Co-evolving parasites improve simulated evolution as an optimization procedure
CNLS '89 Proceedings of the ninth annual international conference of the Center for Nonlinear Studies on Self-organizing, Collective, and Cooperative Phenomena in Natural and Artificial Computing Networks on Emergent computation
COLT '92 Proceedings of the fifth annual workshop on Computational learning theory
Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
Evolving visually guided robots
Proceedings of the second international conference on From animals to animats 2 : simulation of adaptive behavior: simulation of adaptive behavior
Evolutionary robotics and the radical envelope-of-noise hypothesis
Adaptive Behavior
Hardware Solutions for Evolutionary Robotics
Proceedings of the First European Workshop on Evolutionary Robotics
Decision-Theoretic Control of Planetary Rovers
Revised Papers from the International Seminar on Advances in Plan-Based Control of Robotic Agents,
Online Choice of Active Learning Algorithms
The Journal of Machine Learning Research
Distributed Cooperative Outdoor Multirobot Localization and Mapping
Autonomous Robots
'Managed challenge' alleviates disengagement in co-evolutionary system identification
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Ideal Evaluation from Coevolution
Evolutionary Computation
Active Coevolutionary Learning of Deterministic Finite Automata
The Journal of Machine Learning Research
Action-selection and crossover strategies for self-modeling machines
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Nonlinear System Identification Using Coevolution of Models and Tests
IEEE Transactions on Evolutionary Computation
Accelerating self-modeling in cooperative robot teams
IEEE Transactions on Evolutionary Computation
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In previous work [8] a computational framework was demonstrated that allows a mobile robot to autonomously evolve models its own body for the purposes of adaptive behavior generation or recovery from damage. Conceivably, robots working in tandem could share their experiences such that one robot, when faced with a situation already encountered by another robot, could draw on that experience and adapt more rapidly. A first demonstration of this is given here: multiple robots with the same or similar body plan, but acting independently, combine self-models such that they accelerate modeling. Two approaches are investigated: the robots feed their experiences back into a common modeling engine, or they maintain their own modeling engine but share their best self-models with each other. It was found that the latter approach achieves a significant improvement in modeling compared to a single robot and compared to the former approach. This finding has implications for how to design autonomous robots acting in concert to achieve large-scale tasks.