Incremental Knowledge Acquisition with Selective Active Learning

Abstract

This paper describes an architecture for robots interacting with non-expert humans to incrementally acquire domain knowledge. Candidate questions are generated using contex-tual information and ranked using different measures, with the objective of maximizing the potential utility of the re-sponse. We report results of some preliminary experiments evaluating the architecture in a simulated environment.