Learning by gaming:ANT and critical making

Relationships among theory, gaming, learning and socio-technical design are explored in the two contributions which compose the section. The theory in question is ANT, re-interpreted through critical making - an umbrella term for various distinctive practices that link traditional scholarship in the humanities and social sciences to forms of material engagement. Sergio Minniti describes an ongoing project called Game of ANT, which draws upon the critical making approach to design an interactive technology and a workshop experience through which scholars and students can conceptually-materially engage with ANT, hence exploring and approaching it from novel points of view. Game of ANT adopts the Latourian vision of technoscience as war and physically embodies this idea by proposing a sort of war game during which participants play the roles of human or non-human actors engaging with the competitive dynamics of socio-technical life. The commentary by Stefano De Paoli proposes new directions to develop the project, by deepening the concept of game and its value for design and learning processes.</p

A Serious Game Approach in Anti-Doping Education: the Game Project

Anti-doping education has largely relied on traditional educational approaches such as face-to-face interaction and e-learning material. Current challenges in anti-doping education involve a) the development of modern educational tools suitable for the new generation of athletes, b) the use of state-of- art learning pedagogies that will enable effective engagement, learning and retention of the learned material, c) a systematic evaluation of the outcomes of anti-doping educational interventions on behavior and related cognition, and d) a positive approach to doping prevention. Project GAME aims to address these needs through the development of a serious game that will incorporate current empirical evidence on the psychological mechanisms underpinning the decision making process towards doping use in competitive and recreational sports. The aim of the present study is to highlight the importance of anti-doping education, conduct a state of the art literature review on serious games' design, present the prototype of a scenario that will be included in a serious game for anti-doping education, and discuss the project's activities related to the use of technologies in anti-doping education. +++++++++++++ Anti-doping education has largely relied on traditional educational aproaches such as face-to-face interaction and e-learning material. Current chalenges in anti-doping education involve a) the development of modern educational tools suitable for the new generation of athletes, b) the use of state-of- art learning pedagogies that will enable effective engagement, learning and retention of the learned material, c) a systematic evaluation of the outcomes of anti-doping educational interventions on behavior and related cognition, and d) a positive approach to doping prevention. Project GAME aims to address these needs through the development of a serious game that will incorporate current empirical evidence on the psychological mechanisms underpinning the decision making process towards doping use in competitive and recreational sports. The aim of the present study is to highlight the importance of anti-doping education, conduct a state of the art literature review on serious games' design, present the prototype of a scenario that will be included in a serious game for anti-doping education, and discuss the project's activities related to the use of technologies in anti-doping educatio

Minimax Exploiter: A Data Efficient Approach for Competitive Self-Play

Recent advances in Competitive Self-Play (CSP) have achieved, or even surpassed, human level performance in complex game environments such as Dota 2 and StarCraft II using Distributed Multi-Agent Reinforcement Learning (MARL). One core component of these methods relies on creating a pool of learning agents -- consisting of the Main Agent, past versions of this agent, and Exploiter Agents -- where Exploiter Agents learn counter-strategies to the Main Agents. A key drawback of these approaches is the large computational cost and physical time that is required to train the system, making them impractical to deploy in highly iterative real-life settings such as video game productions. In this paper, we propose the Minimax Exploiter, a game theoretic approach to exploiting Main Agents that leverages knowledge of its opponents, leading to significant increases in data efficiency. We validate our approach in a diversity of settings, including simple turn based games, the arcade learning environment, and For Honor, a modern video game. The Minimax Exploiter consistently outperforms strong baselines, demonstrating improved stability and data efficiency, leading to a robust CSP-MARL method that is both flexible and easy to deploy

Semantic Labelling and Learning for Parity Game Solving in LTL Synthesis

We propose "semantic labelling" as a novel ingredient for solving games in the context of LTL synthesis. It exploits recent advances in the automata-based approach, yielding more information for each state of the generated parity game than the game graph can capture. We utilize this extra information to improve standard approaches as follows. (i) Compared to strategy improvement (SI) with random initial strategy, a more informed initialization often yields a winning strategy directly without any computation. (ii) This initialization makes SI also yield smaller solutions. (iii) While Q-learning on the game graph turns out not too efficient, Q-learning with the semantic information becomes competitive to SI. Since already the simplest heuristics achieve significant improvements the experimental results demonstrate the utility of semantic labelling. This extra information opens the door to more advanced learning approaches both for initialization and improvement of strategies

A Novel Graphical Lasso based approach towards Segmentation Analysis in Energy Game-Theoretic Frameworks

Energy game-theoretic frameworks have emerged to be a successful strategy to encourage energy efficient behavior in large scale by leveraging human-in-the-loop strategy. A number of such frameworks have been introduced over the years which formulate the energy saving process as a competitive game with appropriate incentives for energy efficient players. However, prior works involve an incentive design mechanism which is dependent on knowledge of utility functions for all the players in the game, which is hard to compute especially when the number of players is high, common in energy game-theoretic frameworks. Our research proposes that the utilities of players in such a framework can be grouped together to a relatively small number of clusters, and the clusters can then be targeted with tailored incentives. The key to above segmentation analysis is to learn the features leading to human decision making towards energy usage in competitive environments. We propose a novel graphical lasso based approach to perform such segmentation, by studying the feature correlations in a real-world energy social game dataset. To further improve the explainability of the model, we perform causality study using grangers causality. Proposed segmentation analysis results in characteristic clusters demonstrating different energy usage behaviors. We also present avenues to implement intelligent incentive design using proposed segmentation method.Comment: Proceedings of the Special Session on Machine Learning in Energy Application, International Conference on Machine Learning and Applications (ICMLA) 2019. arXiv admin note: text overlap with arXiv:1810.1053