We propose to perform the optimization task of Universal Artificial Intelligence (UAI) through learning a reference machine on which good programs are short. Further, we also acknowledge that the choice of reference machine that the UAI objective is based on is arbitrary and, therefore, we learn a suitable machine for the environment we are in. This is based on viewing Occam's razor as an imperative instead of as a proposition about the world. Since this principle cannot be true for all reference machines, we need to find a machine that makes the principle true. We both want good policies and the environment to have short implementations on the machine. Such a machine is learnt iteratively through a procedure that generalizes the principle underlying the Expectation-Maximization algorithm. © 2014 Springer International Publishing.
CITATION STYLE
Sunehag, P., & Hutter, M. (2014). Intelligence as inference or forcing Occam on the world. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 8598 LNAI, pp. 186–195). Springer Verlag. https://doi.org/10.1007/978-3-319-09274-4_18
Mendeley helps you to discover research relevant for your work.