Title |
Smart Swarms of Bacteria-Inspired Agents with Performance Adaptable Interactions
|
---|---|
Published in |
PLoS Computational Biology, September 2011
|
DOI | 10.1371/journal.pcbi.1002177 |
Pubmed ID | |
Authors |
Adi Shklarsh, Gil Ariel, Elad Schneidman, Eshel Ben-Jacob |
Abstract |
Collective navigation and swarming have been studied in animal groups, such as fish schools, bird flocks, bacteria, and slime molds. Computer modeling has shown that collective behavior of simple agents can result from simple interactions between the agents, which include short range repulsion, intermediate range alignment, and long range attraction. Here we study collective navigation of bacteria-inspired smart agents in complex terrains, with adaptive interactions that depend on performance. More specifically, each agent adjusts its interactions with the other agents according to its local environment--by decreasing the peers' influence while navigating in a beneficial direction, and increasing it otherwise. We show that inclusion of such performance dependent adaptable interactions significantly improves the collective swarming performance, leading to highly efficient navigation, especially in complex terrains. Notably, to afford such adaptable interactions, each modeled agent requires only simple computational capabilities with short-term memory, which can easily be implemented in simple swarming robots. |
X Demographics
Geographical breakdown
Country | Count | As % |
---|---|---|
Chile | 1 | 25% |
Canada | 1 | 25% |
United States | 1 | 25% |
Unknown | 1 | 25% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Members of the public | 3 | 75% |
Scientists | 1 | 25% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
United States | 7 | 6% |
Germany | 2 | 2% |
Spain | 2 | 2% |
Belgium | 1 | <1% |
Brazil | 1 | <1% |
United Kingdom | 1 | <1% |
Croatia | 1 | <1% |
Unknown | 104 | 87% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Student > Ph. D. Student | 37 | 31% |
Researcher | 23 | 19% |
Professor > Associate Professor | 12 | 10% |
Student > Master | 11 | 9% |
Professor | 8 | 7% |
Other | 19 | 16% |
Unknown | 9 | 8% |
Readers by discipline | Count | As % |
---|---|---|
Agricultural and Biological Sciences | 31 | 26% |
Computer Science | 17 | 14% |
Engineering | 17 | 14% |
Physics and Astronomy | 14 | 12% |
Environmental Science | 5 | 4% |
Other | 23 | 19% |
Unknown | 12 | 10% |