Wolfgang Stolzmann & Martin Butz: Latent Learning and Action-Planning in Robots with Anticipatory Classifier Systems
Two applications of Anticipatory Classifier Systems (ACS) in robotics are discussed. The first one is a simulation of an experiment about latent learning in rats with a mobile robot. It shows that an ACS is able to learn latently, i.e. in the absence of environmental reward and that ACS can do action-planning. The second one is about learning of the hand-eye coordination of a robot arm in conjunction with a camera. Goal-directed learning will be introduced. This combination of action-planning and latent learning leads to a substantial reduction of the number of trials which are required to learn a complete model of a prototypically environment.
In Lanzi, Stolzmann, & Wilson (Eds.), Learning Classifier Systems - From Foundations to Applications. LNAI 1813, Heidelberg: Springer-Verlag.
generated: 24 February 2000; last update: 24 February 2000 / WST