Title

Incorporating Advice Into Neuroevolution Of Adaptive Agents

Abstract

Neuroevolution is a promising learning method in tasks with extremely large state and action spaces and hidden states. Recent advances allow neuroevolution to take place in real time, making it possible to e.g. construct video games with adaptive agents. Often some of the desired behaviors for such agents are known, and it would make sense to prescribe them, rather than requiring evolution to discover them. This paper presents a technique for incorporating human-generated advice in real time into neuroevolution. The advice is given in a formal language and converted to a neural network structure through KBANN. The NEAT neuroevolution method then incorporates the structure into existing networks through evolution of network weights and topology. The method is evaluated in the NERO video game, where it makes learning faster even when the tasks change and novel ways of making use of the advice are required. Such ability to incorporate human knowledge into neuroevolution in real time may prove useful in several interactive adaptive domains in the future. © 2006, American Association for Artificial Intelligence.

Publication Date

12-1-2006

Publication Title

Proceedings of the 2nd Artificial Intelligence and Interactive Digital Entertainment Conference, AIIDE 2006

Number of Pages

98-104

Document Type

Article; Proceedings Paper

Personal Identifier

scopus

Socpus ID

77955975273 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/77955975273

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