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» Instance-Based Action Models for Fast Action Planning
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ATAL
2010
Springer
15 years 6 months ago
Closing the learning-planning loop with predictive state representations
A central problem in artificial intelligence is to choose actions to maximize reward in a partially observable, uncertain environment. To do so, we must learn an accurate model of ...
Byron Boots, Sajid M. Siddiqi, Geoffrey J. Gordon
EXACT
2007
15 years 7 months ago
An MDP Approach for Explanation Generation
In order to assist a power plant operator to face unusual situations, we have developed an intelligent assistant that explains the suggested commands generated by an MDP-based pla...
Francisco Elizalde, Luis Enrique Sucar, Alberto Re...
AIPS
1998
15 years 6 months ago
Making Forward Chaining Relevant
Planning by forward chaining through the world space has long been dismissed as being "obviously" infeasible. Nevertheless, this approach to planning has many advantages...
Fahiem Bacchus, Yee Whye Teh
FLAIRS
2008
15 years 7 months ago
Planning for Welfare to Work
We are interested in building decision-support software for social welfare case managers. Our model in the form of a factored Markov decision process is so complex that a standard...
Liangrong Yi, Raphael A. Finkel, Judy Goldsmith
ICRA
2008
IEEE
173views Robotics» more  ICRA 2008»
15 years 11 months ago
Bayesian reinforcement learning in continuous POMDPs with application to robot navigation
— We consider the problem of optimal control in continuous and partially observable environments when the parameters of the model are not known exactly. Partially Observable Mark...
Stéphane Ross, Brahim Chaib-draa, Joelle Pi...