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PRICAI
2000
Springer
15 years 7 months ago
Generating Hierarchical Structure in Reinforcement Learning from State Variables
This paper presents the CQ algorithm which decomposes and solves a Markov Decision Process (MDP) by automatically generating a hierarchy of smaller MDPs using state variables. The ...
Bernhard Hengst
BCSHCI
2007
15 years 5 months ago
"The devil you know knows best": how online recommendations can benefit from social networking
The defining characteristic of the Internet today is an abundance of information and choice. Recommender Systems (RS), designed to alleviate this problem, have so far not been ver...
Philip Bonhard, Martina Angela Sasse, Clare Harrie...
141
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BCSHCI
2009
15 years 5 months ago
Putting the physical into the digital: issues in designing hybrid interactive surfaces
Hybrid surfaces are interactive systems combining techniques of direct-manipulation multi-touch surface interaction with elements of tangible user interfaces (TUIs). The design sp...
David S. Kirk, Abigail Sellen, Stuart Taylor, Nico...
AMAI
2006
Springer
15 years 4 months ago
Symmetric approximate linear programming for factored MDPs with application to constrained problems
A weakness of classical Markov decision processes (MDPs) is that they scale very poorly due to the flat state-space representation. Factored MDPs address this representational pro...
Dmitri A. Dolgov, Edmund H. Durfee
AIEDAM
1998
87views more  AIEDAM 1998»
15 years 3 months ago
Learning to set up numerical optimizations of engineering designs
Gradient-based numerical optimization of complex engineering designs offers the promise of rapidly producing better designs. However, such methods generally assume that the object...
Mark Schwabacher, Thomas Ellman, Haym Hirsh