapt their procedures to others with less physical contact. Here, we propose two serious games to motivate children with sigmatism on doing the speech exercises, which can be used at home and during face-to-face and online speech therapy sessions. The games use automatic speech recognition to classify speech productions. Visual and auditory feedback are used to help children understand their performance, and a hint system is used to help them perform the exercises correctly. A dynamic difficulty adjustment system is used to change the level of difficulty according to the child's speech performance in previous trials.

Evaluating and Comparing Skill Chains and Rating Systems for Dynamic Difficulty Adjustment

Skill chains define how in-game skills build on each other and the order in which players ideally acquire them during gameplay. This can enable dynamic difficulty adjustment (DDA) by serving levels based on the skills that players currently have and those required to solve a given level. Similarly, DDA can also be achieved by using rating systems to match players with suitable levels by assigning ratings to players and levels based on ability and difficulty respectively. However, the relative effects of these two methods remain unclear, particularly in the context of human computation games (HCGs). In this paper, we present a general model for using skill chains and rating systems in a combined DDA system along with an evaluation comparing the two for difficulty balancing within HCGs, focusing on the relative merits of both methods when used separately as well as together. We evaluate our methods using the HCGs Iowa James and Paradox. Our findings suggest that incorporating skill chains can improve upon previously shown benefits of using only rating systems for DDA in HCGs.

A Novel Approach to Working Memory Training Based on Robotics and AI

Working memory is an important function for human cognition since several day-to-day activities are related to it, such as remembering a direction or developing a mental calculation. Unfortunately, working memory deficiencies affect performance in work or education related activities, mainly due to lack of concentration, and, with the goal to improve this, many software applications have been developed. However, sometimes the user ends up bored with these games and drops out easily. To cope with this, our work explores the use of intelligent robotics and dynamic difficulty adjustment mechanisms to develop a novel working memory training system. The proposed system, based on the Nao robotic platform, is composed of three main components: First, the N-back task allows stimulating the working memory by remembering visual sequences. Second, a BDI model implements an intelligent agent for decision-making during the progress of the game. Third, a fuzzy controller, as a dynamic difficulty adjustment system, generates customized levels according to the user. The experimental results of our system, when compared to a computer-based implementation of the N-back game, show a significant improvement on the performance of the user in the game, which might relate to an improvement in their working memory. Additionally, by providing a friendly and interactive interface, the participants have reported a more immersive and better game experience when using the robotic-based system.

Dynamic Difficulty Adjustment with a simplification ability using neuroevolution

This paper describes a concept of a dynamic difficulty adjustment system that learns and adapts online to the style of the player’s in-game behavior based on the neuroevolution using additionally the base of catalogues. A neural network trained through evolutionary algorithms is used to achieve better adaptation. In this case, genetic algorithms alter the weights of the neural network. In addition to adaptation, the proposed method provides an opportunity to downgrade difficulty when it is necessary or respond to rapid changes in the player skill level. The test game in the genre of real-time first-person fighting is designed to validate the efficiency of the proposed model. Multilayer perceptron (MLP) architecture was selected as a neural network’s topology to achieve the maximum correlation between an approximation of the loss function and speed of the neural network. The article describes the advantages and limitations of the proposed concept in comparison with other approaches.

Approaches to Measuring the Difficulty of Games in Dynamic Difficulty Adjustment Systems

ABSTRACTIn 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.