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98
Game Model
3 Gates
6 Gates
9 Gates
12 Gates
15 Gates
PT
12.783
18.261
24.13
21.522
21.304
PT-Attract
12.261
20.087
20.783
22.783
26.217
COBRA (Alpha = 0.15)
11.652
20.696
24.609
24.087
20.913
COBRA (Alpha = 0.5)
11.957
22.652
13.304
23.087
19.652
DOBSS
10.435
18.391
28.261
17.043
23.478
QRE (Lambda = 0.45)
10.783
12.304
24.696
14.826
36.435
QRE (Lambda = 0.76)
11
14.043
25.609
21.435
19.609
Table 108: Average Response Times (Uncapped) for Cluster 3
Figure 45: Average Response Times (Uncapped) against Number of Gates for Cluster 3
Object Description
| Title | Computational model of human behavior in security games with varying number of targets |
| Author | Goenka, Mohit |
| Author email | mgoenka@usc.edu; mohitgoenka@gmail.com |
| Degree | Master of Science |
| Document type | Thesis |
| Degree program | Computer Science |
| School | Viterbi School of Engineering |
| Date defended/completed | 2011-03-30 |
| Date submitted | 2011 |
| Restricted until | Unrestricted |
| Date published | 2011-04-19 |
| Advisor (committee chair) | Tambe, Milind |
| Advisor (committee member) |
John, Richard S. Maheswaran, Rajiv T. |
| Abstract | Security is one of the biggest concerns all around the world. There are only a limited number of resources that can be allocated in security coverage. Terrorists can exploit any pattern of monitoring deployed by the security personnel. It becomes important to make the security pattern unpredictable and randomized. In such a scenario, the security forces can be randomized using algorithms based on Stackelberg games.; Stackelberg games have recently gained significant importance in deployment for real world security. Game-theoretic techniques make a standard assumption that adversaries' actions are perfectly rational. It is a challenging task to account for human behavior in such circumstances.; What becomes more challenging in applying game-theoretic techniques to real-world security problems is the standard assumption that the adversary is perfectly rational in responding to security forces' strategy, which can be unrealistic for human adversaries. Different models in the form of PT, PT-Attract, COBRA, DOBSS and QRE have already been proposed to address the scenario in settings with fixed number of targets. My work focuses on the evaluation of these models when the number of targets is varied, giving rise to an entirely new problem set. |
| Keyword | artificial intelligence; behavioral sciences; game theory; human behavior; COBRA; DOBSS; PT; PT-Attract; QRE; Stackelberg |
| Language | English |
| Part of collection | University of Southern California dissertations and theses |
| Publisher (of the original version) | University of Southern California |
| Place of publication (of the original version) | Los Angeles, California |
| Publisher (of the digital version) | University of Southern California. Libraries |
| Provenance | Electronically uploaded by the author |
| Type | texts |
| Legacy record ID | usctheses-m3757 |
| Contributing entity | University of Southern California |
| Rights | Goenka, Mohit |
| Repository name | Libraries, University of Southern California |
| Repository address | Los Angeles, California |
| Repository email | cisadmin@lib.usc.edu |
| Filename | etd-Goenka-4204 |
| Archival file | uscthesesreloadpub_Volume62/etd-Goenka-4204.pdf |
Description
| Title | Page 113 |
| Contributing entity | University of Southern California |
| Repository email | cisadmin@lib.usc.edu |
| Full text | 98 Game Model 3 Gates 6 Gates 9 Gates 12 Gates 15 Gates PT 12.783 18.261 24.13 21.522 21.304 PT-Attract 12.261 20.087 20.783 22.783 26.217 COBRA (Alpha = 0.15) 11.652 20.696 24.609 24.087 20.913 COBRA (Alpha = 0.5) 11.957 22.652 13.304 23.087 19.652 DOBSS 10.435 18.391 28.261 17.043 23.478 QRE (Lambda = 0.45) 10.783 12.304 24.696 14.826 36.435 QRE (Lambda = 0.76) 11 14.043 25.609 21.435 19.609 Table 108: Average Response Times (Uncapped) for Cluster 3 Figure 45: Average Response Times (Uncapped) against Number of Gates for Cluster 3 |
