Explaining and interpretable task planning
The issue of explanations for AI systems cooperating with humans has been a topic of considerable interest of late. But it is widely argued that current solutions that are based on local representations do not fully capture the reasoning behind the underlying decision. So, the idea here is taking a fresh approach to explainability by appealing to causality and abstraction, as well as model reconciliation and value alignment with the intentions of human users. That is, the goal is to generate explanations that humans can understand and to accept suggestions from humans in a vocabulary that human users will be comfortable with. A partial list of references include:
Extensions to multi-agent settings are also possible.