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Evolving Agent Behavior In Multiobjective Domains Using Fitness-Based Shaping (2010)
Jacob Schrum
and
Risto Miikkulainen
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.
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PDF
Citation:
In
Proceedings of the Genetic and Evolutionary Computation Conference (GECCO 2010)
, 439--446, Portland, Oregon, July 2010.
Bibtex:
@inproceedings{schrum:gecco10, title={Evolving Agent Behavior In Multiobjective Domains Using Fitness-Based Shaping}, author={Jacob Schrum and Risto Miikkulainen}, booktitle={Proceedings of the Genetic and Evolutionary Computation Conference (GECCO 2010)}, month={July}, address={Portland, Oregon}, pages={439--446}, url="http://nn.cs.utexas.edu/?schrum:gecco10", year={2010} }
Presentation:
Slides (PPT)
People
Risto Miikkulainen
Faculty
risto [at] cs utexas edu
Jacob Schrum
Ph.D. Alumni
schrum2 [at] southwestern edu
Projects
Learning Strategic Behavior in Sequential Decision Tasks
2009 - 2014
Demos
Fitness-based Shaping in Multi-objective Domains
Jacob Schrum
2010
Software/Data
BREVE Monsters
BREVE is a system for designing Artificial Life simulations available at
http://spiderlan...
2010
Areas of Interest
Evolutionary Computation
Neuroevolution
Multiobjective Optimization
Reinforcement Learning