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IJCAI
2007
15 years 6 days ago
Using Learned Policies in Heuristic-Search Planning
Many current state-of-the-art planners rely on forward heuristic search. The success of such search typically depends on heuristic distance-to-the-goal estimates derived from the ...
Sung Wook Yoon, Alan Fern, Robert Givan
AIPS
2009
14 years 12 months ago
Preferred Operators and Deferred Evaluation in Satisficing Planning
Heuristic forward search is the dominant approach to satisficing planning to date. Most successful planning systems, however, go beyond plain heuristic search by employing various...
Silvia Richter, Malte Helmert
CORR
2010
Springer
134views Education» more  CORR 2010»
14 years 11 months ago
Incremental Sampling-based Algorithms for Optimal Motion Planning
During the last decade, incremental sampling-based motion planning algorithms, such as the Rapidly-exploring Random Trees (RRTs), have been shown to work well in practice and to po...
Sertac Karaman, Emilio Frazzoli
LOGCOM
2007
97views more  LOGCOM 2007»
14 years 10 months ago
A General Framework for Expressing Preferences in Causal Reasoning and Planning
We consider the problem of incorporating arbitrary preferences in planning systems. A preference may be seen as a goal or constraint that is desirable, but not necessary, to satis...
James P. Delgrande, Torsten Schaub, Hans Tompits
ICRA
2010
IEEE
142views Robotics» more  ICRA 2010»
14 years 9 months ago
Learning and planning high-dimensional physical trajectories via structured Lagrangians
— We consider the problem of finding sufficiently simple models of high-dimensional physical systems that are consistent with observed trajectories, and using these models to s...
Paul Vernaza, Daniel D. Lee, Seung-Joon Yi