Can AI predict animal movements? Filling gaps in animal trajectories using inverse reinforcement learning

[1]  Edward A. Codling,et al.  Random walk models in biology , 2008, Journal of The Royal Society Interface.

[2]  K. M. Schaefer,et al.  Tracking apex marine predator movements in a dynamic ocean , 2011, Nature.

[3]  S. Creel,et al.  Underestimating the frequency, strength and cost of antipredator responses with data from GPS collars: an example with wolves and elk , 2013, Ecology and evolution.

[4]  Henri Weimerskirch,et al.  Interpolation of animal tracking data in a fluid environment , 2006, Journal of Experimental Biology.

[5]  Andrew W. Moore,et al.  Reinforcement Learning: A Survey , 1996, J. Artif. Intell. Res..

[6]  Iain D Couzin,et al.  Habitat and social factors shape individual decisions and emergent group structure during baboon collective movement , 2016, eLife.

[7]  Jacob S. Ivan,et al.  Using environmental features to model highway crossing behavior of Canada lynx in the Southern Rocky Mountains , 2017 .

[8]  Geoffrey E. Hinton,et al.  Feudal Reinforcement Learning , 1992, NIPS.

[9]  Rebecca A. Dunlop,et al.  Comparing multiple sampling platforms for measuring the behavior of humpback whales (Megaptera novaeangliae) , 2016 .

[10]  Anil K. Jain,et al.  A modified Hausdorff distance for object matching , 1994, Proceedings of 12th International Conference on Pattern Recognition.

[11]  Simon Benhamou,et al.  Detecting an orientation component in animal paths when the preferred direction is individual-dependent. , 2006, Ecology.

[12]  Sakiko Matsumoto,et al.  Compass orientation drives naïve pelagic seabirds to cross mountain ranges , 2017, Current Biology.

[13]  D. Driscoll,et al.  Guidelines for Using Movement Science to Inform Biodiversity Policy , 2015, Environmental Management.

[14]  Andrea Manica,et al.  Foraging behaviour and habitat use by brown skuas Stercorarius lonnbergi breeding at South Georgia , 2014 .

[15]  Christopher C Wilmers,et al.  The golden age of bio-logging: how animal-borne sensors are advancing the frontiers of ecology. , 2015, Ecology.

[16]  Ben. G. Weinstein.  A computer vision for animal ecology. , 2018, The Journal of animal ecology.

[17]  Martial Hebert,et al.  Activity Forecasting , 2012, ECCV.

[18]  Göran Ericsson,et al.  Opportunities for the application of advanced remotely-sensed data in ecological studies of terrestrial animal movement , 2015, Movement Ecology.

[19]  Andrew W. Trites,et al.  Foraging a new trail with northern fur seals (Callorhinus ursinus): Lactating seals from islands with contrasting population dynamics have different foraging strategies, and forage at scales previously unrecognized by GPS interpolated dive data , 2015 .

[20]  Henri Weimerskirch,et al.  Ontogeny of foraging behaviour in juvenile red-footed boobies (Sula sula) , 2017, Scientific Reports.

[21]  Roderic A. Grupen,et al.  Robust Reinforcement Learning in Motion Planning , 1993, NIPS.

[22]  William J. Sutherland,et al.  Comparison of methods for determining key marine areas from tracking data , 2013 .

[23]  Tim Appelhans,et al.  Evaluating machine learning approaches for the interpolation of monthly air temperature at Mt. Kilimanjaro, Tanzania , 2015 .

[24]  Simon Benhamou,et al.  Incorporating Movement Behavior and Barriers to Improve Kernel Home Range Space Use Estimates , 2010 .

[25]  R. Kays,et al.  Terrestrial animal tracking as an eye on life and planet , 2015, Science.

[26]  Steven J Cooke,et al.  Energy Landscapes and the Landscape of Fear. , 2017, Trends in ecology & evolution.

[27]  Gregory A. Kiker,et al.  GPS Monitoring of Cattle Location Near Water Features in South Florida , 2009 .

[28]  Albert-László Barabási,et al.  Limits of Predictability in Human Mobility , 2010, Science.

[29]  Scott A. Shaffer,et al.  Analytical approaches to investigating seabird-environment interactions: a review , 2009 .

[30]  Bart Kranstauber,et al.  Bias in estimating animal travel distance: the effect of sampling frequency , 2012 .

[31]  Ella Browning,et al.  Predicting animal behaviour using deep learning: GPS data alone accurately predict diving in seabirds , 2017 .

[32]  M. W. Jones,et al.  Step by step: reconstruction of terrestrial animal movement paths by dead-reckoning , 2015, Movement Ecology.

[33]  Maud Berlincourt,et al.  Changing with the times: little penguins exhibit flexibility in foraging behaviour and low behavioural consistency , 2017, Marine Biology.

[34]  J. Kocik,et al.  Aquatic animal telemetry: A panoramic window into the underwater world , 2015, Science.

[35]  Magnus Andersen,et al.  Aquatic behaviour of polar bears (Ursus maritimus) in an increasingly ice-free Arctic , 2018, Scientific Reports.

[36]  Juan M. Morales,et al.  EXTRACTING MORE OUT OF RELOCATION DATA: BUILDING MOVEMENT MODELS AS MIXTURES OF RANDOM WALKS , 2004 .

[37]  D. Grémillet,et al.  GPS tracking a marine predator: the effects of precision, resolution and sampling rate on foraging tracks of African Penguins , 2004 .

[38]  Jian Zhang,et al.  Seeing the forest from drones: Testing the potential of lightweight drones as a tool for long-term forest monitoring , 2016 .

[39]  Bernie J. McConnell,et al.  The effects of interpolation error and location quality on animal track reconstruction , 2009 .

[40]  Stephen M. Krone,et al.  Analyzing animal movements using Brownian bridges. , 2007, Ecology.

[41]  Maosheng Zhao,et al.  A Continuous Satellite-Derived Measure of Global Terrestrial Primary Production , 2004 .

[42]  Mevin B Hooten,et al.  Animal movement constraints improve resource selection inference in the presence of telemetry error. , 2015, Ecology.

[43]  Nicolas E. Humphries,et al.  Environmental context explains Lévy and Brownian movement patterns of marine predators , 2010, Nature.

[44]  Caroline L Poli,et al.  Dynamic oceanography determines fine scale foraging behavior of Masked Boobies in the Gulf of Mexico , 2017, PloS one.

[45]  E. Revilla,et al.  A movement ecology paradigm for unifying organismal movement research , 2008, Proceedings of the National Academy of Sciences.

[46]  Leslie Pack Kaelbling,et al.  Effective reinforcement learning for mobile robots , 2002, Proceedings 2002 IEEE International Conference on Robotics and Automation (Cat. No.02CH37292).

[47]  José P. Granadeiro,et al.  Do individual seabirds specialize in fisheries' waste? The case of black‐browed albatrosses foraging over the Patagonian Shelf , 2014 .

[48]  Patrick W. Robinson,et al.  A Parsimonious Approach to Modeling Animal Movement Data , 2009, PloS one.

[49]  Stuart Bearhop,et al.  White-capped albatrosses alter fine-scale foraging behavior patterns when associated with fishing vessels , 2011 .

[50]  Tabitha A. Graves,et al.  Understanding the Causes of Missed Global Positioning System Telemetry Fixes , 2006 .

[51]  Robert Weibel,et al.  From A to B, randomly: a point-to-point random trajectory generator for animal movement , 2015, Int. J. Geogr. Inf. Sci..

[52]  I. Boyd,et al.  Bio-logging science: sensing beyond the boundaries , 2004 .

[53]  Yan Ropert-Coudert,et al.  Fine-scale spatial age segregation in the limited foraging area of an inshore seabird species, the little penguin , 2014, Oecologia.

[54]  P. Krausman,et al.  Influence of topography and GPS fix interval on GPS collar performance , 2005 .

[55]  A. M. Edwards,et al.  Revisiting Lévy flight search patterns of wandering albatrosses, bumblebees and deer , 2007, Nature.

[56]  Sakiko Matsumoto,et al.  Sex-Related Differences in the Foraging Movement of Streaked Shearwaters Calonectris leucomelas Breeding on Awashima Island in the Sea of Japan , 2017, Ornithological Science.

[57]  Sara M. Maxwell,et al.  Foraging Behavior and Success of a Mesopelagic Predator in the Northeast Pacific Ocean: Insights from a Data-Rich Species, the Northern Elephant Seal , 2012, PloS one.

[58]  Pietro Perona,et al.  Automated image-based tracking and its application in ecology. , 2014, Trends in ecology & evolution.

[59]  Brett T. McClintock,et al.  Bridging the gaps in animal movement: hidden behaviors and ecological relationships revealed by integrated data streams , 2017 .

[60]  W. Turner.  Sensing biodiversity , 2014, Science.

[61]  George Wittemyer,et al.  Movement reveals scale dependence in habitat selection of a large ungulate. , 2016, Ecological applications : a publication of the Ecological Society of America.

[62]  Stefano Focardi,et al.  The Lévy flight foraging hypothesis in a pelagic seabird. , 2014, The Journal of animal ecology.

[63]  Sergey Levine,et al.  Learning Hand-Eye Coordination for Robotic Grasping with Large-Scale Data Collection , 2016, ISER.

[64]  Pieter Abbeel,et al.  Apprenticeship learning via inverse reinforcement learning , 2004, ICML.

[65]  Michael A. Fedak,et al.  A simple new algorithm to filter marine mammal Argos locations , 2008 .

[66]  Peter M. Kappeler,et al.  Walk the line—dispersal movements of gray mouse lemurs (Microcebus murinus) , 2012, Behavioral Ecology and Sociobiology.

[67]  Stanley M Tomkiewicz,et al.  Global positioning system and associated technologies in animal behaviour and ecological research , 2010, Philosophical Transactions of the Royal Society B: Biological Sciences.

[68]  Ken Yoda,et al.  Foraging spots of streaked shearwaters in relation to ocean surface currents as identified using their drift movements , 2014 .

[69]  G. Odell,et al.  Swarms of Predators Exhibit "Preytaxis" if Individual Predators Use Area-Restricted Search , 1987, The American Naturalist.

[70]  Adam L. Berger,et al.  A Maximum Entropy Approach to Natural Language Processing , 1996, CL.

[71]  Tommaso Russo,et al.  New insights in interpolating fishing tracks from VMS data for different métiers , 2011 .

[72]  T. Guilford,et al.  Migration and stopover in a small pelagic seabird, the Manx shearwater Puffinus puffinus: insights from machine learning , 2009, Proceedings of the Royal Society B: Biological Sciences.

[73]  Takanori Sugawara,et al.  Albatross-borne loggers show feeding on deep-sea squids: implications for the study of squid distributions , 2018 .

[74]  Stuart J. Russell.  Learning agents for uncertain environments (extended abstract) , 1998, COLT' 98.

[75]  Alan M. Wilson,et al.  Improving the accuracy of estimates of animal path and travel distance using GPS drift‐corrected dead reckoning , 2016, Ecology and evolution.

[76]  Catherine E. Meathrel,et al.  A new method for the long-term attachment of data-loggers to shearwaters (Procellariidae) , 2009 .

[77]  E. Jaynes.  Information Theory and Statistical Mechanics , 1957 .

[78]  John Joseph Valletta,et al.  Applications of machine learning in animal behaviour studies , 2017, Animal Behaviour.

[79]  Manfred K. Warmuth,et al.  Exponentiated Gradient Versus Gradient Descent for Linear Predictors , 1997, Inf. Comput..

[80]  Scott A. Shaffer,et al.  State‐space framework for estimating measurement error from double‐tagging telemetry experiments , 2012 .

[81]  F. Weissing,et al.  Lévy Walks Evolve Through Interaction Between Movement and Environmental Complexity , 2011, Science.

[82]  Anind K. Dey,et al.  Maximum Entropy Inverse Reinforcement Learning , 2008, AAAI.

[83]  Molly E. Lutcavage,et al.  Filtering and interpreting location errors in satellite telemetry of marine animals , 2008 .

[84]  Mohammad A. Jaradat,et al.  Reinforcement based mobile robot navigation in dynamic environment , 2011 .

[85]  Ken Yoda,et al.  Social Interactions of Juvenile Brown Boobies at Sea as Observed with Animal-Borne Video Cameras , 2011, PloS one.

[86]  Horst Bornemann,et al.  All at sea with animal tracks; methodological and analytical solutions for the resolution of movement , 2007 .

[87]  M. Fortin,et al.  INFLUENCE OF FOREST COVER ON THE MOVEMENTS OF FOREST BIRDS: A HOMING EXPERIMENT , 2001 .

[88]  Stefan Schaal,et al.  Reinforcement Learning With Sequences of Motion Primitives for Robust Manipulation , 2012, IEEE Transactions on Robotics.

[89]  Michael J. Plank,et al.  Differentiating the Lévy walk from a composite correlated random walk , 2014, 1406.4355.

[90]  Orr Spiegel,et al.  AcceleRater: a web application for supervised learning of behavioral modes from acceleration measurements , 2014, Movement ecology.

[91]  Brett T. McClintock,et al.  Animal Movement: Statistical Models for Telemetry Data , 2017 .

[92]  Kris M. Kitani,et al.  Predicting wide receiver trajectories in American football , 2016, 2016 IEEE Winter Conference on Applications of Computer Vision (WACV).

[93]  Rory P. Wilson,et al.  Construction of energy landscapes can clarify the movement and distribution of foraging animals , 2012, Proceedings of the Royal Society B: Biological Sciences.

[94]  Ken Yoda,et al.  Influence of Local Wind Conditions on the Flight Speed of the Great Cormorant Phalacrocorax carbo , 2012 .

[95]  Jonathan P. How,et al.  Socially aware motion planning with deep reinforcement learning , 2017, 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).

[96]  Bettina Speckmann,et al.  Analysis and visualisation of movement: an interdisciplinary review , 2015, Movement Ecology.

[97]  Francesca Cagnacci,et al.  Resolving issues of imprecise and habitat-biased locations in ecological analyses using GPS telemetry data , 2010, Philosophical Transactions of the Royal Society B: Biological Sciences.

[98]  J. Wall,et al.  Elephants avoid costly mountaineering , 2006, Current Biology.

[99]  Siddhartha S. Srinivasa,et al.  Planning-based prediction for pedestrians , 2009, 2009 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[100]  M. Kearney,et al.  Habitat, environment and niche: what are we modelling? , 2006 .

[101]  Sergio A. Lambertucci,et al.  Energy Landscapes Shape Animal Movement Ecology , 2013, The American Naturalist.

[102]  C H Fleming,et al.  Estimating where and how animals travel: an optimal framework for path reconstruction from autocorrelated tracking data. , 2015, Ecology.

[103]  Kevin Buchin,et al.  Deriving movement properties and the effect of the environment from the Brownian bridge movement model in monkeys and birds , 2015, Movement ecology.

[104]  Helen Bailey,et al.  Identifying and comparing phases of movement by leatherback turtles using state-space models , 2008 .

[105]  Henrik Madsen,et al.  Estimating animal behavior and residency from movement data , 2011 .