Task decomposition with neuroevolution in extended predator-prey domain (2012)
Learning complex behaviour is a difficult task for any artificial agent. Decomposing a task into multiple sub-tasks, learning the sub-tasks separately, and then learning to use them as a whole is a natural way to reduce the dimensionality and complexity of the task function. This approach is demonstrated on a predator agent in the predator-prey-hunter domain. This extended domain has a new agent, a 'hunter', that chases the predators. The evading and chasing behaviours are learnt as separate sub-tasks by separate networks using the NEAT neuro-evolution method. A separate network is then evolved to use these networks based on the situation. Task decomposition using this approach performs significantly better in the predator-prey-hunter domain compared to a monolithic network evolved directly on the whole task.
In Proceedings of Thirteenth International Conference on the Synthesis and Simulation of Living Systems, East Lansing, MI, USA 2012.

Risto Miikkulainen Faculty risto [at] cs utexas edu
Anand Subramoney Masters Alumni anands [at] cs utexas edu