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ICML
1999
IEEE
13 years 11 months ago
Learning Hierarchical Performance Knowledge by Observation
Developing automated agents that intelligently perform complex real world tasks is time consuming and expensive. The most expensive part of developing these intelligent task perfo...
Michael van Lent, John E. Laird
AGENTS
1998
Springer
13 years 11 months ago
Learning Situation-Dependent Costs: Improving Planning from Probabilistic Robot Execution
Physical domains are notoriously hard to model completely and correctly, especially to capture the dynamics of the environment. Moreover, since environments change, it is even mor...
Karen Zita Haigh, Manuela M. Veloso
ATAL
2006
Springer
13 years 11 months ago
RPD-enabled agents teaming with humans for multi-context decision making
Team decision making under stress involving multiple contexts is an extremely challenging issue faced by various real world application domains. This research is targeted at coupl...
Xiaocong Fan, Bingjun Sun, Shuang Sun, Michael D. ...
IJCAI
2007
13 years 9 months ago
Color Learning on a Mobile Robot: Towards Full Autonomy under Changing Illumination
A central goal of robotics and AI is to be able to deploy an agent to act autonomously in the real world over an extended period of time. It is commonly asserted that in order to ...
Mohan Sridharan, Peter Stone
NIPS
2001
13 years 8 months ago
K-Local Hyperplane and Convex Distance Nearest Neighbor Algorithms
Guided by an initial idea of building a complex (non linear) decision surface with maximal local margin in input space, we give a possible geometrical intuition as to why K-Neares...
Pascal Vincent, Yoshua Bengio