Summary of Patrol Security Game: Defending Against Adversary with Freedom in Attack Timing, Location, and Duration, by Hao-tsung Yang et al.
Patrol Security Game: Defending Against Adversary with Freedom in Attack Timing, Location, and Duration
by Hao-Tsung Yang, Ting-Kai Weng, Ting-Yu Chang, Kin Sum Liu, Shan Lin, Jie Gao, Shih-Yu Tsai
First submitted to arxiv on: 21 Oct 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computer Science and Game Theory (cs.GT); Robotics (cs.RO)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed Patrol Security Game (PSG) is an extensive-form Stackelberg game where the attacker decides when, where, and how long to attack. The goal is to develop a patrolling schedule with an infinite time horizon that minimizes the attacker’s payoff. By transforming PSG into a combinatorial minimax problem with a closed-form objective function, the optimal solution can be computed efficiently. The defender’s strategy is constrained to a time-homogeneous first-order Markov chain, and the solutions involve either minimizing expected hitting time or return time, depending on the attacker model. Increasing randomness in the patrol schedule reduces the attacker’s expected payoff in high-penalty cases, but the minimax problem becomes non-convex in other scenarios. To address this, a bi-criteria optimization problem is formulated to balance two objectives: expected maximum reward and entropy. Three graph-based algorithms and one deep reinforcement learning model are proposed to efficiently achieve this trade-off. Experimental results demonstrate that the approaches outperform state-of-the-art baselines on both synthetic and real-world crime datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A team of researchers created a game called Patrol Security Game (PSG) to help robots patrol areas safely. In this game, an attacker tries to find and attack a robot patroller. The goal is to create a schedule for the patroller that makes it hard for the attacker to succeed. They found a way to turn PSG into a problem that can be solved using math formulas. This helps them figure out the best solution quickly. The researchers also found that adding some randomness to the patrol route makes it harder for the attacker to succeed, but only in certain situations. To make the game even more challenging, they created a new optimization problem that balances two goals: getting rewards and being unpredictable. They came up with four different ways to solve this problem using math and machine learning. The results show that their approaches are better than existing methods for both synthetic and real-world crime data. |
Keywords
» Artificial intelligence » Machine learning » Objective function » Optimization » Reinforcement learning
