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» Learning to Optimize Plan Execution in Information Agents
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2007
176views Robotics» more  RSS 2007»
15 years 1 months ago
Active Policy Learning for Robot Planning and Exploration under Uncertainty
Abstract— This paper proposes a simulation-based active policy learning algorithm for finite-horizon, partially-observed sequential decision processes. The algorithm is tested i...
Ruben Martinez-Cantin, Nando de Freitas, Arnaud Do...
AAAI
2000
15 years 1 months ago
Extracting Effective and Admissible State Space Heuristics from the Planning Graph
Graphplan and heuristic state space planners such as HSP-R and UNPOP are currently two of the most effective approaches for solving classical planning problems. These approaches h...
XuanLong Nguyen, Subbarao Kambhampati
80
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AAAI
1998
15 years 1 months ago
Machine Learning of Generic and User-Focused Summarization
A key problem in text summarization is finding a salience function which determines what information in the source should be included in the summary. This paper describes the use ...
Inderjeet Mani, Eric Bloedorn
ECML
2005
Springer
15 years 5 months ago
Active Learning in Partially Observable Markov Decision Processes
This paper examines the problem of finding an optimal policy for a Partially Observable Markov Decision Process (POMDP) when the model is not known or is only poorly specified. W...
Robin Jaulmes, Joelle Pineau, Doina Precup
IAT
2006
IEEE
15 years 5 months ago
Resolution-Based Policy Search for Imperfect Information Differential Games
Differential games (DGs), considered as a typical model of game with continuous states and non-linear dynamics, play an important role in control and optimization. Finding optimal...
Minh Nguyen-Duc, Brahim Chaib-draa