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A Modified Multi-size Convolution Neural Network for Winner Prediction Based on Time Serial Datasets

Published: 12 April 2019 Publication History

Abstract

Researches of AI planning in Real-Time Strategy (RTS) games have been widely applied to human behavior modeling and combat simulation. Winner prediction is an important research area for AI planning, which ensures the decision accuracy. Convolution neural network has proved effective in predicting winner for RTS games. This paper focuses on modify the neural network to handle the time serial datasets. Experiments show that the modified evaluating algorithm can effectively improve the accuracy of winner prediction for time serial data in RTS games.

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Cited By

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  • (2023)An Integrated Framework for Team Formation and Winner Prediction in the FIRST Robotics Competition: Model, Algorithm, and Analysis2023 IEEE International Conference on High Performance Computing & Communications, Data Science & Systems, Smart City & Dependability in Sensor, Cloud & Big Data Systems & Application (HPCC/DSS/SmartCity/DependSys)10.1109/HPCC-DSS-SmartCity-DependSys60770.2023.00126(868-876)Online publication date: 17-Dec-2023
  • (2021)RTS Game AI Robots Winner Prediction Based on Replay Data by using Deep LearningProceedings of the 2021 9th International Conference on Information Technology: IoT and Smart City10.1145/3512576.3512596(115-122)Online publication date: 22-Dec-2021

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  1. A Modified Multi-size Convolution Neural Network for Winner Prediction Based on Time Serial Datasets

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    cover image ACM Other conferences
    ICMAI '19: Proceedings of the 2019 4th International Conference on Mathematics and Artificial Intelligence
    April 2019
    232 pages
    ISBN:9781450362580
    DOI:10.1145/3325730
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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    • Southwest Jiaotong University
    • Xihua University: Xihua University

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    Published: 12 April 2019

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    Author Tags

    1. Google-Net
    2. LSTM algorithm
    3. Real-time strategy games
    4. Winner prediction

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    • (2023)An Integrated Framework for Team Formation and Winner Prediction in the FIRST Robotics Competition: Model, Algorithm, and Analysis2023 IEEE International Conference on High Performance Computing & Communications, Data Science & Systems, Smart City & Dependability in Sensor, Cloud & Big Data Systems & Application (HPCC/DSS/SmartCity/DependSys)10.1109/HPCC-DSS-SmartCity-DependSys60770.2023.00126(868-876)Online publication date: 17-Dec-2023
    • (2021)RTS Game AI Robots Winner Prediction Based on Replay Data by using Deep LearningProceedings of the 2021 9th International Conference on Information Technology: IoT and Smart City10.1145/3512576.3512596(115-122)Online publication date: 22-Dec-2021

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