NeuralAC: Learning Cooperation and Competition Effects for Match Outcome Prediction

نویسندگان

چکیده

Match outcome prediction in group comparison setting is a challenging but important task. Existing works mainly focus on learning individual effects or mining limited interactions between teammates, which not sufficient for capturing complex teammates as well opponents. Besides, the importance of interacting with different characters still largely underexplored. To this end, we propose novel Neural Attentional Cooperation-competition model (NeuralAC), incorporates weighted-cooperation (i.e., intra-team interactions) and weighted-competition inter-team predicting match outcomes. Specifically, first project individuals to latent vectors learn through deep neural networks. Then, design two attention-based mechanisms capture interactions, enhance NeuralAC both accuracy interpretability. Furthermore, demonstrate can generalize several previous works. evaluate performances NeuralAC, conduct extensive experiments four E-sports datasets. The experimental results clearly verify effectiveness compared state-of-the-art methods.

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ژورنال

عنوان ژورنال: Proceedings of the ... AAAI Conference on Artificial Intelligence

سال: 2021

ISSN: ['2159-5399', '2374-3468']

DOI: https://doi.org/10.1609/aaai.v35i5.16528