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100
Appendix P: Average Response Times (Capped)
The average response times of the participants for various game models with a constraint of 120 seconds are illustrated in Tables 110 to 113 and Figures 47 to 50.
Game Model
3 Gates
6 Gates
9 Gates
12 Gates
15 Gates
PT
11.476
11.905
12.381
19.333
9.619
PT-Attract
9.238
16.952
15.429
15.571
17
COBRA (Alpha = 0.15)
8.952
13
16.048
16.429
19.476
COBRA (Alpha = 0.5)
7.619
16.381
14.905
21.905
13.905
DOBSS
11.857
11.286
19.714
10.19
16.619
QRE (Lambda = 0.45)
7.524
11.81
16.667
11.905
15.619
QRE (Lambda = 0.76)
9.571
10
31
17.81
14.476
Table 110: Average Response Times (Capped) for Cluster 1
Figure 47: Average Response Times (Capped) against Number of Gates for Cluster 1
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 115 |
| Contributing entity | University of Southern California |
| Repository email | cisadmin@lib.usc.edu |
| Full text | 100 Appendix P: Average Response Times (Capped) The average response times of the participants for various game models with a constraint of 120 seconds are illustrated in Tables 110 to 113 and Figures 47 to 50. Game Model 3 Gates 6 Gates 9 Gates 12 Gates 15 Gates PT 11.476 11.905 12.381 19.333 9.619 PT-Attract 9.238 16.952 15.429 15.571 17 COBRA (Alpha = 0.15) 8.952 13 16.048 16.429 19.476 COBRA (Alpha = 0.5) 7.619 16.381 14.905 21.905 13.905 DOBSS 11.857 11.286 19.714 10.19 16.619 QRE (Lambda = 0.45) 7.524 11.81 16.667 11.905 15.619 QRE (Lambda = 0.76) 9.571 10 31 17.81 14.476 Table 110: Average Response Times (Capped) for Cluster 1 Figure 47: Average Response Times (Capped) against Number of Gates for Cluster 1 |
