The trace-reinforced ants process does not find shortest paths
Journal de l’École polytechnique — Mathématiques, Volume 9 (2022), pp. 505-536.

In this paper, we study a probabilistic reinforcement-learning model for ants searching for the shortest path(s) from their nest to a food source, represented here by two vertices of a finite graph. In this model, the ants each take a random walk on the graph, starting from the nest vertex, until they reach the food vertex, and then reinforce the weight of the set of crossed edges. We show that if the graph is a finite tree, in which the set of leaves is identified with a single food vertex, and the root with the nest vertex, and if there is an edge between the nest and the food, then almost surely the proportion of ants that end up taking this last edge tends to 1. On the other hand we show on three other examples that in general ants do not always choose the shortest path. Our techniques use stochastic approximation methods, as well as couplings with urn processes.

Nous étudions un modèle de renforcement par apprentissage pour les fourmis cherchant le chemin le plus court pour aller de leur nid à une source de nourriture, représentés ici par deux sommets d’un graphe fini. Dans ce modèle les fourmis accomplissent chacune à leur tour une marche aléatoire sur le graphe, partant du sommet nid, jusqu’à atteindre le sommet nourriture, puis renforcent le poids de l’ensemble des arêtes traversées. Nous montrons que si le graphe est un arbre fini, dans lequel l’ensemble des feuilles sont identifiées à un seul sommet nourriture, et la racine au sommet nid, et s’il existe une arête entre le nid et la nourriture, alors presque sûrement la proportion de fourmis qui finit par emprunter cette dernière arête tend vers 1. D’un autre côté nous montrons sur trois autres exemples qu’en général les fourmis ne choisissent pas toujours le chemin le plus court. Nos techniques utilisent des méthodes d’approximation stochastique, ainsi que des couplages avec des processus d’urnes.

Received:
Accepted:
Published online:
DOI: 10.5802/jep.188
Classification: 60F15, 60K35
Keywords: Reinforced processes, stochastic approximation, urn processes
Mot clés : Processus renforcés, approximation stochastique, processus d’urnes
Daniel Kious 1; Cécile Mailler 1; Bruno Schapira 2

1 Department of Mathematical Sciences, University of Bath Claverton Down, BA2 7AY Bath, UK.
2 Aix-Marseille Université, CNRS, Centrale Marseille, I2M, UMR 7373 13453 Marseille, France
License: CC-BY 4.0
Copyrights: The authors retain unrestricted copyrights and publishing rights
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Daniel Kious; Cécile Mailler; Bruno Schapira. The trace-reinforced ants process does not find shortest paths. Journal de l’École polytechnique — Mathématiques, Volume 9 (2022), pp. 505-536. doi : 10.5802/jep.188. https://jep.centre-mersenne.org/articles/10.5802/jep.188/

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