Multi‑Agent Foraging: state‑of‑the‑art and research challenges

Ouarda Zedadra (1) , Nicolas Jouandeau (2) , Hamid Seridi (1) , Giancarlo Fortino (3)

1 جامعة 8 مايو 45، قالمة [الجزائر] = Université du 8 mai 1945 Guelma [Algérie] = University 8 mai 1945 Guelma [Algeria]
2 LIASD - Laboratoire d'Informatique Avancée de Saint-Denis
3 DEIS - Dipartimento di Elettronica Informatica e Sistemistica [Rende]

The foraging task is one of the canonical testbeds for cooperative robotics, in which a collection of robots has to search and transport objects to specific storage point(s). In this paper, we investigate the Multi-Agent Foraging (MAF) problem from several perspectives that we analyze in depth. First, we define the Foraging Problem according to literature definitions. Then we analyze previously proposed taxonomies, and propose a new foraging taxonomy characterized by four principal axes: Environment, Collective, Strategy and Simulation, summarize related foraging works and classify them through our new foraging taxonomy. Then, we discuss the real implementation of MAF and present a comparison between some related foraging works considering important features that show extensibility, reliability and scalability of MAF systems. Finally we present and discuss recent trends in this field, emphasizing the various challenges that could enhance the existing MAF solutions and make them realistic.

Mots clés: Swarm robotics; Multiagents systems MAS; Foraging behavior; Swarm intelligence

Domaines: Intelligence artificielle [cs.AI]

Ouarda Zedadra, Nicolas Jouandeau, Hamid Seridi, Giancarlo Fortino. Multi‑Agent Foraging: state‑of‑the‑art and research challenges. Complex Adaptive Systems Modeling, 2017, 5 (1), ⟨10.1186/s40294-016-0041-8⟩. ⟨hal-02317268⟩