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Complex Agent Representation

Agents – are imparted with artificial intelligence, that guides them based on one or more functions, such as sight, hearing, basic emotion, energy level, aggressiveness level, etc. These agents are given goals to achieve while there are obstacles in a simulated environment. They interact with each other and the environment to achieve their goal just like how a human crowd would. In Computer Graphics, Crowd Simulation is the process of simulating such Intelligent agents. Their intelligence is derived from studying human behavior and interaction in crowd and imparting the learned knowledge to simulate collective behavior. Complex Agent representation deals with the various ways by which agents are simulated to mimic realistic crowd behavior. Agent based models is one such class of Computational models that deals with simulating the actions and interaction of autonomous agents with a view to assessing their effects on the environment as a whole.

Advancement

There are a lot of advancements in the field of complex agent representation. [1] [2] [3] [4] [5] [6] [7] [8] [9] [10] Cite error: There are <ref> tags on this page without content in them (see the help page).

See also

References

  1. ^ S. R. Musse and D. Thalman. "Hierarchical model for real time simulation of virtual human crowds." in IEEE Transactions on Visualization and Computer Graphics, vol. 7, no. 2, pp. 152-164, Apr-Jun 2001. doi: 10.1109/2945.928167 URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=928167&isnumber=20060.
  2. ^ SAUNDERS, R. and GERO, J.S. (2005) "Curious agents and situated design evaluations.", Artificial Intelligence for Engineering Design, Analysis and Manufacturing, 18(2), pp. 153–161. doi: 10.1017/S0890060404040119.
  3. ^ Adrien Treuille, Seth Cooper, and Zoran Popović. 2006. Continuum crowds. In ACM SIGGRAPH 2006 Papers (SIGGRAPH '06). ACM, New York, NY, USA, 1160-1168. DOI= http://dx.doi.org/10.1145/1179352.1142008
  4. ^ Rahul Narain, Abhinav Golas, Sean Curtis, and Ming C. Lin. 2009. "Aggregate dynamics for dense crowd simulation." In ACM SIGGRAPH Asia 2009 papers (SIGGRAPH Asia '09). ACM, New York, NY, USA, , Article 122 , 8 pages. DOI= http://dx.doi.org/10.1145/1661412.1618468
  5. ^ Stephen J. Guy, Jatin Chhugani, Sean Curtis, Pradeep Dubey, Ming Lin, and Dinesh Manocha. 2010. PLEdestrians: a least-effort approach to crowd simulation. In Proceedings of the 2010 ACM SIGGRAPH/Eurographics Symposium on Computer Animation (SCA '10). Eurographics Association, Aire-la-Ville, Switzerland, Switzerland, 119-128.
  6. ^ Jarosław Wąs, Robert Lubaś, Towards realistic and effective Agent-based models of crowd dynamics, Neurocomputing, Volume 146, 25 December 2014, Pages 199-209, ISSN 0925-2312, http://dx.doi.org/10.1016/j.neucom.2014.04.057. URL: http://www.sciencedirect.com/science/article/pii/S0925231214007838
  7. ^ S. Stuvel; N. Magnenat-Thalmann; D. Thalmann; A. F. van der Stappen; A. Egges, "Torso Crowds," in IEEE Transactions on Visualization and Computer Graphics , vol.PP, no.99, pp.1-1 doi: 10.1109/TVCG.2016.2545670 URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7439844&isnumber=4359476
  8. ^ Jinghui Zhong, Wentong Cai, Linbo Luo and Mingbi Zhao. "Learning behavior patterns from video for agent-based crowd modeling and simulation." (2016) 30: 990. doi:10.1007/s10458-016-9334-8
  9. ^ L. He, J. Pan, W. Wang and D. Manocha, "Proxemic group behaviors using reciprocal multi-agent navigation," 2016 IEEE International Conference on Robotics and Automation (ICRA), Stockholm, 2016, pp. 292-297. doi: 10.1109/ICRA.2016.7487147 URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7487147&isnumber=7487087
  10. ^ F. Durupınar, U. Güdükbay, A. Aman and N. I. Badler, "Psychological Parameters for Crowd Simulation: From Audiences to Mobs," in IEEE Transactions on Visualization and Computer Graphics, vol. 22, no. 9, pp. 2145-2159, Sept. 1 2016. doi: 10.1109/TVCG.2015.2501801 URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7331666&isnumber=7524660

Real-time search

Real-Time search refers to queries made to search engines that index news sites, blogs, Twitter feeds and other sources of data that are being updated continuously ( real-time). General-purpose search engines ( google) increasingly add more real-time results for users; however, search engines, such as [1] [2] [3] [4], focus exclusively on the latest postings [5]. [6] [7] [8] [9]

See also

References

  1. ^ http://www.socialmention.com/about/
  2. ^ www.OneRiot.com
  3. ^ www.Collecta.com
  4. ^ www.Topsy.com
  5. ^ http://www.pcmag.com/encyclopedia/term/60611/real-time-search
  6. ^ Fanning, S. and Fanning, J. and Kessler, E., "Real-time search engine.", (2002), Google Patents, US patent 6,366,907, URL = https://www.google.com/patents/US6366907
  7. ^ Fanning, S. and Fanning, J. and Kessler, E., "Real-time search engine.", (2007), Google Patents, US Patent 7,165,071, URL = https://www.google.com/patents/US7165071
  8. ^ B. Ostermaier, K. Römer, F. Mattern, M. Fahrmair and W. Kellerer, "A real-time search engine for the Web of Things," Internet of Things (IOT), 2010, Tokyo, 2010, pp. 1-8. doi: 10.1109/IOT.2010.5678450 URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5678450&isnumber=5677827
  9. ^ A.-M. Corley "Real-Time Search Stumbles Out of the Gate" IEEE Spectrum February 2010 [online] Available: http://spectrum.ieee.org/telecom/internet/realtime-search-stumbles-out-of-the-gate.
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