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AIPS
2010
14 years 1 months ago
When Policies Can Be Trusted: Analyzing a Criteria to Identify Optimal Policies in MDPs with Unknown Model Parameters
Computing a good policy in stochastic uncertain environments with unknown dynamics and reward model parameters is a challenging task. In a number of domains, ranging from space ro...
Emma Brunskill
DAGSTUHL
2009
13 years 12 months ago
Engineering Self-Adaptive Systems through Feedback Loops
To deal with the increasing complexity of software systems and uncertainty of their environments, software engineers have turned to self-adaptivity. Self-adaptive systems are capab...
Yuriy Brun, Giovanna Di Marzo Serugendo, Cristina ...
LION
2007
Springer
192views Optimization» more  LION 2007»
14 years 5 months ago
Learning While Optimizing an Unknown Fitness Surface
This paper is about Reinforcement Learning (RL) applied to online parameter tuning in Stochastic Local Search (SLS) methods. In particular a novel application of RL is considered i...
Roberto Battiti, Mauro Brunato, Paolo Campigotto
WSCG
2001
114views more  WSCG 2001»
14 years 6 days ago
Interactive Data Exploration with Customized Glyphs
We present a visualization system allowing non-programmers to visualize, explore, and analyze unknown multivariate data by designing an appropriate glyph representation with minim...
Martin Kraus, Thomas Ertl
ICRA
2007
IEEE
113views Robotics» more  ICRA 2007»
14 years 5 months ago
Sampling-Based Motion Planning With Sensing Uncertainty
Abstract— Sampling-based algorithms have dramatically improved the state of the art in robotic motion planning. However, they make restrictive assumptions that limit their applic...
Brendan Burns, Oliver Brock