File photo - U.S. soldiers from the 3rd Cavalry Regiment watch as CH-47 Chinook helicopter from the 82nd Combat Aviation Brigade lands after an advising mission at the Afghan National Army headquarters for the 203rd Corps in the Paktia province of Afghanistan December 21, 2014. Warrior Maven: What is the primary purpose of the Army's AI Task Force? Matty: The Army AI Task Force was established with a Secretary of the Army directive in October of 2018. There are four thrusts or top initiatives from the Secretary's directive. One component is we are leveraging AI to help our talent management in human resources.
MIT's version of the "robotoddler" is just the latest MIT entry in the world of robots that can move themselves in a variety of settings. There's still a long way to go before today's robots evolve into practical, everyday technologies, but even now, autonomous robotic vehicles developed at MIT are exploring uncharted or hazardous places, assisting troops in combat and performing household tasks. In addition to his well-known work on humanoid robots such as Kismet, Professor Rodney Brooks led the development of several robotic vehicles and co-founded a company, iRobot, that develops these machines commercially. Troops in Afghanistan use PackBots to explore enemy caves, and soldiers in Iraq use them to detect improvised explosive devices and inspect weapons caches. "In 20 years, we've gone from robots that can hardly maneuver around objects to ones that can navigate in unstructured environments," said Brooks, director of the Computer Science and Artificial Intelligence Laboratory (CSAIL).
Recently, geospatial abduction was introduced by the authors in [Shakarian et. al. 2010] as a way to infer unobserved geographic phenomena from a set of known observations and constraints between the two. In this paper, we introduce the SCARE-S2 software tool which applies geospatial abduction to the environment of Afghanistan. Unlike previous work, where we looked for small weapon caches supporting local attacks, here we look for insurgent high-value targets (HVT's), supporting insurgent operations in two provinces. These HVT's include the locations of insurgent leaders and major supply depots. Applying this method of inference to Afghanistan introduces several practical issues not addressed in previous work. Namely, we are conducting inference in a much larger area (24,940 sq km as compared to 675 sq km in previous work), on more varied terrain, and must consider the influence of many local tribes. We address all of these problems and evaluate our software on 6 months of real-world counter-insurgency data. We show that we are able to abduce regions of a relatively small area (on average, under 100 sq km and each containing, on average, 4.8 villages) that are more dense with HVT's (35 X more than the overall area considered).