Collaborating Authors

NYPD seeks driver who assaulted 2 Bronx traffic agents

FOX News

Fox News Flash top headlines are here. Check out what's clicking on The NYPD has released a video of a man police say assaulted two department traffic safety agents in the Bronx this week. The incident began with the agents asking the driver of a 2008 Nissan Altima to move his vehicle because it was blocking a fire hydrant Wednesday night, a police spokeswoman told Fox News Saturday. The driver exited the vehicle irate and started punching both traffic agents in the face, she said.

Immigrant advocates urge bus company to block federal agents

FOX News

MIAMI – Immigrant rights attorneys are urging a major bus company to stop letting federal agents on board to conduct immigration sweeps. The American Civil Liberties Union's affiliates in 10 states sent a letter Wednesday to officials for the Greyhound bus company asking them to deny agents permission to board without a warrant or on the U.S. border. The lawyers say U.S. Customs and Border Protection has been searching buses more often to check the immigration status of travelers, singling out people based on race or their appearance. The advocates say the checks have taken place in at least seven states including California, Florida and Vermont. Last month, advocates in Florida warned immigrants about the checks when traveling to the state.

Rational Agents for Artificial Intelligence – Hacker Noon


There are multiple approaches that you might take to create Artificial Intelligence, based on what we hope to achieve with it and how will we measure its success. It ranges from extremely rare and complex systems, like self driving cars and robotics, to something that is a part of our daily lives, like face recognition, machine translation and email classification. The article below gives an insight into what it takes to truly create Artificial Intelligence. The path you take will depend upon what are the goals of your AI and how well you understand the complexity and feasibility of various approaches. In this article we will discuss the approach that is considered more feasible and general for scientific development, i.e. study of the design of rational/intelligent agents.

How A.I. could help United Airlines avoid problems with overbooking passengers


The entire airline industry is at first glance highly technical and, at the same time, highly dependent on personnel to handle tough issues. The big story this week for United Airlines -- which is quickly turning into a public relations nightmare and even creating conflicts with China -- has to do with a passenger who was forcibly removed due to overbooking the seats. There are many variables to the story -- yet, it's a wake-up call because it could have been prevented using artificial intelligence. Even if the overbooking is partly a way to ensure that flights are always full, A.I. could handle that problem. Here's how it would work.

Multi-Agent Continuous Transportation with Online Balanced Partitioning Artificial Intelligence

We introduce the concept of continuous transportation task to the context of multi-agent systems. A continuous transportation task is one in which a multi-agent team visits a number of fixed locations, picks up objects, and delivers them to a final destination. The goal is to maximize the rate of transportation while the objects are replenished over time. Examples of problems that need continuous transportation are foraging, area sweeping, and first/last mile problem. Previous approaches typically neglect the interference and are highly dependent on communications among agents. Some also incorporate an additional reconnaissance agent to gather information. In this paper, we present a hybrid of centralized and distributed approaches that minimize the interference and communications in the multi-agent team without the need for a reconnaissance agent. We contribute two partitioning-transportation algorithms inspired by existing algorithms, and contribute one novel online partitioning-transportation algorithm with information gathering in the multi-agent team. Our algorithms have been implemented and tested extensively in the simulation. The results presented in this paper demonstrate the effectiveness of our algorithms that outperform the existing algorithms, even without any communications between the agents and without the presence of a reconnaissance agent.