My research centers on modeling complex real-world processes, e.g., for enabling the optimization of planning, business, manufacturing, and logistics processes. Modeling requires the explicit definition of actors, application area knowledge and its processing, especially if it is dynamic knowledge that must be kept up-to-date through continuous learning by the actors. One methodology to achieve this are Artificial Intelligence Multi-Agent Systems.
■ Optimization of Dynamic Resources in Open-Pit Mines with Multi-Agent Systems : Resource optimization of the heavy mine equipment.
■ Simulation of Urban Planning Processes with Multi-Agent Systems: According to city planning rules, establish the simulation of city growth for 20+ years in order to support decision making for city development.
■ Big Data Analytics for Traffic Simulation and Prognosis: Extract rules from traffic data through Big Data Analytics, and use them to drive traffic simulation for traffic control.
■ J. O. Berndt & O. Herzog, Anticipatory behavior of software agents in self- organizing negotiations. In M. Nadin (ed.). Anticipation across Disciplines. Springer International Publishing: Berlin, pp. 231-253 (2016).
■ M. Gath et al., Autonomous, adaptive, and self-organized multiagent systems for the optimization of decentralized industrial processes. In J. Kolodziej, L. Correia, J. M. Molina (eds.). Intelligent Agents in Data-intensive Computing. Springer: Cham, Heidelberg, pp. 71-98 (2016).
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