Abstract
As sensors and robots become ubiquitous for security or surveillance missions, efficiently assigning resources to ensure security becomes crucial. However, patrol/ surveillance scheduling has different characteristics compared to other resource-constrained problems. First, the agents are required to visit/ re-visit targets and the feedback may not be obtained in the short run. Secondly, it naturally consists of the adversary in the model and the problem are varied based on different assumptions of the adversary behaviors.
In this talk, I will show how we tackle the patrolling problems from both game-theoretic and geometric views. The problem is formulated into a generalization of the patrol security game. We show that there are two simple objective functions, latency, and entropy, that affect the utility of the adversary greatly. I will first introduce heuristic algorithms specifically for the game-theoretic setting and then provide designs that for each of the objectives. At the end of this talk, I will also briefly introduce some new topics our lab currently work on. These regard a strategic behavior on classical ML problem and how it influence the system in the end.
In this talk, I will show how we tackle the patrolling problems from both game-theoretic and geometric views. The problem is formulated into a generalization of the patrol security game. We show that there are two simple objective functions, latency, and entropy, that affect the utility of the adversary greatly. I will first introduce heuristic algorithms specifically for the game-theoretic setting and then provide designs that for each of the objectives. At the end of this talk, I will also briefly introduce some new topics our lab currently work on. These regard a strategic behavior on classical ML problem and how it influence the system in the end.
Bio
Hao-Tsung Yang is an assistant professor at National Central University. Before that, he was a research associate at the School of Informatics in University of Edinburgh, U.K., supervised by Prof. Rik Sarkar. He receives his Ph.D. degree in Computer Science, Stony Brook University, in 2020, advised by Prof. Jie Gao and Prof. Shan Lin.
Hao-Tsung Yang's research theme lies between autonomous systems, data privacy, algorithm, and machine learning. He focuses on new problems and challenges when A.I. comes into human life, including serving humans, interacting & cooperating with humans, or defense from the human-like adversary. For example, an autonomous system such as multi-robot path planning involves multiple works; the control-feedback loop, the algorithm design, privacy, and data misuse. The solutions of these works influence one another, especially considering the human factor in the environment. One can use machine learning techniques to learn and generate good path planning solutions but also may invade people's privacy such as revealing their routine schedule, misusing sensitive data,...etc. On the other hand, the solution may also reveal to the adversary who wants to damage the system and take advantage of it. In a patrol mission, the adversary can predict the arrival time of patrolling robots and launch attacks in vulnerable time slots. These bring new challenges and solutions could be found from algorithmic or machine learning perspectives, and sometimes, combining both together.
Hao-Tsung Yang's research theme lies between autonomous systems, data privacy, algorithm, and machine learning. He focuses on new problems and challenges when A.I. comes into human life, including serving humans, interacting & cooperating with humans, or defense from the human-like adversary. For example, an autonomous system such as multi-robot path planning involves multiple works; the control-feedback loop, the algorithm design, privacy, and data misuse. The solutions of these works influence one another, especially considering the human factor in the environment. One can use machine learning techniques to learn and generate good path planning solutions but also may invade people's privacy such as revealing their routine schedule, misusing sensitive data,...etc. On the other hand, the solution may also reveal to the adversary who wants to damage the system and take advantage of it. In a patrol mission, the adversary can predict the arrival time of patrolling robots and launch attacks in vulnerable time slots. These bring new challenges and solutions could be found from algorithmic or machine learning perspectives, and sometimes, combining both together.