KEYWORDS: Information security, Network security, Systems modeling, Neural networks, Control systems, Defense and security, Silver, Nickel, Rhodium, Neurons
This work presents a game theory-based consensus problem for leaderless multi-agent systems in the presence of
adversarial inputs that are introducing disturbance to the dynamics. Given the presence of enemy components
and the possibility of malicious cyber attacks compromising the security of networked teams, a position agreement
must be reached by the networked mobile team based on environmental changes. The problem is addressed under
a distributed decision making framework that is robust to possible cyber attacks, which has an advantage over
centralized decision making in the sense that a decision maker is not required to access information from all the
other decision makers. The proposed framework derives three tuning laws for every agent; one associated with
the cost, one associated with the controller, and one with the adversarial input.
This paper first presents an overall view for dynamical decision-making in teams, both cooperative and competitive.
Strategies for team decision problems, including optimal control, zero-sum 2-player games (H-infinity control) and
so on are normally solved for off-line by solving associated matrix equations such as the Riccati equation.
However, using that approach, players cannot change their objectives online in real time without calling for a
completely new off-line solution for the new strategies. Therefore, in this paper we give a method for learning
optimal team strategies online in real time as team dynamical play unfolds. In the linear quadratic regulator case, for
instance, the method learns the Riccati equation solution online without ever solving the Riccati equation. This
allows for truly dynamical team decisions where objective functions can change in real time and the system dynamics can be time-varying.
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