Technically, our focus in the first reporting period (M1-M18) was therefore concentrated on the development of concepts, methods, and software tools that help us to leverage a KG for the above-described vision. This includes:
• Monitoring and tracking w.r.t the KG model to extract and match evidence cues, e.g. for task assignment between humans and AI („trust model“)
• Extension to a multi-layer KG to encode also high-level policies, e.g. safety and ergonomic guidelines („auditable ethics“)
• Utilizing KG embeddings for reasoning about the situation awareness model, e.g. uncertainty model about consistency or incompleteness („self-diagnosis“)
• Novel techniques for the synchronization between KG and embeddings on it („KG dynamics“)
In particular, we made concentrated efforts towards the following aspects:
The Knowledge Graph as Model for Situation Awareness
Development of a layered, modular knowledge graph to support teaming scenarios and the shared decision making within. The design of KG focused on the representation of dynamic and evolving knowledge, particularly in the context of processes. The setup and implementation of the KG follows a knowledge graph management lifecycle (see Fig. 2, left) that structures KG population activities as well as the monitoring of the KG once it is deployed. We developed components for orchestrating dynamic human AI tasks assignment and keeping the KG synchronized with its embedding space, so that changes in the KG can be immediately applied to the embeddings of the affected nodes to handle downstream relational machine learning tasks.
The Teaming Intelligence Aspect of Human-AI Collaboration
In TEAMING.AI the human-AI teaming is centered on a coordination mechanism that supports teaming intelligence for human-AI collaboration by means of operationalization of the 4S framework based on the interdependence analysis of teamwork. We developed concepts for its technical realization in the use cases. The main components are the Teaming Model based on the Dynamic KG and its sub-meta-models for representing and modeling activities, events, processes, and high-level policies as basis for synchronizing and aligning human-AI communication, reciprocal learning (AI assists human in decision making and, vice versa, KG enhancement by humans) and task assignment between humans and AI.
Development and Integration of Components for a Proof of Concept
The development of the Use Cases started with a requirement engineering and the description of user stories that will be used as anchor points for validation tests of teaming scenarios. The requirement engineering included an importance evaluation of an extended list software quality attributes (scalability, robustness) that also included attributes towards teaming aspects (trustworthiness, explicability). Based on this assessment, a reference software architecture for the TEAMING.AI platform has been developed that has a special focus on scalability and timing requirements of ML components and the KG runtime layer (see Fig. 3).
