Knowledge Acquisition with Selective Active Learning for Human-Robot Interaction

Batbold Myagmarjav, Mohan Sridharan

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)

Abstract

This paper describes an architecture for robots interacting with non-expert humans to incrementally acquire domain knowledge. Contextual information is used to generate candidate questions that are ranked using measures of information gain, ambiguity, and human confusion, with the objective of maximizing the potential utility of the response. We report results of preliminary experiments evaluating the
architecture in a simulated environment.
Original languageEnglish
Title of host publicationProceedings of the Tenth Annual ACM/IEEE International Conference on Human-Robot Interaction Extended Abstracts
PublisherAssociation for Computing Machinery (ACM)
Pages147-148
ISBN (Print)978-1-4503-3318-4
DOIs
Publication statusPublished - 2 Mar 2015
EventTenth Annual ACM/IEEE International Conference on Human-Robot Interaction - Portland, United Kingdom
Duration: 2 Mar 20155 Mar 2015

Conference

ConferenceTenth Annual ACM/IEEE International Conference on Human-Robot Interaction
Country/TerritoryUnited Kingdom
CityPortland
Period2/03/155/03/15

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