Risk-averse decision-making under uncertainty in partially observable domains is a fundamental problem in AI and essential for reliable autonomous agents, e.g. in the context of safety. In this research project, we develop online risk-averse planning approaches within the framework of Partially Observable Markov Decision Process (POMDP). Of particular interest to us in this project are rigorous planning performance guarantees
