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CORR
2010
Springer
147views Education» more  CORR 2010»
14 years 9 months ago
Learning Probabilistic Hierarchical Task Networks to Capture User Preferences
While much work on learning in planning focused on learning domain physics (i.e., action models), and search control knowledge, little attention has been paid towards learning use...
Nan Li, William Cushing, Subbarao Kambhampati, Sun...
HICSS
2002
IEEE
155views Biometrics» more  HICSS 2002»
15 years 2 months ago
Towards Knowledge-Sharing and Learning in Virtual Professional Communities
This paper describes a program of research designed to understand how knowledge-sharing and learning can be supported in virtual communities. To conduct this research, we propose ...
Michael Bieber, Il Im, Ronald E. Rice, Ricki Goldm...
AI
2007
Springer
14 years 9 months ago
Learning action models from plan examples using weighted MAX-SAT
AI planning requires the definition of action models using a formal action and plan description language, such as the standard Planning Domain Definition Language (PDDL), as inp...
Qiang Yang, Kangheng Wu, Yunfei Jiang
IROS
2006
IEEE
121views Robotics» more  IROS 2006»
15 years 3 months ago
Planning and Acting in Uncertain Environments using Probabilistic Inference
— An important problem in robotics is planning and selecting actions for goal-directed behavior in noisy uncertain environments. The problem is typically addressed within the fra...
Deepak Verma, Rajesh P. N. Rao
IVC
2008
182views more  IVC 2008»
14 years 9 months ago
Ontology based complex object recognition
This paper presents an object categorization method. Our approach involves the following aspects of cognitive vision : machine learning and knowledge representation. A major eleme...
Nicolas Maillot, Monique Thonnat