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Sun 10 - Sat 16 October 2021
Thu 14 Oct 2021 23:40 - 23:50 at Room 1 - Machine learning and Recommender systems II Chair(s): Antonio Cicchetti

Model-driven optimization has gained much interest in the last years which resulted in several dedicated extensions for in-place model transformation engines. The main idea is to exploit domain-specific languages to define models which are optimized by applying a set of model transformation rules. Objectives are guiding the optimization processes which are currently mostly realized by meta-heuristic searchers such as different kinds of Genetic Algorithms. However, meta-heuristic search approaches are currently challenged by reinforcement learning approaches for solving optimization problems.

In this new idea paper, we apply for the first time reinforcement learning for in-place model transformations. In particular, we extend an existing model-driven optimization approach with reinforcement learning techniques. We experiment with value-based and policy-based techniques. We run several case studies for validating the feasibility of using reinforcement learning for model-driven optimization and compare the performance against existing approaches. The initial evaluation shows promising results but also helped in identifying a dedicated research roadmap for the whole community.

Thu 14 Oct

Displayed time zone: Osaka, Sapporo, Tokyo change

23:00 - 00:00
Machine learning and Recommender systems IITechnical Papers at Room 1
Chair(s): Antonio Cicchetti Mälardalen University
Recommender Systems in Model-Driven Engineering: A Systematic Mapping ReviewJ1ST
Technical Papers
Lissette Almonte Universidad Autónoma de Madrid, Esther Guerra , Iván Cantador Universidad Autonoma de Madrid, Juan de Lara Autonomous University of Madrid
A GNN-based Recommender System to Assist the Specification of Metamodels and ModelsFT
Technical Papers
Juri Di Rocco University of L'Aquila, Claudio Di Sipio University of L'Aquila, Davide Di Ruscio University of L'Aquila, Phuong T. Nguyen University of L’Aquila
Towards Reinforcement Learning for In-Place Model TransformationsVISION
Technical Papers