using action abstraction to evolve effective controllers
We propose that abstracting the actions of a behavior coordination mechanism promotes the faster development and higher fitness of an effective controller for complex, composite tasks. Various techniques are well suited for the development of controllers for individual simple tasks. However, as individual tasks are combined into complex, composite tasks, many of these techniques quickly become impractical. By reusing existing behaviors, the focus of development for a controller can be shifted from low-level control to high-level coordination of these existing behaviors. As a result, the development of an effective controller becomes far more practical. Experiments using a single-agent task in a continuous environment demonstrate that grammatical evolution is capable of discovering fuzzy rulesets which effectively coordinate existing behaviors in a controller in fewer generations and with higher fitness than monolithic controllers.
artificial intelligence fuzzy systems evolutionary robotics modelling behaviours and ecosystems