Potential Game Research Articles

Distributed Task Allocation Algorithm for Heterogeneous UAV Cluster Based on Game Theory

This paper proposes a distributed task allocation algorithm based on game theory to solve the complex task allocation optimization problem when UAV clusters carry heterogeneous resources and tasks have heterogeneous demands. Considering the heterogeneity of resources, two pre-processing methods are proposed: one is the grouping algorithm that combines greedy algorithm with simulated annealing algorithm, and the other is the improved K-medoids clustering algorithm based on heterogeneous resources. These pre-process methods, through grouping and clustering, can reduce the complexity of task allocation. The entropy weight method is utilized to prioritize tasks based on multiple metrics. Considering task demands, airborne resources and path cost, a coalition formation game model is established, which is proved to be a potential game. Then a distributed task allocation algorithm based on coalition formation game is designed to address the task allocation problem. Finally, the simulation involving 30 tasks with heterogeneous requirements assigned to 100 UAVs validates the effectiveness of the proposed algorithm, showing that it can achieve good task allocation results with great real-time performance.

Multicenter outcome analysis of different sheath sizes for Flexible and Navigable Suction ureteral access sheath (FANS) ureteroscopy: an EAU Endourology collaboration with the global FANS study group.

Flexible and navigable suction ureteral access sheath (FANS) is a potential game changer in flexible ureteroscopy (FURS). The influence of sheath size on outcomes needs research. The primary aim was to analyze 30-day single stage stone free status (SFS), zero fragment rate (ZFR) and complications when using 10/12Fr sheaths vis a vis other sheath sizes. The global FANS research group published the 30-day outcomes in patients who underwent FANS and reasoned this can be a potential game changer. We included 295 patients from this anonymized dataset with division into two groups: Group 1 (Smaller sheath) - 10/12Fr FANS, and Group 2 (Larger sheath) - 11/13Fr or 12/14Fr sheaths. Stone volume was similar between both groups (median 1320 mm3, p = 0.88). Ureteroscopy and total operative time was longer in the smaller sheath group (35 vs. 32min, p = 0.02 and 50 vs. 45min, p = 0.001, respectively). While 30-day computed tomography SFS (100% stone free or single residual fragment ≤ 2mm) were not significantly different (96% vs. 95%, p > 0.99), ZFR (100% stone-free) was better with smaller sheaths (68% vs. 53%, p = 0.02). There was no difference in postoperative complication rates, and no sepsis in both groups. Urologists should consider individualizing appropriate sheath size in normal adult kidneys. Sheath size did not affect complication rates, risk of perioperative injury to the pelvicalyceal system or ureteric injury, but smaller FANS sheaths had similar high SFS. The ZFR with smaller sheaths was better, but this needs to be validated. These smaller sheath outcomes need to be balanced with longer ureteroscopy time, operative time, reach to the lower pole, ease of suction and visibility during lithotripsy. Large volume studies in different types of pelvicalyceal anatomy can determine if indeed smaller FANS is the best choice in FURS.

Distributed dynamic scheduling algorithm of target coverage for wireless sensor networks with hybrid energy harvesting system.

The integration of energy harvesting techniques has the potential to significantly prolong target monitoring in wireless sensor networks (WSNs). However, the stochastic nature of hybrid solar-wind energy arrivals poses a significant challenge to optimizing energy utilization for target coverage. To address this issue, we propose a dynamic and distributed node scheduling algorithm based on Lyapunov optimization for hybrid energy-harvesting WSNs (HEH-WSNs). By formulating the maximum long-term average coverage utility subject to peak power constraints, we utilize Lyapunov optimization theory to develop a dynamic potential game framework for target coverage optimization in HEH-WSNs. The proposed distributed dynamic target-coverage node scheduling algorithm (DTNSA) is then derived from the potential game. We present a comprehensive performance analysis of the distributed implementation and evaluate its efficiency through extensive simulations. The results demonstrate that in two distinct scenarios, specifically with different numbers of sensor nodes and target nodes, the average coverage utility of our proposed DTNSA exceeds that of existing algorithms by and , respectively. The performance of the average number of active sensor nodes decreased by and compared to existing algorithms, while the average coverage redundancy decreased by and relative to existing algorithms. Furthermore, our algorithm adapts effectively to dynamic changes in hybrid harvested energy and exhibits lower computational complexity compared to existing target coverage algorithms.

Games: The WD-40 of Learning

The educational landscape has undergone a paradigm shift in recent years, particularly due to the COVID-19 pandemic. The rapid transition to remote learning environments, while ensuring educational continuity, may have inadvertently compromised the development of essential soft skills necessary for in-person interactions, such as communication, collaboration, and teamwork—skills that are vital for success in the workplace (Knight et al., 2021). While games have long been used in education (Conney & Darcy, 2020; Pagel et al., n.d.; Yuan et al., 2020), this study explores the potential of modernizing traditional game-based learning by integrating digital consoles and physical games into contemporary business classroom settings. This approach leverages the inherent appeal and engagement of games (Conney & Darcy, 2020; Hunicke et al., 2004), while fostering the development of critical soft skills. Building upon established frameworks for evaluating educational games, we explore the effectiveness of collaborative games in promoting positive learning experiences. Studies have shown that GBL can enhance motivation, knowledge retention, and problem-solving skills (Lester et al., 2023). Furthermore, collaborative games, by their very nature, necessitate effective communication, teamwork, and strategic planning among players (Pagel et al., n.d.). This project delves deeper into this aspect, investigating the potential of collaborative games to specifically address the regression of soft skills observed in the post-pandemic era. The initial pilot phase of this research focused on Pico Park on the Nintendo Switch and the Team 3 board game as potential collaborative games to cultivate soft skills. Using the MDA Framework (Hunicke et al., 2004), a well-established educational game evaluation tool, we assessed the games' ability to foster desired soft skills. Both games require effective communication, coordination, and strategic thinking, which are essential for fostering teamwork, collaboration, and problem-solving. Additionally, their accessibility and diverse formats (digital and physical) offer opportunities for students to practice soft skills in different learning environments, ultimately contributing to the research's goal of investigating the effectiveness of collaborative games in promoting positive learning experiences. The pilot involved approximately 50 students across three classes of the same course. Anonymous open-ended feedback was collected from participants using a survey questionnaire adapted from Conney and Darcy's (2020) design, which was based on the Kirkpatrick Four Level Evaluation Model (Kirkpatrick & Kirkpatrick, 1959, 1994). Participants were asked to provide their perceptions of the games, what they learnt, and the potential impact on their future group work. Preliminary findings from the pilot data are promising. Students found the games "fun", "different learning", “useful”, “refreshing”, "amazing", "engaging", "excellent for communication" and more (n=50). 98% of students (n=50) felt more confident in their ability to collaborate with others after playing the games and nearly 95% (n=50) indicated that it helped them identify potential teammates for their group assignments. Anecdotal evidence from academics and students corroborates the quantitative results, reporting increased motivation, improved team dynamics, and a more positive overall learning experience, suggesting that these activities significantly improved teamwork skills. To compliment this qualitative feedback, academics (n=3) observed a decrease in group issues and an increase in grades of around 5% for the major group assessment, bringing the average up from 60% in previous semesters to 65% in the trial period. This research aims to contribute to the existing literature on educational games and soft skills development. By demonstrating the positive impact of collaborative games on student engagement, teamwork, and soft skill development, we hope to encourage further exploration and implementation of this approach in classrooms. The findings of this pilot study highlight the potential of games to not only revitalise traditional pedagogy but also exhibits how technology can be used to address the critical need of fostering essential soft skills in a post-pandemic educational landscape.

Navigating the landscape of metabolic-associated steatotic liver disease treatment: aspirin as a potential game changer.

Metabolic-associated steatotic liver disease (MASLD) is the most common cause of chronic liver disease in Western countries, with rapidly increasing prevalence worldwide, estimated at around 40% due to modernization and urbanization. MASLD is defined as hepatic steatosis and identified through histology, imaging, blood markers, and in the absence of other secondary causes of hepatic fat accumulation, such as significant alcohol consumption, use of steatogenic medication, or hereditary disorders. The current management strategies addressing MASLD involve lifestyle modifications and treating coexisting conditions such as obesity, hyperlipidemia, insulin resistance, and type 2 diabetes. Several studies demonstrate that antiplatelet drugs, including acetylsalicylic acid, have beneficial effects on hepatocytes by decreasing hepatic inflammation, oxidative stress, and insulin resistance and may prevent hepatic fibrosis progression in MASLD. This review article discusses the impact of aspirin on steatosis and triglyceride accumulation in the hepatocytes.

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.

Open E-Bus Blueprint

Buses form the backbone of public transportation in India, catering to 39.9 crore (399 million) passengers daily and accounting for over 90% of all public transport trips. As part of a broader goal to reduce transport emissions, India is looking to electrify up to 800,000 buses in the next seven years, including public, private and institutional fleet. Given the different dynamics of operating electric buses, a more connected and digital ecosystem can be a potential game changer in this transition.

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.

INVENTARISASI VEGETASI KUNCI YANG MENDUKUNG KEBERADAAN SATWA DI BLOK PEMANFAATAN TAMAN BURU MASIGIT KAREUMBI

Taman Buru Masigit Kareumbi is a conservation forest area serving as a habitat for various wildlife species in West Java. This area is dedicated to regulated hunting to manage the populations of Timor deer, muntjac, wild dogs, leopards, jungle cats, wild chickens, slow lorises, binturongs, monkeys, langurs, and hornbills. The presence of these species indicates the need for conservation efforts in TB Masigit Kareumbi to maintain the biodiversity of its flora and fauna and preserve the ecological functions of the forest. Research methods included a vegetation survey conducted from May to August 2023 across seven trails within the Utilization Block of TB Masigit Kareumbi. The study identified various vegetation types along these seven different trails, which play a crucial role as habitat and food sources for other game species. These include: rasamala trees, saint trees, puspa trees, are trees, kendang trees, and grassland vegetation such as alang-alang. The potential game species supported by these types of vegetation are: primates and birds that inhabit and forage on rasamala, puspa, and saninten trees; civets that feed on aren trees; porcupines that forage on kondang trees; and alang-alang grass which provides food for deer and habitat for reptiles. This study demonstrates that the Utilization Block of TB Masigit Kareumbi is a forest area with a plant species composition that continues supporting wildlife's survival and presence. Thus, the updated information from this research will assist the managers of TB Masigit Kareumbi in maintaining the balance and preservation of the existing vegetation species.

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.