Competitions

FUZZ-IEEE 2021 is proud to announce three competitions:

FML-based Machine Learning Competition for Human and Smart Machine Co-Learning on Real-World Applications

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The goal of this competition is to optimize the FML knowledge base and rule base through the methodologies of evolutionary computation and machine learning in order to develop a regression model based on FML-based fuzzy inference system and apply it to prediction models of the winning rates for Alpha Go Master Series or for real-world applications. In addition to game of Go, the topic of the competition includes AIoT applications or real-world applications to encourage elementary-school students, high-school students, or undergraduate students in the world to join the competition for education and learning on fuzzy logic and system.

For details please visit the competition webpage.


Interpretable Models for Energy Prediction from Smart Meter Data

Predicting energy consumption is currently a key challenge for the energy industry as a whole. Predicting the consumption in a certain area is massively complicated due to the sudden changes in the way that energy is being consumed and generated at the current point in time. However, this prediction becomes extremely necessary to minimize costs and to enable adjusting (automatically) the production of energy and better balance the load between different energy sources. The goal of this competition is to build energy prediction models that are accurate and interpretable, helping decision makers to better understand the outputs of machine learning models.
For details please visit the competition webpage.


Autonomous drone racing competition

The goal of this competition is to design a fuzzy controller for autonomous drone racing. The task for the drone is to fly the route with gate locations and orientations provided.

For details please visit the competition webpage.