Knowledge-augmented approaches to sequential decision-making try to alleviate the limitations of data-driven techniques caused by noise, stochasticity and asymmetry of knowledge. They leverage rich knowledge assimilated by domain experts through years of experience to learn better behaviour. Recent years have witnessed a major research thrust in this direction and our group is an active contributor to this cause; focused on building human-in-the-loop frameworks for representation and elicitation of knowledge from, potentially multiple, expert(s) for sequential decision-making (RL), planning and prediction.
My research focuses on real-time/active elicitation of human knowledge, at varying levels of generality, for guided planning and problem-solving. Specifically, we address ‘asymmetry of knowledge’ where an AI planning system may have access to certain resources and vast computational power but may lack the necessary knowledge to prioritize among critical tasks. Human experts understand such priorities implicitly and we leverage that to generate better plans. Our DARPA-funded project “Communicating with Computers” has motivated research towards human-AI collaborative planning systems where both humans and AI agents solve problems together and learn from each other. Humans will teach new concepts, the agents will seek guidance when uncertain, and both will grow to augment one another. Part of my research also involves scaling Probabilistic Logic Models via approximation.
I am involved in the Human-Allied Problem Solving and Planning project.