What is it about?
Hybrid Intelligence (HI) aims at creating collaborative systems where humans and intelligent machines (agents) cooperate in mixed teams towards shared goals. Without clear characterization of the tasks and knowledge exchanged between the agents and humans, both standardization and reuse of new HI systems is hampered. We investigate whether Knowledge Engineering (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, sub-symbolic and human-in-the-loop components are involved.
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Why is it important?
The interaction between humans and AI agents becomes more reliable and trustworthy, when such systems are well-designed. By using and reusing patterns and standard components, the development becomes more efficient and the systems more secure and predictable.
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This page is a summary of: Knowledge Engineering for Hybrid Intelligence, December 2023, ACM (Association for Computing Machinery),
DOI: 10.1145/3587259.3627541.
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