Xu Kai, Zeng Yunxiu, Qin Long, Yin Quanjun
College of Systems Engineering, National University of Defense Technology, Changsha 410073, China.
Entropy (Basel). 2020 Jan 10;22(1):88. doi: 10.3390/e22010088.
Deceptive path-planning is the task of finding a path so as to minimize the probability of an observer (or a defender) identifying the observed agent's final goal before the goal has been reached. It is one of the important approaches to solving real-world challenges, such as public security, strategic transportation, and logistics. Existing methods either cannot make full use of the entire environments' information, or lack enough flexibility for balancing the path's deceptivity and available moving resource. In this work, building on recent developments in probabilistic goal recognition, we formalized a single real goal magnitude-based deceptive path-planning problem followed by a mixed-integer programming based deceptive path maximization and generation method. The model helps to establish a computable foundation for any further imposition of different deception concepts or strategies, and broadens its applicability in many scenarios. Experimental results showed the effectiveness of our methods in deceptive path-planning compared to the existing one.
欺骗性路径规划是指在目标达成之前，找到一条路径以最小化观察者（或防御者）识别被观察智能体最终目标的概率。它是解决诸如公共安全、战略运输和物流等现实世界挑战的重要方法之一。现有方法要么无法充分利用整个环境的信息，要么在平衡路径的欺骗性和可用移动资源方面缺乏足够的灵活性。在这项工作中，基于概率目标识别的最新进展，我们将基于单个实际目标量级的欺骗性路径规划问题形式化，随后提出了一种基于混合整数规划的欺骗性路径最大化和生成方法。该模型有助于为进一步施加不同的欺骗概念或策略奠定可计算的基础，并拓宽其在许多场景中的适用性。实验结果表明，与现有方法相比，我们的方法在欺骗性路径规划中是有效的。
