Abstract
This paper presents methods for improving the attention span of workers in tasks that heavily rely on their attention to the occurrence of rare events. The underlying idea in our approach is to dynamically augment the task with some dummy (artificial) events at different times throughout the task, rewarding the worker upon identifying and reporting them. The proposed approach is an alternative to the traditional approach of exclusively relying on rewarding the worker for successfully identifying the event of interest itself. We propose three methods for timing the dummy events throughout the task. Two of these methods are static and determine the timing of the dummy events at random or uniformly throughout the task. The third method is dynamic and uses the identification (or misidentification) of dummy events as a signal for the worker's attention to the task, adjusting the rate of dummy events generation accordingly.
Original language | English |
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Title of host publication | 26th International Joint Conference on Artificial Intelligence, IJCAI 2017 |
Editors | Carles Sierra |
Publisher | International Joint Conferences on Artificial Intelligence |
Pages | 4826-4830 |
Number of pages | 5 |
ISBN (Electronic) | 9780999241103 |
DOIs | |
State | Published - 2017 |
Event | 26th International Joint Conference on Artificial Intelligence, IJCAI 2017 - Melbourne, Australia Duration: 19 Aug 2017 → 25 Aug 2017 |
Publication series
Name | IJCAI International Joint Conference on Artificial Intelligence |
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Volume | 0 |
ISSN (Print) | 1045-0823 |
Conference
Conference | 26th International Joint Conference on Artificial Intelligence, IJCAI 2017 |
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Country/Territory | Australia |
City | Melbourne |
Period | 19/08/17 → 25/08/17 |
Bibliographical note
Funding Information:The research presented in this paper was partially supported by the Israel Science Foundation (grant No. 1083/13),the ISF-NSFC joint research program (grant No. 2240/15), the Ministry of Science, Technology and Space, Israel with the National Science Council (NSC) of Taiwan, and a Harvard Center for Research on Computation and Society fellowship.