NOTE ABOUT COVID-19: We will follow advice from the ICLP2020 organizers on the situation, and we will revise our workshop timeline and other procedures accordingly if needed.
CAUSAL 2020 will be a fully virtual workshop. More details will be announced soon.
CAUSAL 2020 is a workshop of ICLP 2020 to be held September 19 2020 in University of Calabria, Rende, Italy.
Sophisticated causal reasoning has long been prevalent in human society and continues to have an undeniable impact on the advancement of science, technology, medicine, and other significant fields. From the development of ancient tools to modern roots of causal analysis in business and industry, reasoning about and understanding causality enables us to identify how an outcome of interest has come to be and gives us insight into how to bring about, or even prevent, similar outcomes in future scenarios.
This workshop aims to bring together researchers and practitioners of logic programming with a dedicated focus on methods and trends emerging from the study of causality and explanation. We welcome the submission of papers on systems, tools, and applications of logic programming methods for causal reasoning and explanation. In particular, we encourage submissions presenting recent developments, including works in progress. The workshop will present the latest research and application developments in these areas and provide opportunities to discuss current and future research directions and relationships to other fields (e.g. Machine Learning, Explainable AI, Diagnosis, Natural Language Processing and Understanding, Philosophy of Science). An important expected outcome of this workshop is to collect first-hand feedback from the ICLP community about the role and placement of causal reasoning and explanation in the landscape of modern computer theory as well as in the software industry.
Topics of interests include (but are not limited to):
Modeling causal theories in logic programming
Formalization of types of causes: sufficient, necessary, actual, etc
Causality, temporal reasoning and action theories
Causality and counterfactual reasoning
Causality, learning and experimental design
Causality and probability
Causality and equivalence
Causality and ontology
Relating LP based causality and Causal Networks
Challenging problems and benchmark examples
Justifications and argumentation
Explainable AI
Explanations for diagnosis and debugging
Tools, systems and applications
Emily LeBlanc, US Naval Research Lab, USA (emily.leblanc@nrl.navy.mil)
Joost Vennekens, KU Leuven, Belgium
Tran Cao Son, New Mexico State University, USA
Pedro Cabalar, Corunna University, Spain
Jorge Fandiño, University of Potsdam, Germany
Marcello Balduccini, Saint Joseph's University, USA
Yuliya Lierler, University of Nebraska at Omaha, USA