A new formulation of adaptive categorization game of ART-type networks is described in this papet Learning automata dynamics is introduced into adaptive categorization game in ART-type networks so that the vigilance parameters p are adjusted to give variable sized clusters. Moreovel; repeated game analysis is also conducted on the learning automata based adaptive categorization game to study th...

A b s t r a c t l e m h a s c o n c e n t r a t e d t h e a t t e n t i o n o f m a n y r e s e a r c h e r s. T h e s t o c h a s t i c a u t o m a t o n a c t i n g i n a s t a t i o n a r y r a n L a k s h m i v a r a h a n a n d T h a t h a c h a r (1 9 7 2) ; N a r e n d r a a n d T h a t h a c h a r ((L a k s h m i v a r a h a n (1 9 8 1) ; N a r e n d r a a n d T h a t h a c h a r (

In this paper, we give an overview on some algorithms for learning automata. Starting with Biermann’s and Angluin’s algorithms, we describe some of the extensions catering for specialized or richer classes of automata. Furthermore, we survey their recent application to verification problems.

Deteministic finit state (DFA) automata have emerged as an effective tools for agent modeling applications. The problem of automata learning is to determine a DFA from a series of observation and has recently been studied extensively and a number of algorithms has been proposed. These algorithms can be divided into groups : supervised and unsupervised . In supervised algorithms, we have access ...