Abstract: Control theory and machine learning theory handle different sets of challenges. However, the need for the design of autonomous systems for operation in uncertain environments has led to the development of a rich class of tools pertaining to joint control and learning techniques. The proliferation of intelligence and an abundance of cheap sensing, communication and computing resources in autonomous systems, and the conflicting nature of the objectives of multiple agents require a re-think about the control and learning algorithms functioning therein. Examples of such scenarios are plentiful: interaction between a cognitive radar and a cognitive target, competition between drone swarms, interaction between a defender and an attacker in a large cyber-physical system involving inference and control over a network, to name a few. Traditional control and learning theory often fail to model such complex interactions. For example, traditional stochastic filtering algorithms used for target tracking by a radar do not account for the possibility of the target learning the radar’s strategy and location from the received pulses, as explored in a number of recent works. Another such example is that of competition between swarms of collaborating drone fleets; this system is difficult to model solely using the team decision theory or stochastic game theory. This workshop aims to bring researchers to discuss problems, potential methodologies and first solutions to some of the challenging problems in this rapidly evolving field of counter-adversarial inference, control and learning. It covers a broad range of applications and tools, including but not limited to multi-agent control, stochastic games, reinforcement learning, Bayesian/adversarial inference, electronic warfare, cybersecurity, autonomous systems, and multi-armed bandits. 

Topics of interest: Inverse cognition in non-linear stochastic filtering, inverse reinforcement learning, stochastic games, team decision theory, cyber-security, multi-agent reinforcement learning, multi-armed bandits, multi-agent control, consensus theory, scalable control, networked control.

Organizing committee