A weighted-tree similarity algorithm for multi-agent systems in e-Business environments
A tree similarity algorithm for match-making of agents in e-Business environments is presented. Product/service descriptions of seller and buyer agents are represented as node-labeled, arc-labeled, arc-weighted trees. A similarity algorithm for such trees is developed as the basis for semantic match-making in a virtual marketplace. The trees are exchanged using an XML serialization in Object-Oriented RuleML. Correspondingly, we use the declarative language Relfun to implement the similarity algorithm as a parameterized, recursive functional program. Three main recursive functions perform a top-down traversal of trees and the bottom-up computation of similarity. Results from our experiments aiming to match buyers and sellers are found to be effective and promising for e-Business/e-Learning environments. The algorithm can be applied in all environments where weighted trees are used.
multi-agent system e-business arc-weighted trees object-oriented ruleml relfun e-learning similarity measure buyer-seller matching arc-labeled trees