| ID | 30021 |
| フルテキストURL | |
| 著者 |
Ninomiya, Akira
Okayama University
Horiuchi, Tadashi
Matsue National College of Technology
Konishi, Tadataka
Chugoku Polytechnic College
Baba, Mitsuru
Okayama University
|
| 抄録 | This paper applies our state construction method by ART neural network to robot navigation problems. Agents in this paper consist of ART neural network and contradiction resolution mechanism. The ART neural network serves as a mean of state recognition which maps stimulus inputs to a certain state and state construction which creates a new state when a current stimulus input cannot be categorized into any known states. On the other hand, the contradiction resolution mechanism (CRM) uses agents' state transition table to detect inconsistency among constructed states. In the proposed method, two kinds of inconsistency for the CRM are introduced: "Different results caused by the same states and the same actions" and "Contradiction due to ambiguous states." The simulation results on the robot navigation problems confirm the effectiveness of the proposed method |
| キーワード | Adaptive State Construction
ART Neural Network
Reinforcement Learning
|
| 備考 | Digital Object Identifier: 10.1109/ICSMC.2001.973484
Published with permission from the copyright holder. This is the institute's copy, as published in Systems, Man, and Cybernetics, 2001 IEEE International Conference on, 7-10 Oct. 2001, Vol. 3, Pages 1436-1441. Publisher URL:http://dx.doi.org/10.1109/ICSMC.2001.973484 Copyright © 2001 IEEE. All rights reserved. |
| 発行日 | 2001-10
|
| 出版物タイトル |
Systems
|
| 巻 | 3巻
|
| 開始ページ | 1436
|
| 終了ページ | 1441
|
| 資料タイプ |
学術雑誌論文
|
| 言語 |
英語
|
| 査読 |
有り
|
| DOI | |
| Submission Path | industrial_engineering/35
|
