Data-driven AI is the dominating trend in AI at this time. From a planning and scheduling perspective – and for sequential decision making in general – this is manifested in two major kinds of technical artifacts that are rapidly gaining importance.
The first are planning models that are (partially) learned from data (e.g., a weather forecast in a model of flight actions).
The second are action-decision components learned from data, in particular, action policies or planning-control knowledge for making decisions in dynamic environments (e.g., manufacturing processes under resource-availability and job-length fluctuations).

Given the nature of such data-driven artifacts, reliability is a key concern, prominently including safety, robustness, and fairness in various forms, but possibly other concerns as well. Arguably, this is one of the grand challenges in AI for the foreseeable future.

The Workshop website here

ICAPS’24 Workshop on Reliable Data-Driven Planning and Scheduling (RDDPS)
Banff, Alberta, Canada
June 2-3, 2024

Organizing Committee

Daniel Höller, Saarland University, Germany
Timo P. Gros, German Research Center for Artificial Intelligence, Germany
Marcel Steinmetz, University of Toulouse, France
Eyal Weiss, Bar-Ilan University, Israel
Jörg Hoffmann, Saarland University, Germany
Sylvie Thiebaux, University of Toulouse, France, and Australian National University, Australia