The TUPLES project published 68 publications on Trustworthy Planning and Scheduling

The TUPLES project has focused on building trustworthy planning and scheduling systems that are scalable, safe, robust, and explainable—bridging fundamental research with impactful applications.
To achieve these objectives, the cornerstones of our scientific contributions were:
(1) combining symbolic P&S methods with data-driven methods to benefit from the scalability and modeling power of the latter, while gaining the transparency, robustness, and safety of the former.
(2) developing rigorous explanations and verification approaches for ensuring the transparency, robustness, and safety of a sequence of interacting machine learned decisions.

From a practical standpoint, the project demonstrated and evaluated our novel and rigorous methods in a laboratory environment, on a range of use-cases in manufacturing, aircraft operations, sport management, waste collection, and energy management. These methods include novel hybrid approaches (model-based / data-driven) for planning and scheduling that aim at increasing the robustness and/or scalability of existing methods; methods for verifying, testing, improving or enforcing the safety and robustness of the muti-step decisions recommended by these approaches; interactive explanation approaches allowing users of planning and scheduling systems to understand why a solution was recommended over others, and to use this understanding to guide the system towards a solution satisfying their preferences.

Altogether, the project produced 68 scientific publications, over 50 of which were accepted at top-tier AI conferences and journals, with two receiving awards. Many of these contributions were accompanied by publicly released code, ensuring accessibility and reusability by the broader community.
All publications are available in the dedicated section of the TUPLES website, where each entry directly links to the corresponding paper: https://tuples.ai/publications/