Collaborating Authors

Opponent Says Sheriff's Drug Interdiction Halt Was Political

U.S. News

Sgt. Mike Vance is running to replace his boss, Joe Yocum, as sheriff of Seward county. Vance said he's twice been recognized by the National Criminal Enforcement Association as one of the top six drug interdiction officers in the U.S. and Canada.

Inverse Reinforcement Learning Based Human Behavior Modeling for Goal Recognition in Dynamic Local Network Interdiction

AAAI Conferences

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.

Toward Psycho-robots Artificial Intelligence

We try to perform geometrization of psychology by representing mental states, <>, by points of a metric space, <>. Evolution of ideas is described by dynamical systems in metric mental space. We apply the mental space approach for modeling of flows of unconscious and conscious information in the human brain. In a series of models, Models 1-4, we consider cognitive systems with increasing complexity of psychological behavior determined by structure of flows of ideas. Since our models are in fact models of the AI-type, one immediately recognizes that they can be used for creation of AI-systems, which we call psycho-robots, exhibiting important elements of human psyche. Creation of such psycho-robots may be useful improvement of domestic robots. At the moment domestic robots are merely simple working devices (e.g. vacuum cleaners or lawn mowers) . However, in future one can expect demand in systems which be able not only perform simple work tasks, but would have elements of human self-developing psyche. Such AI-psyche could play an important role both in relations between psycho-robots and their owners as well as between psycho-robots. Since the presence of a huge numbers of psycho-complexes is an essential characteristic of human psychology, it would be interesting to model them in the AI-framework.

Optimal Planning Strategy for Ambush Avoidance

AAAI Conferences

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.

Trump Says That His Wall Will Stop Opioids--Two High-Profile Legal Cases Suggest That He's Wrong

The New Yorker

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!"