Methods for discovering causal knowledge from observational data have been a persistent topic of AI research for several decades. Essentially all of this work focuses on knowledge...
Marc Maier, Brian Taylor, Huseyin Oktay, David Jen...
—This paper introduces a new approach to develop robots that can learn general affordance relations from their experiences. Our approach is a part of larger efforts to develop a ...
Erdem Erdemir, Carl B. Frankel, Kazuhiko Kawamura,...
We present several new algorithms for multiagent reinforcement learning. A common feature of these algorithms is a parameterized, structured representation of a policy or value fu...
Carlos Guestrin, Michail G. Lagoudakis, Ronald Par...
Using the methods demonstrated in this paper, a robot with an unknown sensorimotor system can learn sets of features and behaviors adequate to explore a continuous environment and...
An approach to extend process monitoring with the help of information agents (IA) handling semantic data is presented in this paper. According to this approach, an operator of a pr...
Teppo Pirttioja, Ilkka Seilonen, Antti Pakonen, Aa...