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ECAI
1994
Springer
14 years 7 days ago
Exploiting Causal Domain Knowledge for Learning to Control Dynamic Systems
This paper introduces a simple yete ective method for using causal domain knowledge for learning to control dynamic systems. Elementary qualitative causal dependencies of the domai...
Achim G. Hoffmann
CI
2005
106views more  CI 2005»
13 years 8 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
AUTOMATICA
2005
116views more  AUTOMATICA 2005»
13 years 8 months ago
Monotonically convergent iterative learning control for linear discrete-time systems
In iterative learning control schemes for linear discrete time systems, conditions to guarantee the monotonic convergence of the tracking9 error norms are derived. By using the Ma...
Kevin L. Moore, Yangquan Chen, Vikas Bahl
AIIDE
2006
13 years 9 months ago
Incorporating Advice into Neuroevolution of Adaptive Agents
Neuroevolution is a promising learning method in tasks with extremely large state and action spaces and hidden states. Recent advances allow neuroevolution to take place in real t...
Chern Han Yong, Kenneth O. Stanley, Risto Miikkula...
CONCUR
2007
Springer
14 years 2 months ago
Mobility Control Via Passports
Dπ is a simple distributed extension of the π-calculus in which agents are explicitly located, and may use an explicit migration construct to move between locations. In this pap...
Samuel Hym