An ACS cooperative learning approach for route finding in natural environment

This paper introduces an ant-based colony system for the representation of a verbal route description. It is grounded on a natural metaphor that mimics the behavior of ant colonies. While conventional ant-based algorithms are based on the optimization of path strategies on an existing network, the approach presented in this paper differs in the way the network is dynamically derived during the optimization process, and evaluated according to its degree of match regarding the semantics exhibited by a verbal route description. The algorithm is applied to a route searching process in a natural environment, and studied in terms of its performance capabilities. Categories and Subject Descriptors I.2.8 [Artificial Intelligence]: Distributed Artificial Intelligence - Intelligent agents General Terms Algorithms, Human factors, Performance Keywords Verbal route description, route finding, ant colony algorithm

• Type: Journal
• Year: 2008
• Where: GIS
• Authors: David Brosset, Christophe Claramunt, Eric Saux
• Keywords: Algorithm, Artificial Intelligence, Automated Reasoning, GIS 2008, Verbal Route Description