Glossary of Artificial Intelligence


Most of the terms listed in defaultlogic.com resource glossaries are already defined and explained within defaultlogic.com resource itself. However, glossaries like this one are useful for looking up, comparing and reviewing large numbers of terms together. You can help enhance this page by adding new terms or writing definitions for existing ones.

This glossary of artificial intelligence terms is about artificial intelligence, its sub-disciplines, and related fields.



A


B


C


D


E

F


G

H

  • Heuristic - is a technique designed for solving a problem more quickly when classic methods are too slow, or for finding an approximate solution when classic methods fail to find any exact solution. This is achieved by trading optimality, completeness, accuracy, or precision for speed. In a way, it can be considered a shortcut. A heuristic function, also called simply a heuristic, is a function that ranks alternatives in search algorithms at each branching step based on available information to decide which branch to follow. For example, it may approximate the exact solution.[66]
  • Hidden layer - an internal layer of neurons in an artificial neural network, not dedicated to input or output
  • Hidden unit - an neuron in a hidden layer in an artificial neural network
  • Hyper-heuristic - is a heuristic search method that seeks to automate, often by the incorporation of machine learning techniques, the process of selecting, combining, generating or adapting several simpler heuristics (or components of such heuristics) to efficiently solve computational search problems. One of the motivations for studying hyper-heuristics is to build systems which can handle classes of problems rather than solving just one problem.[67][68][69]

I


J

K

L

M


N

O

P


Q

R

S


T

U

V

W

X

Y

Z

See also

References and notes

  1. ^ a b For example: Josephson, John R.; Josephson, Susan G., eds. (1994). Abductive Inference: Computation, Philosophy, Technology. Cambridge, UK; New York: Cambridge University Press. doi:10.1017/CBO9780511530128. ISBN 0521434610. OCLC 28149683.
  2. ^ "Retroduction | Dictionary | Commens". Commens - Digital Companion to C. S. Peirce. Mats Bergman, Sami Paavola & João Queiroz. Retrieved .
  3. ^ Colburn, Timothy; Shute, Gary (2007-06-05). "Abstraction in Computer Science". Minds and Machines. 17 (2): 169-184. doi:10.1007/s11023-007-9061-7. ISSN 0924-6495.
  4. ^ Kramer, Jeff (2007-04-01). "Is abstraction the key to computing?". Communications of the ACM. 50 (4): 36-42. doi:10.1145/1232743.1232745. ISSN 0001-0782.
  5. ^ Michael Gelfond, Vladimir Lifschitz (1998) "Action Languages", Linköping Electronic Articles in Computer and Information Science, vol 3, nr 16.
  6. ^ "What is an Activation Function?". deepai.org.
  7. ^ Russell, S.J.; Norvig, P. (2002). Artificial Intelligence: A Modern Approach. Prentice Hall. ISBN 0-13-790395-2.
  8. ^ Rana el Kaliouby (Nov-Dec 2017). "We Need Computers with Empathy". Technology Review. 120 (6). p. 8.
  9. ^ Tao, Jianhua; Tieniu Tan (2005). "Affective Computing: A Review". Affective Computing and Intelligent Interaction. LNCS 3784. Springer. pp. 981-995. doi:10.1007/11573548.
  10. ^ Comparison of Agent Architectures Archived August 27, 2008, at the Wayback Machine.
  11. ^ Goodfellow, Ian; Bengio, Yoshua; Courville, Aaaron (2016) Deep Learning. MIT Press. p. 196. ISBN 9780262035613
  12. ^ "What is Backpropagation?". deepai.org.
  13. ^ Nielsen, Michael A. (2015). "Chapter 6". Neural Networks and Deep Learning.
  14. ^ "Deep Networks: Overview - Ufldl". ufldl.stanford.edu. Retrieved .
  15. ^ Mozer, M. C. (1995). "A Focused Backpropagation Algorithm for Temporal Pattern Recognition". In Chauvin, Y.; Rumelhart, D. Backpropagation: Theory, architectures, and applications. ResearchGate. Hillsdale, NJ: Lawrence Erlbaum Associates. pp. 137-169. Retrieved .
  16. ^ Robinson, A. J. & Fallside, F. (1987). The utility driven dynamic error propagation network (Technical report). Cambridge University, Engineering Department. CUED/F-INFENG/TR.1.
  17. ^ Werbos, Paul J. (1988). "Generalization of backpropagation with application to a recurrent gas market model". Neural Networks. 1 (4): 339-356. doi:10.1016/0893-6080(88)90007-x.
  18. ^ Feigenbaum, Edward (1988). The Rise of the Expert Company. Times Books. p. 317. ISBN 0-8129-1731-6.
  19. ^ Sivic, Josef (April 2009). "Efficient visual search of videos cast as text retrieval" (PDF). IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, VOL. 31, NO. 4. IEEE. pp. 591-605.
  20. ^ McTear et al 2016, p. 167.
  21. ^ "Understanding the backward pass through Batch Normalization Layer". kratzert.github.io. Retrieved 2018.
  22. ^ Ioffe, Sergey; Szegedy, Christian. "Batch Normalization: Accelerating Deep Network Training b y Reducing Internal Covariate Shift" (PDF).
  23. ^ "Glossary of Deep Learning: Batch Normalisation". medium.com. Retrieved 2018.
  24. ^ "Batch normalization in Neural Networks". towardsdatascience.com. Retrieved 2018.
  25. ^ Pham DT, Ghanbarzadeh A, Koc E, Otri S, Rahim S and Zaidi M. The Bees Algorithm. Technical Note, Manufacturing Engineering Centre, Cardiff University, UK, 2005.
  26. ^ Pham, D.T., Castellani, M. (2009), The Bees Algorithm - Modelling Foraging Behaviour to Solve Continuous Optimisation Problems. Proc. ImechE, Part C, 223(12), 2919-2938.
  27. ^ Pham, D.T. and Castellani, M. (2013), Benchmarking and Comparison of Nature-Inspired Population-Based Continuous Optimisation Algorithms, Soft Computing, 1-33.
  28. ^ Pham, D.T. and Castellani, M. (2015), A comparative study of the bees algorithm as a tool for function optimisation, Cogent Engineering 2(1), 1091540.
  29. ^ Nasrinpour, H. R., Massah Bavani, A., Teshnehlab, M., (2017), Grouped Bees Algorithm: A Grouped Version of the Bees Algorithm, Computers 2017, 6(1), 5; (doi: 10.3390/computers6010005)
  30. ^ Colledanchise Michele, and Ögren Petter 2016. How Behavior Trees Modularize Hybrid Control Systems and Generalize Sequential Behavior Compositions, the Subsumption Architecture, and Decision Trees. In IEEE Transactions on Robotics vol.PP, no.99, pp.1-18 (2016)
  31. ^ Colledanchise Michele, and Ögren Petter 2017. Behavior Trees in Robotics and AI: An Introduction.
  32. ^ Breur, Tom (July 2016). "Statistical Power Analysis and the contemporary "crisis" in social sciences". Journal of Marketing Analytics. 4 (2-3): 61-65. doi:10.1057/s41270-016-0001-3. ISSN 2050-3318.
  33. ^ Sabour, Sara; Frosst, Nicholas; Hinton, Geoffrey E. (2017-10-26). "Dynamic Routing Between Capsules". arXiv:1710.09829 [cs.CV].
  34. ^ "Cloud Robotics and Automation A special issue of the IEEE Transactions on Automation Science and Engineering". IEEE. Retrieved 2014.
  35. ^ "RoboEarth".
  36. ^ Goldberg, Ken. "Cloud Robotics and Automation".
  37. ^ Li, R. "Cloud Robotics-Enable cloud computing for robots". Retrieved 2014.
  38. ^ Fisher, Douglas (1987). "Knowledge acquisition via incremental conceptual clustering" (PDF). Machine Learning. 2 (2): 139-172. doi:10.1007/BF00114265.
  39. ^ Fisher, Douglas H. (July 1987). "Improving inference through conceptual clustering". Proceedings of the 1987 AAAI Conferences. AAAI Conference. Seattle Washington. pp. 461-465.
  40. ^ William Iba and Pat Langley. "Cobweb models of categorization and probabilistic concept formation". In Emmanuel M. Pothos and Andy J. Wills,. Formal approaches in categorization. Cambridge: Cambridge University Press. pp. 253-273. ISBN 9780521190480.
  41. ^ Tian, Yuandong; Zhu, Yan (2015). "Better Computer Go Player with Neural Network and Long-term Prediction". arXiv:1511.06410v1 [cs.LG].
  42. ^ "How Facebook's AI Researchers Built a Game-Changing Go Engine". MIT Technology Review. December 4, 2015. Retrieved .
  43. ^ "Facebook AI Go Player Gets Smarter With Neural Network And Long-Term Prediction To Master World's Hardest Game". Tech Times. 2016-01-28. Retrieved .
  44. ^ "Facebook's artificially intelligent Go player is getting smarter". VentureBeat. Retrieved .
  45. ^ Solomonoff, R.J.The Time Scale of Artificial Intelligence; Reflections on Social Effects, Human Systems Management, Vol 5 1985, Pp 149-153
  46. ^ Moor, J., The Dartmouth College Artificial Intelligence Conference: The Next Fifty years, AI Magazine, Vol 27, No., 4, Pp. 87-9, 2006
  47. ^ Kline, Ronald R., Cybernetics, Automata Studies and the Dartmouth Conference on Artificial Intelligence, IEEE Annals of the History of Computing, October-December, 2011, IEEE Computer Society
  48. ^ M. Haghighat, M. Abdel-Mottaleb, & W. Alhalabi (2016). Discriminant Correlation Analysis: Real-Time Feature Level Fusion for Multimodal Biometric Recognition. IEEE Transactions on Information Forensics and Security, 11(9), 1984-1996.
  49. ^ Maurizio Lenzerini (2002). "Data Integration: A Theoretical Perspective" (PDF). PODS 2002. pp. 233-246.
  50. ^ Big Data Integration
  51. ^ Frederick Lane (2006). "IDC: World Created 161 Billion Gigs of Data in 2006".
  52. ^ Dhar, V. (2013). "Data science and prediction". Communications of the ACM. 56 (12): 64. doi:10.1145/2500499.
  53. ^ Jeff Leek (2013-12-12). "The key word in "Data Science" is not Data, it is Science". Simply Statistics.
  54. ^ Hayashi, Chikio (1998-01-01). "What is Data Science? Fundamental Concepts and a Heuristic Example". In Hayashi, Chikio; Yajima, Keiji; Bock, Hans-Hermann; Ohsumi, Noboru; Tanaka, Yutaka; Baba, Yasumasa. Data Science, Classification, and Related Methods. Studies in Classification, Data Analysis, and Knowledge Organization. Springer Japan. pp. 40-51. doi:10.1007/978-4-431-65950-1_3. ISBN 9784431702085.
  55. ^ Hendrickx, Iris; Van den Bosch, Antal (October 2005). "Hybrid algorithms with Instance-Based Classification". Machine Learning: ECML2005. Springer. pp. 158-169.
  56. ^ a b Adam Ostrow (March 5, 2011). "Roger Ebert's Inspiring Digital Transformation". Mashable Entertainment. Retrieved . With the help of his wife, two colleagues and the Alex-equipped MacBook that he uses to generate his computerized voice, famed film critic Roger Ebert delivered the final talk at the TED conference on Friday in Long Beach, California....
  57. ^ JENNIFER 8. LEE (March 7, 2011). "Roger Ebert Tests His Vocal Cords, and Comedic Delivery". The New York Times. Retrieved . Now perhaps, there is the Ebert Test, a way to see if a synthesized voice can deliver humor with the timing to make an audience laugh.... He proposed the Ebert Test as a way to gauge the humanness of a synthesized voice.
  58. ^ "Roger Ebert's Inspiring Digital Transformation". Tech News. March 5, 2011. Retrieved . Meanwhile, the technology that enables Ebert to "speak" continues to see improvements - for example, adding more realistic inflection for question marks and exclamation points. In a test of that, which Ebert called the "Ebert test" for computerized voices,
  59. ^ Alex_Pasternack (Apr 18, 2011). "A MacBook May Have Given Roger Ebert His Voice, But An iPod Saved His Life (Video)". Motherboard. Retrieved . He calls it the "Ebert Test," after Turing's AI standard...
  60. ^ Herbert Jaeger and Harald Haas. Harnessing Nonlinearity: Predicting Chaotic Systems and Saving Energy in Wireless Communication. Science 2 April 2004: Vol. 304. no. 5667, pp. 78 - 80 doi:10.1126/science.1091277 PDF
  61. ^ Herbert Jaeger (2007) Echo State Network. Scholarpedia.
  62. ^ Martignon, Laura; Vitouch, Oliver; Takezawa, Masanori; Forster, Malcolm. "Naive and Yet Enlightened: From Natural Frequencies to Fast and Frugal Decision Trees", published in Thinking : Psychological perspectives on reasoning, judgement and decision making (David Hardman and Laura Macchi; editors), Chichester: John Wiley & Sons, 2003.
  63. ^ Hodgson, Dr. J. P. E., "First Order Logic", Saint Joseph's University, Philadelphia, 1995.
  64. ^ Hughes, G. E., & Cresswell, M. J., A New Introduction to Modal Logic (London: Routledge, 1996), p.161.
  65. ^ Feigenbaum, Edward (1988). The Rise of the Expert Company. Times Books. p. 318. ISBN 0-8129-1731-6.
  66. ^ Pearl, Judea (1984). Heuristics: intelligent search strategies for computer problem solving. United States: Addison-Wesley Pub. Co., Inc., Reading, MA. p. 3. Retrieved 2017.
  67. ^ E. K. Burke, E. Hart, G. Kendall, J. Newall, P. Ross, and S. Schulenburg, Hyper-heuristics: An emerging direction in modern search technology, Handbook of Metaheuristics (F. Glover and G. Kochenberger, eds.), Kluwer, 2003, pp. 457-474.
  68. ^ P. Ross, Hyper-heuristics, Search Methodologies: Introductory Tutorials in Optimization and Decision Support Techniques (E. K. Burke and G. Kendall, eds.), Springer, 2005, pp. 529-556.
  69. ^ E. Ozcan, B. Bilgin, E. E. Korkmaz, A Comprehensive Analysis of Hyper-heuristics, Intelligent Data Analysis, 12:1, pp. 3-23, 2008.





  This article uses material from the Wikipedia page available here. It is released under the Creative Commons Attribution-Share-Alike License 3.0.

Glossary_of_artificial_intelligence
 



 

Connect with defaultLogic
What We've Done
Led Digital Marketing Efforts of Top 500 e-Retailers.
Worked with Top Brands at Leading Agencies.
Successfully Managed Over $50 million in Digital Ad Spend.
Developed Strategies and Processes that Enabled Brands to Grow During an Economic Downturn.
Taught Advanced Internet Marketing Strategies at the graduate level.


Manage research, learning and skills at defaultlogic.com. Create an account using LinkedIn to manage and organize your omni-channel knowledge. defaultlogic.com is like a shopping cart for information -- helping you to save, discuss and share.


  Contact Us