Relevant bibliographies by topics / Foraging

Academic literature on the topic 'Foraging'

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Published: 4 June 2021
Last updated: 31 July 2024

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Journal articles on the topic "Foraging"

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STENSETH, N. CHR. "Optimal Foraging: Foraging Behavior." Science 240, no. 4856 (May 27, 1988): 1212–13. http://dx.doi.org/10.1126/science.240.4856.1212.

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Sato, Masaya, and Takayuki Sakagami. "Is simulated foraging similar to natural foraging?" Behavioral and Brain Sciences 8, no. 2 (July 1985): 346–47. http://dx.doi.org/10.1017/s0140525x00021051.

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King, Andrew J., and Harry H. Marshall. "Optimal foraging." Current Biology 32, no. 12 (June 2022): R680—R683. http://dx.doi.org/10.1016/j.cub.2022.04.072.

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Freire, Maria L. Montoya, Antti Oulasvirta, and Mario Di Francesco. "Inverse Foraging." Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 5, no. 3 (September 9, 2021): 1–18. http://dx.doi.org/10.1145/3478103.

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Users' engagement with pervasive displays has been extensively studied, however, determining how their content is interesting remains an open problem. Tracking of body postures and gaze has been explored as an indication of attention; still, existing works have not been able to estimate the interest of passers-by from readily available data, such as the display viewing time. This article presents a simple yet accurate method of estimating users' interest in multiple content items shown at the same time on displays. The proposed approach builds on the information foraging theory, which assumes that users optimally decide on the content they consume. Through inverse foraging, the parameters of a foraging model are fitted to the values of viewing times observed in practice, to yield estimates of user interest. Different foraging models are evaluated by using synthetic data and with a controlled user study. The results demonstrate that inverse foraging accurately estimates interest, achieving an R2 above 70% in comparison to self-reported interest. As a consequence, the proposed solution allows to dynamically adapt the content shown on pervasive displays, based on viewing data that can be easily obtained in field deployments.
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Aphramor, Lucy. "Foraging Ahead." Critical Dietetics 2, no. 1 (March 14, 2014): 1. http://dx.doi.org/10.32920/cd.v2i1.756.

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Sutherland, William J., D. W. Stephens, and J. R. Krebs. "Foraging Theory." Journal of Ecology 76, no. 1 (March 1988): 295. http://dx.doi.org/10.2307/2260475.

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Gardiner, Jennifer R. "Foraging further." Nature 526, no. 7575 (October 2015): 646. http://dx.doi.org/10.1038/526646a.

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Ornes, Stephen. "Foraging flights." Proceedings of the National Academy of Sciences 110, no. 9 (February 15, 2013): 3202–4. http://dx.doi.org/10.1073/pnas.1301980110.

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Pirolli, Peter, and Stuart Card. "Information foraging." Psychological Review 106, no. 4 (1999): 643–75. http://dx.doi.org/10.1037/0033-295x.106.4.643.

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CARACO, T. "Foraging theory." Bulletin of Mathematical Biology 49, no. 5 (1987): 632–34. http://dx.doi.org/10.1016/s0092-8240(87)90007-3.

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Dissertations / Theses on the topic "Foraging"

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Chalk, Daniel. "Artificially intelligent foraging." Thesis, University of Exeter, 2009. http://hdl.handle.net/10036/96455.

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Bumble bees (bombus spp.) are significant pollinators of many plants, and are particularly attracted to mass-flowering crops such as Oilseed Rape (Brassica Napus), which they cross-pollinate. B. napus is both wind and insect-pollinated, and whilst it has been found that wind is its most significant pollen vector, the influence of bumble bee pollination could be non-trivial when bee densities are large. Therefore, the assessment of pollinator-mediated cross-pollination events could be important when considering containment strategies of genetically modified (GM) crops, such as GM varieties of B. napus, but requires a landscape-scale understanding of pollinator movements, which is currently unknown for bumble bees. I developed an in silico model, entitled HARVEST, which simulates the foraging and consequential inter-patch movements of bumble bees. The model is based on principles from Reinforcement Learning and Individual Based Modelling, and uses a Linear Operator Learning Rule to guide agent learning. The model incoproates one or more agents, or bees, that learn by ‘trial-and-error’, with a gradual preference shown for patch choice actions that provide increased rewards. To validate the model, I verified its ability to replicate certain iconic patterns of bee-mediated gene flow, and assessed its accuracy in predicting the flower visits and inter-patch movement frequencies of real bees in a small-scale system. The model successfully replicated the iconic patterns, but failed to accurately predict outputs from the real system. It did, however, qualitatively replicate the high levels of inter-patch traffic found in the real small-scale system, and its quantitative discrepancies could likely be explained by inaccurate parameterisations. I also found that HARVEST bees are extremely efficient foragers, which agrees with evidence of powerful learning capabilities and risk-aversion in real bumble bees. When applying the model to the landscape-scale, HARVEST predicts that overall levels of bee-mediated gene flow are extremely low. Nonetheless, I identified an effective containment strategy in which a ‘shield’ comprised of sacrificed crops is placed between GM and conventional crop populations. This strategy could be useful for scenarios in which the tolerance for GM seed set is exceptionally low.
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Gallon, Susan Louise. "Foraging strategies in grey seals (Halichoerus grypus) : foraging effort and prey selection." Thesis, St Andrews, 2008. http://hdl.handle.net/10023/704.

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Pavlic, Theodore P. "Optimal Foraging Theory Revisited." Connect to resource, 2007. http://rave.ohiolink.edu/etdc/view?acc%5Fnum=osu1181936683.

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Evison, Sophie Elizabeth Frances. "Foraging Organisation in Ants." Thesis, University of Sheffield, 2008. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.500109.

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Su, Dan Kuan-Nien. "Bumblebee vibration activated foraging." Diss., [La Jolla] : University of California, San Diego, 2009. http://wwwlib.umi.com/cr/ucsd/fullcit?p1467769.

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Thesis (M.S.)--University of California, San Diego, 2009.
Title from first page of PDF file (viewed September 15, 2009). Available via ProQuest Digital Dissertations. Includes bibliographical references (p. 27-29).
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Karaköylü, Erdem Mustafa. "The foraging sorties hypothesis evaluating the effect of gut dynamics on copepod foraging behavior /." Diss., [La Jolla] : University of California, San Diego, 2010. http://wwwlib.umi.com/cr/ucsd/fullcit?p3398254.

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Thesis (Ph. D.)--University of California, San Diego, 2010.
Title from first page of PDF file (viewed May 6, 2010). Available via ProQuest Digital Dissertations. Vita. Includes bibliographical references.
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Klotz, Jared Lee. "Foraging for Demand: Applying Optimal Foraging Theory to Decisions in a Simulated Business Context." OpenSIUC, 2012. https://opensiuc.lib.siu.edu/theses/1053.

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Charnov's (1976) marginal value theorem has had success in predicting that animals will optimize net rate of gain when foraging in a patchy environment. The present study attempts to apply the marginal value theorem (MVT) to human behavior in a business setting in 3 Experiments. Businesses also attempt to optimize net rate of gain when choosing to discontinue one product in lieu of another using a product life cycle (PLC). Experiments 1 & 2 attempted to assess human behavior in a business context by varying time necessary to retool and monetary cost of retooling respectively. Experiment 3 attempted to add ecological validity by introducing variability to the PLC. The results of Experiments 1, 2, & 3 indicate that the MVT does not accurately predict human behavior in a business context, though methodological issues may have affected these results. Future research must be conducted in this area.
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Liesenjohann, Thilo. "Foraging in space and time." Phd thesis, Universität Potsdam, 2010. http://opus.kobv.de/ubp/volltexte/2010/4856/.

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All animals are adapted to the environmental conditions of the habitat they chose to live in. It was the aim of this PhD-project, to show which behavioral strategies are expressed as mechanisms to cope with the constraints, which contribute to the natural selection pressure acting on individuals. For this purpose, small mammals were exposed to different levels and types of predation risk while actively foraging. Individuals were either exposed to different predator types (airborne or ground) or combinations of both, or to indirect predators (nest predators). Risk was assumed to be distributed homogeneously, so changing the habitat or temporal adaptations where not regarded as potential options. Results show that wild-caught voles have strategic answers to this homogeneously distributed risk, which is perceived by tactile, olfactory or acoustic cues. Thus, they do not have to know an absolut quality (e.g., in terms of food provisioning and risk levels of all possible habitats), but they can adapt their behavior to the actual circumstances. Deriving risk uniform levels from cues and adjusting activity levels to the perceived risk is an option to deal with predators of the same size or with unforeseeable attack rates. Experiments showed that as long as there are no safe places or times, it is best to reduce activity and behave as inconspicuous as possible as long as the costs of missed opportunities do not exceed the benefits of a higher survival probability. Test showed that these costs apparently grow faster for males than for females, especially in times of inactivity. This is supported by strong predatory pressure on the most active groups of rodents (young males, sexually active or dispersers) leading to extremely female-biased operative sex ratios in natural populations. Other groups of animals, those with parental duties such as nest guarding, for example, have to deal with the actual risk in their habitat as well. Strategies to indirect predation pressure were tested by using bank vole mothers, confronted with a nest predator that posed no actual threat to themselves but to their young (Sorex araneus). They reduced travelling and concentrated their effort in the presence of shrews, independent of the different nutritional provisioning of food by varying resource levels due to the different seasons. Additionally, they exhibited nest-guarding strategies by not foraging in the vicinity of the nest site in order to reduce conspicuous scent marks. The repetition of the experiment in summer and autumn showed that changing environmental constraints can have a severe impact on results of outdoor studies. In our case, changing resource levels changed the type of interaction between the two species. The experiments show that it is important to analyze decision making and optimality models on an individual level, and, when that is not possible (maybe because of the constraints of field work), groups of animals should be classified by using the least common denominator that can be identified (such as sex, age, origin or kinship). This will control for the effects of the sex or stage of life history or the individual´s reproductive and nutritional status on decision making and will narrow the wide behavioral variability associated with the complex term of optimality.
Das Verhalten von Tieren ist das Ergebnis eines kontinuierlichen Anpassungsprozesses im Laufe der Evolution einer Art und damit der Veränderung der Umgebung in der es lebt und der Interaktion mit anderen Arten. Dies wird besonders deutlich im Verhalten von potentiellen Beutetieren, ihre Strategien beinhalten meist ein möglichst unauffälliges Verhalten im Zusammenspiel mit reduzierter Bewegung und möglichst guter Tarnung. Dementgegen stehen essentielle Bedürfnisse, wie zum Beispiel die Nahrungssuche, die Verteidigung von Ressourcen (zum Beispiel Territorien, Futterstellen) und die Suche nach Paarungspartnern. Beutetiere leben also in einem Spannungsfeld indem sie Ihr Verhalten optimieren müssen. Hierbei stehen die Ernährung, erfolgreiche Verpaarung und andere Chancen auf der einen Seite, die Vermeidung von Begegnungen mit Prädatoren auf der anderen. Vor allem Kleinsäuger sind häufig als Beutetiere mit einer Vielzahl von Prädatoren aus der Luft und auf dem Boden konfrontiert. Sie müssen für die verschiedenen Bedrohungen adaptive Verhaltensanpassungen bereit haben und in der Lage sein, auf die optischen, olfaktorischen oder akustischen Signale, die die Gefahr durch Prädatoren anzeigen, mit plastischen Verhaltensmustern zu reagieren. Die vorliegende Dissertation beschäftigt sich mit bisher als Konstanten behandelten Faktoren und untersucht anhand von Verhaltensexperimenten mit wilden Wühlmäusen (Microtus arvalis) folgende Fragestellungen: - Wie verhalten sich Tiere, die einer homogenen Risikoverteilung ausgesetzt sind, zum Beispiel weil ihr Prädator genauso gross ist wie sie, im gleichen Habitat lebt und es keinen sicheren Ort gibt? - Mit welchen Anpassungen reagieren Tiere, wenn sie gleichzeitig verschiedenen Prädatoren ausgesetzt sind? - Wie unterscheiden sich die Nahrungssuchstrategien von Männchen und Weibchen? - Wie verhalten sich laktierende Weibchen, die einer permanenten, indirekten Gefahr, z.B. durch einen Nestprädator ausgesetzt sind? Die Ergebnisse der verschiedenen Versuche in künstlichen Arenen und Aussengehegen zeigen, dass die Mäuse in der Lage sind, adaptive Verhaltensanpassung an homogenes Risiko und verschiedene Prädationstypen abzurufen. So sind sie in der Lage, Luft- von Bodenprädatoren zu unterscheiden und jeweils das Verhalten zu zeigen, dass die größtmögliche Sicherheit mit sich bringt. Die simultane Kombination von verschiedenen Prädatoren bewirkt hierbei additive Effekte. Gibt es keine Auswahl zwischen Habitaten, sondern nur unterschiedliche homogene Risikolevel, reagieren sie auf steigendes Risiko immer mit verminderter Aktivität und konzentrieren ihre Nahrungssuche auf weniger Futterstellen, beuten diese dafür jedoch länger aus. Die Wertigkeit von Futterstellen und alternativen Optionen verändert sich also mit dem Risikolevel. Ähnliches zeigt sich auch in den unterschiedlichen Futtersuchstrategien von Männchen und Weibchen. Die untersuchte Art ist polygyn und multivoltin, dementsprechend verbinden die Männchen mit ihrer Nahrungssuche Aktivitäten wie die Suche nach Paarungspartnern und unterscheiden sich die Aktivitätsmuster zwischen Männchen und Weibchen. Zusätzlich zeigen die Ergebnisse, das laktierende Weibchen in der Lage sind, das Risiko für sich und für Ihre Jungen abzuschätzen, wenn sie mit einem Nestprädator (Sorex araneus) konfrontiert werden. Für die Interaktion zwischen diesen beiden Arten ist jedoch die Saison (und damit die Ressourcenlage), in der sie sich begegnen, von entscheidender Bedeutung. Wühlmäuse reagieren mit entsprechenden Verhaltensanpassungen zum Schutz des Nestes um die Überlebenschancen ihrer letzten Würfe im Herbst zu erhöhen. Die vorliegende Arbeit konnte grundsätzliche Probleme der antiprädatorischen Verhaltensanpassung von Beutetieren klären und wichtige Faktoren der Entscheidungsfindung unter Prädationsdruck analysieren. Sie zeigt, dass Tiere das Risiko in ihrer Umgebung nicht unbedingt über direkt Signale wahrnehmen, sondern ihre Verhaltensstrategien einem empfundenen Gesamtrisikolevel anpassen. Dies ermöglicht ihnen, adaptive Strategien zu verfolgen, auch wenn sie keine Auswahl an sicheren Habitaten haben. Sie zeigt auch die unterschiedliche Wahrnehmung von Risiken durch Männchen und Weibchen, die durch die unterschiedlichen mit der Aktivität zusätzlich wahrgenommenen Chancen verknüpft zu sein scheint. Zusätzlich wurde der Einfluss des reproduktiven Status (z.B. laktierend), sowie der Ressourcenlage (z. B. je nach Saison) nachgewiesen.
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Vahl, Wouter Karsten. "Interference competition among foraging waders." [S.l. : [Groningen : s.n.] ; University Library Groningen] [Host], 2006. http://irs.ub.rug.nl/ppn/297672886.

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Mayberry, J. H. "The energetics of foraging insects." Thesis, University of Cambridge, 1987. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.382293.

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Books on the topic "Foraging"

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Flinn, Jason. Cyber Foraging. Cham: Springer International Publishing, 2012. http://dx.doi.org/10.1007/978-3-031-02481-8.

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Kamil, Alan C., John R. Krebs, and H. Ronald Pulliam, eds. Foraging Behavior. Boston, MA: Springer US, 1987. http://dx.doi.org/10.1007/978-1-4613-1839-2.

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R, Krebs J., ed. Foraging theory. Princeton, N.J: Princeton University Press, 1986.

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Lyle, Katie Letcher. The foraging gourmet. New York, NY: Lyons & Burford, 1997.

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Brabazon, Anthony, and Seán McGarraghy. Foraging-Inspired Optimisation Algorithms. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-59156-8.

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Fitzhugh, Ben, and Junko Habu, eds. Beyond Foraging and Collecting. Boston, MA: Springer US, 2002. http://dx.doi.org/10.1007/978-1-4615-0543-3.

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1955-, Stephens David W., Brown Joel S. 1959-, and Ydenberg Ronald C, eds. Foraging: Behavior and ecology. Chicago: University of Chicago Press, 2007.

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Wallace, Stacy Ellen. Foraging energetics of diving ducks. Birmingham: University of Birmingham, 1998.

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Aitchison, James. Foraging: New and selected poems. Tonbridge, Kent [England]: Worple Press, 2009.

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Stewart, Lesley Ann. Size and foraging in coccinellids. Norwich: University of East Anglia, 1988.

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Book chapters on the topic "Foraging"

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Grüter, Christoph. "Foraging." In Stingless Bees, 273–321. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-60090-7_8.

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Abrol, Dharam P. "Foraging." In Asiatic Honeybee Apis cerana, 367–429. Dordrecht: Springer Netherlands, 2013. http://dx.doi.org/10.1007/978-94-007-6928-1_11.

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Walker, Stuart. "Foraging." In Design Realities, 251–52. spirit / Stuart Walker. Description: First edition. |: Routledge, 2018. http://dx.doi.org/10.4324/9780429489037-96.

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Abrol, D. P. "Foraging." In Honeybees of Asia, 257–92. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-16422-4_12.

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Chen, Chaomei. "Foraging." In Turning Points, 87–137. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-19160-2_5.

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Bates, Daniel, Judith Tucker, and Ludomir Lozny. "Foraging." In Human Adaptive Strategies, 63–100. 4th ed. New York: Routledge, 2023. http://dx.doi.org/10.4324/b23278-3.

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Schoener, Thomas W. "A Brief History of Optimal Foraging Ecology." In Foraging Behavior, 5–67. Boston, MA: Springer US, 1987. http://dx.doi.org/10.1007/978-1-4613-1839-2_1.

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Hanson, John. "Tests of Optimal Foraging Using an Operant Analogue." In Foraging Behavior, 335–62. Boston, MA: Springer US, 1987. http://dx.doi.org/10.1007/978-1-4613-1839-2_10.

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Milinski, Manfred. "Competition for Non-Depleting Resources: The Ideal Free Distribution in Sticklebacks." In Foraging Behavior, 363–88. Boston, MA: Springer US, 1987. http://dx.doi.org/10.1007/978-1-4613-1839-2_11.

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Caraco, Thomas. "Foraging Games in a Random Environment." In Foraging Behavior, 389–414. Boston, MA: Springer US, 1987. http://dx.doi.org/10.1007/978-1-4613-1839-2_12.

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Conference papers on the topic "Foraging"

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Piorkowski, David, Sean Penney, Austin Z. Henley, Marco Pistoia, Margaret Burnett, Omer Tripp, and Pietro Ferrara. "Foraging goes mobile: Foraging while debugging on mobile devices." In 2017 IEEE Symposium on Visual Languages and Human-Centric Computing (VL/HCC). IEEE, 2017. http://dx.doi.org/10.1109/vlhcc.2017.8103444.

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Otte, Michael, Nikolaus Correll, and Emilio Frazzoli. "Navigation with foraging." In 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2013). IEEE, 2013. http://dx.doi.org/10.1109/iros.2013.6696804.

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Osatuyi, Babajide. "Collaborative information foraging." In the 16th ACM international conference. New York, New York, USA: ACM Press, 2010. http://dx.doi.org/10.1145/1880071.1880138.

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Piorkowski, David, Scott Fleming, Christopher Scaffidi, Christopher Bogart, Margaret Burnett, Bonnie John, Rachel Bellamy, and Calvin Swart. "Reactive information foraging." In the 2012 ACM annual conference. New York, New York, USA: ACM Press, 2012. http://dx.doi.org/10.1145/2207676.2208608.

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Koot, Gijs, Mirjam A. A. Huis in Veld, Joost Hendricksen, Rianne Kaptein, Arnout De Vries, and Egon L. Van Den Broek. "Foraging Online Social Networks." In 2014 IEEE Joint Intelligence and Security Informatics Conference (JISIC). IEEE, 2014. http://dx.doi.org/10.1109/jisic.2014.62.

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Dolan-Stern, Nicholas, Kevin Scrivnor, and Jason Isaacs. "Multimodal Central Place Foraging." In 2018 Second IEEE International Conference on Robotic Computing (IRC). IEEE, 2018. http://dx.doi.org/10.1109/irc.2018.00019.

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Heer, Jeffrey. "Session details: Information foraging." In CHI '09: CHI Conference on Human Factors in Computing Systems. New York, NY, USA: ACM, 2009. http://dx.doi.org/10.1145/3256954.

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Azmilumur, N. F., M. N. Sobri, and W. A. F. W. Othman. "Meerkat foraging behaviour modelling." In 2017 7th IEEE International Conference on Control System, Computing and Engineering (ICCSCE). IEEE, 2017. http://dx.doi.org/10.1109/iccsce.2017.8284410.

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Yichuan Shao and Hanning Chen. "Cooperative Bacterial Foraging Optimization." In 2009 International Conference on Future BioMedical Information Engineering (FBIE). IEEE, 2009. http://dx.doi.org/10.1109/fbie.2009.5405806.

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Mobus, George. "Foraging search: Prototypical intelligence." In Third international conference on computing anticipatory systems (CASYS'99). AIP, 2000. http://dx.doi.org/10.1063/1.1291293.

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Reports on the topic "Foraging"

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Burnett, Margaret M. Information Foraging Theory in Software Maintenance. Fort Belvoir, VA: Defense Technical Information Center, September 2012. http://dx.doi.org/10.21236/ada579505.

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Johnson, Joshua B., W. Mark Ford, Jane L. Rodrigue, and John W. Edwards. Effects of acoustic deterrents on foraging bats. Newtown Square, PA: U.S. Department of Agriculture, Forest Service, Northern Research Station, 2012. http://dx.doi.org/10.2737/nrs-rn-129.

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Herrel, Sherry L., Eric D. Dibble, and K. J. Killgore. Foraging Behavior of Fishes in Aquatic Plants. Fort Belvoir, VA: Defense Technical Information Center, February 2001. http://dx.doi.org/10.21236/ada392062.

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Keane, Kathy, and Lawrence J. Smith. California Least Tern Foraging Ecology in Southern California: A Review of Foraging Behavior Relative to Proposed Dredging Locations. Fort Belvoir, VA: Defense Technical Information Center, May 2016. http://dx.doi.org/10.21236/ada631962.

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Hooper, Robert G., and Richard F. Harlow. Forest Stands Selected by Foraging Red-Cockaded Woodpeckers. Asheville, NC: U.S. Department of Agriculture, Forest Service, Southeastern Forest Experiment Station, 1986. http://dx.doi.org/10.2737/se-rp-259.

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McGehee, Duncan E., Amatzia Genin, and Jules S. Jaffe. Swimming Behavior of Individual Zooplankters During Night-time Foraging. Fort Belvoir, VA: Defense Technical Information Center, September 1998. http://dx.doi.org/10.21236/ada536359.

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McGehee, Duncan E., Amatzia Genin, and Jules S. Jaffe. Swimming Behavior of Individual Zooplankters During Night-time Foraging. Fort Belvoir, VA: Defense Technical Information Center, September 1997. http://dx.doi.org/10.21236/ada629342.

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Bethany Krebs, Bethany Krebs. Using high-tech toys to improve foraging in captive rhinos. Experiment, January 2015. http://dx.doi.org/10.18258/4495.

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Moles, Joshua. Chemical Reaction Network Control Systems for Agent-Based Foraging Tasks. Portland State University Library, January 2000. http://dx.doi.org/10.15760/etd.2200.

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Couture, Marilyn. Recent and contemporary foraging practices of the Harney Valley Paiute. Portland State University Library, January 2000. http://dx.doi.org/10.15760/etd.480.

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