Generative Adversarial Framework with Composite Discriminator for Organization and Process Modelling—Smart City Cases

Abstract

What are the main findings? We successfully apply generative AI techniques (in particular, generative adversarial modeling) to speed up and streamline the development of organizational structures and processes, which is demanded in volatile smart city environments. We demonstrate the developed idea of the original composite discriminator, taking advantage of separate evaluation of tacit and explicit domain knowledge on smart city related scenarios, namely logistics system model generation and smart tourist trip booking process model generation We show that the developed mechanism of generating and using meaningful augmentations enables learning on limited datasets. What is the implication of the main finding? The developed approach offers an effective solution for the fast generation of organizational structures and process models, accounting for both tacit and explicit requirements and constraints. By automating the creation of organization structures and processes, the approach can contribute to the adaptability of smart city structures and processes and their fast adaptation to changing requirements. The approach can be applied to other domains and could be a starting point for research efforts in related scientific fields. Highlights: Smart city operation assumes dynamic infrastructure in various aspects. However, organization and process modelling require domain expertise and significant efforts from modelers. As a result, such processes are still not well supported by IT systems and still mostly remain manual tasks. Today, machine learning technologies are capable of performing various tasks including those that have normally been associated with people; for example, tasks that require creativeness and expertise. Generative adversarial networks (GANs) are a good example of this phenomenon. This paper proposes an approach to generating organizational and process models using a GAN. The proposed GAN architecture takes into account both tacit expert knowledge encoded in the training set sample models and the symbolic knowledge (rules and algebraic constraints) that is an essential part of such models. It also pays separate attention to differentiable functional constraints, since learning those just from samples is not efficient. The approach is illustrated via examples of logistic system modelling and smart tourist trip booking process modelling. The developed framework is implemented in a publicly available open-source library that can potentially be used by developers of modelling software.