Distributed House-Hunting in Ant Colonies

• Main Authors: Ghaffari, Mohsen (Contributor), Musco, Cameron Nicholas (Contributor), Radeva, Tsvetomira T. (Contributor), Lynch, Nancy Ann (Contributor)
• Other Authors: Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory (Contributor), Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science (Contributor)
• Format: Article
• Language: English
• Published: Association for Computing Machinery (ACM), 2016-01-15T01:30:30Z.
• Subjects: Article
• Online Access: Get fulltext

We introduce the study of the ant colony house-hunting problem from a distributed computing perspective. When an ant colony's nest becomes unsuitable due to size constraints or damage, the colony relocates to a new nest. The task of identifying and evaluating the quality of potential new nests is distributed among all ants. They must additionally reach consensus on a final nest choice and transport the full colony to this single new nest. Our goal is to use tools and techniques from distributed computing theory in order to gain insight into the house-hunting process. We develop a formal model for the house-hunting problem inspired by the behavior of the Temnothorax genus of ants. We then show a Omega(log n) lower bound on the time for all n ants to agree on one of k candidate nests. We also present two algorithms that solve the house-hunting problem in our model. The first algorithm solves the problem in optimal O(log n) time but exhibits some features not characteristic of natural ant behavior. The second algorithm runs in O(k log n) time and uses an extremely simple and natural rule for each ant to decide on the new nest.
United States. Air Force Office of Scientific Research (Contract FA9550-13-1-0042)
National Science Foundation (U.S.) (Award 0939370-CCF)
National Science Foundation (U.S.) (Award CCF-AF-0937274)