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y different yet both appear cognitively necessary. The argument for using probability in an AGI system is assailed by some AGI researchers such as Pei Wang, but we are swayed by the theoretical arguments in favor of probability theory's mathematically fundamental nature, as well as the massive demonstra
and Benjamin Kuipers. Learning geometry from sensorimo- tor experience. In Proceedings of the First Joint Conference on Development and Learning and Epigenetic Robotics, 2012. Sol64a. Ray Solomonoff. A Formal Theory of Inductive Inference, Part I. Information and Control, 1964. Sol64b. Ray Solomonoff. A Formal Th
achines. Oxford University Press, 2010. Wan95. P. Wang. Non-Axiomatic Reasoning System. PhD Thesis, Indiana University. Bloomington, 1995. Wan06. Pei Wang. Rigid Flexibility: The Logic of Intelligence. Springer. 2006. Was09. Mark Weser. Ethics for self-improving machines. In ACf-09, 2009. http://vimeo
Jonathan Mugan and Benjamin Kuipers. Towards the application of reinforcement learning to undirected developmental learning. International Conf. on Epigenetic Robotics, 2008. MK09. Jonathan Mugan and Benjamin Kuipers. Autonomously learning an action hierarchy using a learned qualitative state representation. L IC
Machines. Oxford University Press, 2010. Wan95. P. Wang. Non-Aziomatic Reasoning System. PhD Thesis, Indiana University. Bloomington, 1995. Wan06. Pei Wang. Rigid Flexibility: The Logic of Intelligence. Springer, 2006. Was09. Mark Waser. Ethics for self-improving machines. In AGI-09, 2009. http: //vimeo
. Jonathan Mugan and Benjamin Kuipers. Towards the application of reinforcement learning to undirected developmental learning. International Conf. on Epigenetic Robotics, 2008. Ko9. Jonathan Mugan and Benjamin Kuipers. Autonomously learning an action hierarchy using a learned qualitative state representation. [JCAI-09
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