Optimism in the Face of Uncertainty Should be Refutable

Authors:
Ronald Ortner
Affiliations:
Department Mathematik und Informationstechnolgie, Montanuniversität Leoben, Leoben, Austria 8700
Venue:
Minds and Machines
Year:
2008

Citing 4
Cited 0

Markov Decision Processes: Discrete Stochastic Dynamic Programming

Markov Decision Processes: Discrete Stochastic Dynamic Programming
R-max - a general polynomial time algorithm for near-optimal reinforcement learning

The Journal of Machine Learning Research
An Empirical Evaluation of Interval Estimation for Markov Decision Processes

ICTAI '04 Proceedings of the 16th IEEE International Conference on Tools with Artificial Intelligence
A theoretical analysis of Model-Based Interval Estimation

ICML '05 Proceedings of the 22nd international conference on Machine learning

Quantified Score

Hi-index	0.00

Visualization

Abstract

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.