Reward-Reinforced Reinforcement Learning for Multi-agent Systems
(2021)
Journal Article
Zheng, C., Yang, S., Ullauri, J. M. P., García-Domínguez, A., & Bencomo, N. (2021). Reward-Reinforced Reinforcement Learning for Multi-agent Systems
Outputs (6)
A Hitchhiker's Guide to Model-Driven Engineering for Data-Centric Systems (2021)
Journal Article
Combemale, B., Kienzle, J., Mussbacher, G., Ali, H., Amyot, D., Bagherzadeh, M., …Wimmer, M. (2021). A Hitchhiker's Guide to Model-Driven Engineering for Data-Centric Systems. https://doi.org/10.1109/ms.2020.2995125
Agent-Based Framework for Self-Organization of Collective and Autonomous Shuttle Fleets (2021)
Journal Article
Bucchiarone, A., Sanctis, M. D., & Bencomo, N. (2021). Agent-Based Framework for Self-Organization of Collective and Autonomous Shuttle Fleets. https://doi.org/10.1109/tits.2020.3021592
The Secret to Better AI and Better Software (Is Requirements Engineering) (2021)
Journal Article
Bencomo, N., Guo, J., Harrison, R., Heyn, H., & Menzies, T. (2022). The Secret to Better AI and Better Software (Is Requirements Engineering). IEEE Software, 39(1), 105-110. https://doi.org/10.1109/ms.2021.3118099Much has been written about the algorithmic role that AI plays for automation in SE. But what about the role of AI, augmented by human knowledge? Can we make a profound advance by combining human and artificial intelligence? Researchers in requiremen... Read More about The Secret to Better AI and Better Software (Is Requirements Engineering).
Event-driven Temporal Models for Explanations - ETeMoX: Explaining Reinforcement Learning (2021)
Journal Article
Parra-Ullauri, J. M., Garcıa-Domınguez, A., Bencomo, N., Zheng, C., Zhen, C., Boubeta-Puig, J., …Yang, S. (2022). Event-driven Temporal Models for Explanations - ETeMoX: Explaining Reinforcement Learning. Software and Systems Modeling, 21(3), 1091-1113. https://doi.org/10.1007/s10270-021-00952-4Modern software systems are increasingly expected to show higher degrees of autonomy and self-management to cope with uncertain and diverse situations. As a consequence, autonomous systems can exhibit unexpected and surprising behaviours. This is exa... Read More about Event-driven Temporal Models for Explanations - ETeMoX: Explaining Reinforcement Learning.
Cronista: a multi-database automated provenance collection system for runtime models (2021)
Journal Article
Reynolds, O., García-Domínguez, A., & Bencomo, N. (2022). Cronista: a multi-database automated provenance collection system for runtime models. Information and Software Technology, 141, Article 106694. https://doi.org/10.1016/j.infsof.2021.106694Context: Decision making by software systems that face uncertainty needs tracing to support understandability, as accountability is crucial. While logging has been essential to support explanations and understandability of behaviour, several issues s... Read More about Cronista: a multi-database automated provenance collection system for runtime models.