Deep learning for video game playing

In this article, we review recent Deep Learning advances in the context of how they have been applied to play different types of video games such as first-person shooters, arcade games, and real-time strategy games. We analyze the unique requirements that different game genres pose to a deep learning system and highlight important open challenges in the context of applying these machine learning methods to video games, such as general game playing, dealing with extremely large decision spaces and sparse rewards

Challenging Social Media Threats using Collective Well-being Aware Recommendation Algorithms and an Educational Virtual Companion

Social media (SM) have become an integral part of our lives, expanding our inter-linking capabilities to new levels. There is plenty to be said about their positive effects. On the other hand however, some serious negative implications of SM have repeatedly been highlighted in recent years, pointing at various SM threats for society, and its teenagers in particular: from common issues (e.g. digital addiction and polarization) and manipulative influences of algorithms to teenager-specific issues (e.g. body stereotyping). The full impact of current SM platform design -- both at an individual and societal level -- asks for a comprehensive evaluation and conceptual improvement. We extend measures of Collective Well-Being (CWB) to SM communities. As users' relationships and interactions are a central component of CWB, education is crucial to improve CWB. We thus propose a framework based on an adaptive "social media virtual companion" for educating and supporting the entire students' community to interact with SM. The virtual companion will be powered by a Recommender System (CWB-RS) that will optimize a CWB metric instead of engagement or platform profit, which currently largely drives recommender systems thereby disregarding any societal collateral effect. CWB-RS will optimize CWB both in the short term, by balancing the level of SM threat the students are exposed to, as well as in the long term, by adopting an Intelligent Tutor System role and enabling adaptive and personalized sequencing of playful learning activities. This framework offers an initial step on understanding how to design SM systems and embedded educational interventions that favor a more healthy and positive society

RaidEnv: Exploring New Challenges in Automated Content Balancing for Boss Raid Games

The balance of game content significantly impacts the gaming experience. Unbalanced game content diminishes engagement or increases frustration because of repetitive failure. Although game designers intend to adjust the difficulty of game content, this is a repetitive, labor-intensive, and challenging process, especially for commercial-level games with extensive content. To address this issue, the game research community has explored automated game balancing using artificial intelligence (AI) techniques. However, previous studies have focused on limited game content and did not consider the importance of the generalization ability of playtesting agents when encountering content changes. In this study, we propose RaidEnv, a new game simulator that includes diverse and customizable content for the boss raid scenario in MMORPG games. Additionally, we design two benchmarks for the boss raid scenario that can aid in the practical application of game AI. These benchmarks address two open problems in automatic content balancing, and we introduce two evaluation metrics to provide guidance for AI in automatic content balancing. This novel game research platform expands the frontiers of automatic game balancing problems and offers a framework within a realistic game production pipeline.Comment: 14 pages, 6 figures, 6 tables, 2 algorithm

Design and assessment of adaptative hypermdia games for English acquisition in preschool

La creciente popularidad de los juegos hipermedia llega mucho más allá de los límites del entretenimiento e interfieren en muchos ámbitos educativos. El crecimiento de las tecnologías de juegos ha creado nuevos entornos de enseñanza a través de propuestas que combinan el aprendizaje con la diversión y motivan las expectativas que hacen que el uso de juegos digitales sea una tendencia relevante en situaciones de aprendizaje versátil en todos los niveles educativos. Sobre la base de la aplicación de la Shaiex, financiado por el gobierno de Extremadura (España) y desarrollado por el grupo de investigación GexCALL, el objetivo del presente trabajo es doble. En primer lugar, se analizan los pasos y requisitos para el diseño de juegos adaptables para la enseñanza del inglés como lengua extranjera en el nivel preescolar. Explorar las premisas generales para la tarea de adaptación al personalizar el contenido a las necesidades de los niños y sus habilidades, la ejecución del diseño completo se describe en función de su aplicabilidad. En segundo lugar, una evaluación de la adaptación juegos diseñados es realizada, basada en el supuesto de que la selección de experiencias digitales apropiadas al desarrollo para satisfacer las expectativas de los usuarios y maximizar su potencial de aprendizaje deben someterse a una evaluación cuidadosa.The increasing popularity of hypermedia games is reaching far beyond the boundaries of entertainment and edging its way into many educational domains. The growth in game technologies has created new teaching environments through proposals combining learning with fun and motivating expectations which make the use of digital games a relevant trend in versatile learning situations at all educational levels. Based on the implementation of the Shaiex project, funded by the government of Extremadura (Spain) and develop by the research group GexCALL, the aim of this paper is twofold. First, it analyses the steps and requirements for the design of adaptive games for the teaching of English as a foreign language at the preschool level. Exploring the general premises for task adaptation by personalizing content to children’s needs and abilities, the implementation of the whole design is described in light of its applicability. Second, an assessment of the adaptive games designed is conducted, based on the assumption that selecting developmentally appropriate digital experiences to meet users’ expectations and to maximize their learning potential should undergo careful evaluation.peerReviewednotPeerReviewe

BQL-DRS: A Novel Balanced Q-Learning Based Demand Response System for IoT based Smart Grids

The modernization of electricity networks and the integration of renewable energy resources in Internet of Things (IoT) based smart grids have led to increased variability in market prices, necessitating effective demand response (DR) strategies. To address this challenge, this paper proposes a novel Balanced Q-Learning based Demand Response System (BQL-DRS) that combines both optimistic and pessimistic targets in the Q-learning algorithm to achieve a balanced decision-making process in IoT based smart grids. It optimizes DR actions by efficiently managing consumer demand in real-time, considering IoT data from grid conditions, energy prices, and consumer preferences. The significance of the BQL-DRS lies in its ability to handle dynamic and uncertain IoT based grid environments, enabling it to make informed and cautious decisions while pursuing energy efficiency and cost-effectiveness. By effectively addressing both pessimistic and optimistic scenarios, the BQL-DRS ensures grid stability, load balancing, and substantial cost savings compared to representative models

Reinforcement Learning Approaches in Social Robotics

This article surveys reinforcement learning approaches in social robotics. Reinforcement learning is a framework for decision-making problems in which an agent interacts through trial-and-error with its environment to discover an optimal behavior. Since interaction is a key component in both reinforcement learning and social robotics, it can be a well-suited approach for real-world interactions with physically embodied social robots. The scope of the paper is focused particularly on studies that include social physical robots and real-world human-robot interactions with users. We present a thorough analysis of reinforcement learning approaches in social robotics. In addition to a survey, we categorize existent reinforcement learning approaches based on the used method and the design of the reward mechanisms. Moreover, since communication capability is a prominent feature of social robots, we discuss and group the papers based on the communication medium used for reward formulation. Considering the importance of designing the reward function, we also provide a categorization of the papers based on the nature of the reward. This categorization includes three major themes: interactive reinforcement learning, intrinsically motivated methods, and task performance-driven methods. The benefits and challenges of reinforcement learning in social robotics, evaluation methods of the papers regarding whether or not they use subjective and algorithmic measures, a discussion in the view of real-world reinforcement learning challenges and proposed solutions, the points that remain to be explored, including the approaches that have thus far received less attention is also given in the paper. Thus, this paper aims to become a starting point for researchers interested in using and applying reinforcement learning methods in this particular research field