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Multi-objective tree search approaches for general video game playing

Perez-Liebana, D and Mostaghim, S and Lucas, SM (2016) Multi-objective tree search approaches for general video game playing. In: UNSPECIFIED, ? - ?.

MOTS_GVG_CEC2016.pdf - Accepted Version

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The design of algorithms for Game AI agents usually focuses on the single objective of winning, or maximizing a given score. Even if the heuristic that guides the search (for reinforcement learning or evolutionary approaches) is composed of several factors, these typically provide a single numeric value (reward or fitness, respectively) to be optimized. Multi-Objective approaches are an alternative concept to face these problems, as they try to optimize several objectives, often contradictory, at the same time. This paper proposes for the first time a study of Multi-Objective approaches for General Video Game playing, where the game to be played is not known a priori by the agent. The experimental study described here compares several algorithms in this setting, and the results suggest that Multi-Objective approaches can perform even better than their single-objective counterparts.

Item Type: Conference or Workshop Item (Paper)
Additional Information: Published proceedings: 2016 IEEE Congress on Evolutionary Computation, CEC 2016
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions: Faculty of Science and Health > Computer Science and Electronic Engineering, School of
Depositing User: Diego Perez Liebana
Date Deposited: 22 Feb 2017 15:34
Last Modified: 31 Mar 2021 01:15

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