Abstract Traditional Agent-Oriented Software Engineering (AOSE) methods rely on explicit and expert-driven design for MAS, but often lack automation. In contrast, Multi-Agent Reinforcement Learning (MARL) and related fields offer automated ways to model environments and learn suitable agent policies. However, integrating these techniques into AOSE remains underexplored partly due to the lack of control, explainability, and unifying frameworks. We propose MOISE+MARL Assisted MAS Design (MAMAD), a four-activity method framing MAS design as a constrained optimization problem: learning joint policies that maximize rewards while respecting MOISE + roles and goals. The activities include: 1) Modeling the environment, 2) Training under organizational constraints, 3) Analyzing emergent behaviors, 4) Transferring to real-world deployment. We evaluate MAMAD on various environments, showing that the generated MAS exhibit expected performance, compliance with design requirements and are explainable, while reducing manual design overhead.
Soulé et al. (Wed,) studied this question.