This paper presents Unlimited Computable AI, or UCAI, that is a family of computable variants of AIXI. UCAI is more powerful than AIXItl, which is a conventional family of computable variants of AIXI, in the following ways: 1) UCAI supports models of terminating computation, including typed lambda calculi, while AIXItl only supports Turing machine with timeout ˜t, which can be simulated by typed lambda calculi for any ˜t; 2) unlike UCAI, AIXItl limits the program length to some ˜l .
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Bird, R., and Wadler, P. 1988. An Introduction to Functional Programming. Prentice-Hall.
Boehm, H., and Cartwright, R. 1990. Exact Real Arithmetic Formulating Real Numbers As Functions. In Turner, D. A., ed., Research Topics in Functional Programming. Boston, MA, USA: Addison-Wesley Longman Publishing Co., Inc. 43–64.
Claessen, K., and Pałka, M. H. 2013. Splittable Pseudorandom Number Generators Using Cryptographic Hashing. In Proceedings of the 2013 ACM SIGPLAN Symposium on Haskell, Haskell ’13, 47–58. New York, NY, USA: ACM.
Hutter, M. 2007. Universal Algorithmic Intelligence: A Mathematical Top→Down Approach. In Goertzel, B., and Pennachin, C., eds., Artificial General Intelligence, Cognitive Technologies. Berlin: Springer. 227–290.
Katayama, S. 2016. Ideas for a Reinforcement Learning Algorithm that Learns Programs. In Artificial General Intelligence - 9th International Conference, AGI 2016, AGI 2016, New York, USA, July 16–19, 2016, Proceedings, 354–362.
Plume, D. 1998. A Calculator for Exact Real Number Computation. Ph.D. Dissertation, University of Edinburgh.
Sutton, R. S., and Barto, A. G. 1998. Introduction to Reinforcement Learning. Cambridge, MA, USA: MIT Press, 1st edition.
Veness, J.; Ng, K. S.; Hutter, M.; Uther, W.; and Silver, D. 2011. A Monte-Carlo AIXI Approximation. Journal of Artificial Intelligence Research 40:95–142.
Watkins, C. J. C. H., and Dayan, P. 1992. Q-learning. In Machine Learning, 279–292.
Willems, F. M. J.; Shtarkov, Y. M.; and Tjalkens, T. J. 1995. The Context Tree Weighting Method: Basic Properties. IEEE Transactions on Information Theory 41:653–664.