AutoCoMBOT: Autonomy in Cyberspace through rObot learning and Man-BOt Teaming
AutoComBOT – Autonomy in Cyberspace through rObot learning and Man-BOt Teaming, introduces a novel multi-pronged approach to address the standing challenges for future warfare involving multitude of cyber bots. Important constraints in bot warfare scenarios include limited access to a reliable central controller, speed of action, synchronization, and coordination among the distributed cyber-physical, cyber, and human entities. Our interdisciplinary team will develop a new comprehensive foundational framework for safe and robust artificial intelligence intrinsic to autonomous agents, distributed bots, self-adaptivity, introspection, human-AI teaming, as well as automated methods for human-AI games, deception and recovery in dynamic settings.
Principal Investigator: Farinaz Koushanfar (University of California San Diego)
The focus of the AutoComBOT research is on three modular but inter-linked thrusts:
Robust Learning: We formulate key challenging scenarios in robust learning with distributed agents as instances of graph optimization problems; the formulation is leveraged to derive new bounds, metrics, attacks, and defenses.
Introspection/Anti-fragility Adaptation: This is formulated as active learning scenarios with offline preprocessing and training, online introspection/anti-fragility adaptation, and dynamic adversary deception.
New Team Science Concepts for Cyber Bots: This thrust focuses on effective team assembly and robust collaboration networks for human-bot teams, shared cognition for human-AI interaction, and modeling frameworks for human-bot interaction and control.
