|[lsquo]|Infotaxis|[rsquo]| as a strategy for searching without gradients Export

@article{citeulike:1067802, abstract = {Chemotactic bacteria rely on local concentration gradients to guide them towards the source of a nutrient1. Such local cues pointing towards the location of the source are not always available at macroscopic scales because mixing in a flowing medium breaks up regions of high concentration into random and disconnected patches. Thus, animals sensing odours in air or water detect them only intermittently as patches sweep by on the wind or currents2, 3, 4, 5, 6. A macroscopic searcher must devise a strategy of movement based on sporadic cues and partial information. Here we propose a search algorithm, which we call 'infotaxis', designed to work under such conditions. Any search process can be thought of as acquisition of information on source location; for infotaxis, information plays a role similar to concentration in chemotaxis. The infotaxis strategy locally maximizes the expected rate of information gain. We demonstrate its efficiency using a computational model of odour plume propagation and experimental data on mixing flows7. Infotactic trajectories feature 'zigzagging' and 'casting' paths similar to those observed in the flight of moths8. The proposed search algorithm is relevant to the design of olfactory robots9, 10, 11, but the general idea of infotaxis can be applied more broadly in the context of searching with sparse information.}, author = {Vergassola, Massimo and Villermaux, Emmanuel and Shraiman, Boris I.}, citeulike-article-id = {1067802}, citeulike-linkout-0 = {http://dx.doi.org/10.1038/nature05464}, citeulike-linkout-1 = {http://dx.doi.org/10.1038/nature05464}, day = {25}, doi = {10.1038/nature05464}, issn = {0028-0836}, journal = {Nature}, keywords = {artificial-intelligence, bio-inspired, derivative-free, ecology, localization, machine-learning, modelling, optimization, path, robot, simulation}, month = {January}, number = {7126}, pages = {406--409}, posted-at = {2007-01-29 10:47:28}, priority = {3}, publisher = {Nature Publishing Group}, title = {|[lsquo]|Infotaxis|[rsquo]| as a strategy for searching without gradients}, url = {http://dx.doi.org/10.1038/nature05464}, volume = {445}, year = {2007} }

TY - JOUR ID - citeulike:1067802 L3 - citeulike-article-id:1067802 N2 - Chemotactic bacteria rely on local concentration gradients to guide them towards the source of a nutrient1. Such local cues pointing towards the location of the source are not always available at macroscopic scales because mixing in a flowing medium breaks up regions of high concentration into random and disconnected patches. Thus, animals sensing odours in air or water detect them only intermittently as patches sweep by on the wind or currents2, 3, 4, 5, 6. A macroscopic searcher must devise a strategy of movement based on sporadic cues and partial information. Here we propose a search algorithm, which we call 'infotaxis', designed to work under such conditions. Any search process can be thought of as acquisition of information on source location; for infotaxis, information plays a role similar to concentration in chemotaxis. The infotaxis strategy locally maximizes the expected rate of information gain. We demonstrate its efficiency using a computational model of odour plume propagation and experimental data on mixing flows7. Infotactic trajectories feature 'zigzagging' and 'casting' paths similar to those observed in the flight of moths8. The proposed search algorithm is relevant to the design of olfactory robots9, 10, 11, but the general idea of infotaxis can be applied more broadly in the context of searching with sparse information. IS - 7126 JF - Nature SN - 0028-0836 EP - 409 TI - |[lsquo]|Infotaxis|[rsquo]| as a strategy for searching without gradients PB - Nature Publishing Group VL - 445 SP - 406 KW - artificial-intelligence KW - bio-inspired KW - derivative-free KW - ecology KW - localization KW - machine-learning KW - modelling KW - optimization KW - path KW - robot KW - simulation AU - Vergassola, Massimo AU - Villermaux, Emmanuel AU - Shraiman, Boris PY - 2007/01/25/ UR - http://dx.doi.org/10.1038/nature05464 ER -