DocumentCode :
423675
Title :
Response knowledge learning of autonomous agent
Author :
Chow, Chi-Kin ; Tsui, Hung-Tat
Author_Institution :
Dept. of Electron. Eng., Chinese Univ. of Hong Kong, China
Volume :
2
fYear :
2004
fDate :
25-29 July 2004
Firstpage :
1133
Abstract :
In robot applications, the performance of a robot agent is measured by the award received from its response. A lot of literature defines the response as either a state diagram or a neural network. Due to the absence of a desired response, neither is applicable to an unstructured environment. In this paper, a novel response knowledge learning algorithm is proposed to handle this domain. By using a set of experiences, the algorithm can extract the contributed experiences to construct the response function. Two sets of environments are provided to illustrate the performance of the proposed algorithm. The results show that it can effectively construct a response function that receives an award which is very close to the true maximum.
Keywords :
knowledge engineering; learning (artificial intelligence); robots; autonomous agent; neural network; response function; response knowledge learning algorithm; robot agent; robot applications; state diagram; Automatic control; Autonomous agents; Control systems; Hidden Markov models; Laboratories; Neural networks; Robotics and automation; Robots; Signal processing; Signal processing algorithms;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Neural Networks, 2004. Proceedings. 2004 IEEE International Joint Conference on
ISSN :
1098-7576
Print_ISBN :
0-7803-8359-1
Type :
conf
DOI :
10.1109/IJCNN.2004.1380094
Filename :
1380094
Link To Document :
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