Incremental evolution of complex general behavior
Adaptive Behavior - Special issue on environment structure and behavior
COOPERATIVE COEVOLUTION OF MULTI-AGENT SYSTEMS
COOPERATIVE COEVOLUTION OF MULTI-AGENT SYSTEMS
Evolutionary Neural Networks for Value Ordering in Constraint SatisfactionProblems
Evolutionary Neural Networks for Value Ordering in Constraint SatisfactionProblems
Robust non-linear control through neuroevolution
Robust non-linear control through neuroevolution
MASON: A Multiagent Simulation Environment
Simulation
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Collective specialization in multi-rover systems
Proceedings of the 9th annual conference on Genetic and evolutionary computation
| Hi-index | 0.00 |
In Nature, behavioral specialization is ubiquitous. Groups benefit from complementary and specialized behaviors in individuals, especially in tasks requiring collective behavior. We apply four multiagent NeuroEvolution approaches to such a task: Enforced SubPopulations [5], Parallel and Coevolutionary Enforced SubPopulations [16] and Collective NeuroEvolution [11]. Rather than just single controllers we evolve teams of simulated robots to search an unexplored area and gather certain object types for collective construction of a specific sequence. Teams are composed of agents that may evolve from initially homogeneous behavior into specialists that effectively complement each other. Results show that CONE outperforms in the collective behavior task when assisted with target behavior heuristics for lifetime learning to speed up the search. Some evolved specialists however become what we call all-rounders, taking on some more tasks to compensate for their lack in number.