Dependable learning-enabled multiagent systems

Abstract

We are concerned with the construction, formal verification, and safety assurance of dependable multiagent systems. For the case where the system (agents and their environment) can be explicitly modelled, we develop formal verification methods over several logic languages, such as temporal epistemic logic and strategy logic, to reason about the knowledge and strategy of the agents. For the case where the system cannot be explicitly modelled, we study multiagent deep reinforcement learning, aiming to develop efficient and scalable learning methods for cooperative multiagent tasks. In addition to these, we develop (both formal and simulation-based) verification methods for the neural network based perception agent that is trained with supervised learning, considering its safety and robustness against attacks from an adversarial agent, and other approaches (such as explainable AI, reliability assessment, and safety argument) for the analysis and assurance of the learning components. Our ultimate objective is to combine formal methods, machine learning, and reliability engineering to not only develop dependable learning-enabled multiagent systems but also provide rigorous methods for the verification and assurance of such systems.