Raytheon is developing a machine learning technology under a $6 million contract from the Defense Advanced Research Projects Agency (DARPA) for the Competency-Aware Machine Learning (CAML) program.
Systems will be able to communicate the abilities they have learned, the conditions under which the abilities were learned, the strategies they recommend and the situations for which those strategies can be used.
“The CAML system turns tools into partners,” said Ilana Heintz, principal investigator for CAML at Raytheon BBN Technologies. “It will understand the conditions where it makes decisions and communicate the reasons for those decisions.”
The system will learn from a video game-like process. Instead of giving the system rules, the researchers will tell the system what choices it has in the game and what the ultimate goal is. By repeatedly playing the game, the system will learn the most effective ways to meet the goal. The system will explain itself by recording the conditions and strategies it used to come up with successful outcomes.
“People need to understand an autonomous system’s skills and limitations to trust it with critical decisions,” added Heintz.
Once the system has developed these skills, the team will apply it to a simulated search and rescue mission. Users will create the conditions surrounding the mission, while the system will make recommendations and give users information about its competence in those particular conditions. For example, the system might say, “In the rain, at night, I can distinguish between a person and an inanimate object with 90 percent accuracy, and I have done this over 1,000 times.”
According to DARPA, CAML contributes to improved human-machine teaming and realization of the task synergies expected of autonomous systems. By creating a fundamentally new machine learning approach, CAML will facilitate mission planning by giving human operators insight into available machine assets based on task requirements, determining the level of autonomy to be granted, and controlling behaviors to adapt to operating conditions.