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.
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In Proceedings of Thirteenth International Conference on the Synthesis and Simulation of Living Systems, East Lansing, MI, USA, 2012.
Bibtex:

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