Evolving Agent Behavior In Multiobjective Domains Using Fitness-Based Shaping (2010)
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 (GECCO 2010), 439--446, Portland, Oregon, July 2010.

Slides (PPT)
Risto Miikkulainen Faculty risto [at] cs utexas edu
Jacob Schrum Ph.D. Alumni schrum2 [at] southwestern edu
BREVE Monsters BREVE is a system for designing Artificial Life simulations available at http://spiderlan... 2010