NDLI: Evolving agent behavior in multiobjective domains using fitness-based shaping (2010)

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Evolving agent behavior in multiobjective domains using fitness-based shaping (2010)

Content Provider	CiteSeerX
Author	Schrum, Jacob
Description	Multiobjective evolutionary algorithms have long been applied to engineering problems. Lately they have also been used to evolve behaviors for intelligent agents. In such applications, it is often necessary to “shape ” the behavior via increasingly difficult tasks. Such shaping requires extensive domain knowledge. An alternative is fitness-based shaping through changing selection pressures, which requires little to no domain knowledge. Two such methods are evaluated in this paper. The first approach, Targeting Unachieved Goals, dynamically chooses when an objective should be used for selection based on how well the population is performing in that objective. The second method, Behavioral Diversity, adds a behavioral diversity objective to the objective set. These approaches are implemented in the popular multiobjective evolutionary algorithm NSGA-II and evaluated in a multiobjective battle domain. Both methods outperform plain NSGA-II in evolution time and final performance, but differ in the profiles of final solution populations. Therefore, both methods should allow multiobjective evolution to be more extensively applied to various agent control problems in the future. In Proceedings of the Genetic and Evolutionary Computation Conference
File Format	PDF
Language	English
Publisher Date	2010-01-01
Access Restriction	Open
Subject Keyword	Evolution Time Extensive Domain Knowledge Multiobjective Battle Domain Multiobjective Evolution Plain Nsga-ii Multiobjective Evolutionary Algorithm Intelligent Agent Various Agent Control Problem First Approach Multiobjective Domain Behavioral Diversity Objective Selection Pressure Second Method Popular Multiobjective Evolutionary Algorithm Nsga-ii Fitness-based Shaping Agent Behavior Difficult Task Final Solution Population Final Performance Domain Knowledge Engineering Problem Behavioral Diversity Objective Set
Content Type	Text
Resource Type	Article

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