FAIRWork

Current automated and hierarchical structured production processes can only insufficiently deal with the upcoming flexibilization. We foster the “democratization” of decision-making in production processes, hence the participation of all involved stakeholders, by introducing a decentralized AI system. Our Democratic AI-based Decision Support System (DAI-DSS) finds democratically the appropriate decision for a concrete situation during production. Each human or technical actor is represented by an agent who negotiates based on the current status provided by the digital shadow and twins. The future situation is predicted by AI algorithms for each individual actor considering the modelled knowledge base that defines each negotiation strategy. A multiple optimization algorithm finds the most appropriate solution considering the needs of all involved human and technical stakeholders.

Project Abstract

A hybrid decision-making model takes advantage of both machine and human-centred elements. However, pairing humans with machines can be a challenge. The EU-funded FAIRWork project will deliver a decision support system that integrates digital twins optimising the overall production process according to multiple parameters and democratises decision making allowing workers and machines to participate in decision making. The project combines model-based approaches to transparently design, simulate and improve decision making, co-creation laboratory models and physical experiments as communication media and reliable indication of data and AI algorithms.

Objectives

We provide a decision support system that (a) integrates digital twins to optimize the overall production process according multiple parameters, and (b) democratizes decision making granting human worker and machines a say during decision making. Each individual demand is consolidated to achieve a decision support, which is then optimized.
We combine (a) model-based approaches to transparently design, simulate and improve decision making, (b) co-creation laboratory using models and physical experiments as communication media to all actors and, (c) reliability indication of data and AI algorithms.
Those models can be assessed according legal, ethical, security and safety concerns and in case of approval ensure that only compliant models are used for decision making.
Hybrid decision making is provided by combining human- and machine-centric knowledge representation into a holistic model. Simulations on time, cost, quality, safety or energy efficiency contribute to transparent decision making, which evolves via model changes.
BOC provides graphical modelling enabling intuitive knowledge representations for humans. JOTNE provides data repositories for machines. JR provides data marketplaces and optimizations. MORE enable decentralized decision making with AI. RWTH provides both (a) AI and intuitive machine interaction and (b) social and ethical guidance for trustful decision making.
The use cases of the car manufacturer CRF and the robot plant provider FLEX are challenged by human and machine interaction, complex environment interactions and influence factors requiring continuous re-arrangements. The challenge is to consider each actors needs in a concise and complex situation still targeting an overall optimization.
The non-profit organization OMiLAB provides its world-wide network on modelling omilab.org and interacts with DIH, PPP on AI, Data and Robotics and contribute with corresponding CSA as part of their mission.