Potential Game Research Articles

Storytelling Games for General Anxiety: Clinician Perspectives on Walking Simulator Games as Intervention

General anxiety is a common mental health condition across the world, with many barriers preventing people from accessing treatments. A growing body of evidence suggests that serious games offer appealing and effective solutions for anxiety. Despite their potential, an understanding of game design qualities and contextual game mechanics in game-based interventions are significantly under-explored, especially for general anxiety. Alternative and emerging game genres may offer new intervention approaches previously overlooked in digital mental health research. We use story-led exploration games, particularly the environmental storytelling walking simulator game genre, as an analytical template to understand new game design strategies and features that can potentially enable engaging and efficacious serious games as interventions for general anxiety. Using semi-structured interviews and thematic analysis, we summarize input from expert clinical participants who identified and evaluated several game design qualities towards developing walking simulators as potential serious game interventions.

The Ludo-Learning Matrix: A Framework for Understanding Learning in Games

Games have been widely recognized as methods of making learning more engaging. This has proven to be very attractive to those attempting to get complex fields such as cybersecurity training to a wider audience. Unfortunately, there is a failure to understand that each type of game serves a different educational purpose, leading to the situation where the games are being selected incorrectly and are failing to fulfill their promise. To address this issue, key concepts in learning and games were identified from the literature and then operationalized into an analytical framework. The concepts of player agency, message broadcasting, the transfer of learning and the type of challenges present in the game were selected. This generated a framework that is player-focused rather than content-focused and could be applied to any potential learning game. To validate the framework, three cybersecurity games were selected and analyzed using a ludo-textual approach. Cyberciege, a single player game aimed at university students, Spoofy, a kids’ web browser game and Tallinn Soldier, a seminar wargame, were selected to give as wide a sample as possible. The paper goes on to report on the results of the analysis and discuss how this may be used to examine and design learning games for cybersecurity in the future. Each of the selected games focus on different mechanisms for learning and engagement, highlighting the design tradeoffs that were made in the creation of the game. The proposed analytical framework can be applied not just to cybersecurity training games but theoretically to understanding any game that has learning as its objective.

A practical vehicular‐fog‐based privacy‐preserving attribute‐based keyword search system using potential games

AbstractThe growth of fog computing drives Internet of Things users to outsource data for services like storage and sharing. To ensure secure and efficient data search, extensive research has been conducted on searchable encryption, specifically attribute‐based keyword search (ABKS). ABKS facilitates precise access control over encrypted data while maintaining search functionality. However, delegating complex search operations to fog nodes introduces challenges like latency limitations, compromised search results, and unauthorized occupation of server resources. In this manuscript, we propose a Time‐Efficient Data Retrieval (TEDR) scheme to address these challenges. Our scheme incorporates two access policies, allowing data owners (DOs) and data users (DUs) to control data access and search queries. TEDR enables fog nodes to verify search queries before decryption, avoiding unnecessary decryption for invalid queries. We introduce a decentralized query submitting framework to allocate search queries among fog nodes, addressing redundancy concerns and latency issues. We analyze the decentralized query submitting game and classify it as a potential game, making it suitable for decentralized decision‐making. We evaluate TEDR performance on real‐world datasets, demonstrating practicality and efficiency. Our scheme reduces computational and communication costs by approximately 30% compared to existing methods.

Artificial Intelligence (AI): A Potential Game Changer in Regenerative Orthopedics-A Scoping Review.

Regenerative orthopedics involves approaches like stem cell therapy, platelet-rich plasma (PRP) therapy, the use of biological scaffold implants, tissue engineering, etc. We aim to present a scoping review of the role of artificial intelligence (AI) in different treatment approaches of regenerative orthopedics. Using the PRISMA guidelines, a search for articles for the last ten years (2013-2024) on PubMed was done, using several keywords. We have discussed the state-of-the-art, strengths/benefits, and limitations of the published research, and provide a useful resource for the way ahead in future for researchers working in this area. Using the eligibility criteria out of 82 initially screened publications, we included 18 studies for this review. We noticed that the treatment responses to regenerative treatments depend on several factors; hence, to facilitate better comprehensive and patient-specific treatments, AI technology is very useful. Machine learning (ML) and deep learning (DL) are a few of the most frequently used AI techniques. They use a data-driven approach for training models to make human-like decisions. Data are fed to the ML/DL algorithm and the trained model makes classifications or predictions based on its learning. The area of regenerative orthopedics is highly sophisticated and significantly aids in providing cost-effective and non-invasive treatments to patients suffering from orthopedic ailments and injuries. Due to its promising future, the use of AI in regenerative orthopedics is an emerging and promising research field; however, its universal clinical applications are associated with some ethical considerations, which need addressing. The online version contains supplementary material available at 10.1007/s43465-024-01189-1.

A game-theoretic approach to managing the composition and structure of a bearing-only measurement system in conditions of a priori uncertainty

The problem of managing the composition and structure of a bearing-only measurements system (BOMS) in a game-theoretic formulation is considered. An approach of cooperative search for the placement of BOMS points and a method for estimating the work-time indicator of the system are proposed. The search for the placement of BOMS points uses the toolkit of multi-agent potential games. The criteria for selecting the placement of points and the type of potential function are determined. The management of the composition and structure of the BOMS is based on the results of the cluster-variation method. A structural and functional description of the simulation model is presented. The above results of simulation modeling confirm the practical effectiveness of the proposed approaches.

A mesh-independent method for second-order potential mean field games

Abstract This article investigates the convergence of the Generalized Frank–Wolfe (GFW) algorithm for the resolution of potential and convex second-order mean field games. More specifically, the impact of the discretization of the mean-field-game system on the effectiveness of the GFW algorithm is analyzed. The article focuses on the theta-scheme introduced by the authors in a previous study. A sublinear and a linear rate of convergence are obtained, for two different choices of stepsizes. These rates have the mesh-independence property: the underlying convergence constants are independent of the discretization parameters.

Harnessing the potential of public procurement for the protein transition – perceived barriers and facilitators

AbstractShifting dietary patterns from animal-based proteins to more plant-based and alternative protein sources – the protein transition – is urgently needed to improve planetary and human health. Public food procurement is considered to be an effective policy instrument to accelerate the protein transition and to be a potential game changer towards a sustainable food system. However, this potential has remained far from leveraged, and it is largely unknown which barriers and enablers exist in that context. Therefore, this study aimed to gain insight into the barriers and facilitators that are perceived by relevant stakeholders when implementing the protein transition in public food procurement. Our study was conducted in the Netherlands, because of the policy goals set by the Dutch government with regard to the protein transition (50% of the proteins consumed should include animal-based proteins and 50% plant-based proteins by 2030) and because the extent to which the protein transition has been included in the food procurement of Dutch (semi-)public organizations is still largely unknown. However, findings are also relevant for other countries. Semi-structured interviews were conducted with participants from a wide range of (semi-)public organizations (e.g., universities, hospitals, governments), in 2022. Five main themes of barriers and facilitators were identified: (1) support and motivation, (2) food availability and offerings, (3) financial considerations and incentives, (4) policies, processes, and contracts, and (5) environmental factors. The insights from this study can strengthen the scientific evidence base and can serve as a foundation for future research. Moreover, the insights can be beneficial to officials working in (semi-)public organizations to effectively design and execute their procurement process, and can help policymakers in policy development to foster (semi-)public organizations to implement the protein transition in their own contexts.

Dynamic environment UAV deployment algorithm based on potential game theory

AbstractTo address the issue of low coverage resulting from the challenge of acquiring the optimal deployment position in commonly used distributed deployment algorithms, this study presents a three‐dimensional deployment algorithm for Unmanned Aerial Vehicles (UAVs) based on potential games. First, a local mutually beneficial game model is designed to demonstrate the existence of exact potential games and Nash equilibrium. The Nash equilibrium solution corresponds to the maximum coverage. Next, drawing inspiration from exploration, a solution method called Exploration Spatial Adaptive Play is proposed. It utilizes the maximum utility function value from multiple step sizes in the exploration direction to update the action selection probability, thereby ensuring the optimal deployment position in each decision cycle. To address the issue of sensor position error, a method for processing sensor position errors is proposed. The simulation results demonstrate that the proposed distributed deployment algorithm achieves higher coverage compared to commonly used methods.

A High-Level Structured Methodology for Development of AI Systems in Africa

AI is a potential game changer for Africa to address the specific challenges she faces in sectors like healthcare, climate change and water-related issues. However, the regulation of AI is still largely underdeveloped in Africa with some existing policies and frameworks still being young. Therefore, as the adoption of AI systems spreads across Africa, so does the need for a structured methodology to guide organizations in either developing new AI systems or onboarding existing ones while maintaining the quality and ethicality of these systems. This paper aims to develop a holistic methodology that provides comprehensive guidance to companies considering to develop new AI systems or onboard existing systems. The goal is to support the development and deployment of AI systems tailored to the specific needs of Africa. The proposed methodology employs a lifecycle approach that integrates both Agile and Waterfall frameworks. By combining the adaptive flexibility of Agile with the structured progression of Waterfall, this methodology ensures adaptability and thoroughness throughout the AI system's development and implementation phases. The integration of these methodologies offers a robust, adaptable framework that can be tailored to the unique demands of AI projects in Africa, from design to implementation, deployment as well as maintenance phases, thereby maximizing the potential impact of AI technologies in the region.

Cooperative mission planning based on game theory for UAVs and USVs heterogeneous system in dynamic scenario

PurposeThe purpose of this paper is to present and implement a task allocation method based on game theory for reconnaissance mission planning of UAVs and USVs system.Design/methodology/approachIn this paper, the decision-making framework via game theory of mission planning is constructed. The mission planning of UAVs–USVs is transformed into a potential game optimization problem by introducing a minimum weight vertex cover model. The modified population-based game-theoretic optimizer (MPGTO) is used to improve the efficiency of solving this complex multi-constraint assignment problem.FindingsSeveral simulations are carried out to exhibit that the proposed algorithm obtains the superiority on quality and efficiency of mission planning solutions to some existing approaches.Research limitations/implicationsSeveral simulations are carried out to exhibit that the proposed algorithm obtains the superiority on quality and efficiency of mission planning solutions to some existing approaches.Practical implicationsThe proposed framework and algorithm are expected to be applied to complex real scenarios with uncertain targets and heterogeneity.Originality/valueThe decision framework via game theory is proposed for the mission planning problem of UAVs–USVs and a MPGTO with swarm evolution, and the adaptive iteration mechanism is presented for ensuring the efficiency and quality of the solution.

Correlated Equilibria in Large Anonymous Bayesian Games

We consider multipopulation Bayesian games with a large number of players. Each player aims at minimizing a cost function that depends on this player’s own action, the distribution of players’ actions in all populations, and an unknown state parameter. We study the nonatomic limit versions of these games and introduce the concept of Bayes correlated Wardrop equilibrium, which extends the concept of Bayes correlated equilibrium to nonatomic games. We prove that Bayes correlated Wardrop equilibria are limits of action flows induced by Bayes correlated equilibria of the game with a large finite set of small players. For nonatomic games with complete information admitting a convex potential, we prove that the set of correlated and of coarse correlated Wardrop equilibria coincide with the set of probability distributions over Wardrop equilibria and that all equilibrium outcomes have the same costs. We get the following consequences. First, all flow distributions of (coarse) correlated equilibria in convex potential games with finitely many players converge to mixtures of Wardrop equilibria when the weight of each player tends to zero. Second, for any sequence of flows satisfying a no-regret property, its empirical distribution converges to the set of distributions over Wardrop equilibria, and the average cost converges to the unique Wardrop cost. Funding: This work was partially supported by European Cooperation in Science and Technology Action 16228 GAMENET. F. Koessler acknowledges the support of the Agence Nationale de la Recherche [Grant StratCom ANR-19-CE26-0010-01]. M. Scarsini acknowledges the support of the Gruppo Nazionale per l’Analisi Matematica, la Probabilità e le loro Applicazioni project [Grant CUP_E53C22001930001], the Ministero dell’Università e della Ricerca Progetti di Rilevante Interesse Nazionale [Grant 2022EKNE5K], and the European Union-Next Generation EU, component M4C2, investment 1.1 (Ministero dell’Università e della Ricerca Progetti di Rilevante Interesse Nazionale Piano Nazionale di Ripresa e Resilienza) [Grant P2022XT8C8]. T. Tomala gratefully acknowledges the support of the HEC foundation and Agence Nationale de la Recherche/Investissements d’Avenir [Grant ANR-11-IDEX-0003/Labex Ecodec/ANR-11-LABX-0047].

Revisiting the Algorithms Behind the Headlines. How Journalists Respond to Professional Competition of Generative AI

ABSTRACT Due to its ability to process and create human-like texts, Generative AI such as ChatGPT is a potential game changer in news making. This paper re-examines central research areas that were analyzed around the emergence of earlier versions of automated news writing in the 2012 article The Algorithms behind the Headlines. The current paper employs a sociology of professions- and boundary work- perspective to understand how journalists (re)assert their professional authority in the face of Generative AI. An analysis of metajournalistic discourse where journalists reflect on their profession in the light of ChatGPT’s launch shows that journalists saw Generative AI as a larger threat to their profession than previous forms of automated news writing. They strongly defended their authoritative position over the societal task of information provision, either by reaffirming journalists as a professional collective, or by focusing on journalists as individuals with a unique tone of voice. While rejecting the idea of algorithmic objectivity, humanness served as a shortcut for originality. Journalists argued that they should remain firmly in the loop when Generative AI is integrated in news making. The new technical possibilities of Generative AI were not accommodated in the journalistic profession by broadening the understanding of journalism.

New ways for carsharing – Can mobility as a service boost carsharing?

Mobility as a Service is a potential game changer in the transition from individual motorized to sustainable and multimodal mobility in urban areas. These innovative concepts are an opportunity for existing forms of sustainable mobility to reach a broader user base and extend their service offerings. Although there are some practical examples of carsharing applications in such concepts, there is a lack of data-driven research about Mobility as a Service in practice and, above all, findings about the influence on usage behavior within such systems. We analyze usage data of the “Mobil-Flat”, a subscription-based mobility offer that integrates carsharing, public transportation, and bike sharing into a single service in a medium-sized German city, to determine the impact of Mobility as a Service on carsharing use. This results in a structured overview of the business model and the implications of such concepts on user behavior and acceptance in the context of carsharing, based on a real-world dataset.

Antibody-drug conjugates combined with immunotherapy: a potential game changer in the treatment of upper tract urothelial carcinoma

Currently, treatment strategies for upper tract urothelial carcinoma (UTUC) are changing rapidly. However, there are many limitations in the implementation of new surgical methods and systemic treatment options, which cannot reverse the current status quo of UTUC treatment. In recent years, antibody-drug conjugates have shown great potential in the field of cancer treatment, which can activate the immune system and enhance the effect of immunotherapy while accurately killing targeted tumor cells. The results of multiple clinical trials, including the EV-302 study, have confirmed that combination therapy can improve the survival rate of patients with advanced urothelial cancer, and may replace chemotherapy as the first-line treatment for advanced urothelial cancer. However, there are still a series of challenges in the application of combination therapy in UTUC, such as low level of evidence, adverse reactions, and drug resistance. In the future, it is necessary to focus on clinical trials of UTUC combination therapy, further optimize antibody drug conjugates and immunotherapy drugs to adapt to the disease characteristics of UTUC, and further study the molecular biology characteristics of UTUC to meet this series of challenges.

Identification methods for ordinal potential differential games

This paper introduces two new identification methods for linear quadratic (LQ) ordinal potential differential games (OPDGs). Potential games are notable for their benefits, such as the computability and guaranteed existence of Nash Equilibria. While previous research has analyzed ordinal potential static games, their applicability to various engineering applications remains limited. Despite the earlier introduction of OPDGs, a systematic method for identifying a potential game for a given LQ differential game has not yet been developed. To address this gap, we propose two identification methods to provide the quadratic potential cost function for a given LQ differential game. Both methods are based on linear matrix inequalities (LMIs). The first method aims to minimize the condition number of the potential cost function’s parameters, offering a faster and more precise technique compared to earlier solutions. In addition, we present an evaluation of the feasibility of the structural requirements of the system. The second method, with a less rigid formulation, can identify LQ OPDGs in cases where the first method fails. These novel identification methods are verified through simulations, demonstrating their advantages and potential in designing and analyzing cooperative control systems.

Recent Advances in Gene Therapy for Hemophilia: Projecting the Perspectives.

One of the well-known X-linked genetic disorders is hemophilia, which could be hemophilia A as a result of a mutation in the F8 (factor VIII) gene or hemophilia B as a result of a mutation in the F9 (factor IX) gene, leading to insufficient levels of the proteins essential for blood coagulation cascade. In patients with severe hemophilia, factor VIII or factor IX activities in the blood plasma are considerably low, estimated to be less than 1%. This is responsible for spontaneous or post-traumatic bleeding episodes, or both, leading to disease complications and death. Current treatment of hemophilia relies on the prevention of bleeding, which consists of expensive lifelong replacement infusion therapy of blood plasma clotting factors, their recombinant versions, or therapy with recombinant monoclonal antibodies. Recently emerged gene therapy approaches may be a potential game changer that could reshape the therapeutic outcomes of hemophilia A or B using a one-off vector in vivo delivery and aim to achieve long-term endogenous expression of factor VIII or IX. This review examines both traditional approaches to the treatment of hemophilia and modern methods, primarily focusing on gene therapy, to update knowledge in this area. Recent technological advances and gene therapeutics in the pipeline are critically reviewed and summarized. We consider gene therapy to be the most promising method as it may overcome the problems associated with more traditional treatments, such as the need for constant and expensive infusions and the presence of an immune response to the antibody drugs used to treat hemophilia.

Optimizing the cascade of prevention to protect people from tuberculosis: A potential game changer for reducing global tuberculosis incidence.

The provision of tuberculosis preventive treatment is one of the critical interventions to reduce tuberculosis incidence and ultimately eliminate the disease, yet we still miss appropriate tools for an impactful intervention and treatment coverage remains low. We used recent data, epidemiological estimates, and research findings to analyze the challenges of each step of the cascade of tuberculosis prevention that currently delay the strategy implementation. We addressed research gaps and implementation bottlenecks that withhold key actions in tuberculosis case finding, testing for tuberculosis infection, provision of preventive treatment with safer, shorter regimens and supporting people to complete their treatment. Empowering communities to generate demand for preventive therapy and other prevention services in a holistic manner and providing adequate financial support to sustain implementation are essential requirements. The adoption of an effective, universal monitoring and evaluation system is a prerequisite to provide general and granular insight, and to steer progress of the tuberculosis infection strategy at global and local level.

Edge computing collaborative offloading strategy for space‐air‐ground integrated networks

SummaryDue to geographical factors, it is impossible to build large‐scale communication network infrastructure in remote areas, which leads to poor network communication quality in these areas and a series of delay‐sensitive tasks cannot be timely processed and responded. Aiming at the problem of limited coverage in remote areas, the space‐air‐ground integrated networks (SAGIN) combined with mobile edge computing (MEC) can provide low latency and high reliability transmission for offloading delay‐sensitive tasks for users in remote areas. Considering the strong limitation of satellite resources in the space‐ground integrated network and insufficient energy of local user equipment, firstly, a satellite‐UAV cluster‐ground three‐layer edge computing network architecture is proposed in this paper. Under the condition that the delay requirements of various ground tasks are met, the task offloading problem is transformed into a Stackelberg game between ground user equipment and edge servers. In addition, it is proved that the existence of Nash equilibrium in non‐cooperative game between ground user equipment by using potential game. Finally, a Nash equilibrium iterative offloading algorithm based on Stackelberg game (NEIO‐SG) is proposed to find the optimal offloading strategy for tasks to minimize the system offloading cost and the optimal forwarding percentage strategy for offloading tasks to maximize the utility function of the edge server. Simulation results show that compared to other baseline algorithms, NEIO‐SG reduces the total system latency during task offloading by about 13 and the energy consumption of the edge server by about 35.

Exploring uncharted territory: new frontiers in environmental DNA for tropical fisheries management.

Tropical ecosystems host a significant share of global fish diversity contributing substantially to the global fisheries sector. Yet their sustainable management is challenging due to their complexity, diverse life history traits of tropical fishes, and varied fishing techniques involved. Traditional monitoring techniques are often costly, labour-intensive, and/or difficult to apply in inaccessible sites. These limitations call for the adoption of innovative, sensitive, and cost-effective monitoring solutions, especially in a scenario of climate change. Environmental DNA (eDNA) emerges as a potential game changer for biodiversity monitoring and conservation, especially in aquatic ecosystems. However, its utility in tropical settings remains underexplored, primarily due to a series of challenges, including the need for a comprehensive barcode reference library, an understanding of eDNA behaviour in tropical aquatic environments, standardized procedures, and supportive biomonitoring policies. Despite these challenges, the potential of eDNA for sensitive species detection across varied habitats is evident, and its global use is accelerating in biodiversity conservation efforts. This review takes an in-depth look at the current state and prospects of eDNA-based monitoring in tropical fisheries management research. Additionally, a SWOT analysis is used to underscore the opportunities and threats, with the aim of bridging the knowledge gaps and guiding the more extensive and effective use of eDNA-based monitoring in tropical fisheries management. Although the discussion applies worldwide, some specific experiences and insights from Indian tropical fisheries are shared to illustrate the practical application and challenges of employing eDNA in a tropical context.

Incentive-based fault-tolerant cooperative payoff design in weakly acyclic games

This paper proposes an incentive-based cooperative payoff design method to accommodate faults in weakly acyclic games, where the payoff functions of normal players are designed such that all players’ optimal payoffs are recovered into an admissible level. Besides, the stochastically stable Nash equilibrium (SSNE) is regarded as the optimal state to avoid some inefficient Nash equilibria. Firstly, without cooperation among players, the effect of faults on SSNE and the optimal payoff is analyzed deeply. Further, the conditions of fault accommodation are discussed under the proposed cooperative fault tolerance method, based on which an algorithm is designed to calculate the feasible incentive. Secondly, with cooperation among players, the effect of faults is analyzed again in special weakly acyclic games, namely potential games, and the simpler conditions of fault accommodation are provided. Finally, two examples of frequency assignment in distributed systems and power management in sensor networks illustrate the effectiveness of the proposed methods.