News – Page 4 – Cognitive Computation Lab

学位記授与式(6月修了)

日髙研の博士後期課程斉藤功樹さんが、短期修了(2年9か月)・優秀修了者として博士（知識科学）の学位を取得しました。

日髙研としては初の博士学位の取得者です。今後も優れた研究を継続していくことを期待しています！

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Entropy誌に論文が採録

北陸先端科学技術大学院大学の日髙昇平准教授と鳥居拓馬助教との共著の論文が6月8日付でEntropy誌に採録されました。

Hidaka, S. & Torii, T. (2021). Designing bivariate auto-regressive timeseries with controlled Granger causality. Entropy. 23(6), 742. (link)

Abstract:
In this manuscript, we analyzed a bivariate vector auto-regressive (VAR) model in order to draw a design principle of a timeseries with a controlled statistical inter-relationship. We show how to generate bivariate timeseries with given covariance and Granger causality (or equivalently transfer entropy), and show the trade-off relationship between these two types of statistical interaction. In principle, covariance and Granger causality are independently controllable, but the feasible ranges of their values, which allow the VAR to be proper and have a stationary distribution, are constrained by each other. Thus, our analysis identiﬁes the essential tri-lemma structure among the stability and properness of VAR, the controllability of covariance, and that of Granger causality.

Keywords: Granger causality; Transfer entropy; Vector auto-regressive model; Lyapunov equation

関連論文:
(1) Kenichi Oguma, Takuma Torii, Shohei Hidaka (2021). Animacy perception of a pair of movements under quantitative control of its temporal contingency: a preliminary study. In Proceedings of The 26th International Symposium on Artificial Life and Robotics (AROB2021). 274-279. (GS12-1) (Presented online on January, 22th, 2021) (link) (pdf)

(2) Hidaka, S., & Torii, T. (2021). Designing bivariate timeseries with controlled Granger causality. psyarxiv. (link)

(3) 科研費研究課題「潜在的な階層性をもつ非言語行為の意図推定過程の解明」

(4) JSTさきがけ (信頼されるAIの基盤技術領域) 研究課題「機械理解の創成に向けた随伴関手の統計的推定理論の構築」

Neural Computation誌に論文が採録

北陸先端科学技術大学院大学の鳥居拓馬助教と日髙昇平准教授の共著の論文が6月4日付でNeural Computation誌に採録されました。

Torii, T. & Hidaka, S. (accepted). Completion of the infeasible actions of others: Goal inference by dynamical invariant. Neural Computation.

Abstract:
To help another person, we need to infer his/her goal and intention, and then perform the action that he/she was unable to perform to meet his/her intended goal. In this study, we investigate a computational mechanism for inferring someone’s intention and goal from his/her incomplete action to enable the action to be completed on their behalf. As a minimal and idealized motor control task of this type, we analyzed single-link pendulum control tasks by manipulating the underlying goals. By analyzing behaviors generated by multiple types of pendulum control tasks, we found that a type of fractal dimension of movements is characteristic of the difference in the underlying motor controllers, which reflect the difference in the underlying goals. To test whether an incomplete action can be completed using this property of the action trajectory, we demonstrated that the simulated pendulum controller can perform an action in the direction of the underlying goal by using the fractal dimension as a criterion for similarity in movements.

さきがけ(信頼されるAI領域)に採択

さきがけ・信頼されるAIの基盤技術に以下の研究課題が採択されました(2020年12月開始)。

課題名：機械理解の創成に向けた随伴関手の統計的推定理論の構築
研究者：日髙　昇平
概要：
本研究は、再現性と検証可能性をもち、見方・解釈に伴ってその意味付けが変わる認知現象の一つとして、曖昧図形の知覚に着目する。解釈・理解を圏、データの解釈への対応づけを関手とする「随伴関手の統計的推定」モデルにより、曖昧図形の知覚の数理構造を説明し、これを実験的に検証する。この研究の過程で新たに生まれる、圏論を基礎とした情報技術・統計的モデリングを概念実証として、「機械理解」分野創生の基礎を構築する。