Software Engineering must increasingly address the issues of complexity and uncertainty that arise when systems are to be deployed into a dynamic software ecosystem. There is also interest in using digital twins of systems in order to design, adapt and control them when faced with such issues. The use of multi-agent systems in combination with reinforcement learning is an approach that will allow software to intelligently adapt to respond to changes in the environment. This paper proposes a language extension that encapsulates learning-based agents and system building operations and shows how it is implemented in ESL. The paper includes examples the key features and describes the application of agent-based learning implemented in ESL applied to a real-world supply chain.
|Title of host publication||iSOFT - Proceedings of the 13th Innovations in Software Engineering Conference (Formerly known as India Software Engineering Conference), ISEC 2020|
|Publication status||Published - 27 Feb 2020|
|Event||13th Innovations in Software Engineering Conference, ISEC 2020 - Jabalpur, India|
Duration: 27 Feb 2020 → 29 Feb 2020
|Conference||13th Innovations in Software Engineering Conference, ISEC 2020|
|Period||27/02/20 → 29/02/20|
- Reinforcement learning