Category: News

CogSci2022などでの発表

CogSci 2022とJCoLE2022, NALOMA 2022にて 以下4件の発表を行います。

  • Torii, T., Maeda, A., & Hidaka, S. (2022). Embedding parallelepiped in co-occurrence matrix: simulation and empirical evidence. Joint Conference on Language Evolution (JCoLE2022).
  • Akihiro Maeda, Takuma Torii and Shohei Hidaka (2022). Parallelogram structure of analogy in word co-occurrence matrix, Natural Logic Meets Machine Learning III Workshop @ESSLLI 2022, August 8-12 2022.
  • Miyamoto, M. & Hidaka, S. (2022). Identifying a Phonetic Factors of Onomatopoeias Correlated to Sound Symbolic Commons between Japanese and Non-Japanese Speakers. The 44th Annual Meeting of the Cognitive Science Society (CogSci2022). (link)
  • Imai, M. et al. (2022). The contingency symmetry bias as a foundation of word learning: Evidence from 8-mont-olds on a matching-to-sample task. The 44th Annual Meeting of the Cognitive Science Society (CogSci2022). (link)

挑戦的研究(萌芽) 2022

以下の競争的研究資金を獲得いたしました。引き続き研究に励んで参ります。

  • 令和4年度科学研究費補助金挑戦的研究(萌芽), 日本学術振興会, 研究課題「四次元知覚可能性の実証」(代表：日髙昇平). (JSPS KAKENHI Grant-in-Aid for Challenging Research (Exploratory) JP22K19790) (link)

論文掲載 (Artificial Life and Robotics)と今後の発表予定

昨年AROB2021で発表した研究を改訂した論文がArtificial Life and Robotics誌に掲載されました。
Our paper, which was revised after the AROB conference 2021, has been published online.

Torii, T., Oguma, K. & Hidaka, S. Animacy perception of a pair of movements under quantitative control of its temporal contingency: a preliminary study. Artificial Life and Robotics (2022). (link)
また、2022年3月3日に第16回錯覚ワークショップ(online)にて以下の２件の発表を行う予定です。

プレスリリース

鳥居助教と日髙准教授の研究「失敗する他者の行動からその意図を推定する人工知能技術を開発」についてのプレスリリースを行いました。詳細はリンク先よりご覧ください。

【論文情報】

掲載誌 Neural Computation
論文題目 Completion of the Infeasible Actions of Others: Goal Inference by Dynamical Invariant
著者 Takuma Torii, Shohei Hidaka
掲載日 2021年10月12日(米国東部標準時間)にオンライン版に掲載
DOI 10.1162/neco_a_01437

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！

saitophd

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.

関連論文:
(1) 模倣学習の機序解明に向けた意図推定のモデル化：倒立振子課題の分析.
(2) 模倣の計算理論に向けて：力学的不変量による模倣機序の説明
(3) 科研費研究課題「力学系の不変量による身体運動の分節化・認識・生成理論の構築」