Abstract
State-of-the-art applications of Stackelberg security games - including wildlife protection - offer a wealth of data, which can be used to learn the behavior of the adversary. But existing approaches either make strong assumptions about the structure of the data, or gather new data through online algorithms that are likely to play severely suboptimal strategies. We develop a new approach to learning the parameters of the behavioral model of a bounded rational attacker (thereby pinpointing a near optimal strategy), by observing how the attacker responds to only three defender strategies. We also validate our approach using experiments on real and synthetic data.
Original language | English (US) |
---|---|
Pages (from-to) | 308-314 |
Number of pages | 7 |
Journal | IJCAI International Joint Conference on Artificial Intelligence |
Volume | 2016-January |
State | Published - 2016 |
Externally published | Yes |
Event | 25th International Joint Conference on Artificial Intelligence, IJCAI 2016 - New York, United States Duration: Jul 9 2016 → Jul 15 2016 |
All Science Journal Classification (ASJC) codes
- Artificial Intelligence