  This study investigates the combined use of generative grammar rules and
Monte Carlo Tree Search (MCTS) for optimizing truss structures. Our approach
accommodates intermediate construction stages characteristic of progressive
construction settings. We demonstrate the significant robustness and
computational efficiency of our approach compared to alternative reinforcement
learning frameworks from previous research activities, such as Q-learning or
deep Q-learning. These advantages stem from the ability of MCTS to
strategically navigate large state spaces, leveraging the upper confidence
bound for trees formula to effectively balance exploitation-exploration
trade-offs. We also emphasize the importance of early decision nodes in the
search tree, reflecting design choices crucial for identifying the global
optimum. Additionally, we show how MCTS dynamically adapts to complex and
extensive state spaces without significantly affecting solution quality. While
the focus of this paper is on truss optimization, our findings suggest MCTS as
a powerful tool for addressing other increasingly complex engineering
applications.
