Human Inspired Evolving Machines - The Next Generation of Evolving Intelligent Systems?

In today's real-world applications, there is an increasing need to integrate new information and knowledge into modelbuilding processes to account for changing system dynamics, new operating conditions or environmental influences. This is essential to increase the efficiency of models in terms of performance and process safety during on-line operation and production phases. Evolving Intelligent Systems (EIS) [1] constitute a powerful methodology to address this need, integrating mechanism for continuous adaptation of parameters, expansion of model structures, on-the-fly memory extension of system models and real-time modeling. The current situation in EIS is that system knowledge builds largely upon data (usually measurements, features extracted from the process). There is little active intervention by experts and/or operators working with the systems. Interaction is generally confined to passive supervision or – at most - in form of good/bad rewards on model decisions. This briefing article discusses requirements, possible methods and key components to enhance communication by dynamically integrating the previous system experiences from human beings into evolving models (human feedback integration component). Model interpretability and understandability are important topics to motivate users to provide enhanced feedback (model interpretation component). Finally, the complete concept will lead to an enriched human-model interaction scenario, termed human-inspired evolving machines, which might serve as a cornerstone for the next generation of evolving intelligent systems.