Abstract
Efficient pragmatic methods in artificial intelligence can be treated as results of specialization of models of universal intelligence with respect to a certain task or class of environments. Thus, specialization can help to create efficient AGI preserving its universality. This idea is promising, but has not yet been applied to concrete models. Here, we considered the task of mass induction, which general solution can be based on Kolmogorov complexity parameterized by reference machine. Futamura-Turchin projections of this solution were derived and implemented in combinatory logic. Experiments with search for common regularities in strings show that efficiency of universal induction can be considerably increased for mass induction using proposed approach.
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Potapov, A., Rodionov, S. (2014). Making Universal Induction Efficient by Specialization. In: Goertzel, B., Orseau, L., Snaider, J. (eds) Artificial General Intelligence. AGI 2014. Lecture Notes in Computer Science(), vol 8598. Springer, Cham. https://doi.org/10.1007/978-3-319-09274-4_13
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DOI: https://doi.org/10.1007/978-3-319-09274-4_13
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