Ant-inspired density estimation via random walks
Many ant species use distributed population density estimation in applications ranging from quorum sensing, to task allocation, to appraisal of enemy colony strength. It has been shown that ants estimate local population density by tracking encounter rates: The higher the density, the more often the...
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| Format: | Article |
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National Academy of Sciences (U.S.)
2018
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| Online Access: | http://hdl.handle.net/1721.1/114568 https://orcid.org/0000-0003-2197-6806 https://orcid.org/0000-0003-3045-265X |
| _version_ | 1826210193340366848 |
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| author | Musco, Cameron Nicholas Su, Hsin-Hao Lynch, Nancy Ann |
| author2 | Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory |
| author_facet | Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory Musco, Cameron Nicholas Su, Hsin-Hao Lynch, Nancy Ann |
| author_sort | Musco, Cameron Nicholas |
| collection | MIT |
| description | Many ant species use distributed population density estimation in applications ranging from quorum sensing, to task allocation, to appraisal of enemy colony strength. It has been shown that ants estimate local population density by tracking encounter rates: The higher the density, the more often the ants bump into each other. We study distributed density estimation from a theoretical perspective. We prove that a group of anonymous agents randomly walking on a grid are able to estimate their density within a small multiplicative error in few steps by measuring their rates of encounter with other agents. Despite dependencies inherent in the fact that nearby agents may collide repeatedly (and, worse, cannot recognize when this happens), our bound nearly matches what would be required to estimate density by independently sampling grid locations. From a biological perspective, our work helps shed light on how ants and other social insects can obtain relatively accurate density estimates via encounter rates. From a technical perspective, our analysis provides tools for understanding complex dependencies in the collision probabilities of multiple random walks. We bound the strength of these dependencies using local mixing properties of the underlying graph. Our results extend beyond the grid to more general graphs, and we discuss applications to size estimation for social networks, density estimation for robot swarms, and random walk-based sampling for sensor networks. Keywords: population density estimation; random walk sampling; network exploration; ant colony; algorithms; biological distributed algorithms |
| first_indexed | 2024-09-23T14:45:24Z |
| format | Article |
| id | mit-1721.1/114568 |
| institution | Massachusetts Institute of Technology |
| last_indexed | 2024-09-23T14:45:24Z |
| publishDate | 2018 |
| publisher | National Academy of Sciences (U.S.) |
| record_format | dspace |
| spelling | mit-1721.1/1145682022-10-01T22:19:20Z Ant-inspired density estimation via random walks Musco, Cameron Nicholas Su, Hsin-Hao Lynch, Nancy Ann Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory Musco, Cameron Nicholas Su, Hsin-Hao Lynch, Nancy Ann Many ant species use distributed population density estimation in applications ranging from quorum sensing, to task allocation, to appraisal of enemy colony strength. It has been shown that ants estimate local population density by tracking encounter rates: The higher the density, the more often the ants bump into each other. We study distributed density estimation from a theoretical perspective. We prove that a group of anonymous agents randomly walking on a grid are able to estimate their density within a small multiplicative error in few steps by measuring their rates of encounter with other agents. Despite dependencies inherent in the fact that nearby agents may collide repeatedly (and, worse, cannot recognize when this happens), our bound nearly matches what would be required to estimate density by independently sampling grid locations. From a biological perspective, our work helps shed light on how ants and other social insects can obtain relatively accurate density estimates via encounter rates. From a technical perspective, our analysis provides tools for understanding complex dependencies in the collision probabilities of multiple random walks. We bound the strength of these dependencies using local mixing properties of the underlying graph. Our results extend beyond the grid to more general graphs, and we discuss applications to size estimation for social networks, density estimation for robot swarms, and random walk-based sampling for sensor networks. Keywords: population density estimation; random walk sampling; network exploration; ant colony; algorithms; biological distributed algorithms National Science Foundation (U.S.) (Grant BIO-1455983) National Science Foundation (U.S.) (Grant CCF-1461559) National Science Foundation (U.S.) (Grant CCF-0939370) United States. Air Force Office of Scientific Research (Grant FA9550-13-1-0042) 2018-04-05T15:29:45Z 2018-04-05T15:29:45Z 2017-10 2017-04 2018-03-30T18:33:39Z Article http://purl.org/eprint/type/JournalArticle 0027-8424 1091-6490 http://hdl.handle.net/1721.1/114568 Musco, Cameron et al. “Ant-Inspired Density Estimation via Random Walks.” Proceedings of the National Academy of Sciences 114, 40 (September 2017): 10534–10541 © 2017 National Academy of Sciences https://orcid.org/0000-0003-2197-6806 https://orcid.org/0000-0003-3045-265X http://dx.doi.org/10.1073/PNAS.1706439114 Proceedings of the National Academy of Sciences Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use. application/pdf National Academy of Sciences (U.S.) National Academy of Sciences |
| spellingShingle | Musco, Cameron Nicholas Su, Hsin-Hao Lynch, Nancy Ann Ant-inspired density estimation via random walks |
| title | Ant-inspired density estimation via random walks |
| title_full | Ant-inspired density estimation via random walks |
| title_fullStr | Ant-inspired density estimation via random walks |
| title_full_unstemmed | Ant-inspired density estimation via random walks |
| title_short | Ant-inspired density estimation via random walks |
| title_sort | ant inspired density estimation via random walks |
| url | http://hdl.handle.net/1721.1/114568 https://orcid.org/0000-0003-2197-6806 https://orcid.org/0000-0003-3045-265X |
| work_keys_str_mv | AT muscocameronnicholas antinspireddensityestimationviarandomwalks AT suhsinhao antinspireddensityestimationviarandomwalks AT lynchnancyann antinspireddensityestimationviarandomwalks |