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  • Table of Contents
    IEEE Trans. Games (IF 1.886) Pub Date : 2020-06-16

    Presents the table of contents for this issue of the publication.

  • IEEE Transactions on Games
    IEEE Trans. Games (IF 1.886) Pub Date : 2020-06-16

    Presents a listing of the editorial board, board of governors, current staff, committee members, and/or society editors for this issue of the publication.

  • Learning the Game of Go by Scalable Network Without Prior Knowledge of Komi
    IEEE Trans. Games (IF 1.886) Pub Date : 2020-05-07
    Bohong Yang; Lin Wang; Hong Lu; Youzhao Yang

    AlphaGo trains a value network to predict the win rate of the current state with 7.5 komi on a 19 × 19 board. The komi of most rectangular boards is unknown, so we do not know who the winner is at the end of the game. We need to use the human experience to guess a komi and then train the value network with this komi. Therefore, the accuracy of the value network is related to the accuracy of the guess

  • Winning Is Not Everything: Enhancing Game Development With Intelligent Agents
    IEEE Trans. Games (IF 1.886) Pub Date : 2020-05-29
    Yunqi Zhao; Igor Borovikov; Fernando de Mesentier Silva; Ahmad Beirami; Jason Rupert; Caedmon Somers; Jesse Harder; John Kolen; Jervis Pinto; Reza Pourabolghasem; James Pestrak; Harold Chaput; Mohsen Sardari; Long Lin; Sundeep Narravula; Navid Aghdaie; Kazi Zaman

    Recently, there have been several high-profile achievements of agents learning to play games against humans and beat them. In this article, we study the problem of training intelligent agents in service of game development. Unlike the agents built to “beat the game,” our agents aim to produce human-like behavior to help with game evaluation and balancing. We discuss two fundamental metrics based on

  • IEEE Computational Intelligence Society
    IEEE Trans. Games (IF 1.886) Pub Date : 2020-06-16

    Presents a listing of the editorial board, board of governors, current staff, committee members, and/or society editors for this issue of the publication.

  • Information for Authors
    IEEE Trans. Games (IF 1.886) Pub Date : 2020-06-16

    These instructions give guidelines for preparing papers for this publication. Presents information for authors publishing in this journal.

  • Infinite Loot Box: A Platform for Simulating Video Game Loot Boxes
    IEEE Trans. Games (IF 1.886) Pub Date : 2019-04-25
    Dominic Kao

    Loot boxes are garnering increased attention in both the industry and media. One focal point of the discussion is whether loot boxes should be considered a form of gambling. While parallels can be drawn between loot boxes and random reward schedules, researchers have argued that the “glorification” aspect of loot boxes that have heightened player awareness (e.g., opening a box, a pack of cards, or

  • Crawling in Rogue's Dungeons With Deep Reinforcement Techniques
    IEEE Trans. Games (IF 1.886) Pub Date : 2019-02-13
    Andrea Asperti; Daniele Cortesi; Carlo De Pieri; Gianmaria Pedrini; Francesco Sovrano

    This paper is a report of our extensive experimentation, during the last two years, of deep reinforcement techniques for training an agent to move in the dungeons of the famous Rogue video game. The challenging nature of the problem is tightly related to the procedural, random generation of new dungeon maps at each level, which forbids any form of level-specific learning and forces us to address the

  • “Are You Playing a Shooter Again?!” Deep Representation Learning for Audio-Based Video Game Genre Recognition
    IEEE Trans. Games (IF 1.886) Pub Date : 2019-01-21
    Shahin Amiriparian; Nicholas Cummins; Maurice Gerczuk; Sergey Pugachevskiy; Sandra Ottl; Björn Schuller

    In this paper, we present a novel computer audition task: audio-based video game genre classification. The aim of this study is threefold: 1) to check the feasibility of the proposed task; 2) to introduce a new corpus: The Game Genre by Audio + Multimodal Extracts (G $^{2}$ AME), collected entirely from social multimedia; and 3) to compare the efficacy of various acoustic feature spaces to classify

  • Comparison Training for Computer Chinese Chess
    IEEE Trans. Games (IF 1.886) Pub Date : 2019-01-16
    Jr-Chang Chen; Wen-Jie Tseng; I-Chen Wu; Ting-Han Wei

    This paper describes the application of modified comparison training for automatic feature weight tuning. The final objective is to improve the evaluation functions used in Chinese chess programs. First, we apply n -tuple networks to extract features. N -tuple networks require very little expert knowledge through its large numbers of features, while simultaneously allowing easy access. Second, we propose

  • Self-Adaptive Monte Carlo Tree Search in General Game Playing
    IEEE Trans. Games (IF 1.886) Pub Date : 2018-12-03
    Chiara F. Sironi; Jialin Liu; Mark H. M. Winands

    Many enhancements for Monte Carlo tree search (MCTS) have been applied successfully in general game playing (GGP). MCTS and its enhancements are controlled by multiple parameters that require extensive and time-consuming offline optimization. Moreover, as the played games are unknown in advance, offline optimization cannot tune parameters specifically for single games. This paper proposes a self-adaptive

  • Dual Indicators to Analyze AI Benchmarks: Difficulty, Discrimination, Ability, and Generality
    IEEE Trans. Games (IF 1.886) Pub Date : 2018-11-28
    Fernando Martínez-Plumed; José Hernández-Orallo

    With the purpose of better analyzing the result of artificial intelligence (AI) benchmarks, we present two indicators on the side of the AI problems, difficulty and discrimination , and two indicators on the side of the AI systems, ability and generality . The first three are adapted from psychometric models in item response theory (IRT), whereas generality is defined as a new metric that evaluates

  • Toward Personalized Adaptive Gamification: A Machine Learning Model for Predicting Performance
    IEEE Trans. Games (IF 1.886) Pub Date : 2018-11-27
    Christian López; Conrad Tucker

    Personalized adaptive gamification has the potential to improve individuals’ motivation and performance. Current methods aim to predict the perceived affective state (i.e., emotion) of an individual in order to improve their motivation and performance by tailoring an application. However, existing methods may struggle to predict the state of an individual that it has not been trained for. Moreover

  • A Mobile Game for Automatic Emotion-Labeling of Images.
    IEEE Trans. Games (IF 1.886) Pub Date : 2018-10-22
    Haik Kalantarian,Khaled Jedoui,Peter Washington,Dennis P Wall

    In this short paper, we describe challenges in the development of a mobile charades-style game for delivery of social training to children with autism spectrum disorder (ASD). Providing real-time feedback and adapting game difficulty in response to the child's performance necessitates the integration of emotion classifiers into the system. Due to the limited performance of existing emotion recognition

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