Paper Info
Title
New learning algorithm for hierarchical structure learning automata operating in p-model stationary random environment
Abstract
In this paper, based on the concept of Discretized Generalized Pursuit Algorithm (DGPA), the discretized generalized pursuit hierarchical structure learning algorithm is constructed which is applied to the hierarchical structure learning automata oprating in the P-model stationary random environment. The efficacy of our algorithm is demonstrated by the numerical simulation, in which the hierarchical structure learning automata is applied to the problem of the mobile robots going through an unknown maze (the maze passage problem of mobile robots).
Year
DOI
Venue
2005
10.1007/11596448_16
CIS (1)
Keywords
Field
DocType
p-model stationary random environment,automata operating,mobile robot,discretized generalized pursuit algorithm,unknown maze,hierarchical structure,discretized generalized pursuit,automata oprating,numerical simulation,maze passage problem,new learning algorithm
Discretization,Computer simulation,Computer science,Automaton,Structure learning,Algorithm,Artificial intelligence,Mobile robot,Machine learning,Random environment
Conference
Volume
ISSN
ISBN
3801
0302-9743
3-540-30818-0
Citations 
PageRank 
References 
0
0.34
4
Authors
1
Name
Order
Citations
PageRank
Yoshio Mogami1308.63