=Paper=
{{Paper
|id=Vol-3121/preface
|storemode=property
|title=AAAI-MAKE 2022: Machine Learning and Knowledge Engineering for Hybrid Intelligence
|pdfUrl=https://ceur-ws.org/Vol-3121/preface.pdf
|volume=Vol-3121
|authors=Andreas Martin,Knut Hinkelmann
|dblpUrl=https://dblp.org/rec/conf/aaaiss/0001H22
}}
==AAAI-MAKE 2022: Machine Learning and Knowledge Engineering for Hybrid Intelligence==
<pdf width="1500px">https://ceur-ws.org/Vol-3121/preface.pdf</pdf>
<pre>
AAAI-MAKE 2022: Machine Learning and Knowledge
Engineering for Hybrid Intelligence
Andreas Martin1 , Knut Hinkelmann1,2
1
  FHNW University of Applied Sciences and Arts Northwestern Switzerland, School of Business, Riggenbachstrasse 16,
4600, Olten, Switzerland
2
  University of Pretoria, Department of Informatics, Pretoria, South Africa

   The AAAI 2022 Spring Symposium on Machine Learning and Knowledge Engineering for
Hybrid Intelligence (AAAI-MAKE 2022) brought together researchers and practitioners of the
two fields to reflect on advances in combining them, and to present the first results in creating
hybrid intelligence with the two AI methods. AAAI-MAKE 2022 is the fourth consecutive edition
of this symposium, which combines two prominent AI approaches, symbolic and sub-symbolic
AI, as hybrid AI.
   In such hybrid architectures, agents using different types of AI work together to solve
problems where separate approaches do not provide satisfactory results, e.g., in terms of
explainability and data efficiency. Explainability is needed to complement human intelligence
in the AI loop, and data efficiency (learning from small data sets) is needed in many domains
where data availability is limited. Hybrid approaches that combine machine learning with the
use of logic can explain inferences and increase data efficiency.
   The combination of machine learning and knowledge engineering opens up new possibilities
for the redesign of knowledge work at the interface of humans and machines, with the aim
of combining complementary strengths. Knowledge workers without strong AI expertise can
contribute to hybrid teams where humans and machines work synergistically to achieve common
goals better in collaboration than separately. More efforts need to be made to democratize the
combination of machine learning and knowledge engineering and unleash the complementary
strengths.
   The 2022 edition was held as a hybrid event with an on-site presence at Stanford and re-
mote participation. The remarkable number of submissions again showed a huge demand
for combined/hybrid AI approaches that address hybrid intelligence. These proceedings are a
collection of papers that contribute to the symposium’s aim of combining machine learning
and knowledge engineering, hybrid intelligence / intelligent systems, as well as hybrid AI and
neuro-symbolic approaches/methods.




In A. Martin, K. Hinkelmann, H.-G. Fill, A. Gerber, D. Lenat, R. Stolle, F. van Harmelen (Eds.), Proceedings of the AAAI
2022 Spring Symposium on Machine Learning and Knowledge Engineering for Hybrid Intelligence (AAAI-MAKE 2022),
Stanford University, Palo Alto, California, USA, March 21–23, 2022.
$ andreas.martin@fhnw.ch (A. Martin); knut.hinkelmann@fhnw.ch (K. Hinkelmann)
€ https://andreasmartin.ch (A. Martin); http://knut.hinkelmann.ch (K. Hinkelmann)
 0000-0002-7909-7663 (A. Martin); 0000-0002-1746-6945 (K. Hinkelmann)
                                       © 2022 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
    CEUR
    Workshop
    Proceedings
                  http://ceur-ws.org
                  ISSN 1613-0073
                                       CEUR Workshop Proceedings (CEUR-WS.org)

</pre>