A game playing robot that can learn from experience

Raafat Ahmed Abd El-Azim, A. Ueno, S. Tatsumi
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引用次数: 3

Abstract

We present a new approach for online learning an x-o game strategy by humanoid robot ldquohoap-3rdquo*. No preset data for game playing are provided in advance. The proposed system mechanism simulates human decision making to carry out the online game learning. ldquohaop-3rdquo autonomously gains experience needed for learning the game strategy from the human partner. The more intelligent human partner, the faster humanoid robot ldquohoap-3rdquo learning.
一个可以从经验中学习的游戏机器人
提出了一种由仿人机器人ldquohoap-3rdquo*在线学习x-o博弈策略的新方法。没有预先提供游戏数据。提出的系统机制模拟人的决策来进行网络游戏学习。Ldquohaop-3rdquo自动从人类伙伴那里获得学习游戏策略所需的经验。人类伴侣越聪明,仿人机器人的学习速度就越快。
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