TUPLES (TrUstworthy Planning and scheduling with Learning and ExplanationS) is a 3 year project aiming to obtain scalable, yet transparent, robust and safe algorithmic solutions for P&S. 
It will contribute to a more integrated and human-centered approach to the development of P&S tools, in order to increase confidence in these systems and accelerate their adoption.

The cornerstones of our scientific contributions will be:

- combining symbolic P&S methods with data-driven methods to benefit from the scalability and modelling power of the latter, while gaining the transparency, robustness, and safety of the former;

- developing rigorous explanations and verification approaches for ensuring the transparency, robustness, and safety of a sequence of interacting machine learned decisions. Both of these challenges are at the forefront of AI research.

We will demonstrate and evaluate 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.

OUTCOME 1 
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. 

OUTCOME 2:
To develop 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. 

OUTCOME 3: 
To demonstrate these approaches on real practical case studies, from airplane pilot assistance, to soccer team. recruitment, and waste collection.

Funded by the European Union under grant agreement No 101070149. 

The views expressed are those of the authors and do not necessarily reflect those of the European Union and therefore the latter cannot be held responsible for them.

TUPLES Trustworthy AI

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