Ant-inspired density estimation via random walks
Abstract
Highly complex distributed algorithms are ubiquitous in nature: from the behavior of social insect colonies and bird flocks, to cellular differentiation in embryonic development, to neural information processing. In our research, we study biological computation theoretically, combining a scientific perspective, which seeks to better understand the systems being studied, with an engineering perspective, which takes inspiration from these systems to improve algorithm design. In this work, we focus on the problem of population density estimation in ant colonies, demonstrating that extremely simple algorithms, similar to those used by ants, solve the problem with strong theoretical guarantees and have a number of interesting computational applications.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Sep 19, 2017
- Source ID
- 10.1073/pnas.1706439114
Entities
People
- Cameron Musco
- Hsin-hao Su
- Nancy Lynch
Organizations
- Air Force Office of Scientific Research
- Massachusetts Institute of Technology
- National Science Foundation