The popular Belief-Desire-Intention (BDI) architecture structures agents' behavior around beliefs, desires, and intentions, enabling effective pursuit of collective objectives and optimal system performance. Many complex situations require agent developers to design a coordination model/plan involving multiple BDI agents working together to achieve a given goal. However, a semantic notion of state has been missing in most current accounts of BDI agent execution, making it impossible to assess at design time if a coordination model/plan is able to achieve the goal. Thus, in this paper, we introduce a framework that permits us to identify the state that accrues at any point in agent execution. This enables us to determine, at design time, if a multi-agent coordination model/plan accomplishes a given goal. Our framework facilitates the verification of coordination correctness in such complex multi-agent systems.

Robotic Process Automation (RPA) design and implementation requires an architecture which facilitates the seamless transition between human agents, robotic agents, and intelligent agents to automate information acquisition tasks and decision-making tasks. Coordination of those agents must consider various factors, such as efficiency of a resource when completing tasks, the quality of completed complex tasks, and the cost of the used resources. This article proposes a novel approach for generating an optimal architecture based on distinct types of resources, including human agents, intelligent agents, and robotic agents. An optimal architecture is the optimal enactment of process instances executed by a combination of human and automation agents based on their characteristics. The architecture provides a set of resources and their characteristics that are tailored to meet multiple objectives for process execution. The proposed approach is validated through an empirical evaluation based on a real-world business process. An empirical evaluation demonstrates that, given equal computational time, our approach outperforms conventional constraint optimization ILOG CPLEX (Manual 1987).