Abstract
The study of social networks has increased rapidly in the past few decades. Of recent interest are the dynamics of changing opinions over a network. Some research has investigated how interpersonal influence can affect opinion change, how to maximize/minimize the spread of opinion change over a network, and recently, if/how agents can act strategically to effect some outcome in the network's opinion distribution. This latter problem can be modeled and addressed as a reinforcement learning problem; we introduce an approach to help network agents find strategies that outperform hand-crafted policies. Our preliminary results show that our approach is promising in networks with dynamic topologies.
Original language | English |
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Title of host publication | AAAI 2020 - 34th AAAI Conference on Artificial Intelligence |
Pages | 13734-13735 |
Number of pages | 2 |
ISBN (Electronic) | 9781577358350 |
DOIs | |
State | Published - 2020 |
Event | 34th AAAI Conference on Artificial Intelligence, AAAI 2020 - New York, United States Duration: Feb 7 2020 → Feb 12 2020 |
Publication series
Name | AAAI 2020 - 34th AAAI Conference on Artificial Intelligence |
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Conference
Conference | 34th AAAI Conference on Artificial Intelligence, AAAI 2020 |
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Country/Territory | United States |
City | New York |
Period | 2/7/20 → 2/12/20 |
Bibliographical note
Publisher Copyright:Copyright © 2020 Association for the Advancement of Artificial Intelligence. All rights reserved.
ASJC Scopus subject areas
- Artificial Intelligence