Knowledge Engineering for Hybrid IntelligenceIlaria Tiddi; Victor De Boer; Stefan Schlobach; André Meyer-Vitali
In: Brent Venable; Daniel Garijo; Brian Jalaian (Hrsg.). K-CAP '23: Proceedings of the 12th Knowledge Capture Conference 2023. International Conference on Knowledge Capture (K-Cap-2023), Knowledge Capture, December 5-7, Pensacola, Florida, USA, ISBN 979-8-4007-0141-2, Association for Computing Machinery, New York, 12/2023.
Hybrid Intelligence (HI) is a rapidly growing field aiming at creating collaborative systems where humans and intelligent machines cooperate in mixed teams towards shared goals. A clear characterization of the tasks and knowledge exchanged by the agents in HI applications is still missing, hampering both standardization and reuse when designing new HI systems. Knowledge Engineering (KE) methods have been used to solve such issue through the formalization of tasks and roles in knowledge-intensive processes. We investigate whether KE methods can be applied to HI scenarios, and specifically whether common, reusable elements such as knowledge roles, tasks and subtasks can be identified in contexts where symbolic, subsymbolic and human-in-the-loop components are involved. We first adapt the well-known CommonKADS methodology to HI, and then use it to analyze several HI projects and identify common tasks. The results are (i) a high-level ontology of HI knowledge roles, (ii) a set of novel, HI-specific tasks and (iii) an open repository to store scenarios1 – allowing reuse, validation and design of existing and new HI applications.
TAILOR - Netzwerk aus KI-Exzellenzzentren zur Erforschung der Grundlagen für eine vertrauenswürdige und zuverlässige Künstliche Intelligenz