書誌事項
- タイトル別名
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- Significance of Function Emergence Approach based on End-to-end Reinforcement Learning as suggested by Deep Learning, and Novel Reinforcement Learning Using a Chaotic Neural Network toward Emergence of Thinking
- シンソウ ガクシュウ ガ シサ スル end-to-end キョウカ ガクシュウ ニ モトズク キノウソウハツ アプローチ ノ ジュウヨウセイ ト シコウ ノ ソウハツ ニ ムケタ カオスニューラルネット オ モチイタ アタラシイ キョウカ ガクシュウ
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説明
It is propounded that in order to avoid the “frame problem” or “symbol grounding<br> problem” and to create a way to analyze and realize human-like intelligence with higher<br> functions, it is not enough just to introduce deep learning, but it is significant to get<br> out of deeply penetrated “division into functional modules” and to take the approach of<br> “function emergence through end-to-end reinforcement learning.” The functions that<br> have been shown to emerge according to this approach in past works are summarized,<br>and the reason for the difficulty in the emergence of thinking that is a typical higher<br> function is made clear.<br> It is claimed that the proposed hypothesis that exploration grows towards think-<br>ing through learning, becomes a key to break through the difficulty. To realize that,<br>“reinforcement learning using a chaotic neural network” in which adding external ex-<br>ploration noises is not necessary is introduced. It is shown that a robot with two<br> wheels and a simple visual sensor can learn an obstacle avoidance task by using this<br> new reinforcement learning method.
収録刊行物
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- 認知科学
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認知科学 24 (1), 96-117, 2017
日本認知科学会
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詳細情報 詳細情報について
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- CRID
- 1390282679461498496
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- NII論文ID
- 130006038535
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- NII書誌ID
- AN1047304X
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- ISSN
- 18815995
- 13417924
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- NDL書誌ID
- 028395724
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- 本文言語コード
- ja
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- 資料種別
- journal article
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- データソース種別
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- JaLC
- NDLサーチ
- CiNii Articles
- KAKEN
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- 抄録ライセンスフラグ
- 使用不可