publication . Preprint . 2011

Teraflop-scale Incremental Machine Learning

Özkural, Eray;
Open Access English
  • Published: 04 Mar 2011
We propose a long-term memory design for artificial general intelligence based on Solomonoff's incremental machine learning methods. We use R5RS Scheme and its standard library with a few omissions as the reference machine. We introduce a Levin Search variant based on Stochastic Context Free Grammar together with four synergistic update algorithms that use the same grammar as a guiding probability distribution of programs. The update algorithms include adjusting production probabilities, re-using previous solutions, learning programming idioms and discovery of frequent subprograms. Experiments with two training sequences demonstrate that our approach to incremen...
free text keywords: Computer Science - Artificial Intelligence
Download from
16 references, page 1 of 2

1. Solomonoff, R.J.: A system for incremental learning based on algorithmic probability. In: Proceedings of the Sixth Israeli Conference on Artificial Intelligence, Tel Aviv, Israel (1989) 515-527

2. Hutter, M.: The fastest and shortest algorithm for all well-defined problems. International Journal of Foundations of Computer Science 13(3) (2002) 431-443 [OpenAIRE]

3. Schmidhuber, J.: Ultimate cognition `a la G¨odel. Cognitive Computation 1(2) (2009) 177-193 [OpenAIRE]

4. Schmidhuber, J.: Optimal ordered problem solver. Machine Learning 54 (2004) 211-256

5. R. Cilibrasi, P.V.: Clustering by compression. Technical report, CWI (2003)

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

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

8. Schmidhuber, J., Zhao, J., Wiering, M.: Shifting inductive bias with successstory algorithm, adaptive levin search, and incremental self-improvement. Machine Learning 28(1) (1997) 105-130

9. Richard Kelsey, William Clinger, J.R.: Revised5 report on the algorithmic language scheme. Higher-Order and Symbolic Computation 11(1) (1998) (editors).

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

11. John E. Hopcroft, Rajeev Motwani, J.U.: Introduction to Automata Theory, Languages, and Computation. Second edn. Addison Wesley (2001) [OpenAIRE]

12. Merialdo, B.: Tagging english text with a probabilistic model. Computational Linguistics 20 (1993) 155-171 [OpenAIRE]

13. Solomonoff, R.J.: Algorithmic probability, heuristic programming and agi. In: Third Conference on Artificial General Intelligence. (2010) 251-157

14. Solomonoff, R.J.: Three kinds of probabilistic induction: Universal distributions and convergence theorems. The Computer Journal 51(5) (2008) 566-570

15. Hutter, M.: Universal algorithmic intelligence: A mathematical top→down approach. In Goertzel, B., Pennachin, C., eds.: Artificial General Intelligence. Cognitive Technologies. Springer, Berlin (2007) 227-290

16 references, page 1 of 2
Any information missing or wrong?Report an Issue