INTEGRATION OF A PHYSICAL SYSTEM, MACHINE LEARNING, SIMULATION, VALIDATION AND CONTROL SYSTEMS TOWARDS SYMBIOTIC MODEL ENGINEERING Sebastian Bohlmann Institute of Applied Mathematics Gottfried Wilhelm Leibniz University Hannover Welfengarten 1 D-30167 Hannover, Germany bohlmann@ifam.uni-hannover.de Volkhard Klinger Department of Embedded Systems and Information Engineering FHDW Hannover Freundallee 15 D-30173 Hannover, Germany volkhard.klinger@fhdw.de Helena Szczerbicka Department of Simulation and Modelling Gottfried Wilhelm Leibniz University Hannover Welfengarten 1 D-30167 Hannover, Germany hsz@sim.uni-hannover.de ABSTRACT System simulation without detailed prior knowledge or data of the system is a complex challenge. In this paper we present an approach to automatically generate a model on the fly in a symbiotic way. Basically the data based model generation system introduced is an agent based evolutionary optimization system creating continuous differential equations from simple predefined operators. The well known paradigm of symbiotic simulation is then enhanced with this agent based machine learning system. Here we focus on the emergent behavior of the model generation system resulting from the interaction of multiple agents optimizing a common model and the effects arising from the direct coupling and steering of the connected physical system. Different emergent mechanisms and effects can be observed speeding up the model generation process. To measure and evaluate this effects multiple experiments with a robotic system are discussed. Keywords: symbiotic simulation, symbiotic circle, system identification, agent-based evolutionary compu- tation, memetic optimization algorithms. SpringSim-MSCIAAS 2017, April 23-26, Virginia Beach, VA, USA ©2017 Society for Modeling & Simulation International (SCS)