Faculty Recruiting Support CICS

An Introspective Approach for Competence-Aware Autonomy

28 Mar
Tuesday, 03/28/2023 2:00pm to 4:00pm
Hybrid - LGRC A311 and Zoom
PhD Thesis Defense
Speaker: Connor Basich

Abstract: Building and deploying autonomous systems in the open world has long been a goal of both the artificial intelligence (AI) and robotics communities. From autonomous driving, to health care, to office assistance, these systems have the potential to transform society and alter our everyday lives. The open world, however, presents numerous challenges that question the typical assumptions made by the models and frameworks often used in contemporary AI and robotics. Systems in the open world are faced with an unconstrained and non-stationary environment with a range of heterogeneous actors that is too complex to be modeled in plenum. Moreover, many of these systems are expected to operate on the order of months or even years. To more reliably handle these challenges, many autonomous systems deployed in human environments entail some measure of reliance on human assistance. This reliance on human assistance is an acknowledgement of a limited competence of the autonomous agent to complete its tasks fully autonomously in all situations. Consequently, in order for such systems to be effective in the open world, they, like humans, must be aware of their own competence and both capable and incentivized to solicit external assistance when needed. This thesis therefore proposes planning approaches based on the concept of competence modeling that equip an autonomous system with knowledge about both its capabilities and limitations to better optimize its autonomy and operate more effectively in the open world.

Advisor: Shlomo Zilberstein

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