Julian Togelius, “Evolution of a Subsumption Architecture Neurocontroller”, ?
I’ve read this paper. It was interesting as it strenghten my idea of using (or possibility of using) incremental ideas in learning. I have done some experiments doing incremental learning, but I’m not yet in a place to make conclusions.
Before rewriting its abstract, let’s copy this informative table:
1-One layer – One fitness: Monolithic evolution
2-One layer – Many fitness: Incremental evolution
3-Many layers – One fitness: Modularized evolution
4-Many layers – Many fitness: Layered evolution
(One may call have another names for this one, i.e. I used to name every incrementally making “many layers” system, “incremental”.)
He found out that the forth method of evolution has indeed very good performance. It is the one I’m thinking about.
Here is the paper’s abstract:
Abstract. An approach to robotics called layered evolution and merging features from the subsumption architecture into evolutionary robotics is presented, and its advantages are discussed. This approach is used to construct a layered controller for a simulated robot that learns which light source to approach in an environment with obstacles. The evolvability and performance of layered evolution on this task is compared to (standard) monolithic evolution, incremental and modularised evolution. To corroborate the hypothesis that a layered controller performs at least as well as an integrated one, the evolved layers are merged back into a single network. On the grounds of the test results, it is argued that layered evolution provides a superior approach for many tasks, and it is suggested that this approach may be the key to scaling up evolutionary robotics.