Search Results (736)

Awaken, aware, arise, perform, pause, and repeat. The actions of the everyday. Without it, we fall into dysregulation. This paper seeks to examine creative research developed as an experiment during COVID-19, an audiovisualscape in virtual reality (VR). Rhythmanalysis+ is a social, ecological, and sensorial enquiry into materiality, grounded in archipelagic thinking, through the lens of Rhythmanalysis, a form of analysis focusing on the everyday, through the lens of cyclical and linear rhythms. (Lefebvre). The research will also draw on Deleuze and Guattari’s rhizome theory, a botanical and philosophical investigation into networks. Networks form the backbone of the research. Lars Bang Larsen also argues that networks offer a distinctive view on how factual, speculative, historical, and non-human elements envelop and intertwine. Glissant’s archipelagic thought promotes transformation, multiplicity, and a sense of unpredictability. For this work, four inhabitants from Sherkin, a small island off the southwest coast of Ireland with a population of 100, became the research focus. Across four weeks, islanders gathered data from their daily sensory rhythms. Flight patterns of birds and bats were recorded, daily tasks noted, pathways cycled. Relational impacts of animal-odour on farming, weather, and tides were processed remotely, and an immersive cartographic score was created as a direct response in a three-dimensional virtual space. Rhythmanalysis+ analyses our newly altered perceptions of time and space as a material within a virtual world. VR, created as a gaming platform, is being pushed by art itself, forcing us to relook at the natural world, which is not static, but relational. Fluid but equally extractive, it is important to look at technology’s impact on all that is human and how it is perceived within the body as it is reframed digitally. Full article

There has been significant recent progress in algorithms for approximation of Nash equilibrium in large two-player zero-sum imperfect-information games and exact computation of Nash equilibrium in multiplayer normal-form games. While counterfactual regret minimization and fictitious play are scalable to large games and have convergence guarantees in two-player zero-sum games, they do not guarantee convergence to Nash equilibrium in multiplayer games. We present an approach for exact computation of Nash equilibrium in multiplayer imperfect-information games that solves a quadratically-constrained program based on a nonlinear complementarity problem formulation from the sequence-form game representation. This approach capitalizes on recent advances for solving nonconvex quadratic programs. Our algorithm is able to quickly solve three-player Kuhn poker after removal of dominated actions. Of the available algorithms in the Gambit software suite, only the logit quantal response approach is successfully able to solve the game; however, the approach takes longer than our algorithm and also involves a degree of approximation. Our formulation also leads to a new approach for computing Nash equilibrium in multiplayer normal-form games which we demonstrate to outperform a previous quadratically-constrained program formulation. Full article

Driven by the “dual carbon” goal and the strategy for cultivating new productive forces, China’s economy is undergoing a crucial transformation from high-speed growth to high-quality development. As a typical high-energy consumption and high-emission sector, the green and low-carbon transformation of the building materials industry directly affects the optimization of the national energy structure and the realization of ecological goals. However, traditional building material enterprises generally face practical challenges such as low resource utilization efficiency, insufficient digitalization and greening integration of the industrial chain, and weak green innovation momentum. The transformation actions of a single entity are difficult to break through systemic bottlenecks, and it is urgently necessary to establish a dynamic evolution mechanism involving multiple entities in collaboration. This paper aims to explore the evolutionary rules and stability of digital green (DG) transformation strategies of building materials enterprises (BMEs) under multi-agent interactions involving government, universities, and consumers. Centering on BMEs, a four-party evolutionary game model among the government, enterprises, universities, and consumers is constructed, and the evolutionary processes of strategic behaviors are characterized through replicator dynamic equations. Using MATLAB R2022 (Version number: 9.13.0.2049777) bnumerical simulations, this study investigates how key parameters, such as government subsidies, penalty intensity, and consumers’ green preferences, affect the transformation pathways of enterprises. The results reveal that the DG transformation behavior of BMEs is significantly influenced by governmental policy incentives and universities’ knowledge innovation. Stronger subsidies and penalties enhance enterprises’ willingness to adopt proactive DG strategies, while consumers’ green preferences further accelerate transformation through market mechanisms. Among multiple strategic combinations, active DG transformation emerges as the main evolutionarily stable strategy. This study provides a systematic multi-agent collaborative analysis framework for the transformation of BME DG, revealing the mechanisms by which policies, knowledge, and market demands influence enterprise decisions. Thus, it offers theoretical and decision-making references for the green and low-carbon transformation of the building materials industry. Full article

Active methodologies place the student at the core of the teaching–learning process, with the teacher becoming a companion and guide. Among these methodologies, gamification is demonstrating great capacity to attract students and promote interest, being of particular relevance in STEM subjects. While gamification and Project-Based Learning (PBL) have been extensively studied independently, their integration into Vocational Education and Training (VET) computer science courses remains underexplored, particularly regarding approaches where students develop games themselves rather than merely incorporating game elements or playing serious games. This work presents a novel gamified PBL approach specifically designed for VET Programming education, with three distinctive features: (i) students develop a complete game based on graph theory and Object-Oriented Programming, with each student working under personalised conditions and constraints; (ii) a custom-developed software tool that simultaneously serves as a pedagogical scaffold for students to validate their solutions iteratively and as an automated evaluation platform for teachers; and (iii) empirical validation through action-research with first-year VET students, employing mixed-methods analysis including qualitative observations and descriptive quantitative comparisons. The approach was implemented with first-year Web Application Design students in the Programming subject, where students developed a game integrating graph theory algorithms, Object-Oriented Programming, and Markup Language. Despite the small sample size (10 students), qualitative observations and descriptive analysis indicated promising results, and grade distributions were comparable to those in more accessible subjects. Teacher diary observations, follow-ups, and questionnaires documented sustained engagement, peer collaboration, and strategic problem-solving throughout the project phase. These preliminary findings suggest that gamification through game development, particularly when supported by automated tools enabling personalised conditions and iterative validation, represents a promising approach for teaching and learning Programming in VET contexts. Full article

In this study, we develop a game-theoretic optimization framework to analyze competing vessels’ technology choices between shore power (SP) and low-sulfur fuel oil (LSFO) within a maritime supply chain which is regulated by a cap-and-trade mechanism. Using a Stackelberg game approach, we construct two models—one port-led and the other vessel-led—to derive closed-form equilibrium for pricing, service quantities, profits, emissions, and social welfare. The results reveal three key findings. First, the leader in either Stackelberg structure always achieves higher profits, while total supply chain profits remain identical across power structures. Second, at low carbon prices, LSFO-equipped vessels provide more services and earn higher profits due to cost advantages. As the carbon price rises—which directly incentivizes emission reduction and accelerates maritime decarbonization—SP becomes more attractive and eventually dominates in profitability despite higher initial investment. Notably, although SP has lower unit emissions, its total emissions may surpass those of LSFO at certain carbon-price thresholds because the SP-equipped vessel optimally expands output. Third, intensified competition reduces service quantities, profits, and emissions, with a more substantial reduction effect on LSFO vessels. Overall, our results provide mathematically grounded insights for optimizing low-carbon technology adoption in maritime transport and offer actionable policy implications for carbon pricing that balance environmental objectives and supply chain efficiency. This research contributes specifically to the United Nations’ Sustainable Development Goals (SDGs), specifically SDG 13 (Climate Action) and SDG 9 (Industry, Innovation and Infrastructure). Full article

In this paper, an explainable decision-making and guidance integration method is developed based on dynamic Bayesian network and the optimized control method. The proposed method can be applied for the autonomous decision-making and guidance in the game of attacking and defending of unmanned combat aerial vehicles in close air combat. Firstly, the target maneuver recognition and target trajectory prediction are carried out according to the target information detected by the sensor. Then, a dynamic Bayesian network model for close combat decision is established by combining space occupancy situation and equipment performance information with target maneuver identification results. The decision model realizes the intelligent selection of the optimization index function of the maneuver. The optimal control constrained gradient method is adopted to realize the optimal calculation of the unmanned combat aerial vehicle occupancy guidance quantity by considering the constraint of unmanned combat aerial vehicle flight performance. The simulation results of several typical close air combat show that the proposed method can realize rationalized autonomous decision-making and space occupancy guidance of unmanned combat aerial vehicles, overcome the solidification of mobile action mode by traditional methods, and has better real-time performance and optimization performance. Full article

Recognizing emotions accurately in virtual reality (VR) enables adaptive and personalized experiences across gaming, therapy, and other domains. However, most existing facial emotion recognition models rely on acted expressions collected under controlled settings, which differ substantially from the spontaneous and subtle emotions that arise during real VR experiences. To address this challenge, the objective of this study is to develop and evaluate generalizable emotion recognition models that jointly learn from both acted and natural facial expressions in virtual reality. We integrate two complementary datasets collected using the Meta Quest Pro headset, one capturing natural emotional reactions and another containing acted expressions. We evaluate multiple model architectures, including convolutional and domain-adversarial networks, and a mixture-of-experts model that separates natural and acted expressions. Our experiments show that models trained jointly on acted and natural data achieve stronger cross-domain generalization. In particular, the domain-adversarial and mixture-of-experts configurations yield the highest accuracy on natural and mixed-emotion evaluations. Analysis of facial action units (AUs) reveals that natural and acted emotions rely on partially distinct AU patterns, while generalizable models learn a shared representation that integrates salient AUs from both domains. These findings demonstrate that bridging acted and natural expression domains can enable more accurate and robust VR emotion recognition systems. Full article

Deep reinforcement learning (DRL) has been widely adopted to solve decision-making problems in complex environments, demonstrating high performance across various domains. However, DRL-based FPS agents are typically trained with a traditional, monolithic policy that integrates heterogeneous functionalities into a single network. This design hinders policy interpretability and severely limits structural flexibility, since even minor design changes in the action space often necessitate complete retraining of the entire network. These constraints are particularly problematic in game development, where behavioral characteristics are distinct and design updates are frequent. To address these issues, this study proposes a Modular Reinforcement Learning (MRL) framework. Unlike monolithic approaches, this framework decomposes complex agent behaviors into semantically distinct action modules, such as movement and attack, which are optimized in parallel with specialized reward structures. Each module learns a policy specialized for its own behavioral characteristics, and the final agent behavior is obtained by combining the outputs of these modules. This modular design enhances structural flexibility by allowing selective modification and retraining of specific functions, thereby reducing the inefficiency associated with retraining a monolithic policy. Experimental results on the 1-vs-1 training map show that the proposed modular agent achieves a maximum win rate of 83.4% against a traditional monolithic policy agent, demonstrating superior in-game performance. In addition, the retraining time required for modifying specific behaviors is reduced by up to 30%, confirming improved efficiency for development environments that require iterative behavioral updates. Full article

Curling presents a challenging continuous-control problem in which shot outcomes depend on long-horizon interactions between complex physical dynamics, strategic intent, and opponent responses. Despite recent progress in applying reinforcement learning (RL) to games and sports, curling lacks a unified environment that jointly supports stable, rule-consistent simulation, structured state abstraction, and scalable agent training. To address this gap, we introduce a comprehensive learning framework for curling AI, consisting of a full-sized simulation environment, a task-aligned Markov decision process (MDP) formulation, and a two-phase training strategy designed for stable long-horizon optimization. First, we propose a novel MDP formulation that incorporates stone configuration, game context, and dynamic scoring factors, enabling an RL agent to reason simultaneously about physical feasibility and strategic desirability. Second, we present a two-phase curriculum learning procedure that significantly improves sample efficiency: Phase 1 trains the agent to master delivery mechanics by rewarding accurate placement around the tee line, while Phase 2 transitions to strategic learning with score-based rewards that encourage offensive and defensive planning. This staged training stabilizes policy learning and reduces the difficulty of direct exploration in the full curling action space. We integrate this MDP and training procedure into a unified Curling RL Framework, built upon a custom simulator designed for stability, reproducibility, and efficient RL training and a self-play mechanism tailored for strategic decision-making. Agent policies are optimized using Soft Actor–Critic (SAC), an entropy-regularized off-policy algorithm designed for continuous control. As a case study, we compare the learned agent’s shot patterns with elite match records from the men’s division of the Le Gruyère AOP European Curling Championships 2023, using 6512 extracted shot images. Experimental results demonstrate that the proposed framework learns diverse, human-like curling shots and outperforms ablated variants across both learning curves and head-to-head evaluations. Beyond curling, our framework provides a principled template for developing RL agents in physics-driven, strategy-intensive sports environments. Full article

The Extended Digital Narrative (XDN) research project explores how experimental creative practice with emerging technologies generates critical insights into algorithmic narrativity—the intersection of human narrative understanding and computational data processing. This article presents five case studies demonstrating that direct engagement with AI and Extended Reality platforms is essential for humanities research on new genres of digital storytelling. Lina Harder’s Hedy Lamar Chatbot examines how generative AI chatbots construct historical personas, revealing biases in training data and platform constraints. Scott Rettberg’s Republicans in Love investigates text-to-image generation as a writing environment for political satire, documenting rapid changes in AI aesthetics and content moderation. David Jhave Johnston’s Messages to Humanity demonstrates how Runway’s Act-One enables solo filmmaking, collapsing traditional production hierarchies. Haoyuan Tang’s video game project reframes LLM integration by prioritizing player actions over dialogue, challenging assumptions about AI’s role in interactive narratives. Sérgio Galvão Roxo’s Her Name Was Gisberta employs Virtual Reality for social education against transphobia, utilizing perspective-taking techniques for empathy development. These projects demonstrate that practice-based research is not merely artistic production but a vital methodology for understanding how AI and XR platforms shape—and are shaped by—human narrative capacities. Full article

We develop a model of monetary policy committee decision-making, building on the framework of games played through agents (GPTA). Interest groups seek to influence policy by offering action-contingent contracts to committee members. The resulting equilibrium admits a simple characterization and shows how institutional features—such as committee size—shape the extent of external influence. When political pressure pushes for expansive and inflationary policy, larger committees can enhance de facto independence by diluting this influence. We also show that when anti-inflationary pressures dominate, an appropriate choice of committee size can replicate the preference shift towards more conservativeness familiar from delegation frameworks, even when it is not feasible to appoint a conservative central banker in a systematic way. Full article

Gamification is widely used to enhance student motivation, yet many educational design proposals remain conceptual and provide limited operational guidance for digital learning environments. This paper introduces APAR (Activities, Points, Achievements and Rewards), a content-independent structural framework for designing and implementing educational gamification in learning platforms. Grounded in motivation theories (including Self-Determination Theory and Relatedness–Autonomy–Mastery–Purpose) and reward taxonomies (Status, Access, Power and Stuff), APAR distinguishes high-level design constructs from concrete game elements (e.g., points, badges and leaderboards) and provides a systematic design loop linking learning activities, feedback, intermediate goals and reinforcement. The contribution includes (i) a mapping table relating each APAR construct to motivation models, supported dynamics and typical learning-platform implementations; (ii) an actionable design guide; and (iii) an empirical illustration implemented in Moodle in a higher-education Computer Networks course. In this setting, the proportion of enrolled students taking the final exam increased from 58% to 72% in the first year, and the proportion of enrolled students passing increased from 17% to 38%; in 2022–2023 these values were 70% and 39%, respectively (56% of exam takers passed). While the use case relies on quantitative course-level indicators and is observational, the findings support the potential of structural gamification as an integrated methodological tool and motivate further mixed-method validations. Full article

This study examines decision-making in intelligent virtual agents (IVAs) and formalizes the distinction between tactical decisions (individual actions) and strategic decisions (composed of sequences of tactical actions) using a mathematical model based on set theory and the Bellman equation. Although the equation itself is not modified, the analysis reveals that the discount factor (γ) influences the type of decision: low values favor tactical decisions, while high values favor strategic ones. The model was implemented and validated in a proof-of-concept simulated environment, namely the Snake Coin Change Problem (SCCP), using a Deep Q-Network (DQN) architecture, showing significant differences between agents with different decision profiles. These findings suggest that adjusting γ can serve as a useful mechanism to regulate both tactical and strategic decision-making processes in IVAs, thus offering a conceptual basis that could facilitate the design of more intelligent and adaptive agents in domains such as video games, and potentially in robotics and artificial intelligence as future research directions. Full article

Emergency supplies allocation is a critical task in post-disaster response, as ineffective or delayed decisions can directly lead to increased human suffering and loss of life. In practice, emergency managers must make rapid allocation decisions over multiple periods under incomplete information and highly unpredictable demand, making robust and adaptive decision support essential. However, existing allocation approaches face several challenges: (1) Those traditional approaches rely heavily on predefined uncertainty sets or probabilistic models, and are inherently static, making them unsuitable for multi-period, dynamically allocation problems; and (2) while reinforcement learning (RL) technique is inherently suitable for dynamic decision-making, most existing RL-base approaches assume fixed demand, making them unable to cope with the non-stationary demand patterns seen in real disasters. To address these challenges, we first establish a multi-period and multi-objective emergency supplies allocation problem with demand uncertainty and then formulate it as a two-player zero-sum Markov game (TZMG). Demand uncertainty is modeled through an adversary rather than predefined uncertainty sets. We then propose RESA, a novel RL framework that uses adversarial training to learn robust allocation policies. In addition, RESA introduces a combinatorial action representation and reward clipping methods to handle high-dimensional allocations and nonlinear objectives. Building on RESA, we develop RESA_PPO by employing proximal policy optimization as its policy optimizer. Experiment results with realistic post-disaster data show that RESA_PPO achieves near-optimal performance, with an average gap of only 3.7% in terms of the objective value of the formulated problem, from the theoretical optimum derived by exact solvers. Moreover, RESA_PPO outperforms all baseline methods, including heuristic and standard RL methods, by at least 5.25% on average. Full article

This study examines patterns of Information and Communication Technology (ICT) use during leisure time among non-university students in the Autonomous Community of Andalusia (Spain) and explores their potential to inform environmental education initiatives. Two research questions guided the study: (1) Which devices and usage times characterize students’ digital leisure according to gender and educational level? (2) How can these patterns inform the design of contextualized environmental education actions? A cross-sectional quantitative study was conducted using a survey administered to 1251 students enrolled in Primary Education, Compulsory Secondary Education, Upper Secondary Education (Baccalaureate), and Vocational Training in the cities of Seville, Malaga, Cádiz, and Granada. The questionnaire, consisting of 49 items, assessed the use of television, tablets, mobile phones, computers, and video games during leisure time. Data were analysed using descriptive statistics, inferential analysis (ANOVA), and multivariate analysis (MANOVA). The results highlight the central role of the mobile phone as the dominant device across all educational stages, as well as significant age-related differences in the use of television, tablets, and video games. Gender differences were found only in the time devoted to video gaming. The main contribution of this study lies in providing updated empirical evidence on youth digital leisure within a specific geographical context, identifying opportunities to integrate digital resources into environmental education initiatives that are sensitive to educational stage and gender and aligned with sustainability goals. The use of ICTs is proposed to create interactive educational experiences that prepare students to address ecosocial challenges, promote sustainable development, and foster a stronger connection with the natural environment. Full article