Zeng, Yunxiu (National University of Defense Technology) | Xu, Kai (National University of Defense Technology) | Yin, Quanjun (National University of Defense Technology) | Qin, Long (National University of Defense Technology) | Zha, Yabing (National University of Defense Technology) | Yeoh, William (Washington University in St. Louis)
Goal recognition is the task of inferring an agent's goals given some or all of the agent’s observed actions. Among different ways of problem formulation, goal recognition can be solved as a model-based planning problem using off-the-shell planners. However, obtaining accurate cost or reward models of an agent and incorporating them into the planning model becomes an issue in real applications. Towards this end, we propose an Inverse Reinforcement Learning (IRL)-based opponent behavior modeling method, and apply it in the goal recognition assisted Dynamic Local Network Interdiction (DLNI) problem. We first introduce the overall framework and the DLNI problem domain of our work. After that, an IRL-based human behavior modeling method and Markov Decision Process-based goal recognition are introduced. Experimental results indicate that our learned behavior model has a higher tracking accuracy and yields better interdiction outcomes than other models.
We try to perform geometrization of psychology by representing mental states, <
Operating vehicles in adversarial environments between a recurring origin-destination pair requires new planning techniques. Such a technique, presented in this paper, is a game inspired by Ruckle’s original contribution. The goal of the first player is to minimize the expected casualties undergone by a moving agent. The goal of the second player is to maximize this damage. The outcome of the game is obtained via a linear program that solves the corresponding minmax optimization problem over this outcome. The formulation originally proposed by Feron and Joseph is extended to different environment models in order to compute routing strategies over unstructured environments. To compare these methods for increasingly accurate representations of the environment, a grid-based model is chosen to represent the environment and the existence of a sufficient network size is highlighted. A global framework for the generation of realistic routing strategies between any two points is described. Finally the practicality of the proposed framework is illustrated on real world environments.
In two very different court proceedings last week, evidence was presented which you may find helpful if you're still trying to assess President Trump's rationale for his border wall. Along with bigness and elemental beauty, he credits it with crime-stopping powers: in particular, he says, it will stanch the flow of drugs that feed our country's raging opioid habit. In his primetime address to the nation, on January 8th, Trump told Americans that "our southern border is a pipeline for vast quantities of illegal drugs including meth, heroin, cocaine, and fentanyl." Without a wall, he tweeted on January 11th, "Criminals, Gangs, Human Traffickers, Drugs & so much other big trouble can easily pour in. It can be stopped cold!"