Robust player imitation using multiobjective evolution

Authors:
Niels Van Hoorn;Julian Togelius;Daan Wierstra;Jürgen Schmidhuber
Affiliations:
Dalle Molle Institute for Artificial Intelligence, Manno- Lugano, Switzerland;Dalle Molle Institute for Artificial Intelligence, Manno-Lugano, Switzerland;Dalle Molle Institute for Artificial Intelligence, Manno-Lugano, Switzerland;Dalle Molle Institute for Artificial Intelligence, Manno-Lugano, Switzerland
Venue:
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Year:
2009

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Abstract

The problem of how to create NPC AI for videogames that believably imitates particular human players is addressed. Previous approaches to learning player behaviour is found to either not generalize well to new environments and noisy perceptions, or to not reproduce human behaviour in sufficient detail. It is proposed that better solutions to this problem can be built on multiobjective evolutionary algorithms, with objectives relating both to traditional progress-based fitness (playing the game well) and similarity to recorded human behaviour (behaving like the recorded player). This idea is explored in the context of a modern racing game.