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103
Game Model
3 Gates
6 Gates
9 Gates
12 Gates
15 Gates
PT
11.409
18.227
19.227
19.955
14.545
PT-Attract
10
17
14.545
12.636
17.955
COBRA (Alpha = 0.15)
10.455
19
27.864
15.773
16.727
COBRA (Alpha = 0.5)
10.591
14.591
18.409
18.5
20.136
DOBSS
8.545
11.364
22.091
16.227
12.682
QRE (Lambda = 0.45)
9.591
12.273
23.182
12.682
13.682
QRE (Lambda = 0.76)
11.727
18.955
20.182
14.273
17.5
Table 113: Average Response Times (Capped) for Cluster 4
Figure 50: Average Response Times (Capped) against Number of Gates for Cluster 4
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 118 |
| Contributing entity | University of Southern California |
| Repository email | cisadmin@lib.usc.edu |
| Full text | 103 Game Model 3 Gates 6 Gates 9 Gates 12 Gates 15 Gates PT 11.409 18.227 19.227 19.955 14.545 PT-Attract 10 17 14.545 12.636 17.955 COBRA (Alpha = 0.15) 10.455 19 27.864 15.773 16.727 COBRA (Alpha = 0.5) 10.591 14.591 18.409 18.5 20.136 DOBSS 8.545 11.364 22.091 16.227 12.682 QRE (Lambda = 0.45) 9.591 12.273 23.182 12.682 13.682 QRE (Lambda = 0.76) 11.727 18.955 20.182 14.273 17.5 Table 113: Average Response Times (Capped) for Cluster 4 Figure 50: Average Response Times (Capped) against Number of Gates for Cluster 4 |
