By David L. Dowe (auth.), David L. Dowe (eds.)

ISBN-10: 3642449573

ISBN-13: 9783642449574

ISBN-10: 3642449581

ISBN-13: 9783642449581

Algorithmic likelihood and buddies: complaints of the Ray Solomonoff eighty fifth memorial convention is a suite of unique paintings and surveys. The Solomonoff eighty fifth memorial convention used to be held at Monash University's Clayton campus in Melbourne, Australia as a tribute to pioneer, Ray Solomonoff (1926-2009), honouring his quite a few pioneering works - so much fairly, his innovative perception within the early Nineteen Sixties that the universality of common Turing Machines (UTMs) will be used for common Bayesian prediction and synthetic intelligence (machine learning). This paintings keeps to more and more effect and under-pin data, econometrics, computer studying, information mining, inductive inference, seek algorithms, facts compression, theories of (general) intelligence and philosophy of technology - and functions of those parts. Ray not just anticipated this because the route to actual synthetic intelligence, but in addition, nonetheless within the Nineteen Sixties, expected levels of development in computer intelligence which might eventually bring about machines surpassing human intelligence. Ray warned of the necessity to count on and talk about the capability effects - and hazards - faster instead of later. potentially foremostly, Ray Solomonoff used to be a superb, satisfied, frugal and adventurous man or woman of light unravel who controlled to fund himself whereas electing to behavior lots of his paradigm-changing examine outdoor of the collage procedure. the amount includes 35 papers touching on the abovementioned subject matters in tribute to Ray Solomonoff and his legacy.

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**Extra info for Algorithmic Probability and Friends. Bayesian Prediction and Artificial Intelligence: Papers from the Ray Solomonoff 85th Memorial Conference, Melbourne, VIC, Australia, November 30 – December 2, 2011**

**Sample text**

Springer, Heidelberg (2013) 68. : New Error Bounds for Solomonoﬀ Prediction. J. Comput. Syst. Sci. 62(4), 653–667 (2001) 69. : Comparing humans and AI agents. , Looks, M. ) AGI 2011. LNCS, vol. 6830, pp. 122–132. L. Dowe 70. : Evaluating a reinforcement learning algorithm with a general intelligence test. A. ) CAEPIA 2011. LNCS, vol. 7023, pp. 1–11. Springer, Heidelberg (2011) 71. : Complexity measures for meta-learning and their optimality. L. ) Solomonoﬀ Festschrift. LNCS (LNAI), vol. 7070, pp.

Zhang, D. ) AI 2012. LNCS, vol. 7691, pp. 902–913. Springer, Heidelberg (2012) 98. : Toward an algorithmic metaphysics. L. ) Solomonoﬀ Festschrift. LNCS (LNAI), vol. 7070, pp. 306–317. Springer, Heidelberg (2013) 99. : Generalized Kraft inequality and arithmetic coding. IBM J. Res. Develop. 20(3), 198–203 (1976) 100. : Modeling by shortest data description. Automatica 14, 465–471 (1978) 101. : Information and Complexity in Statistical Modeling. Information Science and Statistics. Springer (2007) 102.

Springer, Heidelberg (2013) 85. : An Introduction to Kolmogorov Complexity and its applications. Springer (1997) 86. : Text compression as a test for artiﬁcial intelligence. In: Proc. National Conf. , p. 970. AAAI / John Wiley & Sons (1999) 87. : MMLD inference of multilayer perceptrons. L. ) Solomonoﬀ Festschrift. LNCS (LNAI), vol. 7070, pp. 261–272. Springer, Heidelberg (2013) 88. : Minimum message length analysis of the behrens– ﬁsher problem. L. ) Solomonoﬀ Festschrift. LNCS (LNAI), vol. 7070, pp.

### Algorithmic Probability and Friends. Bayesian Prediction and Artificial Intelligence: Papers from the Ray Solomonoff 85th Memorial Conference, Melbourne, VIC, Australia, November 30 – December 2, 2011 by David L. Dowe (auth.), David L. Dowe (eds.)

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