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AAAI
2007
15 years 4 months ago
Temporal Difference and Policy Search Methods for Reinforcement Learning: An Empirical Comparison
Reinforcement learning (RL) methods have become popular in recent years because of their ability to solve complex tasks with minimal feedback. Both genetic algorithms (GAs) and te...
Matthew E. Taylor, Shimon Whiteson, Peter Stone
EWCBR
2008
Springer
15 years 3 months ago
Semantics and Experience in the Future Web
Abstract. The Web is a vibrant environment for innovation in computer science, AI, and social interaction; these innovations come in such great number and speed that it is unlikely...
Enric Plaza
133
Voted
UAI
2008
15 years 3 months ago
CORL: A Continuous-state Offset-dynamics Reinforcement Learner
Continuous state spaces and stochastic, switching dynamics characterize a number of rich, realworld domains, such as robot navigation across varying terrain. We describe a reinfor...
Emma Brunskill, Bethany R. Leffler, Lihong Li, Mic...
108
Voted
KBS
2007
56views more  KBS 2007»
15 years 1 months ago
Domain-dependent distributed models for railway scheduling
Many combinatorial problems can be modelled as Constraint Satisfaction Problems (CSPs). Solving a general CSP is known to be NP-complete, so closure and heuristic search are usual...
Miguel A. Salido, Montserrat Abril, Federico Barbe...
EDBT
2011
ACM
231views Database» more  EDBT 2011»
14 years 5 months ago
Data integration with dependent sources
Data integration systems offer users a uniform interface to a set of data sources. Previous work has typically assumed that the data sources are independent of each other; however...
Anish Das Sarma, Xin Luna Dong, Alon Y. Halevy