Stigmergic landmark foraging

By N. Lemmens, Karl Tuyls. 2009

15 citations recorded by the source corpus.

In this paper, we describe a nature-inspired optimization algorithm based on bee foraging behavior. This algorithm combines the high performance of bee path-integration navigation with ant-like stigmergic behavior in the form of landmarks. More precisely, each individual landmark can be created at any walkable state in the environment and contains a collection of direction markers with which visiting agents can find their way in an unknown environment. A landmark can either be represented by an agent or any other information distributing object (e.g., a RFID). Essentially, we implement ant recruitment behavior based on pheromone. However, instead of using attracting or repelling pheromone in every state of the environment, we only update directional information at key locations in the environment. The resulting algorithm, which we call Stigmergic Landmark Foraging (SLF), proves to be very efficient in terms of building and adapting solutions.

Citations in the corpus

• Multiagent Learning: Basics, Challenges, and Prospects (2012, background): “A prime example is the formal link between coevolutionary algorithms and multiagent Q-learning established through the replicator equations from evolutionary game theory (EGT) (Panait, Tuyls, and Luke 2008), but also relations between swarm intelligence and reinforcement learning are being discovere”
• Bee-inspired foraging in an embodied swarm (2011, unknown): “We will also extend the embodied algorithm to mechanisms developed in simulation already, such as landmark navigation [4].”