Title :
A construction of neural-net based AI systems
Author :
Kawada, Manabu ; Wu, Xu ; Ae, Tadashi
Author_Institution :
Dept. of Electron. Eng., Hiroshima Univ., Japan
Abstract :
The paper investigates the integration of the neural network technique and traditional AI techniques towards the realization of a real-time neuron-based AI architecture. As the first step of our project, we propose a neural network based AI system, called NAI. NAI is a kind of real-time CBR (case-based reasoning) system in which the WTA (winner-take-all) type neural network is embedded for supporting the real-time classification and retrieval of a massive case-base. For flexible learning with the WTA neural network, two learning algorithms (supervised and unsupervised) have been developed on the basis of the LVQ1 and self-organizing learning algorithms
Keywords :
algorithm theory; case-based reasoning; knowledge based systems; learning (artificial intelligence); neural net architecture; pattern classification; real-time systems; LVQ1 learning algorithm; NAI; flexible learning; learning algorithms; massive case-base; neural network technique; neural-net based AI system construction; real-time case-based reasoning system; real-time classification; real-time neuron-based AI architecture; real-time retrieval; self-organizing learning algorithm; supervised learning; unsupervised learning; winner-take-all type neural network; Artificial intelligence; Artificial neural networks; Expert systems; Intelligent networks; Knowledge engineering; Learning; Neural networks; Neurons; Organizing; Real time systems;
Conference_Titel :
Engineering of Complex Computer Systems, 1995. Held jointly with 5th CSESAW, 3rd IEEE RTAW and 20th IFAC/IFIP WRTP, Proceedings., First IEEE International Conference on
Conference_Location :
Ft. Lauderdale, FL
Print_ISBN :
0-8186-7123-8
DOI :
10.1109/ICECCS.1995.479369
