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...
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 ...
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...
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...
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...