Task decomposition with neuroevolution in extended predator-prey domain
| Autor: | Risto Miikulainen, Anand Subramoney, Ashish Jain |
|---|---|
| Rok vydání: | 2012 |
| Předmět: |
Neuroevolution
business.industry Computer science media_common.quotation_subject Machine learning computer.software_genre ComputingMethodologies_ARTIFICIALINTELLIGENCE GeneralLiterature_MISCELLANEOUS Task (project management) Domain (software engineering) Decomposition (computer science) Artificial intelligence business Function (engineering) computer Curse of dimensionality media_common |
| Zdroj: | ALIFE |
| DOI: | 10.7551/978-0-262-31050-5-ch045 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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