Flexible and Explainable Solutions for Multi-Agent Path Finding Problems

Artificial Intelligence (AI) applications are being used widely among people with different background and interests. For these applications to be successful, two of the important features (and challenges) needed by AI methods are flexibility and explainability. A flexible AI method developed to solve a problem can accommodate variations of the problem, and thus can be used to investigate different options for a better understanding. An explainable AI method can provide answers to queries about the (in)feasibility and the optimality of solutions. One of the well-studied problems in AI that necessitates solutions for these two challenges is the multi-agent path finding (MAPF) problem.