In order to transform machine learning systems from tools into partners, users need to trust their machine counterpart. One component to building a trusted relationship is knowledge of a partner’s competence (an accurate insight into a partner’s skills, experience, and reliability in dynamic environments). While state-of-the-art machine learning systems can perform well when their behaviors are applied in contexts similar to their learning experiences, they are unable to communicate their task strategies, the completeness of their training relative to a given task, the factors that may influence their actions, or their likelihood to succeed under specific conditions.
DARPA’s Competency-Aware Machine Learning (CAML) program addresses this challenge by enabling learning systems to be aware of their own competency. Systems will have knowledge of their learned abilities, the conditions under which those abilities were learned, knowledge of their resultant task strategies, and the situations for which those strategies are applicable.
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 for operating conditions.
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