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Evolving Multimodal Behavior Through Modular Multiobjective Neuroevolution (2014)
Jacob Schrum
Intelligent organisms do not simply perform one task, but exhibit multiple distinct modes of behavior. For instance, humans can swim, climb, write, solve problems, and play sports. To be fully autonomous and robust, it would be advantageous for artificial agents, both in physical and virtual worlds, to exhibit a similar diversity of behaviors. This dissertation develops methods for discovering such behavior automatically using multiobjective neuroevolution. First, sensors are designed to allow multiple different interpretations of objects in the environment (such as predator or prey). Second, evolving networks are given ways of representing multiple policies explicitly via modular architectures. Third, the set of objectives is dynamically adjusted in order to lead the population towards the most promising areas of the search space.
These methods are evaluated in five domains that provide examples of three different types of task divisions. Isolated tasks are separate from each other, but a single agent must solve each of them. Interleaved tasks are distinct, but switch back and forth within a single evaluation. Blended tasks do not have clear barriers, because an agent may have to perform multiple behaviors at the same time, or learn when to switch between opposing behaviors. The most challenging of the domains is Ms. Pac-Man, a popular classic arcade game with blended tasks. Methods for developing multimodal behavior are shown to achieve scores superior to other Ms. Pac-Man results previously published in the literature. These results demonstrate that complex multimodal behavior can be evolved automatically, resulting in robust and intelligent agents.
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Citation:
PhD Thesis, The University of Texas at Austin, Austin, TX 78712, May 2014. Tech Report TR-14-07.
Bibtex:
@phdthesis{schrum:phd14, title={Evolving Multimodal Behavior Through Modular Multiobjective Neuroevolution}, author={Jacob Schrum}, month={May}, school={The University of Texas at Austin}, address={Austin, TX 78712}, note={Tech Report TR-14-07}, url="http://nn.cs.utexas.edu/?schrum:phd2014", year={2014} }
Presentation:
Slides (PDF)
People
Jacob Schrum
Ph.D. Alumni
schrum2 [at] southwestern edu
Projects
The Role of Emotion and Communication in Cooperative Behavior
2013 - 2016
Demos
Multimodal Behavior in Imprison Ms. Pac-Man
Jacob Schrum
2014
Multimodal Behavior in Multiple Lives Ms. Pac-Man
Jacob Schrum
2014
Multimodal Behavior in One Life Ms. Pac-Man
Jacob Schrum
2014
Multi-modal Approaches to Evolving Behavior for Multi-task Games
Jacob Schrum
2011
Fitness-based Shaping in Multi-objective Domains
Jacob Schrum
2010
Software/Data
MM-NEAT
Download at GitHub
Modular Multiobjective NEAT is a software fra...
2014
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
Game Playing