An experimental ant colony approach for the geolocation of verbal route descriptions

This paper introduces an experimental cooperative and stochastic algorithm for the derivation of spatial routes that fits the semantics of a verbal route description in natural environments. The algorithm mimics the behavior of ants, where positive feedbacks consist of pheromone trails, deposited on attractive paths. The novelty of the approach relies on the integration of the semantics of a verbal route description within the heuristic of the search algorithm. A route is modeled using a graph-based description where landmarks and spatial relationships play a central role. The algorithm is experimented and illustrated by a prototype implementation applied to foot orienteering. Preliminary computational experiments show that the ant colony developed and applied to route finding in natural environments performs relatively well when compared with other meta-heuristics.