Some Theorems on Incremental Compression
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The ability to induce short descriptions of, i.e. compressing, a wide class of data is essential for any system exhibiting general intelligence. In all generality, it is proven that incremental compression – extracting features of data strings and continuing to compress the residual data variance – leads to a time complexity superior to universal search if the strings are incrementally compressible. It is further shown that such a procedure breaks up the shortest description into a set of pairwise orthogonal features in terms of algorithmic information.
KeywordsIncremental compression Data compression Algorithmic complexity Universal induction Universal search Feature extraction
I would like to express my gratitude to Alexey Potapov and Alexander Priamikov for proof reading and helpful comments.
- 3.Hutter, M.: Universal Artificial Intelligence: Sequential Decisions based on Algorithmic Probability, 300p. Springer, Heidelberg (2005). http://www.hutter1.net/ai/uaibook.htm
- 5.Potapov, A., Rodionov, S.: Making universal induction efficient by specialization. In: Goertzel, B., Orseau, L., Snaider, J. (eds.) AGI 2014. LNCS, vol. 8598, pp. 133–142. Springer, Heidelberg (2014)Google Scholar
- 6.Franz, A.: Artificial general intelligence through recursive data compression and grounded reasoning: a position paper. CoRR, abs/1506.04366 (2015). http://arXiv.org/abs/1506.04366