Koopman Spectral Kernels for Comparing Complex Dynamics: Application to Multiagent Sport Plays

Keisuke Fujii, Yuki Inaba, Yoshinobu Kawahara

研究成果: 書籍/レポート タイプへの寄稿会議への寄与

16 被引用数 (Scopus)

抄録

Understanding the complex dynamics in the real-world such as in multi-agent behaviors is a challenge in numerous engineering and scientific fields. Spectral analysis using Koopman operators has been attracting attention as a way of obtaining a global modal description of a nonlinear dynamical system, without requiring explicit prior knowledge. However, when applying this to the comparison or classification of complex dynamics, it is necessary to incorporate the Koopman spectra of the dynamics into an appropriate metric. One way of implementing this is to design a kernel that reflects the dynamics via the spectra. In this paper, we introduced Koopman spectral kernels to compare the complex dynamics by generalizing the Binet-Cauchy kernel to nonlinear dynamical systems without specifying an underlying model. We applied this to strategic multiagent sport plays wherein the dynamics can be classified, e.g., by the success or failure of the shot. We mapped the latent dynamic characteristics of multiple attacker-defender distances to the feature space using our kernels and then evaluated the scorability of the play by using the features in different classification models.

本文言語英語
ホスト出版物のタイトルMachine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2017, Proceedings
編集者Michelangelo Ceci, Saso Dzeroski, Donato Malerba, Yasemin Altun, Kamalika Das, Jesse Read, Marinka Zitnik, Jerzy Stefanowski, Taneli Mielikäinen
出版社Springer Verlag
ページ127-139
ページ数13
ISBN(印刷版)9783319712727
DOI
出版ステータス出版済み - 2017
外部発表はい
イベントEuropean Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML PKDD 2017 - Skopje, マケドニア
継続期間: 9月 18 20179月 22 2017

出版物シリーズ

名前Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
10536 LNAI
ISSN(印刷版)0302-9743
ISSN(電子版)1611-3349

会議

会議European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML PKDD 2017
国/地域マケドニア
CitySkopje
Period9/18/179/22/17

!!!All Science Journal Classification (ASJC) codes

  • 理論的コンピュータサイエンス
  • コンピュータ サイエンス(全般)

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