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DAGSTUHL
2007
15 years 7 months ago
Learning Probabilistic Relational Dynamics for Multiple Tasks
The ways in which an agent’s actions affect the world can often be modeled compactly using a set of relational probabilistic planning rules. This paper addresses the problem of ...
Ashwin Deshpande, Brian Milch, Luke S. Zettlemoyer...
AAAI
2004
15 years 7 months ago
The Independent LifeStyle AssistantTM (I.L.S.A.): AI Lessons Learned
The Independent LifeStyle AssistantTM (I.L.S.A.) is an agentbased monitoring and support system to help elderly people to live longer in their homes by reducing caregiver burden. ...
Karen Zita Haigh, Liana M. Kiff, Janet Myers, Vale...
IFIP3
1998
91views Education» more  IFIP3 1998»
15 years 7 months ago
Internet Support for Collaborative Learning in Workgroups
This paper intends to describe an ongoing experiment that provides a collaborative learning method applied to a case study in higher education. With this purpose, a computer-based...
Elena Campo Montalv, Vicente Martinez-Orga, Nelson...
AIEDU
2005
98views more  AIEDU 2005»
15 years 6 months ago
The Andes Physics Tutoring System: Lessons Learned
The Andes system is a mature intelligent tutoring system that has helped hundreds of students improve their learning of university physics. It replaces pencil and paper problem so...
Kurt VanLehn, Collin Lynch, Kay G. Schulze, Joel A...
JAIR
2002
120views more  JAIR 2002»
15 years 6 months ago
Learning Geometrically-Constrained Hidden Markov Models for Robot Navigation: Bridging the Topological-Geometrical Gap
Hidden Markov models hmms and partially observable Markov decision processes pomdps provide useful tools for modeling dynamical systems. They are particularly useful for represent...
Hagit Shatkay, Leslie Pack Kaelbling