Date: Apr 20, 2018 4:00 pm – Apr 21, 2018 3:45 pm
Location: Thessaloniki, Greece

First Workshop on Formal Methods for ML-Enabled Autonomous Systems (FOMLAS 2018)


After the well-known DARPA Urban challenge, there have been significant improvements towards autonomous driving. In the past few years, the major theme when building self-driving cars has shifted to deep learning and probabilistic techniques. When these new algorithms act as key components in autonomous driving, they create substantial technological challenges in terms of explainability (e.g., can I explain what is happening inside the machine-learning algorithm?), predictability and correctness (e.g., can I predict what will happen next in the algorithm, or how good can the machine learning component generalize?), and accountability (e.g., when an accident occurs, can one find the root cause, or who is the one to blame?).

This goal of this workshop is to facilitate discussion regarding how formal methods can be used to increase predictability, explainability, and accountability of ML-enabled autonomous systems. The workshop welcomes results from concept formulation (by connecting these concepts with existing research topics in logic and games), as well as algorithms, tools or concrete case studies.



Submission date: 10 February 2018 
Notification date: 3 March 2018 
Final papers due: 24 March 2018
EPTCS proceedings due: 1 May 2018



Workshop Co-chairs

Advisory board

Program Committee (till 2017.09.11)

Local arrangement


Chih-Hong Cheng <>
Indranil Saha <>

  • Workshop
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