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AAAI
2008
13 years 10 months ago
Maximum Entropy Inverse Reinforcement Learning
Recent research has shown the benefit of framing problems of imitation learning as solutions to Markov Decision Problems. This approach reduces learning to the problem of recoveri...
Brian Ziebart, Andrew L. Maas, J. Andrew Bagnell, ...
ACMIDC
2008
13 years 9 months ago
Tangible programming and informal science learning: making TUIs work for museums
In this paper we describe the design and initial evaluation of a tangible computer programming exhibit for children on display at the Boston Museum of Science. We also discuss fiv...
Michael S. Horn, Erin Treacy Solovey, Robert J. K....
AAAI
2010
13 years 9 months ago
To Max or Not to Max: Online Learning for Speeding Up Optimal Planning
It is well known that there cannot be a single "best" heuristic for optimal planning in general. One way of overcoming this is by combining admissible heuristics (e.g. b...
Carmel Domshlak, Erez Karpas, Shaul Markovitch
VMV
2008
107views Visualization» more  VMV 2008»
13 years 9 months ago
Learning with Few Examples using a Constrained Gaussian Prior on Randomized Trees
Machine learning with few training examples always leads to over-fitting problems, whereas human individuals are often able to recognize difficult object categories from only one ...
Erik Rodner, Joachim Denzler
CORR
2010
Springer
104views Education» more  CORR 2010»
13 years 7 months ago
Empirical learning aided by weak domain knowledge in the form of feature importance
Standard hybrid learners that use domain knowledge require stronger knowledge that is hard and expensive to acquire. However, weaker domain knowledge can benefit from prior knowle...
Ridwan Al Iqbal