Multiobjective Optimization
Instead of finding a single optimal solution to any given problem, multiobjective methods aim at finding a Pareto-front, which represents all of the trade-offs between objectives within the domain. A human decision maker can then decide which of the available trade-offs works best. Our work in this area focuses on generating multi-modal behavior, as well as maintaining diversity using multiobjective approaches.
Jacob Schrum Ph.D. Student schrum2@cs.utexas.edu
Effective Diversity Maintenance in Deceptive Domains 2013
Joel Lehman, Kenneth O. Stanley and Risto Miikkulainen
Evolving Multimodal Networks for Multitask Games 2012
Jacob Schrum and Risto Miikkulainen
Evolving Multimodal Networks for Multitask Games 2011
Jacob Schrum and Risto Miikkulainen
Evolving Agent Behavior In Multiobjective Domains Using Fitness-Based Shaping 2010
Jacob Schrum and Risto Miikkulainen
Evolving Multi-modal Behavior in NPCs 2009
Jacob Schrum and Risto Miikkulainen
Constructing Complex NPC Behavior via Multi-Objective Neuroevolution 2008
Jacob Schrum and Risto Miikkulainen
MARLEDA: Effective Distribution Estimation Through Markov Random Fields 2007
Matthew Alden
UT^2: Winning Botprize 2012 Entry The Botprize Competition is an annual competition to program bots that appear human-l... 2012