Outcomes
We have demonstrated and evaluated our novel and rigorous methods in a laboratory environment, on a range of use-cases. TUPLES has released some software tools and a testing environment to enable the human-centered development and assessment of trustworthy P&S systems.
Tuples Lab
TO DEVELOP HYBRID PLANNING AND SCHEDULING METHODS THAT COMBINE THE EFFICIENCY, FLEXIBILITY, AND ADAPTABILITY OF DATA-DRIVEN LEARNING APPROACHES WITH THE ROBUSTNESS, RELIABILITY, AND CLARITY OF MODEL-BASED REASONING METHODS
The TUPLES lab is a step towards combining the ability of synthetic benchmarks to support controlled experiments and the complexity of real world problem.
The lab lab consists of a software library featuring instance generators and simulators for simplified versions of some of the project use cases, designed to enable controlled experiments of trustworthy P&S methods. The library is open source and accessible through a Python API.
The TUPLES project addresses very challenging problems that involve decision making under uncertainty and complex constraints. While problems of this type frequently occur in the real worlds, standard benchmarks from the planning and scheduling domain often miss some of their unique properties.
The lab enables investigations that would be impossible on many of the original use cases. For example, one may be able to evaluate the robustness and safety of a system against adjustable degrees of uncertainty.
The SCIKIT ENVIRONMENT is an extensions to the scikit-decide from Airbus, which provides a framework for applying sequential decision-making algorithms (planning, scheduling and reinforcement learning) to multiple application domains.
It offers a versatile development platform for both domain providers and developers, while allowing users to quickly compare different solvers from different communities on the same domain.
Self Assessment Tool
A web-based diagnostic survey that supports the evaluation of trustworthiness in AI systems for Planning & Scheduling. Developed from EU guidelines and refined through consortium feedback.
The TUPLES self-assessment tool translates abstract principles into clear, actionable recommendations on robustness, safety, transparency, and accountability.
It is aimed at developing verification and explanation methods capable of reasoning about the properties of the solutions produced by planning and scheduling systems, in particular when these are represented by neural networks.
This tool allows DSS developers to verify adherence to Trustworthy AI guidelines.
It’s first users are intended to be the EU business and academic community.
We will assess whether relevant metrics emerge that have the potential to evolve towards a real rating system that could eventually become a step towards a future certification standard.
The TUPLES self-assessment tool is an independent tool that increases awareness of best practices and guidelines on Human-centered AI.
Try it here.
Whether you are developing AI-driven planning tools, assessing third-party systems, or seeking alignment with EU best practices, explore how to enhance the trustworthiness of your AI solutions.
Impacts
We demonstrated these approaches on real practical case studies, from airplane pilot assistance, to soccer team recruitment, and waste collection.
The central impact of the TUPLES project lies in advancing the trustworthiness of AI, driving forward research, development, and deployment of world-class technologies that serve individuals, organizations, and society as a whole. These advancements are firmly rooted in European values, including respect for fundamental rights and a commitment to environmental sustainability.
When AI systems are designed to be safe, robust, explainable, and scalable—and are perceived as such—they can be confidently integrated into the daily practices of industries and citizens. This integration not only delivers measurable performance gains but also supports better, more informed decision-making across domains.
See also the dedicated sections of this website.
Optimal and robust P&S processes yield significant practical benefits, that can be classified across several areas:
AI-supported P&S will generally increase both efficiency and effectiveness of operations, reducing respectively the planning time and, most importantly, ensuring that models and algorithm capture the complexity of ever changing contextual conditions and recommend cost-optimal solutions, be they strategic design or operational management, supporting a general improvement in performance (which will ensure investment in AI is sustained by rapid returns).
Improved energy efficiency, reduced CO2 emissions and pollutants, lower dependence on fossil fuels and more efficient use of scarce (and costy) resources is the result of good planning in any resource- or energy-intensive process. The project includes a specific task to monitor and track environmental impacts across the 5 use cases, to provide real-life benchmarks of the potential of widespread application of AI-powered Best practices
The work environment will be positively impacted by the introduction of reliable and widely accepted P&S tools, increasing the competence, skills, and agency of human planners, foremen on the shop floor, and their workforce leading, for example, to more balanced shifts with reduced stress, limiting repetitive activities that can be performed by the AI (reducing cognitive load), and facilitating better management practices aided by explainable AI.