The robustness of relaxation rates in constraint satisfaction networks
نویسندگان
چکیده
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.
منابع مشابه
On the Parallel Complexity of Some Constraint Satisfaction Problems
Constraint satisfaction networks have been shown to be a very useful tool for knowledge representation in Artificial Intelligence applications. These networks often utilize local constraint propagation techniques to achieve global consistency (consistent labelling in vision). Such methods have been used extensively in the context of image understanding and interpretation, as well as planning, n...
متن کاملOptimal Coding Subgraph Selection under Survivability Constraint
Nowadays communication networks have become an essential and inevitable part of human life. Hence, there is an ever-increasing need for expanding bandwidth, decreasing delay and data transfer costs. These needs necessitate the efficient use of network facilities. Network coding is a new paradigm that allows the intermediate nodes in a network to create new packets by combining the packets recei...
متن کاملSupervised Texture Classification Using a Probabilistic Neural Network and Constraint Satisfaction M - Neural Networks, IEEE Transactions on
In this paper, the texture classification problem is projected as a constraint satisfaction problem. The focus is on the use of a probabilistic neural network (PNN) for representing the distribution of feature vectors of each texture class in order to generate a feature-label interaction constraint. This distribution of features for each class is assumed as a Gaussian mixture model. The feature...
متن کاملRelaxation of Qualitative Constraint Networks
In this paper, we propose to study the interest of relaxing qualitative constraints networks by using the formalism of discrete Constraint Satisfaction Problem (CSP). This allows us to avoid the introduction of new definitions and properties in the domain of qualitative reasoning. We first propose a general (but incomplete) approach to show the unsatisfiability of qualitative networks, by using...
متن کاملImage segmentation by relaxation using constraint satisfaction neural network
The problem of image segmentation using constraint satisfaction neural networks (CSNN) has been considered. Several variations of the CSNN theme have been advanced to improve its performance or to explore new structures. These new segmentation algorithms are based on interplay of additional constraints, of varying the organization of the network or modifying the relaxation scheme. The proposed ...
متن کامل