Future Prediction with Hierarchical Episodic Memories under Deterministic and Stochastic Environments

Material type: 図書

Author: 青田, 佳士ほか

Publisher: Springer-Verlag Berlin Heidelberg

Publication date: 2012

Material Format: Paper

Capacity, size, etc.: -

NDC: -

View All

Notes on use

Note (General)：: In agreement with Bond’s suggestion, we consider that episodic memories are hierarchized autonomously by simple rule. In this research, our model solv...

Search by Bookstore

Holdings of Libraries in Japan

This page shows libraries in Japan other than the National Diet Library that hold the material.

List of Cooperating Institutions and Databases

Please contact your local library for information on how to use materials or whether it is possible to request materials from the holding libraries.

other

Tokyo Tech Research Repository
Paper
You can check the holdings of institutions and databases with which 学術機関リポジトリデータベース（IRDB）（機関リポジトリ） is linked at the site of 学術機関リポジトリデータベース（IRDB）（機関リポジトリ）.
Tokyo Tech Research Repository

Search by Bookstore

Bibliographic Record

You can check the details of this material, its authority (keywords that refer to materials on the same subject, author's name, etc.), etc.

Paper

Material Type: 図書
Title: Future Prediction with Hierarchical Episodic Memories under Deterministic and Stochastic Environments
Author/Editor: 青田, 佳士
Aota, Yoshito
三宅, 美博
Miyake, Yoshihiro
Author Heading: 青田, 佳士
Aota, Yoshito
三宅, 美博
Miyake, Yoshihiro
Publication, Distribution, etc.: Springer-Verlag Berlin Heidelberg
Publication Date: 2012
Publication Date (W3CDTF): 2012
Periodical title: T. Huang et al. (Eds.): ICONIP 2012, Part I, LNCS 7663
Text Language Code: eng
Subject Heading: episodic memory, hierarchization, maze task, POMDPs, episodic future thinking, prediction, environment modeling, incremental learning
Target Audience: 一般
Note (General): In agreement with Bond’s suggestion, we consider that episodic memories are hierarchized autonomously by simple rule. In this research, our model solves maze tasks. Each episodic memory corresponds to the model’s each track. In our previous research, we suggested that our model concatenates episodic memories into one long episodic memory. Our previous model showed successful prediction of any long periodical and deterministic environmental changes with editing (selecting and concatenating with adequate timing) stored episodic memories autonomously. However, the previous models could not select adequate actions under a stochastic environment like POMDPs. Here, we suggest hierarchical episodic memories implement into the model. It is shown that the model improved not only their action under POMDPs but also prediction of long-term environmental change and incremental learning.
identifier:oai:t2r2.star.titech.ac.jp:50278988
Data Provider (Database): 国立情報学研究所 : 学術機関リポジトリデータベース（IRDB）（機関リポジトリ）
https://irdb.nii.ac.jp
Original Data Provider (Database): 東京工業大学 : 東京工業大学リサーチリポジトリ