Skip to main content
SpringerLink
Log in

Introduction to Ray Solomonoff 85th Memorial Conference

  • Chapter
Algorithmic Probability and Friends. Bayesian Prediction and Artificial Intelligence

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 7070))

Abstract

This piece is an introduction to the proceedings of the Ray Solomonoff 85th memorial conference, paying tribute to the works and life of Ray Solomonoff, and mentioning other papers from the conference.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Akaike, H.: Statistical prediction information. Ann. Inst. Statist. Math. 22, 203–217 (1970)

    Article  MathSciNet  MATH  Google Scholar 

  2. Akaike, H.: Information theory and an extension of the maximum likelihood principle. In: Petrov, B.N., Csaki, F. (eds.) Proceedings of the 2nd International Symposium on Information Theory, pp. 267–281 (1973)

    Google Scholar 

  3. Akaike, H.: Factor Analysis and AIC. Psychometrika 52(3), 317–332 (1987)

    Article  MathSciNet  MATH  Google Scholar 

  4. Amir, A., Amin, A.H.M., Khan, A.: Developing machine intelligence within P2P networks using a distributed associative memory. In: Dowe, D.L. (ed.) Solomonoff Festschrift. LNCS (LNAI), vol. 7070, pp. 439–443. Springer, Heidelberg (2013)

    Google Scholar 

  5. Balduzzi, D.: Falsification and future performance. In: Dowe, D.L. (ed.) Solomonoff Festschrift. LNCS (LNAI), vol. 7070, pp. 65–78. Springer, Heidelberg (2013)

    Google Scholar 

  6. Barmpalias, G., Dowe, D.L.: Universality probability of a prefix-free machine. Philosophical Transactions of the Royal Society A [Mathematical, Physical & Engineering Sciences] (Phil Trans. A) 370, 3488–3511 (2012)

    Article  MathSciNet  Google Scholar 

  7. Barron, A.R., Cover, T.M.: Minimum complexity density estimation. IEEE Transactions on Information Theory 37, 1034–1054 (1991)

    Article  MathSciNet  MATH  Google Scholar 

  8. Baxter, R.A., Oliver, J.J.: MDL and MML: Similarities and differences. Technical report TR 94/207, Dept. of Computer Science, Monash University, Clayton, Victoria 3168, Australia (1995)

    Google Scholar 

  9. Bergen, M.S., Bishop, W.B., Buchanan, B.L., Dilworth, R.P., Ackerlind, E., Solomonoff, R.J., et al.: Part n-circuit theory; information theory. In: IEEE International Convention Record, p. 293. Institute of Electrical and Electronics Engineers, U.S.A. (1957)

    Google Scholar 

  10. Boulton, D.M.: Numerical classification based on an information measure. Master’s thesis, M.Sc. thesis, Basser Computing Dept., University of Sydney, Sydney, Australia (1970)

    Google Scholar 

  11. Boulton, D.M.: The Information Measure Criterion for Intrinsic Classification. PhD thesis, Dept. Computer Science, Monash University, Clayton, Australia (August 1975)

    Google Scholar 

  12. Boulton, D.M., Wallace, C.S.: The information content of a multistate distribution. J. Theor. Biol. 23, 269–278 (1969)

    Article  MathSciNet  Google Scholar 

  13. Boulton, D.M., Wallace, C.S.: A program for numerical classification. Computer Journal 13(1), 63–69 (February 1970)

    Google Scholar 

  14. Boulton, D.M., Wallace, C.S.: A comparison between information measure classification. In: Proc. of the Australian & New Zealand Association for the Advancement of Science (ANZAAS) Congress (August 1973) (abstract)

    Google Scholar 

  15. Boulton, D.M., Wallace, C.S.: An information measure for hierarchic classification. Computer Journal 16(3), 254–261 (1973)

    Article  Google Scholar 

  16. Boulton, D.M., Wallace, C.S.: Occupancy of a rectangular array. Computer Journal 16(1), 57–63 (1973)

    Article  MATH  Google Scholar 

  17. Boulton, D.M., Wallace, C.S.: An information measure for single link classification. Computer Journal 18(3), 236–238 (1975)

    Article  MATH  Google Scholar 

  18. Brennan, M.H.: Data processing in the early cosmic ray experiments in Sydney. Computer Journal 51(5), 561–565 (2008); Christopher Stewart WALLACE (1933-2004) memorial special issue

    Google Scholar 

  19. Brennan, M.H., Millar, D.D., Wallace, C.S.: Air showers of size greater than 105 particles - (1) core location and shower size determination. Nature 182, 905–911 (October 4, 1958)

    Google Scholar 

  20. Campbell, D.: The Semimeasure Property of Algorithmic Probability - “Feature” or “Bug”? In: Dowe, D.L. (ed.) Solomonoff Festschrift. LNCS (LNAI), vol. 7070, pp. 79–90. Springer, Heidelberg (2013)

    Google Scholar 

  21. Chaitin, G.J.: On the length of programs for computing finite sequences. Journal of the Association for Computing Machinery 13, 547–569 (1966)

    Article  MathSciNet  MATH  Google Scholar 

  22. Chaitin, G.J.: On the simplicity and speed of programs for computing infinite sets of natural numbers. Journal of the Association for Computing Machinery 16(3), 407–422 (1969)

    Article  MathSciNet  MATH  Google Scholar 

  23. Chaitin, G.J.: Randomness and Mathematical Proof. Scientific American 232(5), 47–52 (May 1975)

    Google Scholar 

  24. Chaitin, G.J.: Godel’s theorem and information. International J. of Theoretical Physics 21(12), 941–954 (1982)

    Article  MathSciNet  MATH  Google Scholar 

  25. Comley, J.W., Dowe, D.L.: General Bayesian networks and asymmetric languages. In: Proc. Hawaii International Conference on Statistics and Related Fields, June 5-8 (2003)

    Google Scholar 

  26. Comley, J.W., Dowe, D.L.: Minimum message length and generalized Bayesian nets with asymmetric languages. In: Grünwald, P., Pitt, M.A., Myung, I.J. (eds.) Advances in Minimum Description Length: Theory and Applications (MDL Handbook), ch. 11, pp. 265–294. M.I.T. Press (April 2005) ISBN 0-262-07262-9; Final camera-ready copy submitted in October 2003. [Originally submitted with title: “Minimum Message Length, MDL and Generalised Bayesian Networks with Asymmetric Languages”.]

    Google Scholar 

  27. Balduzzi, D.: Falsification and future performance. In: Dowe, D.L. (ed.) Solomonoff Festschrift. LNCS (LNAI), vol. 7070, pp. 65–78. Springer, Heidelberg (2013)

    Google Scholar 

  28. Dale, P.E.R., Dale, M.B., Dowe, D.L., Knight, J.M., Lemckert, C.J., Low Choy, D.C., Sheaves, M.J., Sporne, I.: A conceptual model for integrating physical geography research and coastal wetland management, with an Australian example. Progress in Physical Geography 34(5), 605–624 (October 2010)

    Google Scholar 

  29. Dean, Thomas, Boddy: An analysis of time-dependent planning. In: Proc. 7th National Conference on Artificial Intelligence, pp. 49–54 (1998)

    Google Scholar 

  30. Dessalles, J.-L.: Algorithmic simplicity and relevance. In: Dowe, D.L. (ed.) Solomonoff Festschrift. LNCS (LNAI), vol. 7070, pp. 119–130. Springer, Heidelberg (2013)

    Google Scholar 

  31. Dowe, D.L.: Discussion following “Hedging predictions in machine learning, A. Gammerman and V. Vovk”. Computer Journal 2(50), 167–168 (2007)

    Google Scholar 

  32. Dowe, D.L.: Foreword re C. S. Wallace. Computer Journal 51(5), 523–560 (2008); Christopher Stewart WALLACE (1933-2004) memorial special issue

    Google Scholar 

  33. Dowe, D.L.: Minimum Message Length and statistically consistent invariant (objective?) Bayesian probabilistic inference - from (medical) “evidence”. Social Epistemology 22(4), 433–460 (2008)

    Article  Google Scholar 

  34. Dowe, D.L.: MML, hybrid Bayesian network graphical models, statistical consistency, invariance and uniqueness. In: Bandyopadhyay, P.S., Forster, M.R. (eds.) Handbook of the Philosophy of Science. Philosophy of Statistics, vol. 7, pp. 901–982. Elsevier (2011)

    Google Scholar 

  35. Dowe, D.L., Baxter, R.A., Oliver, J.J., Wallace, C.S.: Point estimation using the Kullback-Leibler loss function and MML. In: Wu, X., Kotagiri, R., Korb, K. (eds.) PAKDD 1998. LNCS (LNAI), vol. 1394, pp. 87–95. Springer, Heidelberg (1998)

    Chapter  Google Scholar 

  36. Dowe, D.L., Farr, G.E., Hurst, A.J., Lentin, K.L.: Information-theoretic football tipping. Technical report TR 96/297, Dept. of Computer Science, Monash University, Clayton, Victoria 3168, Australia (1996)

    Google Scholar 

  37. Dowe, D.L., Gardner, S., Oppy, G.R.: Bayes not bust! Why simplicity is no problem for Bayesians. British Journal for the Philosophy of Science 58(4), 709–754 (2007)

    Article  MathSciNet  MATH  Google Scholar 

  38. Dowe, D.L., Hajek, A.R.: A computational extension to the Turing test. In: Proceedings of the 4th Conference of the Australasian Cognitive Science Society, Newcastle, NSW, Australia (September 1997)

    Google Scholar 

  39. Dowe, D.L., Hajek, A.R.: A computational extension to the Turing test. Technical Report 97/322, Dept. Computer Science, Monash University, Australia 3168 (October 1997)

    Google Scholar 

  40. Dowe, D.L., Hajek, A.R.: A non-behavioural, computational extension to the Turing test. In: Proceedings of the International Conference on Computational Intelligence & Multimedia Applications (ICCIMA 1998), Gippsland, Australia, pp. 101–106 (February 1998)

    Google Scholar 

  41. Dowe, D.L., Hernández-Orallo, J.: I.Q. tests are not for machines, yet. Intelligence 40(2), 77–81 (March 2012)

    Google Scholar 

  42. Dowe, D.L., Hernández-Orallo, J., Das, P.K.: Compression and intelligence: Social environments and communication. In: Schmidhuber, J., Thórisson, K.R., Looks, M. (eds.) AGI 2011. LNCS, vol. 6830, pp. 204–211. Springer, Heidelberg (2011)

    Chapter  Google Scholar 

  43. Dowe, D.L., Krusel, N.: A decision tree model of bushfire activity. Technical report TR 93/190, Dept. of Computer Science, Monash University, Clayton, Vic. 3800, Australia (September 1993)

    Google Scholar 

  44. Dowe, D.L., Lentin, K.L.: Information-theoretic footy-tipping competition - Monash. Computer Science Association Newsletter (Australia), 55–57 (December 1995)

    Google Scholar 

  45. Edgoose, T., Allison, L.: MML Markov classification of sequential data. Stats. and Comp. 9(4), 269–278 (1999)

    Article  Google Scholar 

  46. Edwards, R.T., Dowe, D.L.: Single factor analysis in MML mixture modelling. In: Wu, X., Kotagiri, R., Korb, K.B. (eds.) PAKDD 1998. LNCS, vol. 1394, pp. 96–109. Springer, Heidelberg (April 1998)

    Google Scholar 

  47. Ellison, T.M.: Categorisation as topographic mapping between uncorrelated spaces. In: Dowe, D.L. (ed.) Solomonoff Festschrift. LNCS (LNAI), vol. 7070, pp. 131–141. Springer, Heidelberg (2013)

    Google Scholar 

  48. Evans, T.: A heuristic program of solving geometric analogy problems. PhD thesis, Mass. Inst. Tech., Cambridge, Mass., U.S.A. (1963) Also available from AF Cambridge Research Lab, Hanscom AFB, Bedford, Mass., U.S.A.: Data Sciences Lab., Phys. and Math. Sci. Res. Paper 64, Project 4641 (1963)

    Google Scholar 

  49. Evans, T.: A heuristic program to solve geometric-analogy problems. In: Proc. SJCC, vol. 25, pp. 327–339 (1965)

    Google Scholar 

  50. Da Silva Filho, R.I., da Rocha, R.L.A., Guiraldelli, R.H.G.: Learning in the limit: A mutational and adaptive approach. In: Dowe, D.L. (ed.) Solomonoff Festschrift. LNCS (LNAI), vol. 7070, pp. 106–118. Springer, Heidelberg (2013)

    Google Scholar 

  51. Fitzgibbon, L.J., Dowe, D.L., Allison, L.: Univariate polynomial inference by Monte Carlo message length approximation. In: Proceedings of the 19th International Conference on Machine Learning (ICML 2002), pp. 147–154. Morgan Kaufmann (2002)

    Google Scholar 

  52. Fitzgibbon, L.J., Dowe, D.L., Vahid, F.: Minimum message length autoregressive model order selection. In: Proc. Int. Conf. on Intelligent Sensors and Information Processing, Chennai, India, pp. 439–444 (January 2004)

    Google Scholar 

  53. Freivalds, R.: Algorithmic information theory and computational complexity. In: Dowe, D.L. (ed.) Solomonoff Festschrift. LNCS (LNAI), vol. 7070, pp. 142–154. Springer, Heidelberg (2013)

    Google Scholar 

  54. Fresco, N.: A critical survey of some competing accounts of concrete digital computation. In: Dowe, D.L. (ed.) Solomonoff Festschrift. LNCS (LNAI), vol. 7070, pp. 155–173. Springer, Heidelberg (2013)

    Google Scholar 

  55. Good, I.J.: Rational decisions. J. Roy. Statist. Soc. (B) 14(1), 107–114 (1952)

    Google Scholar 

  56. Good, I.J.: Speculations concerning the first ultraintelligent machine. Advances in Computers 6, 31–88 (1965)

    Article  Google Scholar 

  57. Hall, J.S.: Further reflections on the timescale of AI. In: Dowe, D.L. (ed.) Solomonoff Festschrift. LNCS (LNAI), vol. 7070, pp. 174–183. Springer, Heidelberg (2013)

    Google Scholar 

  58. Hernández-Orallo, J.: Beyond the Turing test. Journal of Logic, Language and Information 9(4), 447–466 (2000)

    Article  MathSciNet  MATH  Google Scholar 

  59. Hernández-Orallo, J., Dowe, D.L.: Measuring universal intelligence: Towards an anytime intelligence test. Artificial Intelligence Journal 174(18), 1508–1539 (2010)

    Article  Google Scholar 

  60. Hernández-Orallo, J., Dowe, D.L.: Potential Properties of Turing Machines. Technical report 2012/271, Clayton School of I.T., Monash University, Clayton, Vic. 3168, Australia, 22 pp. (August 3, 2012)

    Google Scholar 

  61. Hernández-Orallo, J., Dowe, D.L.: On Potential Cognitive Abilities in the Machine Kingdom. Minds and Machines 23, 179–210 (2013), http://dx.doi.org/10.1007/s11023-012-9299-6

    Article  Google Scholar 

  62. Hernández-Orallo, J., Dowe, D.L., España-Cubillo, S., Hernández-Lloreda, M.V., Insa-Cabrera, J.: On more realistic environment distributions for defining, evaluating and developing intelligence. In: Schmidhuber, J., Thórisson, K.R., Looks, M. (eds.) AGI 2011. LNCS, vol. 6830, pp. 82–91. Springer, Heidelberg (2011)

    Chapter  Google Scholar 

  63. Hernández-Orallo, J., Dowe, D.L., Hernández-Lloreda, M.V.: Universal Psychometrics: Measuring Cognitive Abilities in the Machine Kingdom. Accepted to Cognitive Systems Research (See also Technical report 2012/267, Clayton School of I.T., Monash University)

    Google Scholar 

  64. Hernandez-Orallo, J., Minaya-Collado, N.: A formal definition of intelligence based on an intensional variant of Kolmogorov complexity. In: Proceedings of the International Symposium of Engineering of Intelligent Systems, pp. 146–163. ICSC Press (1998)

    Google Scholar 

  65. Hope, L.R., Korb, K.: Bayesian information reward. In: McKay, B., Slaney, J.K. (eds.) AI 2002. LNCS (LNAI), vol. 2557, pp. 272–283. Springer, Heidelberg (2002)

    Google Scholar 

  66. Horning, J.: A procedure for grammatical inference. In: Proc. IFIP Congress, Amsterdam, North Holland, vol. 71, Amsterdam, North Holland

    Google Scholar 

  67. Hu, B., Rakthanmanon, T., Hao, Y., Evans, S., Lonardi, S., Keogh, E.: Towards discovering the intrinsic cardinality and dimensionality of time series using MDL. In: Dowe, D.L. (ed.) Solomonoff Festschrift. LNCS (LNAI), vol. 7070, pp. 184–197. Springer, Heidelberg (2013)

    Google Scholar 

  68. Hutter, M.: New Error Bounds for Solomonoff Prediction. J. Comput. Syst. Sci. 62(4), 653–667 (2001)

    Article  MathSciNet  MATH  Google Scholar 

  69. Insa-Cabrera, J., Dowe, D.L., España-Cubillo, S., Hernández-Lloreda, M.V., Hernández-Orallo, J.: Comparing humans and AI agents. In: Schmidhuber, J., Thórisson, K.R., Looks, M. (eds.) AGI 2011. LNCS, vol. 6830, pp. 122–132. Springer, Heidelberg (2011)

    Chapter  Google Scholar 

  70. Insa-Cabrera, J., Dowe, D.L., Hernández-Orallo, J.: Evaluating a reinforcement learning algorithm with a general intelligence test. In: Lozano, J.A., Gámez, J.A., Moreno, J.A. (eds.) CAEPIA 2011. LNCS, vol. 7023, pp. 1–11. Springer, Heidelberg (2011)

    Chapter  Google Scholar 

  71. Jankowski, N.: Complexity measures for meta-learning and their optimality. In: Dowe, D.L. (ed.) Solomonoff Festschrift. LNCS (LNAI), vol. 7070, pp. 198–210. Springer, Heidelberg (2013)

    Google Scholar 

  72. Jeffreys, H.: An invariant form for the prior probability in estimation problems. Proc. of the Royal Soc. of London A 186, 453–454 (1946)

    Article  MathSciNet  MATH  Google Scholar 

  73. Langdon Jr., G.G.: An introduction to arithmetic coding. IBM Journal of Research and Development 28(2), 135–149 (1984)

    Article  MathSciNet  MATH  Google Scholar 

  74. Langdon Jr., G.G., Rissanen, J.J.: A simple general binary source code. IEEE Transactions on Information Theory 28(5), 800–803 (1982)

    Article  MATH  Google Scholar 

  75. King, P.A.: Design of a conscious machine. In: Dowe, D.L. (ed.) Solomonoff Festschrift. LNCS (LNAI), vol. 7070, pp. 211–222. Springer, Heidelberg (2013)

    Google Scholar 

  76. Kolmogorov, A.N.: Three approaches to the quantitative definition of information. Problems of Information Transmission 1, 4–7 (1965)

    MathSciNet  Google Scholar 

  77. Kolmogorov, A.N.: Logical basis for information theory and probability theory. IEEE Transactions on Information Theory 14, 662–664 (1968)

    Article  MathSciNet  MATH  Google Scholar 

  78. Lattimore, T., Hutter, M.: No free lunch versus occam’s razor in supervised learning. In: Dowe, D.L. (ed.) Solomonoff Festschrift. LNCS (LNAI), vol. 7070, pp. 223–235. Springer, Heidelberg (2013)

    Google Scholar 

  79. Legg, S., Hutter, M.: Universal intelligence: A definition of machine intelligence. Minds and Machines 17(4), 391–444 (November 2007)

    Google Scholar 

  80. Legg, S., Veness, J.: An approximation of the universal intelligence measure. In: Dowe, D.L. (ed.) Solomonoff Festschrift. LNCS (LNAI), vol. 7070, pp. 236–249. Springer, Heidelberg (2013)

    Google Scholar 

  81. Levin, L.A.: Universal sequential search problems. Problems of Information Transmission 9(3), 265–266 (1973)

    Google Scholar 

  82. Levin, L.A.: Universal heuristics: How do humans solve “Unsolvable” problems? In: Dowe, D.L. (ed.) Solomonoff Festschrift. LNCS (LNAI), vol. 7070, pp. 53–54. Springer, Heidelberg (2013)

    Google Scholar 

  83. Lewis, D.K., Shelby-Richardson, J.: Scriven on human unpredictability. Philosophical Studies: An International Journal for Philosophy in the Analytic Tradition 17(5), 69–74 (1966)

    Article  Google Scholar 

  84. Li, M.: Partial match distance. In: Dowe, D.L. (ed.) Solomonoff Festschrift. LNCS (LNAI), vol. 7070, pp. 55–64. Springer, Heidelberg (2013)

    Google Scholar 

  85. Li, M., Vitányi, P.M.B.: An Introduction to Kolmogorov Complexity and its applications. Springer (1997)

    Google Scholar 

  86. Mahoney, M.: Text compression as a test for artificial intelligence. In: Proc. National Conf. on Artificial Intelligence, U.S.A., p. 970. AAAI / John Wiley & Sons (1999)

    Google Scholar 

  87. Makalic, E., Allison, L.: MMLD inference of multilayer perceptrons. In: Dowe, D.L. (ed.) Solomonoff Festschrift. LNCS (LNAI), vol. 7070, pp. 261–272. Springer, Heidelberg (2013)

    Google Scholar 

  88. Makalic, E., Schmidt, D.F.: Minimum message length analysis of the behrens–fisher problem. In: Dowe, D.L. (ed.) Solomonoff Festschrift. LNCS (LNAI), vol. 7070, pp. 250–260. Springer, Heidelberg (2013)

    Google Scholar 

  89. Miyabe, K.: An optimal superfarthingale and its convergence over a computable topological space. In: Dowe, D.L. (ed.) Solomonoff Festschrift. LNCS (LNAI), vol. 7070, pp. 273–284. Springer, Heidelberg (2013)

    Google Scholar 

  90. Molloy, S.B., Albrecht, D.W., Dowe, D.L., Ting, K.M.: Model-Based Clustering of Sequential Data. In: Proceedings of the 5th Annual Hawaii International Conference on Statistics, Mathematics and Related Fields (January 2006)

    Google Scholar 

  91. Needham, S.L., Dowe, D.L.: Message length as an effective Ockham’s razor in decision tree induction. In: Proc. 8th Int. Workshop on Artif. Intelligence and Statistics (AI+STATS 2001), pp. 253–260 (January 2001)

    Google Scholar 

  92. Özkural, E.: Diverse consequences of algorithmic probability. In: Dowe, D.L. (ed.) Solomonoff Festschrift. LNCS (LNAI), vol. 7070, pp. 285–298. Springer, Heidelberg (2013)

    Google Scholar 

  93. van Heerden, P.J.: A general theory of prediction. Technical report, Polaroid Corp., Cambridge 39, Massachusetts, U.S.A., Privately circulated report (1963)

    Google Scholar 

  94. Paul, W.J., Solomonoff, R.J.: Autonomous theory building systems. Neural Networks and Adaptive Learning, Schloss Reisenberg, Knowledge Processing and its Applications Series (1990)

    Google Scholar 

  95. Paul, W.J., Solomonoff, R.J.: Autonomous theory building systems. Annals of Operations Research 55(1), 179–193 (1995)

    Article  MATH  Google Scholar 

  96. Pelckmans, K.: An adaptive compression algorithm in a deterministic world. In: Dowe, D.L. (ed.) Solomonoff Festschrift. LNCS (LNAI), vol. 7070, pp. 299–305. Springer, Heidelberg (2013)

    Google Scholar 

  97. Pérez-Ariza, C.B., Nicholson, A.E., Korb, K.B., Mascaro, S., Hu, C.H.: Causal discovery of dynamic Bayesian networks. In: Thielscher, M., Zhang, D. (eds.) AI 2012. LNCS, vol. 7691, pp. 902–913. Springer, Heidelberg (2012)

    Chapter  Google Scholar 

  98. Petersen, S.: Toward an algorithmic metaphysics. In: Dowe, D.L. (ed.) Solomonoff Festschrift. LNCS (LNAI), vol. 7070, pp. 306–317. Springer, Heidelberg (2013)

    Google Scholar 

  99. Rissanen, J.J.: Generalized Kraft inequality and arithmetic coding. IBM J. Res. Develop. 20(3), 198–203 (1976)

    Article  MathSciNet  MATH  Google Scholar 

  100. Rissanen, J.J.: Modeling by shortest data description. Automatica 14, 465–471 (1978)

    Article  MATH  Google Scholar 

  101. Rissanen, J.J.: Information and Complexity in Statistical Modeling. Information Science and Statistics. Springer (2007)

    Google Scholar 

  102. Rissanen, J.J., Langdon Jr., G.G.: Arithmetic coding. IBM Journal of Research and Development 23(2), 149–162 (1979)

    Article  MathSciNet  MATH  Google Scholar 

  103. Rzepka, R., Muramoto, K., Araki, K.: Limiting context by using the web to minimize conceptual jump size. In: Dowe, D.L. (ed.) Solomonoff Festschrift. LNCS (LNAI), vol. 7070, pp. 318–326. Springer, Heidelberg (2013)

    Google Scholar 

  104. Sanghi, P., Dowe, D.L.: A computer program capable of passing I.Q. tests. In: 4th International Conference on Cognitive Science (and 7th Australasian Society for Cognitive Science Conference), Univ. of NSW, Sydney, Australia, vol. 2, pp. 570–575 (July 2003)

    Google Scholar 

  105. Schmidhuber, J.: Optimal ordered problem solver. Technical report TR IDSIA-12-02, IDSIA, Lugano, Switzerland, (July 31, 2002), http://www.idsia.ch/~juergen/oops.html

  106. Schmidt, D.F.: Minimum Message Length Inference of Autoregressive Moving Average Models. PhD thesis, Faculty of Information Technology, Monash University (2008)

    Google Scholar 

  107. Schmidt, D.F.: Minimum message length order selection and parameter estimation of moving average models. In: Dowe, D.L. (ed.) Solomonoff Festschrift. LNCS (LNAI), vol. 7070, pp. 327–338. Springer, Heidelberg (2013)

    Google Scholar 

  108. Schwartz, J., Solomonoff, R.J.: Photoelectric chopper for guided missiles. Electronics (November 1954)

    Google Scholar 

  109. Schwarz, G.: Estimating dimension of a model. Ann. Stat. 6, 461–464 (1978)

    Article  MATH  Google Scholar 

  110. Scriven, M.: An essential unpredictability in human behavior. In: Wolman, B.B., Nagel, E. (eds.) Scientific Psychology: Principles and Approaches, pp. 411–425. Basic Books (Perseus Books) (1965)

    Google Scholar 

  111. Shannon, C.E.: A mathematical theory of communication. The Bell System Technical Journal 27, 379–423 (July 1948), 623–656 (October 1948)

    Google Scholar 

  112. Silvescu, A., Honavar, V.: Abstraction super-structuring normal forms: Towards a theory of structural induction. In: Dowe, D.L. (ed.) Solomonoff Festschrift. LNCS (LNAI), vol. 7070, pp. 339–350. Springer, Heidelberg (2013)

    Google Scholar 

  113. Solomonoff, A.: Locating a discontinuity in a piecewise-smooth periodic function using bayes estimation. In: Dowe, D.L. (ed.) Solomonoff Festschrift. LNCS (LNAI), vol. 7070, pp. 351–365. Springer, Heidelberg (2013)

    Google Scholar 

  114. Solomonoff, G.: Ray solomonoff and the new probability. In: Dowe, D.L. (ed.) Solomonoff Festschrift. LNCS (LNAI), vol. 7070, pp. 37–52. Springer, Heidelberg (2013)

    Google Scholar 

  115. Solomonoff, R.J.: An exact method for the computation of the connectivity of random nets. Bulletin of Mathematical Biophysics 14(2), 153–157 (1952)

    Article  MathSciNet  Google Scholar 

  116. Solomonoff, R.J.: An optically driven airborne chopper. In: Proceedings of the 3rd Typhoon Symposium, p. 205 (1953)

    Google Scholar 

  117. Solomonoff, R.J.: Effects of Heisenberg’s principle on channel capacity. Proceedings of the I.R.E. 43, 484 (April 1955)

    Google Scholar 

  118. Solomonoff, R.J.: An inductive inference machine. Dartmouth Summer Research Project on Artificial Intelligence, A privately circulated report (August 1956)

    Google Scholar 

  119. Solomonoff, R.J.: An inductive inference machine. In: IRE Convention Record, Section on Information Theory, Part 2, pp. 56–62 (1957)

    Google Scholar 

  120. Solomonoff, R.J.: The mechanization of linguistic learning. In: Proceedings of the Second International Congress on Cybernetics, Namur, Belgium, pp. 180–193 (May 1958)

    Google Scholar 

  121. Solomonoff, R.J.: Utility evaluation. Publication VI23 30, Zator Co. and Air Force Office of Scientific Research, U.S.A. (April 1958)

    Google Scholar 

  122. Solomonoff, R.J.: A new method for discovering the grammars of phrase structure languages. In: Proceedings of the International Conference on Information Processing. UNESCO, Paris, France (1959)

    Google Scholar 

  123. Solomonoff, R.J.: A progress report on machines to learn to translate languages and retrieve information. In: Advances in Documentation and Library Science, Vol. III, Part 2 (Reprint from Proceedings of International Conference for Standards on a Common Language for Machine Searching and Translation 1959), vol. III, pp. 941–953. Interscience Publishers (September/October 1959)

    Google Scholar 

  124. Solomonoff, R.J.: Progress report: Research on inductive inference for the year ending 31 March 1959. Technical Report ZTB-130, Zator Co. and Air Force Office of Scientific Research, U.S.A. (May 1959)

    Google Scholar 

  125. Solomonoff, R.J.: A preliminary report on a general theory of inductive inference. Technical Report V-131, Zator Co. and Air Force Office of Scientific Research, Cambridge, Mass., U.S.A. (February 1960)

    Google Scholar 

  126. Solomonoff, R.J.: A preliminary report on a general theory of inductive inference (revision of Report V-131). Technical Report ZTB-138, Zator Co. and Air Force Office of Scientific Research, Cambridge, Mass., U.S.A. (November 1960)

    Google Scholar 

  127. Solomonoff, R.J.: A coding method for inductive inference. Technical Report ZTB-140, Zator Co. [and perhaps Rockford Research Co.] (Prepared for Air Force Office of Scientific Research, Air Research and Development Command, U.S. Air Force), Cambridge, Mass., U.S.A. (April 1961)

    Google Scholar 

  128. Solomonoff, R.J.: Progress report: Research in inductive inference for the period 1 April 1959 to 30 November 1960. Technical Report ZTB 139, Rockford Research Co. and Air Force Office of Scientific Research, U.S.A. (January 1961)

    Google Scholar 

  129. Solomonoff, R.J.: Comments on Dr. S. Watanabe’s paper. Synthese 14(2), 97–100 (September 1962)

    Google Scholar 

  130. Solomonoff, R.J.: An inductive inference code employing definitions. Technical Report ZTB-141, Zator Co. [and perhaps Rockford Research Co.] (Prepared for Air Force Office of Scientific Research, Air Research and Development Command, U.S. Air Force), Cambridge, Mass., U.S.A. (April 1962)

    Google Scholar 

  131. Solomonoff, R.J.: Training sequences for mechanized induction. In: Yovits, M., Jacobi, Goldstein (eds.) Self-Organizing Systems, pp. 425–434. Spartan Books (1962)

    Google Scholar 

  132. Solomonoff, R.J.: A formal theory of inductive inference. Information and Control 7, 1–22, 224–254 (1964)

    Google Scholar 

  133. Solomonoff, R.J.: A formal theory of inductive inference: Part I. Information and Control 7(1), 1–22 (March 1964)

    Google Scholar 

  134. Solomonoff, R.J.: A formal theory of inductive inference: Part II. Information and Control 7(2), 224–254 (June 1964)

    Google Scholar 

  135. Solomonoff, R.J.: Some recent work in artificial intelligence. Proceedings of the IEEE 54(12), 1687–1697 (December 1966)

    Google Scholar 

  136. Solomonoff, R.J.: Inductive inference research status, spring, 1967. Technical Report RTB 154, Rockford Research Co. and Air Force Office of Scientific Research, 140 1/2 Mt, Auburn St., Cambridge, Mass., U.S.A. (July 1967)

    Google Scholar 

  137. Solomonoff, R.J.: The search for artificial intelligence. Electronics and Power 14(1), 8–11 (January 1968)

    Google Scholar 

  138. Solomonoff, R.J.: The adequacy of complexity models of induction. In: Logic, Methodology and Philosophy of Science: Proceedings of the Fifth International Congress, London, Ontario, Canada, pp. 19–20 (September 1975) (Section VI)

    Google Scholar 

  139. Solomonoff, R.J.: Inductive inference theory - a unified approach to problems in pattern recognition and artificial intelligence. In: Proceedings of the Fourth International Joint Conference on Artificial Intelligence, Tbilisi, Georgia, U.S.S.R, vol. 1, pp. 274–280 (September 1975), http://world.std.com/~rjs/pubs.html , http://world.std.com/~rjs/tblisi75.pdf

  140. Solomonoff, R.J.: Complexity-based induction systems: Comparisons and convergence theorems. IEEE Transaction on Information Theory, IT-24(4), 422–432 (1978)

    Google Scholar 

  141. Solomonoff, R.J.: Perfect training sequences and the costs of corruption — a progress report on inductive inference research. Technical report, Oxbridge Research, Cambridge, MA, U.S.A. (August 1982)

    Google Scholar 

  142. Solomonoff, R.J.: Optimum sequential search. Technical report, Oxbridge Research, Cambridge, Mass., U.S.A. (June 1984)

    Google Scholar 

  143. Solomonoff, R.J.: The time scale of artificial intelligence; reflections on social effects. Human Systems Management 5, 149–153 (1985)

    Google Scholar 

  144. Solomonoff, R.J.: Two kinds of complexity. Technical report, Oxbridge Research, Cambridge, Mass., U.S.A. (1985)

    Google Scholar 

  145. Solomonoff, R.J.: The application of algorithmic probability to problems in artificial intelligence. In: Kanal, L.N., Lemmer, J.F. (eds.) Uncertainty in Artificial Intelligence, pp. 473–491. Elsevier Science Publishers B.V. (1986); Also in: Kochen, M., Hastings, H.M.: Advances in Cognitive Science. AAAS Selected Symposia Series, pp. 210–227. AAAS, Washington, D.C. (1988)

    Google Scholar 

  146. Solomonoff, R.J.: A system for incremental learning based on algorithmic probability. In: Proceedings of the Sixth Israeli Conference on Artificial Intelligence, Computer Vision and Pattern Recognition, Tel Aviv, Israel, pp. 515–527 (December 1989)

    Google Scholar 

  147. Solomonoff, R.J.: Does algorithmic probability solve the problem of induction? In: Dowe, D.L., Korb, K.B., Oliver, J.J. (eds.) Proceedings of the Information, Statistics and Induction in Science (ISIS) Conference, Melbourne, Australia, pp. 7–8. World Scientific (August 1996) ISBN 981-02-2824-4

    Google Scholar 

  148. Solomonoff, R.J.: The discovery of algorithmic probability. Journal of Computer and System Sciences 55(1), 73–88 (1997)

    Article  MathSciNet  MATH  Google Scholar 

  149. Solomonoff, R.J.: Does algorithmic probability solve the problem of induction? Report, Oxbridge Research, P.O.B. 400404, Cambridge, Mass. 02140, U.S.A. (1997), http://world.std.com/~rjs/isis96.pdf

  150. Solomonoff, R.J.: Two kinds of probabilistic induction. Computer Journal 42(4), 256–259 (1999); Special Issue on Kolmogorov Complexity

    Google Scholar 

  151. Solomonoff, R.J.: Progress in incremental machine learning. In: NIPS Workshop on Universal Learning Algorithms and Optimal Search, Whistler, BC, Canada. NIPS (2002)

    Google Scholar 

  152. Solomonoff, R.J.: Progress in incremental machine learning (Preliminary report for NIPS 2002 workshop on universal learners and optimal search). Technical report, Technical Report IDSIA-16-03, IDSIA, Lugano, Switzerland (2003); Given at NIPS Conference, Whistler, B.C., Canada (December 14, 2002)

    Google Scholar 

  153. Solomonoff, R.J.: The universal distribution and machine learning. The Computer Journal 46(6), 598–601 (2003); Inaugural Kolmogorov Lecture, CLRC, Royal Holloway, University of London, England, U.K. (February 27, 2003)

    Google Scholar 

  154. Solomonoff, R.J.: Algorithmic probability, AI and NKS (given at Midwest NKS Conference, U.S.A.) (October 2005), http://world.std.com/~rjs/lects.html ; also www.cs.indiana.edu/~dgerman/2005midwestNKSconference/keynotes/ray-j-solomonoff.ram

  155. Solomonoff, R.J.: Lecture 1: Algorithmic probability (given at M.I.T., Cambridge, Ma., U.S.A.) (2005), http://world.std.com/~rjs/lects.html

  156. Solomonoff, R.J.: Lecture 2: Applications of algorithmic probability. (given at M.I.T., Cambridge, Ma., U.S.A.) (2005), http://world.std.com/~rjs/lects.html

  157. Solomonoff, R.J.: Machine learning - past and future, Dartmouth, N.H., U.S.A., (July 13-15, 2006); Lecture given in 2006 at AI@50, The Dartmouth A. I. Conference: The Next Fifty Years. (Revision August 11, 2009)

    Google Scholar 

  158. Solomonoff, R.J.: Incomputability in games, wars and economics — inductive inference in hostile environments. Logic, Computability and Randomness, page 19 (2007)

    Google Scholar 

  159. Solomonoff, R.J.: The probability of “undefined” (non-converging) output in generating the universal probability distribution. Information Processing Letters 106(6), 238–240 (2007)

    Article  MathSciNet  Google Scholar 

  160. Solomonoff, R.J.: Three kinds of probabilistic induction: Universal distributions and convergence theorems. Computer Journal 51(5), 566–570 (2008); Christopher Stewart WALLACE (1933-2004) Memorial Special Issue

    Google Scholar 

  161. Solomonoff, R.J.: Algorithmic probability: Theory and applications. In: Dehmer, M., Emmert-Streib, F. (eds.) Information Theory and Statistical Learning. Springer Science and Business Media, pp. 1–23. Springer, N.Y., U.S.A. (2009)

    Google Scholar 

  162. Solomonoff, R.J.: Algorithmic probability, heuristic programming and AGI. In: Proceedings of the Third Conference on Artificial General Intelligence, AGI 2010, Lugano, Switzerland, pp. 251–257. IDSIA (March 2010)

    Google Scholar 

  163. Solomonoff, R.J.: Algorithmic Probability – Its Discovery – Its Properties and Application to Strong AI, pp. 149–157. World Scientific Publishing Company (2011)

    Google Scholar 

  164. Solomonoff, R.J., Rapoport, A.: Structure of random nets. In: Proc. Int. Cong. Mathematicians, Providence, R.I., U.S.A., pp. 674–675. American Mathematical Society (1950)

    Google Scholar 

  165. Solomonoff, R.J., Rapoport, A.: Connectivity of random nets. Bulletin of Mathematical Biophysics 13(2), 107–117 (1951)

    Article  MathSciNet  Google Scholar 

  166. Solomonoff, R.J., Saleeby, E.G.: On the application of algorithmic probability to autoregressive models. In: Dowe, D.L. (ed.) Solomonoff Festschrift. LNCS (LNAI), vol. 7070, pp. 366–385. Springer, Heidelberg (2013)

    Google Scholar 

  167. Strannegard, C., Amirghasemi, M., Ulfsbacker, S.: An anthropomorphic method for number sequence problems. In: Cognitive Systems Research (in press, 2013), doi:10.1016/j.cogsys.2012.05.003

    Google Scholar 

  168. Sunehag, P., Hutter, M.: Principles of solomonoff induction and AIXI. In: Dowe, D.L. (ed.) Solomonoff Festschrift. LNCS (LNAI), vol. 7070, pp. 386–398. Springer, Heidelberg (2013)

    Google Scholar 

  169. Suzuki, J.: MDL/Bayesian criteria based on universal coding/Measure. In: Dowe, D.L. (ed.) Solomonoff Festschrift. LNCS (LNAI), vol. 7070, pp. 399–410. Springer, Heidelberg (2013)

    Google Scholar 

  170. Takahashi, H.: Algorithmic analogies to Kamae-Weiss theorem on normal numbers. In: Proceedings of Solomonoff 85th Memorial Conference. Springer (2013)

    Google Scholar 

  171. Tan, P.J., Dowe, D.L.: Decision forests with oblique decision trees. In: Gelbukh, A., Reyes-Garcia, C.A. (eds.) MICAI 2006. LNCS (LNAI), vol. 4293, pp. 593–603. Springer, Heidelberg (2006)

    Chapter  Google Scholar 

  172. Turing, A.M.: On computable numbers, with an application to the Entscheidungsproblem. Proc. London Math. Soc. 2 42, 230–265 (1936)

    MathSciNet  Google Scholar 

  173. Turing, A.M.: Computing machinery and intelligence. Mind 59, 433–460 (1950)

    Article  MathSciNet  Google Scholar 

  174. Ulam, S.: Tribute to John von Neumann. Bull. American Mathematical Soc. 64(3), 1–49 (1958)

    Article  MathSciNet  Google Scholar 

  175. Veness, J., Ng, K.S., Hutter, M., Uther, W., Silver, D.: A Monte-Carlo AIXI Approximation. J. Artificial Intelligence Research 40, 95–142 (2011)

    MathSciNet  MATH  Google Scholar 

  176. Vinge, V.: Technological singularity. In: VISION-21 Symposium Sponsored by NASA Lewis Research Center and the Ohio Aerospace Institute, vol. 30, p. 31 (March 1993)

    Google Scholar 

  177. Visser, G., Dale, P.E.R., Dowe, D.L., Ndoen, E., Dale, M.B., Sipe, N.: A novel approach for modeling malaria incidence using complex categorical household data: The minimum message length (MML) method applied to Indonesian data. Computational Ecology and Software 2(3), 140–159 (2012)

    Google Scholar 

  178. Visser, G., Dowe, D.L.: Minimum message length clustering of spatially-correlated data with varying inter-class penalties. In: Proc. 6th IEEE International Conf. on Computer and Information Science (ICIS) 2007, pp. 17–22 (July 2007)

    Google Scholar 

  179. Visser, G., Dowe, D.L., Uotila, J.P.: Enhancing MML clustering using context data with climate applications. In: Nicholson, A., Li, X. (eds.) AI 2009. LNCS, vol. 5866, pp. 350–359. Springer, Heidelberg (2009)

    Chapter  Google Scholar 

  180. Wallace, C.S.: Digital computers. In: Butler, S.T., Messel, H. (eds.) Atoms to Andromeda, pp. 215–245. Shakespeare-Head, Sydney (1966)

    Google Scholar 

  181. Wallace, C.S.: An improved program for classification. In: Proc. of the 9th Australian Computer Science Conference (ACSC-9), pp. 357–366 (February 1986); Published as Proc. of ACSC-9, vol. 8(1)

    Google Scholar 

  182. Wallace, C.S.: Classification by minimum-message-length encoding. In: Akl, S.G., Fiala, F., Koczkodaj, W.W. (eds.) Advances in Computing and Information - ICCI 1990. LNCS, vol. 468, pp. 72–81. Springer, Heidelberg (1990)

    Google Scholar 

  183. Wallace, C.S.: Classification by minimum-message-length inference. In: Working Notes AAAI Spring Symposium Series, Stanford Uni., Calif., U.S.A., pp. 65–69 (1990)

    Google Scholar 

  184. Wallace, C.S.: False oracles and SMML estimators. In: Dowe, D.L., Korb, K.B., Oliver, J.J. (eds.) Proceedings of the Information, Statistics and Induction in Science (ISIS) Conference, Melbourne, Australia, pp. 304–316. World Scientific (August 1996) ISBN 981-02-2824-4; Was previously Tech. Rept. 89/128, Dept. Comp. Sci., Monash Univ., Australia (June 1989)

    Google Scholar 

  185. Wallace, C.S.: Intrinsic classification of spatially correlated data. Computer Journal 41(8), 602–611 (1998)

    Article  MATH  Google Scholar 

  186. Wallace, C.S.: The MIT Encyclopedia of the Cognitive Sciences (MITECS), chapter Minimum description length (major review), pp. 550–551. The MIT Press, London (1999) ISBN: 0-262-73124-X

    Google Scholar 

  187. Wallace, C.S.: Statistical and Inductive Inference by Minimum Message Length. Springer (May 2005)

    Google Scholar 

  188. Wallace, C.S., Boulton, D.M.: An information measure for classification. Computer J. 11(2), 185–194 (1968)

    Article  MATH  Google Scholar 

  189. Wallace, C.S., Boulton, D.M.: An invariant Bayes method for point estimation. Classification Society Bulletin 3(3), 11–34 (1975)

    Google Scholar 

  190. Wallace, C.S., Dowe, D.L.: Intrinsic classification by MML - the Snob program. In: Proc. 7th Australian Joint Conf. on Artificial Intelligence, pp. 37–44. World Scientific (November 1994)

    Google Scholar 

  191. Wallace, C.S., Dowe, D.L.: Minimum message length and Kolmogorov complexity. Computer J. 42(4), 270–283 (1999)

    Article  MATH  Google Scholar 

  192. Wallace, C.S., Dowe, D.L.: Refinements of MDL and MML coding. Computer Journal 42(4), 330–337 (1999)

    Article  MATH  Google Scholar 

  193. Wallace, C.S., Dowe, D.L.: Rejoinder. Computer Journal 42(4), 345–347 (1999)

    Article  MATH  Google Scholar 

  194. Wallace, C.S., Dowe, D.L.: MML clustering of multi-state, Poisson, von Mises circular and Gaussian distributions. Statistics and Computing 10, 73–83 (January 2000)

    Google Scholar 

  195. Wallace, C.S., Freeman, P.R.: Estimation and inference by compact coding. Journal of the Royal Statistical Society Series B 49(3), 240–252 (1987); See also Discussion on pp. 252-265

    Google Scholar 

  196. Wallace, C.S., Georgeff, M.P.: A general objective for inductive inference. Technical Report #83/32, Department of Computer Science, Monash University, Clayton, Australia, Reissued in June 1984 as TR No. 44 (March 1983)

    Google Scholar 

  197. Wallace, C.S., Patrick, J.D.: Coding decision trees. Machine Learning 11, 7–22 (1993)

    Article  MATH  Google Scholar 

  198. Webb, G.I., Boughton, J., Zheng, F., Ting, K.M., Salem, H.: Learning by extrapolation from marginal to full-multivariate probability distributions: Decreasingly naive Bayesian classification. Machine Learning 86(2), 233–272 (2012)

    Article  MathSciNet  MATH  Google Scholar 

  199. Wei Xing, Croft, W.B.: LDA-based document models for ad-hoc retrieval. In: Proc. 29th ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2006, New York, NY, USA, pp. 178–185 (2006)

    Google Scholar 

  200. Wood, I., Sunehag, P., Hutter, M. (Non-)Equivalence of universal priors. In: Dowe, D.L. (ed.) Solomonoff Festschrift. LNCS (LNAI), vol. 7070, pp. 417–425. Springer, Heidelberg (2013)

    Google Scholar 

  201. Woodward, J., Swan, J.: A syntactic approach to prediction. In: Dowe, D.L. (ed.) Solomonoff Festschrift. LNCS (LNAI), vol. 7070, pp. 426–438. Springer, Heidelberg (2013)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Computer Science and Software Engineering, Clayton School of Information Technology, Monash University, VIC, 3800, Australia

    David L. Dowe

Authors
  1. David L. Dowe
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Faculty of Information Technology, Clayton School of Information Technology, Monash University, Bldg. 63, Wellington Road, 3800, Clayton, VIC, Australia

    David L. Dowe

Rights and permissions

Reprints and permissions

Copyright information

© 2013 Springer-Verlag Berlin Heidelberg

About this chapter

Cite this chapter

Dowe, D.L. (2013). Introduction to Ray Solomonoff 85th Memorial Conference. In: Dowe, D.L. (eds) Algorithmic Probability and Friends. Bayesian Prediction and Artificial Intelligence. Lecture Notes in Computer Science, vol 7070. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-44958-1_1

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-44958-1_1

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-44957-4

  • Online ISBN: 978-3-642-44958-1

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation