On the Role of Local Arguments in the (Timed) Concurrent Language for Argumentation

Modelling the behaviour of concurrent agents that interact and reason in a dynamic environment is a difficult task. It requires tools that can effectively capture different types of interactions, such as persuasion and deliberation, while helping agents make decisions or reach agreements. This paper proposes a revised and extended version of the (timed) concurrent language for argumentation, better suited for modelling real-world scenarios. Our focus is on private information: we have given each agent a local argumentation store for reasoning with private knowledge. With this feature, agents can use the argumentation engine to implement courses of action based on their personal information and only disclose the bare minimum. Finally, we present an application example that models a privacy-preserving multi-agent decision-making process to demonstrate the capabilities of our language.