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» Learning to Optimize Plan Execution in Information Agents
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CI
2005
106views more  CI 2005»
13 years 7 months ago
Incremental Learning of Procedural Planning Knowledge in Challenging Environments
Autonomous agents that learn about their environment can be divided into two broad classes. One class of existing learners, reinforcement learners, typically employ weak learning ...
Douglas J. Pearson, John E. Laird
MATES
2010
Springer
13 years 5 months ago
Context-Aware Route Planning
Context-aware routing is the problem of finding the shortest route from a start location to a destination location while taking into account the planned movements of other agents...
Adriaan ter Mors, Cees Witteveen, Jonne Zutt, Fern...
TBILLC
2005
Springer
14 years 29 days ago
Real World Multi-agent Systems: Information Sharing, Coordination and Planning
Abstract. Applying multi-agent systems in real world scenarios requires several essential research questions to be answered. Agents have to perceive their environment in order to t...
Frans C. A. Groen, Matthijs T. J. Spaan, Jelle R. ...
RAS
2010
117views more  RAS 2010»
13 years 5 months ago
Extending BDI plan selection to incorporate learning from experience
An important drawback to the popular Belief, Desire, and Intentions (BDI) paradigm is that such systems include no element of learning from experience. We describe a novel BDI exe...
Dhirendra Singh, Sebastian Sardiña, Lin Pad...
AAI
1999
147views more  AAI 1999»
13 years 7 months ago
Animated Agents for Procedural Training in Virtual Reality: Perception, Cognition, and Motor Control
This paper describes Steve, an animated agent that helps students learn to perform physical, procedural tasks. The student and Steve cohabit a three-dimensional, simulated mock-up...
Jeff Rickel, W. Lewis Johnson