Literature related to LEIA

A list of papers and books that are related to the LEIA project. See also the LEIA publications.

• Marie desJardins. Pagoda: A Model for Autonomous Learning in Probabilistic Domains. In AI Magazine, 14(1):75–76, 1993.
• Péter Gács, John T. Tromp, and Paul M.B. Vitányi. Algorithmic statistics. In IEEETIT: IEEE Transactions on Information Theory, 47, 2001.
• Murray Gell-Mann and Seth LLoyd. Information measures, effective complexity and total information. In Complexity, 2:44–52, 1996.
• Paul W. Glimcher, Decisions, Uncertainty, and the Brain: The science of Neuroeconomics. The MIT Press, 2003.
• Peter D. Grünwald. The Minimum Description Length Principle and Reasoning under Uncertainty. Institute for Logic, Language and Computation, 1998.
• Marcus Hutter. A theory of universal artificial intelligence based on algorithmic complexity. Technical report, 62 pages, April 2000.
• Ming Li and Paul M.B. Vitányi. An Introduction to Kolmogorov Complexity and its Applications. Springer Verlag, 1993.
• Ming Li and Paul M.B. Vitányi. Inductive reasoning and Kolmogorov complexity. In Journal of Computer and System Sciences, 44(2):343–384, April 1992.
• Brian E. Pangburn. Experience-based Language Acquisition: a computational model of human language acquisition, 2002.
• Jorma J. Rissanen. Stochastic Complexity in Statistical Enquiry. World Scientific, Singapore, 1989.
• Ray J. Solomonoff. A formal theory of inductive inference, part I. In Information and Control, 7:1–22, 1964.
• Ingmar Visser. Rules and Associations: Hidden Markov models and neural networks in the psychology of learning. PrintPartners Ipskamp, Enschede, 2002.
• Marcelo J. Weinberger, Jorma J. Rissanen, and Meir Feder. A universal finite memory source. In IEEE Transactions on Information Theory, IT-41(3):643–652, 1995.

See also Publications and External Links.

Visit the LEIA project page on GitHub for project details.