Academic literature on the topic 'Belief-Tracking'
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Journal articles on the topic "Belief-Tracking"
Baltag, Alexandru, Nina Gierasimczuk, and Sonja Smets. "Truth-Tracking by Belief Revision." Studia Logica 107, no. 5 (July 20, 2018): 917–47. http://dx.doi.org/10.1007/s11225-018-9812-x.
Full textXu, Jian-min, Shu-fang Wu, and Yu Hong. "Topic tracking with Bayesian belief network." Optik 125, no. 9 (May 2014): 2164–69. http://dx.doi.org/10.1016/j.ijleo.2013.10.044.
Full textAdams, Fred, John A. Barker, and Murray Clarke. "Knowledge as Fact-Tracking True Belief." Manuscrito 40, no. 4 (December 2017): 1–30. http://dx.doi.org/10.1590/0100-6045.2017.v40n4.fa.
Full textBrafman, Ronen I., and Guy Shani. "Online belief tracking using regression for contingent planning." Artificial Intelligence 241 (December 2016): 131–52. http://dx.doi.org/10.1016/j.artint.2016.08.005.
Full textGrainger, Sarah A., Julie D. Henry, Claire K. Naughtin, Marita S. Comino, and Paul E. Dux. "Implicit false belief tracking is preserved in late adulthood." Quarterly Journal of Experimental Psychology 71, no. 9 (January 1, 2018): 1980–87. http://dx.doi.org/10.1177/1747021817734690.
Full textKHONGKRAPHAN, Kittiya, and Pakorn KAEWTRAKULPONG. "Efficient Human Body Tracking by Quick Shift Belief Propagation." IEICE Transactions on Information and Systems E94-D, no. 4 (2011): 905–12. http://dx.doi.org/10.1587/transinf.e94.d.905.
Full textSchneider, Dana, Virginia P. Slaughter, and Paul E. Dux. "What do we know about implicit false-belief tracking?" Psychonomic Bulletin & Review 22, no. 1 (May 22, 2014): 1–12. http://dx.doi.org/10.3758/s13423-014-0644-z.
Full textXue, Jianru, Nanning Zheng, Jason Geng, and Xiaopin Zhong. "Tracking Multiple Visual Targets via Particle-Based Belief Propagation." IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics) 38, no. 1 (February 2008): 196–209. http://dx.doi.org/10.1109/tsmcb.2007.910533.
Full textJogan, Matjaz, Alan He, Alexander Tank, and Alan Stocker. "Humans maintain probabilistic belief states when tracking occluded objects." Journal of Vision 15, no. 12 (September 1, 2015): 188. http://dx.doi.org/10.1167/15.12.188.
Full textXue, Jianru, Nanning Zheng, and Xiaopin Zhong. "Sequential stratified sampling belief propagation for multiple targets tracking." Science in China Series F 49, no. 1 (January 2006): 48–62. http://dx.doi.org/10.1007/s11432-004-0140-6.
Full textDissertations / Theses on the topic "Belief-Tracking"
Kominis, Filippos. "Belief tracking for multi-agent planning." Doctoral thesis, Universitat Pompeu Fabra, 2017. http://hdl.handle.net/10803/457705.
Full textLa Planificació Clàssica és un problema que busca una seqüèencia d’accions per arribar a una meta o objectiu des d’un estat inicial, assumint que les accions són deterministes. Per una altra banda, la Lògica Epistèmica Dinàmica (LED), proporciona una eina de treball formal que permet el modelatge de creences complexes en un entorn de múltiples agents, i defineix com aquestes creences varien aplicant accions físiques i de comunicació. En aquesta disertació ens centrem en connectar l’expressivitat de LED amb els diferents enfocaments que s’utilitzen a planificació clàssica. Primer presentem les formulacions que capturen un fragment de l’expressivitat de LED i que pot modelar coneixement anidat en dos configuracions diferents amb múltiples agents. Després abordem el problema computacional de trobar plans, tot proporcionant traduccions de planificació clàssica que permeten utilitzar planificadors clàssics amb búsquedes heurístiques. Finalment, evaluem de forma empírica els nostres enfocaments i parlem sobre les seves propietats formals.
Roberts, Matthew Simon. "Tracking and classification with wireless sensor networks and the transferable belief model." Thesis, Cardiff University, 2010. http://orca.cf.ac.uk/55134/.
Full textBharathan, Vivek. "Belief Revision in Dynamic Abducers through Meta-Abduction." The Ohio State University, 2010. http://rave.ohiolink.edu/etdc/view?acc_num=osu1276787509.
Full textSavic, Vladimir. "Nonparametric Message Passing Methods for Cooperative Localization and Tracking." Doctoral thesis, Technical University of Madrid, Spain, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-81446.
Full textLin, Chung-Ching. "Detecting and tracking moving objects from a moving platform." Diss., Georgia Institute of Technology, 2012. http://hdl.handle.net/1853/49014.
Full textRekik, Wafa. "Vers le suivi d’objets dans un cadre évidentiel : représentation, filtrage dynamique et association." Thesis, Paris 11, 2015. http://www.theses.fr/2015PA112040/document.
Full textIntelligent systems are more and more present in our society, like the systems of surveillance and civilian or military sites protection. Their purpose is to detect intruders and present the alarms or threats to a distant operator. In our work, we are interested in such systems with the aim to better handle the quality of information presented to the operator in terms of reliability and precision. We focus on the image modality and we have to handle detections that are both uncertain and imprecise in order to present reliable objects to the operator.To specify our problem, we consider the following constraints. The first one is that the system is modular; one subpart of the system is the detection of fragments corresponding potentially to objects. Our second constraint is then to use only information derived from the geometry of these fragmentary detections: spatial location in the image and size of the detections. Then, a threat is supposed all the more important as the detections have an important size and are temporally persistent.The chosen formal framework is the belief functions theory that allows modeling imprecise and uncertain data. The contributions of this thesis deal with the objects representation in terms of imprecise and uncertain location of the objects and object filtering.The pertinent representation of information is a key point for estimation problems and decision making. A representation is good when simple and efficient criteria for the resolution of sub problems can be derived. The representation proposed has allowed us to derive, in a simple and rigorous way, an association criterion between new detections (fragments) and objects under construction. We remind that this association is a key step for several problems with unlabelled data that extends our contribution beyond of the considered application.Data filtering is used in many methods and algorithms to robustify the results using the expected data redundancy versus the noise inconsistency. Then, we formulated our problem in terms of dynamic estimation of a discernment frame including the 'true hypotheses'. This frame is dynamically estimated taking into account the new data (or observations) that allow us to detect two main types of errors, namely the duplication of some hypotheses (objects in our application) and the presence of false alarms (due to noise or false detections in our case).Finally, we show the possibility of coupling our sub-functions dealing with object construction and their filtering with a tracking process using higher level information such as classical tracking algorithm in image processing.Keywords: belief functions theory, data association, filtering
Hachour, Samir. "Suivi et classification d'objets multiples : contributions avec la théorie des fonctions de croyance." Thesis, Artois, 2015. http://www.theses.fr/2015ARTO0206/document.
Full textThis thesis deals with multi-objet tracking and classification problem. It was shown that belieffunctions allow the results of classical Bayesian methods to be improved. In particular, a recentapproach dedicated to a single object classification which is extended to multi-object framework. Itwas shown that detected observations to known objects assignment is a fundamental issue in multiobjecttracking and classification solutions. New assignment solutions based on belief functionsare proposed in this thesis, they are shown to be more robust than the other credal solutions fromrecent literature. Finally, the issue of multi-sensor classification that requires a second phase ofassignment is addressed. In the latter case, two different multi-sensor architectures are proposed, aso-called centralized one and another said distributed. Many comparisons illustrate the importanceof this work, in both situations of constant and changing objects classes
Yogeswaran, Arjun. "Self-Organizing Neural Visual Models to Learn Feature Detectors and Motion Tracking Behaviour by Exposure to Real-World Data." Thesis, Université d'Ottawa / University of Ottawa, 2018. http://hdl.handle.net/10393/37096.
Full textChang, Chun-Kai, and 張鈞凱. "Adapting Measurement and Belief Sharing in Multi-Robot Simultaneous Localization and Tracking." Thesis, 2015. http://ndltd.ncl.edu.tw/handle/15113269748197904348.
Full text國立臺灣大學
資訊工程學研究所
103
Existing multi-robot cooperative perception solutions can be mainly classified into two categories, measurement-based and belief-based, according to the information shared among robots. With well-controlled communication, measurement-based approaches are expected to achieve theoretically optimal estimates while belief-based approaches are not. Nevertheless, belief-based approaches perform relatively stable under unstable communication as a belief contains the information of multiple previous measurements. Motivated by the observation that measurement sharing and belief sharing are respectively superior in different conditions, in this thesis an adapting algorithm, communication adaptive multi-robot simultaneous localization and tracking (ComAd MRSLAT), is proposed to combine the advantages of both to tackle the unstable communication conditions. However, the decision process of what kind of information to share is only based on a probability distribution of states, which is estimated according to a set of observations and observation probabilities. Therefore, it could be seen as a multi-robot partially observable Markov decision process (POMDP) problem. The information to share is decided by maximizing the expected uncertainty reduction, based on which the algorithm dynamically alternates between measurement-sharing and belief-sharing without information loss or reuse. With using the expected effective communication and information receiving, the proposed ComAd MR-SLAT can tackle the complexity issue and online decide the sharing strategy to adapt different communication conditions. The proposed ComAd MR-SLAT is evaluated in communication conditions with different packet loss rates, bursty loss lengths, and data association conditions. The proposed ComAd MR-SLAT outperforms both measurement-based and belief-based MR-SLAT in both localization and data association accuracy. In addition, the real data are also collected and evaluated, the experimental results demonstrate the effectiveness of the proposed adapting algorithm and exhibit that the ComAd MR-SLAT is robust in the simulation and real data experiment.
LIU, LUN CHI, and 劉倫綺. "Study on behavior tracking of cases with positive colorectal cancer screening test by using health belief model." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/kyh6w9.
Full text國立臺南大學
行政管理學系碩士在職專班
107
With the impact of lifestyle changes and westernization of diet, the number of people suffering from colorectal polyps and colorectal cancer has increased year by year. In 2017, colorectal cancer mortality was the third cancer leading cause of death (Health Promotion Administration, Ministry of Health and Welfare, 2018). In this study, our research object is patients undergoing colonoscopy in a regional hospital in the south that to conduct research case collection. In the chapter 3, we use research hypotheses, research frameworks and valid questionnaire sample to do data analysis and processing by statistical methods, in order to verify the aforementioned research hypotheses. Describe the questionnaire issuing and response situation for the statistical analysis result of each variable, and analyze the valid sample questionnaire by descriptive statistics. We conducted factor analysis, reliability and validity testing for questionnaire scales. Using analysis of variance and regression analysis. Exploring whether there are significant differences between variables by t-test and ANOVA, and regression analysis was used to verify the hypothesis of dimensions. Positive impact on colorectal cancer screening behavior by the dimensions of perceived susceptibility, perceived severity, perceived benefits, perceived barriers and Cues to action. It shows that people will be affected by health beliefs and generate different feelings for different positive influences. We can speculate that the health belief model does have an influence on the willingness of the people to screen. The samples of this study focus on the people in Tainan, so its representative is slightly higher. It is difficult to conduct a random sampling method for a specific parent group. We recommend that further research extend the scope of samples to outside Tainan or to the country and compare them to do more in-depth research and analysis. There should be different contributions for study of the willingness of the people to screen. Most people think that cancer screening is more health-conscious. In this research, it shows more people did not consider healthy behavior due to the dimension of perceived barriers. It is recommended that follow-up researchers adopt qualitative research methods, and more in-depth discussions provide a reference for policy advocacy.
Books on the topic "Belief-Tracking"
Nagel, Jennifer. 5. Internalism and externalism. Oxford University Press, 2014. http://dx.doi.org/10.1093/actrade/9780199661268.003.0005.
Full textLipton, Gregory A. Tracking the Camels of Love. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780190684501.003.0002.
Full textBratman, Michael E. Planning, Time, and Self-Governance. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780190867850.001.0001.
Full textBook chapters on the topic "Belief-Tracking"
Han, Tony X., and Thomas S. Huang. "Articulated Body Tracking Using Dynamic Belief Propagation." In Computer Vision in Human-Computer Interaction, 26–35. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11573425_3.
Full textXue, Jianru, Nanning Zheng, and Xiaopin Zhong. "Tracking Targets Via Particle Based Belief Propagation." In Computer Vision – ACCV 2006, 348–58. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11612032_36.
Full textLiang, Wei, Yunde Jia, and Cheng Ge. "Visual Hand Tracking Using Nonparametric Sequential Belief Propagation." In Lecture Notes in Computer Science, 679–87. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11538059_71.
Full textHu, Dan, Xingshe Zhou, and Junjie Wu. "Visual Tracking Based on Convolutional Deep Belief Network." In Lecture Notes in Computer Science, 103–15. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-23216-4_8.
Full textDu, Wei, and Justus Piater. "Multi-view Object Tracking Using Sequential Belief Propagation." In Computer Vision – ACCV 2006, 684–93. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11612032_69.
Full textXue, Jianru, Nanning Zheng, and Xiaopin Zhong. "Sequential Stratified Sampling Belief Propagation for Multiple Targets Tracking." In Lecture Notes in Computer Science, 330–39. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11538059_35.
Full textWickramarathne, Thanuka L. "Integrity Preserving Belief Update for Recursive Bayesian Tracking with Non-ideal Sensors." In Belief Functions: Theory and Applications, 231–40. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-45559-4_24.
Full textRenna, Ilaria, Catherine Achard, and Ryad Chellali. "Combination of Annealing Particle Filter and Belief Propagation for 3D Upper Body Tracking." In Intelligent Robotics and Applications, 824–33. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-10817-4_81.
Full textVivet, Marc, Brais Martínez, and Xavier Binefa. "Real-Time Motion Detection for a Mobile Observer Using Multiple Kernel Tracking and Belief Propagation." In Pattern Recognition and Image Analysis, 144–51. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-02172-5_20.
Full textBall, Linden J., and Jeremy D. Quayle. "The Effects of Belief and Logic in Syllogistic Reasoning: Evidence from an Eye-Tracking Analysis." In Proceedings of the Twenty First Annual Conference of the Cognitive Science Society, 49–54. New York: Psychology Press, 2020. http://dx.doi.org/10.4324/9781410603494-14.
Full textConference papers on the topic "Belief-Tracking"
Mrkšić, Nikola, and Ivan Vulić. "Fully Statistical Neural Belief Tracking." In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers). Stroudsburg, PA, USA: Association for Computational Linguistics, 2018. http://dx.doi.org/10.18653/v1/p18-2018.
Full textRamachandran, Deepak, and Adwait Ratnaparkhi. "Belief Tracking with Stacked Relational Trees." In Proceedings of the 16th Annual Meeting of the Special Interest Group on Discourse and Dialogue. Stroudsburg, PA, USA: Association for Computational Linguistics, 2015. http://dx.doi.org/10.18653/v1/w15-4609.
Full textBaltag, Alexandru, Nina Gierasimczuk, and Sonja Smets. "Belief revision as a truth-tracking process." In hte 13th Conference. New York, New York, USA: ACM Press, 2011. http://dx.doi.org/10.1145/2000378.2000400.
Full textMrkšić, Nikola, Diarmuid Ó Séaghdha, Tsung-Hsien Wen, Blaise Thomson, and Steve Young. "Neural Belief Tracker: Data-Driven Dialogue State Tracking." In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). Stroudsburg, PA, USA: Association for Computational Linguistics, 2017. http://dx.doi.org/10.18653/v1/p17-1163.
Full textLindberg, C., L. S. Muppirisetty, K. Dahlen, V. Savic, and H. Wymeersch. "MAC delay in belief consensus for distributed tracking." In 2013 10th Workshop on Positioning, Navigation and Communication (WPNC). IEEE, 2013. http://dx.doi.org/10.1109/wpnc.2013.6533255.
Full textLin Zheng and Quan Liu. "Articulated Body tracking based on sequential belief propagation." In 2010 2nd International Conference on Computer Engineering and Technology. IEEE, 2010. http://dx.doi.org/10.1109/iccet.2010.5486222.
Full textMinwoo Park, Yanxi Liu, and Robert T. Collins. "Efficient mean shift belief propagation for vision tracking." In 2008 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2008. http://dx.doi.org/10.1109/cvpr.2008.4587508.
Full textRamadan, Osman, Paweł Budzianowski, and Milica Gašić. "Large-Scale Multi-Domain Belief Tracking with Knowledge Sharing." In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers). Stroudsburg, PA, USA: Association for Computational Linguistics, 2018. http://dx.doi.org/10.18653/v1/p18-2069.
Full textMourllion, B., D. Gruyer, R. Royere, and S. Theroude. "Multi-hypotheses tracking algorithm based on the belief theory." In 2005 7th International Conference on Information Fusion. IEEE, 2005. http://dx.doi.org/10.1109/icif.2005.1591957.
Full textDu, Wei, and Justus Piater. "Data Fusion by Belief Propagation for Multi-Camera Tracking." In 2006 9th International Conference on Information Fusion. IEEE, 2006. http://dx.doi.org/10.1109/icif.2006.301712.
Full textReports on the topic "Belief-Tracking"
Phillips, Jake. Understanding the impact of inspection on probation. Sheffield Hallam University, 2021. http://dx.doi.org/10.7190/shu.hkcij.05.2021.
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