Tutorial Knowledge Engineering for Hybrid Intelligence (KE4HI @ FOIS)
The second Knowledge Engineering for Hybrid Intelligence (HIKE) tutorial will be held in conjunction with the 14TH INTERNATIONAL CONFERENCE ON FORMAL ONTOLOGY IN INFORMATION SYSTEMS (FOIS 2024) 08-09 July 2024 (online) and 15-19 July 2024 (Enschede, Netherlands)
Description
Hybrid Intelligence (HI) is a rapidly growing field aiming at creating collaborative systems where humans and intelligent machines synergetically 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, formerly often for Expert Systems. In this tutorial we will introduce how KE methods can be applied to HI scenarios, and specifically how 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. In this tutorial we will first introduce the well-known CommonKADS methodology, and recent extensions to make it usable to hybrid scenarios. In a hands-on part, we will then use this methodology to analyze HI projects and identify common tasks.
Program
The tentative program is found below
| Part | Time | Topic | Content | |
|---|---|---|---|---|
| 1 | Introduction to HI, Knowledge Engineering, CommonKADS. Introduction to HIKE | 1) Introduction to Knowledge Engineering, CommonKADS 2) Introduction to the HIKE framework and existing scenarios 3) Handson: Choose a domain, task, agents, application, describe your own scenario and provide structured description using templates | ||
| 2 | Structuring your Scenario using UML notation | 1) Introduction to the basics of UML in KADS 2) Handson: Fill the ontology table 3) Design a UML workflow of the chosen HI scenario Result | ||
| 3 | Measuring your HIness. Introduction of HIness | 1) Introduction to HIness measures 2) Handson: Measure the HIness of your own scenario and its tasks |
Resources
- The tutorial is based on the following paper: Tiddi I, De Boer V, Schlobach S, Meyer-Vitali A. Knowledge Engineering for Hybrid Intelligence. In: Proceedings of the 12th Knowledge Capture Conference 2023; 2023. p. 75-82
- The Paper’s github repository contains a number of scenarios, and their formal descriptions as an example of the proposed steps (not including the measurements for HI-ness, though)
Organizers
Ilaria Tiddi is an Assistant Professor in Hybrid Intelligence at the Knowledge in AI (KAI) group of the Vrije Universiteit Amsterdam (NL). Her research focuses on creating systems that generate complex narratives through a combination of semantic technologies, open data and machine learning, applied mostly in scientific and robotics scenarios.
Victor de Boer is an Associate Professor at the User-Centric Data Science group at the Computer Science department of the Vrije Universiteit Amsterdam (VU) and a senior research fellow at Netherlands Institute for Sound and Vision. In his research, he combines Knowledge Representation and Machine Learning with Human-Computer Interaction to tackle research challenges in various domains.
Stefan Schlobach is an Associate Professor at the Vrije Universiteit Amsterdam. He is leading the Knowledge in Artificial Intelligence group in the Department of Computer Science. Dr. Schlobach has published over 100 research papers in the area of Knowledge Engineering and Knowledge Representation and Reasoning.