Automated Planning and Scheduling is a central research area in AI that has been studied since the inception of the field and where European research has been making strong contributions over decades. Planning is a decision-making technology that consists in reasoning on a predictive model of a system being controlled and deciding how and when to act in order to achieve a desired objective. It is a relevant technology for many application areas that need quick, automated and optimal decisions, like agile manufacturing, agrifood or logistics.
The AIPlan4EU project brought AI planning as a first-class citizen in the European AI On-Demand (AIoD) Platform by developing a uniform, user-centered framework to access the existing planning technology and by devising concrete guidelines for innovators and practitioners on how to use this technology. We considered use-cases from diverse application areas that inspired the design and the development of the framework, and included several available planning systems as engines that can be selected to solve practical problems. We developed a general and planner-agnostic API that is available as a resource to be integrated into the users' systems and is also available through the AI Builder component of the AIoD platform. The framework has been validated on use-cases both from within the consortium and recruited by means of cascade funding; moreover, standard interfaces between the framework and common industrial technologies have been developed.
The main project output is the “Unified Planning Framework” (UPF,
https://github.com/aiplan4eu/unified-planning)(s’ouvre dans une nouvelle fenêtre): a single-access-point service for using and experimenting with planning technology. Planning engines can be used through the UPF by means of a dynamic plugin system. This means that a user can pose planning queries in an engine-independent way and the UPF is capable of discovering, among the available engines, the ones capable of answering the query and can adapt the query to the specificities of the selected engine by means of the engine-dependent plugin.
We also realized solutions prototypes for the use-cases elicited within the project. Each use-case focused on a specific technology to perform its business logic and there is the need of interfacing the specificities of such technologies to the UPF API/language. Hence, we designed and developed Technology Specific Bridges (TSBs) as reusable software components that are used to realize the solution of the various use-cases.
Finally, an overarching objective of the project was to bring this grand view to the AIoD platform. We provided our solutions as downloadable AI-assets and we allow user experimentation with our planning technology and use-cases directly on the platform by means of the AI Builder, which offers a reusable unified planning server component and several interactive planning demonstrations.
