Trustworthy Planning and Scheduling with Learning and Explanations

TUPLES is a 3-year project that has contributed 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. We have enhanced the capacity of scalable, yet transparent, robust and safe algorithmic solutions for planning & scheduling, by designing methods that combine the power of data-driven and knowledge-based symbolic AI.

We adopted a use-case driven process aiming to provide structure to the research activities and ensure their practical viability. The process is schematically depicted in the figure, and has contributed to both usable demonstrators for the considered use cases and simplified laboratory environments suitable for controlled experiments and public release. With input from Human Factors and Organizational Psychology experts, the project has proposed metrics and protocols to assess and monitor the trustworthiness of the developed P&S systems, accounting for workers’ and stakeholders’ needs and opinions.

TUPLES has built on its industry participants’ use cases to define how and when the AI techniques developed in the project should be applied during the various stages of implementing trustworthy P&S systems. The use cases have been selected because of their potential to contribute to progressing the state of the art, their potential of advancing the state of the practice for robust and transparent scheduling and planning systems, and their manageable risk.