Robustness in the long run: Auto-teaching vs
Anticipation in Evolutionary Robotics
Nicolas Godzik, Marc Schoenauer and Michèle Sebag
In: PPSN 2004, September 2004, Birmingham.
In Evolutionary Robotics, auto-teaching networks, neural networks
that modify their own weights during the life-time of the robot, have
been shown to be powerful architectures to develop adaptive
Unfortunately, when run for a longer period of time than
that used during evolution, the long-term behavior of such networks
can become unpredictable. This paper gives an example of such
dangerous behavior, and proposes an alternative solution based on
anticipation: as in auto-teaching networks, a secondary network is
evolved, but its outputs try to predict the next state of the robot sensors.
The weights of the action network are adjusted using
some back-propagation procedure based on the errors made by the
First results -- in simulated environments --
show a tremendous increase in robustness of the long-term
behavior of the controller.