Fast moving but power hungry unmanned aerial vehicles (UAVs) can recharge on slow-moving unmanned ground vehicles (UGVs) to survey large areas in an effective and efficient manner. In order to solve this computationally challenging problem in a reasonable time, we created a two-level optimization heuristics. At the outer level, the UGV route is parameterized by few free parameters and at the inner level, the UAV route is solved by formulating and solving a vehicle routing problem with capacity constraints, time windows, and dropped visits. The UGV free parameters need to be optimized judiciously in order to create high quality solutions. We explore two methods for tuning the free UGV parameters: (1) a genetic algorithm, and (2) Asynchronous Multi-agent architecture (Ateams). The A-teams uses multiple agents to create, improve, and destroy solutions. The parallel asynchronous architecture enables A-teams to quickly optimize the parameters. Our results on test cases show that the A-teams produces similar solutions as genetic algorithm but with a speed up of 2-3 times.

Machine learning is gaining momentum and whether we want to admit it or not, it has become an essential part of our lives. As Adam Geitgey, Director of Software Engineering at Groupon, told JAXenter a few months ago, "anyone who knows how to program can use machine learning tools to solve problems." I think that in five years, machine learning won't be thought of as "magic" anymore. It will be a very common tool that nearly all programmers use to solve problems – just like how most programmers today know about databases and networking. Geitgey explained that even if you don't need a deep mathematical background to be able to apply machine learning, learning Python --"by far the most popular programming language today for machine learning"-- is a must.

Sylvester Kaczmarek is an award-winning entrepreneur and product leader with more than a decade of international, quality-driven IT industry experience. One of the most popular shows on television 30 years ago was "The A-Team" -- the story of five rogue military commandos who teamed together to form an elite fighting unit. Now, a generation later, DARPA and the U.S. military are in search of a new "A-Team" -- only this team won't be comprised of just humans, it will include a few machines, as well. A-team refers to "agile team," which DARPA refers to as hybrid teams of humans teamed with intelligent machines. What DARPA recognizes is that intelligent machines are not just "agents" carrying out the simple commands of humans, but rather are part of an "intelligent fabric" that dynamically evolves over time. The obvious use case for these A-teams, of course, is in the military sphere.