The Robustness of Relaxation Rates in Constraint Satisfaction Networks

Citation data:

IEEE

Publication Year:
1999
Usage 5
Downloads 5
Repository URL:
https://scholarsarchive.byu.edu/facpub/1124; https://scholarsarchive.byu.edu/cgi/viewcontent.cgi?article=2123&context=facpub
Author(s):
Martinez, Tony R.; Ventura, Dan A.; Wilson, D. Randall; Moncur, Brian
Publisher(s):
IEEE
Tags:
relaxation rate; constraint satisfaction networks; Computer Sciences
article description
Constraint satisfaction networks contain nodes that receive weighted evidence from external sources and/or other nodes. A relaxation process allows the activation of nodes to affect neighboring nodes, which in turn can affect their neighbors, allowing information to travel through a network. When doing discrete updates (as in a software implementation of a relaxation network), a goal net or goal activation can be computed in response to the net input into a node, and a relaxation rate can then be used to determine how fast the node moves from its current value to its goal value. An open question was whether or not the relaxation rate is a sensitive parameter. This paper shows that the relaxation rate has almost no effect on how information flows through the network as long as it is small enough to avoid large discrete steps and/or oscillation.