We are glad to announce the workshops that have been accepted for CEC 2023.
| Title | Organizer | Outline |
| Evolutionary Computation for Explainable Artificial Intelligence (ECXAI) | Ying Bi, Bing Xue, Mengjie Zhang | |
| A Sandbox for Teaching and Learning in CI for Pre-University and Undergraduate Students | Chang-Shing Lee, Gui N. DeSouza, Alexander Dockhorn, Yusuke Nojima, Marek Reformat, Li-Wei Ko, Chun-Rong Huang | Poster, Website |
| Computational Intelligence for Adaptive Learning in Human-Machine Interaction | Li-Wei Ko, I-Fang Chung, Giovanni Acampora, Tzyy-Ping Jung |
Workshop Abstracts:
Evolutionary Computation for Explainable Artificial Intelligence (ECXAI):
The theme of this workshop is the use of evolutionary computation for explainable artificial intelligence and machine learning, covering ALL different evolutionary computation paradigms for explainable artificial intelligence and machine learning.
The aim of this workshop is to investigate both the new theories and applications in different evolutionary computation paradigms for explainable artificial intelligence. This workshop will bring together researchers and practitioners from around the world to discuss the latest advances in the field and will act as a major forum for the presentation of recent research.
Authors are invited to submit their original and unpublished work to this workshop. Topics related to all aspects of evolutionary computation for explainable artificial intelligence, such as theories, algorithms, systems, and applications, are welcome.
A Sandbox for Teaching and Learning in CI for Pre-University and Undergraduate Students
The idea is to create a young student workshop with an associated competition during CEC 2023 inspired by the F.I.R.S.T. robotics competition. Before or during the first hours of the event, students will receive learning materials and guidance from tutors to learn about a CI-related topic. Later students will be enabled to test learned materials in a simple real-world application. Namely, programming robots to solve various tasks.
Computational Intelligence for Adaptive Learning in Human-Machine Interaction
Human-Machine Interaction in the real world is the scientific discipline concerned with
understanding the principles underlying interactions between humans and other
elements of a system, and the profession that applies these principles and understanding
to designs in order to optimize human well-being and overall system performance. As
human behavior is always dynamic, making it challenging to predict and access, it is
worth applying fuzzy theories, control systems, and neural networks with intelligent
computational technologies to enhance the interaction performance between humans
and the systems. The scientific goal of the workshop is to better understand how innovative Computational Intelligence developments relate to and enhance human-machine interaction in real-world settings.
