Project description
Building trustworthy AI for planning and scheduling
Current artificial intelligence (AI) methods for planning and scheduling (P&S), whether model-based or data-driven, do not inspire enough confidence to be massively adopted and achieve their potential impact. The EU-funded TUPLES project will contribute to an integrated and human-centred approach for the development of P&S tools in order to increase confidence and adoption. Overall, it has three goals. Firstly, to develop hybrid P&S methods that combine the efficiency, flexibility, and adaptability of data-driven learning approaches with the robustness, reliability, and clarity of model-based reasoning methods. Secondly, designing methods for verifying and explaining the solutions produced by P&S systems. Lastly, conducting case studies, from aeroplane pilot assistance to soccer team management and waste collection.
Objective
Planning and scheduling (P&S) is a core area of AI. Its aim is to build systems that assist humans in planning, organising and optimising courses of action to achieve complex objectives. Despite the pressing need for decision-support systems for P&S applications in industry and public services, current approaches do not satisfy essential properties of trustworthy AI, such as transparency, explainability, robustness, safety and scalability.
TUPLES is a 3 year project aiming to obtain scalable, yet transparent, robust and safe algorithmic solutions for P&S. The cornerstones of our scientific contributions will be (1) 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 and (2) 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. Our results also include practical guidelines derived from the lessons learnt in this process, and open-source software tools and test environments enabling the human-centered development and assessment of trustworthy P&S systems. Expected outcomes include increased productivity, decreased environmental footprint and the empowerment of workers in the above sectors. These could translate into huge economic, environmental and social impacts if trustworthiness ends up driving mass adoption of P&S.
The TUPLES consortium includes world-leading researchers in several fields of AI (P&S, constraints, machine learning, explanations), humanities and social sciences (psychology, law, ethics), and experts of their applications.
Fields of science
- social sciencessociologygovernancepublic services
- engineering and technologyenvironmental engineeringwaste management
- social scienceseconomics and businesseconomicsproduction economicsproductivity
- social sciencespsychology
- natural sciencescomputer and information sciencesartificial intelligencemachine learning
Keywords
Programme(s)
Funding Scheme
HORIZON-RIA - HORIZON Research and Innovation ActionsCoordinator
31000 Toulouse
France