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Publications on AGI.

Potapov A., Rodionov S., Potapova V. «Real-time GA-based Probabilistic Programming in Application to Robot Control». B. Steunebrink et al. (Eds.): AGI 2016, Lecture Notes in Artificial Intelligence. Springer, 2016. V. 9782. P. 95–105.
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Potapov A., Batishcheva V., Rodionov S. "Optimization Framework with Minimum Description Length Principle for Probabilistic Programming". J. Bieger, B. Goertzel, A. Potapov (Eds.): AGI 2015, Lecture Notes in Artificial Intelligence. Berlin: Springer, 2015. V. 9205. P. 331–340.
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Batishcheva V., Potapov A. "Genetic Programming on Program Traces as an Inference Engine for Probabilistic Languages". J. Bieger, B. Goertzel, A. Potapov (Eds.): AGI 2015, Lecture Notes in Artificial Intelligence. Berlin: Springer, 2015. V. 9205. P. 14–24.
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Potapov A., Rodionov S. "Universal empathy and ethical bias for artificial general intelligence". Journal of Experimental & Theoretical Artificial Intelligence, vol. 26, iss. 3, pp. 405-416, 2014.
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Alexey Potapov, Sergey Rodionov. "Making Universal Induction Efficient by Specialization". B. Goertzel et al. (Eds.): AGI 2014, LNAI 8598, pp. 133–142, 2014.
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Alexey Potapov, Sergey Rodionov. "Universal Induction with Varying Sets of Combinators". K.-U. Kühnberger, S. Rudolph, P. Wang (Eds.): AGI 2013, LNAI 7999, pp. 88–97, 2013.
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Alexey Potapov, Sergey Rodionov. "Extending Universal Intelligence Models with Formal Notion of Representation". J. Bach, B. Goertzel, and M. Iklé (Eds.): AGI 2012, LNAI 7716, pp. 242–251, 2012.
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Alexey Potapov, Andrew Svitenkov, Yurii Vinogradov. "Differences between Kolmogorov Complexity and Solomonoff Probability: Consequences for AGI". J. Bach, B. Goertzel, and M. Iklé (Eds.): AGI 2012, LNAI 7716, pp. 252–261, 2012.
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Alexey Potapov, Sergey Rodionov, Andrew Myasnikov, Galymzhan Begimov. "Cognitive Bias for Universal Algorithmic Intelligence". SarXiv:1209.4290v1 [cs.AI]. 2012. 10 p.
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Previous publications.

Potapov A.S. "Artificial Intelligence and Universal Reasoning". Russia, Saint-Petersburg, Polytechnics. 2012. 716 p. (in Russian).
[PDF] [BUY]

Potapov A.S. "Principle of Representational Minimum Description Length in Image Analysis and Pattern Recognition". Pattern Recognition and Image Analysis. 2012. V. 22. No. 1. P. 82–91.
[PDF] [Springerlink]

Potapov A.S. "Automatic Image Analysis and Pattern Recognition. Representational Minimum Description Length Approach". Moscow, Lambert Academic Publishing. 2011. 285 p. (in Russian).
[PDF] [BUY]

Potapov A.S., Malyshev I.A., Puysha A.E., Averkin A.N. "New paradigm of learnable computer vision algorithms based on the representational MDL principle". Proc. SPIE. 2010. V.7696. P. 769606.
[PDF] [Spie]

Potapov A.S., Petrochenko V.G. "Quantitative description of the laws of perceptual grouping by means of the principle of representational minimum description length". J. Opt. Technol. 75 (8). P. 509–513.
[PDF] [J. Opt. Technol.]

Potapov A.S. "Synthetic pattern recognition methods based on the representational minimum description length principle". Proc. OSAV'2008, The 2nd Int. Topical Meeting on Optical Sensing and Artificial Vision. 2008. P. 354–362.
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Potapov A.S. "Theoretic-informational approach to the introduction of feedback into multilevel machine-vision systems". J. Opt. Technol. 74 (10). P. 694–699.
[PDF] [Optics InfoBase]

Potapov A.S. "Pattern Recognition and Machine Perception: General Approach on the Base of the Minimum Description Length Principle". Russia, Saint-Petersburg, Polytechnics. 2007. 548 p. (in Russian).
[PDF] [BUY]

Potapov A.S., Luciv V.R. "Information-theoretic approach to image description and interpretation". Proc. SPIE. 2003. Vol. 5400. P. 277–283.
[PDF] [Spie]