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Table 21: Time Weighted Average Expected Utilities against No. of Gates for Cluster 2 36 Table 22: Time Weighted Average Expected Utilities against No. of Gates for Cluster 3 37 Table 23: Time Weighted Average Expected Utilities against No. of Gates for Cluster 4 38 Table 24: Game Model Point Allocation 39 Table 25: Game Model Point Allocation for Time Weighted Average Expected Utilities 40 Table 26: p-values for 3 Gate settings in Cluster 1 41 Table 27: p-values for 6 Gate settings in Cluster 1 42 Table 28: p-values for 9 Gate settings in Cluster 1 42 Table 29: p-values for 12 Gate settings in Cluster 1 43 Table 30: p-values for 15 Gate settings in Cluster 1 43 Table 31: p-values for 3 Gate settings in Cluster 2 44 Table 32: p-values for 6 Gate settings in Cluster 2 44 Table 33: p-values for 9 Gate settings in Cluster 2 45 Table 34: p-values for 12 Gate settings in Cluster 2 45 Table 35: p-values for 15 Gate settings in Cluster 2 46 Table 36: p-values for 3 Gate settings in Cluster 3 46 Table 37: p-values for 6 Gate settings in Cluster 3 47 Table 38: p-values for 9 Gate settings in Cluster 3 47 Table 39: p-values for 12 Gate settings in Cluster 3 48 Table 40: p-values for 15 Gate settings in Cluster 3 48
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 6 |
| Contributing entity | University of Southern California |
| Repository email | cisadmin@lib.usc.edu |
| Full text | vi Table 21: Time Weighted Average Expected Utilities against No. of Gates for Cluster 2 36 Table 22: Time Weighted Average Expected Utilities against No. of Gates for Cluster 3 37 Table 23: Time Weighted Average Expected Utilities against No. of Gates for Cluster 4 38 Table 24: Game Model Point Allocation 39 Table 25: Game Model Point Allocation for Time Weighted Average Expected Utilities 40 Table 26: p-values for 3 Gate settings in Cluster 1 41 Table 27: p-values for 6 Gate settings in Cluster 1 42 Table 28: p-values for 9 Gate settings in Cluster 1 42 Table 29: p-values for 12 Gate settings in Cluster 1 43 Table 30: p-values for 15 Gate settings in Cluster 1 43 Table 31: p-values for 3 Gate settings in Cluster 2 44 Table 32: p-values for 6 Gate settings in Cluster 2 44 Table 33: p-values for 9 Gate settings in Cluster 2 45 Table 34: p-values for 12 Gate settings in Cluster 2 45 Table 35: p-values for 15 Gate settings in Cluster 2 46 Table 36: p-values for 3 Gate settings in Cluster 3 46 Table 37: p-values for 6 Gate settings in Cluster 3 47 Table 38: p-values for 9 Gate settings in Cluster 3 47 Table 39: p-values for 12 Gate settings in Cluster 3 48 Table 40: p-values for 15 Gate settings in Cluster 3 48 |
