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.
architecture in a simulated environment.
Original language | English |
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Title of host publication | Proceedings of the Tenth Annual ACM/IEEE International Conference on Human-Robot Interaction Extended Abstracts |
Publisher | Association for Computing Machinery (ACM) |
Pages | 147-148 |
ISBN (Print) | 978-1-4503-3318-4 |
DOIs | |
Publication status | Published - 2 Mar 2015 |
Event | Tenth Annual ACM/IEEE International Conference on Human-Robot Interaction - Portland, United Kingdom Duration: 2 Mar 2015 → 5 Mar 2015 |
Conference
Conference | Tenth Annual ACM/IEEE International Conference on Human-Robot Interaction |
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Country/Territory | United Kingdom |
City | Portland |
Period | 2/03/15 → 5/03/15 |