Design and implementation of a platform for discovering and sharing AI planning software

Abstract

One of the branch of Artificial Intelligence is AI planning and over the past few years, there has been significant research in the AI planning field which led to development of multiple techniques, algorithms and tools. Yet while AI planning techniques have been moving forward with research, the lack of a central repository for sharing AI planning software may have hindered their broader adoption. Resources are currently scattered across web-pages, research papers and books and this situation complicates finding appropriate AI planning software as per requirements by people who are from the field and even more difficult for new users of AI planning. The lack of standardisation and accessibility of planning software brings the field in a situation that hinders applicability and reproducibility. The goal of this master’s thesis is to design and implement a single platform that provides access to multiple AI planning software artefacts. The platform is designed with a modular architecture with different functionalities as a service which ensures seamless integration between frontend and backend. This platform provides users with an intuitive UI with features to discover, filter and sort. The solution is evaluated through user studies and it highlights positive feedback on platform’s design and features while it also guides on areas of improvement for overall user experience and widespread adoption.