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  1. Mechanizing Induction.Ronald Ortner & Hannes Leitgeb - 2009 - In Dov Gabby, Hartmann M., Woods Stephan & John (eds.), Handbook of the History of Logic: Inductive Logic. Elsevier: Amsterdam. pp. 719--772.
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    Optimism in the Face of Uncertainty Should Be Refutable.Ronald Ortner - 2008 - Minds and Machines 18 (4):521-526.
    We give an example from the theory of Markov decision processes which shows that the “optimism in the face of uncertainty” heuristics may fail to make any progress. This is due to the impossibility to falsify a belief that a (transition) probability is larger than 0. Our example shows the utility of Popper’s demand of falsifiability of hypotheses in the area of artificial intelligence.
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    Optimal Behavior is Easier to Learn Than the Truth.Ronald Ortner - 2016 - Minds and Machines 26 (3):243-252.
    We consider a reinforcement learning setting where the learner is given a set of possible models containing the true model. While there are algorithms that are able to successfully learn optimal behavior in this setting, they do so without trying to identify the underlying true model. Indeed, we show that there are cases in which the attempt to find the true model is doomed to failure.
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