Classical Conditioning in Social Robots

被引:0
|
作者
Novianto, Rony [1 ]
Williams, Mary-Anne [1 ]
Gaerdenfors, Peter [2 ]
Wightwick, Glenn [3 ]
机构
[1] Univ Technol Sydney, Quantum Computat & Intelligent Syst, Sydney, NSW 2007, Australia
[2] Lund Univ, Lund, Sweden
[3] Univ Technol Sydney, Ultimo, Australia
来源
SOCIAL ROBOTICS | 2014年 / 8755卷
关键词
Classical Conditioning; Maximum Likelihood Estimation; ASMO Cognitive Architecture;
D O I
暂无
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Classical conditioning is important in humans to learn and predict events in terms of associations between stimuli and to produce responses based on these associations. Social robots that have a classical conditioning skill like humans will have an advantage to interact with people more naturally, socially and effectively. In this paper, we present a novel classical conditioning mechanism and describe its implementation in ASMO cognitive architecture. The capability of this mechanism is demonstrated in the Smokey robot companion experiment. Results show that Smokey can associate stimuli and predict events in its surroundings. ASMO's classical conditioning mechanism can be used in social robots to adapt to the environment and to improve the robots' performances.
引用
收藏
页码:279 / 289
页数:11
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