Dynamic Difficulty Adjustment Research Articles

Exploring Dynamic Difficulty Adjustment Methods for Video Games

Exploring Dynamic Difficulty Adjustment Methods for Video Games

Blockchain: A comparative analysis of hybrid consensus algorithm and performance evaluation

The security of data and information is a critical concern in the rapidly evolving IT industry. Blockchain technology emerges as an innovative way of enhancing data security through the secure transmission of digital assets without a trusted third party or central administrator. Blockchain's fundamental principles of decentralization, immutability, and transparency are applicable in a variety of fields, including finance, supply chain management, healthcare, digital voting, and the Internet of Things. This research explores the underlying protocols of blockchain networks, specifically focusing on consensus mechanisms that enable transaction agreement and validation. The widespread adoption of blockchain technology faces significant challenges in achieving scalability and efficient consensus mechanisms. This research addresses these crucial limitations by proposing a novel hybrid consensus approach designed to enhance usability, security, and scalability. The proposed consensus mechanism incorporates a dynamic difficulty adjustment mechanism. The difficulty of validating blocks dynamically adjusts based on transaction volume and overall validator participation. Furthermore, a slashing mechanism is implemented to penalize validators engaging in malicious behavior by reducing their stake. Our analysis demonstrates the effectiveness of our proposed scheme in addressing several key concerns associated with blockchain, including reducing energy consumption, minimizing the probability of forks, and mitigating the level of mining centralization found in the consensus algorithm.

A framework for designing Reinforcement Learning agents with Dynamic Difficulty Adjustment in single-player action video games

Dynamic Difficulty Adjustment (DDA) within video games aims to avoid frustration or boredom. This paper provides the first framework for designing a Reinforcement Learning (RL) agent with DDA for single-player action video games. The framework includes the definitions of states, actions, and rewards. We propose a 2Q-table system that can provide a better winning/losing ratio and extend the duration of the rounds. We apply the framework to a use case study. We address the challenges that the design and implementation of RL agents with DDA for single-player action video games might present, such as (i) large and/or continuous action–state spaces, (ii) an appropriate definition of the rewards for achieving a correct DDA, (iii) learning from each player online from limited samples and (iv) in an arcade shooter video game. The two evaluations performed (with computer-driven and human players) show that the paper’s goals are met since players face personalized challenges according to their playing skills.

Virtual reality games for cognitive rehabilitation of older adults: a review of adaptive games, domains and techniques

In recent decades, the senior adults population worldwide has increased, as well as the medical conditions related to aging, such as cognitive decline. Virtual reality (VR) games are a valuable addition to conventional cognitive rehabilitation as they increase engagement to the therapy through customization, socialization, immersion, and feedback. This review, performed according to PRISMA protocol, addresses the following questions: How VR games have been used for cognitive rehabilitation?, What cognitive domains have been addressed by VR games and in which populations have these games been used?, Which features have been considered to improve engagement in VR games for cognitive rehabilitation?, How is the difficulty adjustment of exercises carried out in VR games for cognitive rehabilitation?. We found 25 scientific works related to these questions, 92% of them treating one cognitive domain at a time, despite the fact that the related literature recognizes the value of training multiple domains simultaneously. Our review indicates that, despite the existence of serious VR games for working memory training, such as those described in Flak et al. (Front Psychol 10:807, 2019. https://doi.org/10.3389/fpsyg.2019.00807), to our knowledge, there are no applications that simultaneously address multiple cognitive domains and incorporate dynamic difficulty adjustment, which are important to ensure ecological validity of therapy and therapy adherence, respectively. In addition, we found that games themselves could be used to monitor the user’s progression. It is also important to determine the impact of multiplayer interactions in the game, test difficulty adjustment approaches that use physiological variables, and define difficulty-skill relationships aligned with the user’s preferences. This paper concludes that the main barriers to implement dynamic difficulty adjustment in VR games for cognitive rehabilitation are: (i) the absence of metrics to estimate when the game offers to the players a challenge adapted their skills, and (ii) the lack of a conceptual framework that integrates relevant theories such as state of flow, cognitive load, cognitive rehabilitation, and feedback systems.

Rethinking dynamic difficulty adjustment for video game design

Dynamic Difficulty Adjustment (DDA) has garnered significant interest in game research fields for many years. Despite high expectations, the effectiveness of DDA in enhancing player experiences has yielded mixed results. This paper aims to rethink the theoretical foundations of DDA by addressing four crucial questions about the issues, definition, scope, value, and design of DDA. A comprehensive literature review was conducted to provide novel insights into DDA and DDA design. We revealed DDA’s unhealthy reliance on Flow theory, redefined DDA based on the concept of game difficulty, and discussed DDA’s scope and value. Additionally, we presented a goal-based DDA design framework and proposed a 6-step DDA design process as a practical approach. This work offers theoretical support for understanding and designing DDA in future game research.

Dynamic difficulty adjustment approaches in video games: a systematic literature review

Dynamic difficulty adjustment approaches in video games: a systematic literature review

A methodology for using players’ chat content for dynamic difficulty adjustment in metaverse multiplayer games

Personalization of game difficulty is a critical task in leveraging artificial intelligence (AI) technologies to enhance player engagement in virtual worlds like metaverse. One of the key challenges in this area is developing methods for assessing a player’s perception of game difficulty. This information can be used to dynamically adjust the game difficulty to match the player’s skill level and preferences, which can improve the player’s experience and engagement. The existing approaches have limitations such as relying on costly external devices, requiring time-consuming feedback or questionnaires, and being specific to certain game genres and narratives. In this paper, we propose a new method called ChatDDA for evaluating a player’s perception of game difficulty by analyzing the content of their chat messages. Our method uses a pre-trained language model to extract semantic features from the chat messages, which are then used to train a feed-forward neural network to predict the player’s level of hopefulness or despair about succeeding in the game. Three pre-trained language models—BERT, RoBERTa, and Twitter-roBERTa—are fine-tuned on a purpose-built dataset of player chat messages of the popular multiplayer online game PlayerUnknown’s Battlegrounds (PUBG) tagged as expressing optimism or pessimism regarding game success. The results showed that our method can accurately predict a player’s perception of game difficulty, with an accuracy of 0.953 on the test dataset of player chat messages. This suggests that our method has the potential to enhance player engagement and immersion within the game, ultimately leading to more satisfying and enjoyable metaverse experiences.

Effects of virtual reality-based cognitive interventions on cognitive function and activity of daily living among stroke patients: Systematic review and meta-analysis.

To examine the effects of virtual reality-based cognitive interventions on cognitive function and activities of daily living among stroke patients, and to identify the optimal design for such intervention. Systematic review and meta-analysis. Medline, EMBASE, Cochrane, CINANL, JBI-EBP and Web of Science from inception to October 2023. Methodological quality was assessed by Risk of Bias Tool. Meta-analyses were assessed by Review Manager 5.4. Subgroup analyses were conducted to explore the influence of study design. Grading of Recommendations Assessment, Development and Evaluation approach was adopted to assess the certainty of evidence. Twenty-five randomized controlled trials (1178 participants) were included. Virtual reality-based cognitive interventions demonstrated moderate-to-large effects in improving global cognitive function (SMD = 0.43; 95% CI [0.01, 0.85]), executive function (SMD = 0.84; 95% CI [0.25, 1.43]) and memory (SMD = 0.65; 95% CI [0.15, 1.16]) compared to control treatments. No significant effects were found on language, visuospatial ability and activities of daily living. Subgroup analyses indicated one-on-one coaching, individualized design and dynamic difficulty adjustment, and interventions lasting ≥ 6 weeks had particularly enhanced effects, especially for executive function. Virtual reality-based cognitive interventions improve global cognitive function, executive function and memory among stroke patients. This review underscores the broad cognitive advantages offered by virtual technology, suggesting its potential integration into standard stroke rehabilitation protocols for enhanced cognitive recovery. The study identifies key factors in virtual technology interventions that effectively improve cognitive function among stroke patients, offering healthcare providers a framework for leveraging such technology to optimize cognitive outcomes in stroke rehabilitation. PRISMA 2020 statement. CRD42022342668.

Dynamic Difficulty Adjustment via Procedural Level Generation Guided by a Markov Decision Process for Platformers and Roguelikes

Procedural level generation can create unseen levels and improve the replayability of games, but there are requirements for a generated level. First, a level must be completable. Second, a level must look and feel like a level that would exist in the game, meaning a random combination of tiles that happens to be completable is not enough. On top of these two requirements, though, is the player experience. If a level is too hard, the player will be frustrated. If too easy, they will be bored. Neither outcome is desirable. A procedural level generation system has to account for the player's skill and generate levels at the correct difficulty. I address this issue by showing how a Markov Decision Process can be used as a director to assemble levels tailored to a player's skill level, but I've only demonstrated that my approach works with surrogate agents. For my thesis, I plan to build on my past work by creating a full roguelike and platformer and running two player studies to validate my approach.

Force Feedback-Based Gamification: Performance Validation of Squat Exergame Using Pneumatic Gel Muscles and Dynamic Difficulty Adjustment

Force Feedback-Based Gamification: Performance Validation of Squat Exergame Using Pneumatic Gel Muscles and Dynamic Difficulty Adjustment

Keep Calm and Aim for the Head: Biofeedback-Controlled Dynamic Difficulty Adjustment in a Horror Game

Who said that violent video games cannot promote calm behaviour? Could we reverse engineer the difficulty system of a horror game to encourage the player to stay calm as opposed to feeling under constant pressure? To explore the feasibility of the approach and its effectiveness, we created a horror game, Caroline, which uses the player's biometric data to adapt the difficulty of the game - if the player is too stressed, the difficulty increases, and the opposite happens if the player is relaxed. We explored the effect of such an approach on players' cognitive, emotional, performative and decision-making challenge as well as their intrinsic motivation by comparing it to the base game without any DDA. Our results showed that players felt more motivated when the gameplay was adjusted according to their heart rate. However, out of the four types of challenge, the only one affected by the DDA was the decision-making challenge. We discuss what these findings mean for video game design and research into affective computing and provide suggestions for future research.

The Use of Deep Learning to Improve Player Engagement in a Video Game through a Dynamic Difficulty Adjustment Based on Skills Classification

The balance between game difficulty and player skill in the evolving landscape of the video game industry is a significant factor in player engagement. This study introduces a deep learning (DL) approach to enhance gameplay by dynamically adjusting game difficulty based on a player’s skill level. Our methodology aims to prevent player disengagement, which can occur if the game difficulty significantly exceeds or falls short of the player’s skill level. Our evaluation indicates that such dynamic adjustment leads to improved gameplay and increased player involvement, with 90% of the players reporting high game enjoyment and immersion levels.

Pareto-based Dynamic Difficulty Adjustment of a competitive exergame for arm rehabilitation

Although Dynamic Difficulty Adjustment (DDA) in rehabilitation-based exergames features the conflicting objectives of intensity and performance, traditional DDA methods have only addressed this problem in terms of single or weighted objectives. Deviating from previous works, this work presents a Pareto-based DDA (PDDA) for a Competitive rehabilitation-based exergame which is capable of generating a set of trade-off solutions between the two conflicting objectives. Consequently, based on the rehabilitative needs, a unique solution is selected via the trade-off worth information of the generated solutions to modify the game parameters or difficulty. In order to evaluate the performance of the proposed PDDA, two experimental studies were conducted based on 12 and 10 unimpaired participants respectively. The first study which was aimed at evaluating the performance of the proposed PDDA in terms of the in-game parameters shows that the proposed PDDA is capable of generating multiple trade-off solutions as well as effectively adjust the game parameters or difficulty. The second study which was aimed at evaluating the proposed PDDA in term of user experience shows that PDDA demonstrate statistical difference with a random DDA method in terms of the Intrinsic Motivation Inventory (IMI) and the Flow Experience Measure (FEM) metrics. Since rehabilitation outcomes generally rely on both intensity and performance, the ability of the proposed PDDA to increase effort, enjoyment, competence, and flow will help to greatly facilitate frequent and intense exercise as well as performance in rehabilitation-based exergame.

How Hard Is It Really? Assessing Game-Task Difficulty Through Real-Time Measures of Performance and Cognitive Load

Background Serious and entertainment game designers strive to create engaging, immersive, and often, challenging games. This task involves modifying game mechanics or environments to create experiences with differing levels of challenge to meet player skill. The balance between different game mechanics or environments, and the differing levels of challenge they pose, is typically understood through iterative testing. Balance and challenge becomes increasingly important in serious games and simulation training as these games commonly need to be engaging and impart learning content. Overburdening players’ cognitive capacity with either too much gameplay challenge or learning content may reduce the educational effectiveness of the game. Aim. In this research, we develop a game-based driving simulation with different gameplay tasks to explore the impact of different types of challenges and game aesthetics on real-time cognitive load and task performance, which may inform serious game design. We also test the validity of a game-embedded real-time cognitive load measuring method. Method A total of 31 participants undertook the driving simulation experiment under three different aesthetic conditions using a within-subject experimental design. Cognitive load was measured using three different methods, and performance was measured via in-game metrics. Additionally, demographic and engagement surveys were also completed. Results Player performance and cognitive load respond differently to different types of challenge, and an appropriate level of game challenge can lower cognitive load. The embedded cognitive load measure was validated as an effective method for evaluating real-time cognitive load during gameplay. Conclusion The results demonstrate the validity of a dual measure approach for future adaptive serious games and simulation training environments combining performance and cognitive load. An easy to implement, and robust, in-game measure for cognitive load has been validated in real-world conditions. From these results, a system for dynamic difficulty adjustment is proposed tailored towards serious games and simulation.

Fuzzy-based dynamic difficulty adjustment of an educational 3D-game

An educational game aims to employ the pleasant and fascinating environment of a game for educational purpose. However, when the game’s educational content or playing environment does not match with the player’s learning needs or game-playing skills, the player can find the game either too easy or too difficult and decide to drop it. Therefore, a successful educational game has to offer Dynamic Difficulty Adjustment (DDA) of both the game’s educational content and the game’s playing environment. Considering the above, in this paper a fuzzy-based DDA mechanism of a 3D-game, that teaches the programming language ‘HTML’, is presented. The game adapts dynamically the difficulty of battles and mazes navigation to each individual player’s skills. The novelty of the presented game lies on the application of DDA in the two dimensions (educational content and playing environment) of an educational game and on the use of fuzzy logic for succeeding balance between the game’s difficulty and the user’s playing skills. The adaptation is realized in real-time, during the playing and not each time the player changes game level/track. Furthermore, fuzzy logic allows to apply DDA without the need of many learning data, like other Artificial Intelligence techniques for DDA (i.e. machine learning techniques). The presented game was evaluated through a comparative evaluation, which included questionnaires, an experiment in real conditions and t-test statistical analysis method. The evaluation results showed that the incorporated adaptation mechanism increases the user’s motivation and succeeds to keep players’ interest for the game undiminished.

Dynamic Difficulty Adjustment of Serious-Game Based on Synthetic Fog using Activity Theory Model

Dynamic Difficulty Adjustment of Serious-Game Based on Synthetic Fog using Activity Theory Model

Effective Selfish Mining Defense Strategies to Improve Bitcoin Dependability

Selfish mining is a typical malicious attack targeting the blockchain-based bitcoin system, an emerging crypto asset. Because of the non-incentive compatibility of the bitcoin mining protocol, the attackers are able to collect unfair mining rewards by intentionally withholding blocks. The existing works on selfish mining mostly focused on cryptography design, and malicious behavior detection based on different approaches, such as machine learning or timestamp. Most defense strategies show their effectiveness in the perspective of reward reduced. No work has been performed to design a defense strategy that aims to improve bitcoin dependability and provide a framework for quantitively evaluating the improvement. In this paper, we contribute by proposing two network-wide defensive strategies: the dynamic difficulty adjustment algorithm (DDAA) and the acceptance limitation policy (ALP). The DDAA increases the mining difficulty dynamically once a selfish mining behavior is detected, while the ALP incorporates a limitation to the acceptance rate when multiple blocks are broadcast at the same time. Both strategies are designed to disincentivize dishonest selfish miners and increase the system’s resilience to the selfish mining attack. A continuous-time Markov chain model is used to quantify the improvement in bitcoin dependability made by the proposed defense strategies. Statistical analysis is applied to evaluate the feasibility of the proposed strategies. The proposed DDAA and ALP methods are also compared to an existing timestamp-based defense strategy, revealing that the DDAA is the most effective in improving bitcoin’s dependability.

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.

Improved Belgian AI Algorithm for Dynamic Management in Action Role-Playing Games

Artificial intelligence in games is one of the most challenging tasks in academia and industry. In action role-playing games, how to manage combat effectively is a key issue related to game development and the player’s experience. The Belgian artificial intelligence (BAI) algorithm is a classic but limited method that is widely used for combat management between the player and enemies. To address the poor adaptability of BAI, this paper proposes an improved Belgian artificial intelligence (IBAI) algorithm with dynamic difficulty adjustment (DDA) and implements two systems separately based on BAI and IBAI in Unreal Engine 4. Advantages on 12 parameters—10.086 mean total score greater, and 0.079 standard deviation smaller—demonstrate that the system based on IBAI has higher adaptability and a better player experience by comparing the two systems in different situations and inviting players to participate in gameplay experiences and questionnaires. The robust dynamic management mechanism of IBAI can help game designers and developers achieve the combat system of action role-playing games more efficiently, thus, shortening the development cycle and improving the player retention rate.

Effects of Player-Level Matchmaking Methods in a Live Citizen Science Game

Citizen science games must balance task difficulty with player skill to ensure optimal engagement and performance. This issue has been previously addressed via player-level matchmaking, a dynamic difficulty adjustment method in which player and level ratings are used to present levels best suited for players' individual abilities. However, this work has been done in small, isolated test games and left out potential techniques that could further improve player performance. Therefore, we examined the effects of player-level matchmaking in Foldit, a live citizen science game. An experiment with 221 players demonstrated that dynamic matchmaking approaches led to significantly more levels completed, as well as a more challenging highest level completed, compared to random level ordering, but not greater than a static approach. We conclude that player-level matchmaking is worth consideration in the context of live citizen science games, potentially paired with other dynamic difficulty adjustment methods.