Assessing Learned Representations under Open-World Novelty

Abstract My dissertation research focuses on sequential decision-making (SDM) in complex environments, and how agents can perform well even when novelty is introduced to those environments. The problem of how agents can respond intelligently to novelty has been a long-standing challenge in AI, and poses unique problems across approaches to SDM. This question has been studied in various formulations, including open-world learning and reasoning, transfer learning, concept drift, and statistical relational learning. Classical and modern approaches in agent design offer tradeoffs in human effort for feature encoding, ease of deployment in new domains, and the development of both provably and empirically reliable policies. I propose a formalism for studying open-world novelty in SDM processes with feature-rich observations. I study the conditions under which causal-relational queries can be estimated from non-novel observations, and empirically examine the effects of open-world novelty on agent behavior.