We have been awarded one of the projects under the DARPA Explainable AI programme, to be kicked off next week. Our project, entitled COGLE (Common Ground Learning and Explanation), will be coordinated by Xerox Palo Alto Research Centre, and I will a PI leading technical efforts on the machine learning side of the architecture.
COGLE will be a highly interactive sense-making system for explaining the learned performance capabilities of an autonomous system and the history that produced that learning. COGLE will be initially developed using an autonomous Unmanned Aircraft System (UAS) test bed that uses reinforcement learning (RL) to improve its performance. COGLE will support user sensemaking of autonomous system decisions, enable users to understand autonomous system strengths and weaknesses, convey an understanding of how the system will behave in the future, and provide ways for the user to improve the UAS’s performance.
To do this, COGLE will:
- Provide specific interactions in sensemaking user interfaces that directly support modes of human explanation known to be effective and efficient in human learning and understanding.
- Support mapping (grounding) of human conceptualizations onto the RL representations and processes.
This area is becoming one that is increasingly being discussed in the public sphere, in the context of the increasing adoption of AI into daily lives, e.g., see this article in the MIT Technology Review and this one in Nautilus, both referring directly to this DARPA programme. I look forward to contributing to this theme!