## 4 Results and Evaluation
### Ablation Experiments  
Our ablation study aimed to delineate the specific impact of the Gaussian-Voronoi map and the chain-of-thought behavior tree on the terrain generation quality. We sought to understand how each component contributes to the realism and complexity of the generated terrains. This investigation aimed to evaluate the influence of the Gaussian-Voronoi map, the chain-of-thought behavior tree, and the multiagent update strategy on the proficiency of generating terrain from textual descriptions. For the purposes of this study, we chose to exclude the multiagent update strategy. This decision was informed by insights gained from previous quality assessment experiments, which convincingly demonstrated the benefits of incorporating this strategy. As a result, by omitting the multiagent update strategy from our ablation study, our goal was to more precisely discern the effects of the Gaussian-Voronoi map and the chain-of-thought behavior tree on enhancing the quality, authenticity, and intricacy of the generated terrain models. This refined focus enabled us to draw more direct correlations between these components and the overall success of our terrain generation method. Figure 7 visually demonstrates the multiagent update strategy's role, highlighting its collaboration with other components to enhance terrain generation's efficiency and accuracy. This strategy's integration is pivotal for refining terrain details and aligning them closely with textual descriptions. This inclusion ensures that readers can fully appreciate how the multiagent update strategy collaborates with other key components to facilitate an efficient and accurate process of generating terrain from textual descriptions. By doing so, we underscore that although the multiagent update strategy was not considered in this specific analysis, it plays a crucial role within the overall framework, being instrumental in achieving the ultimate objectives of our method. The findings from this study were derived exclusively from experiments conducted on the hard dataset, chosen for its complexity to maximally accentuate the impact of each individual component. The outcomes, as detailed in the subsequent table, juxtapose the efficacy of our comprehensive approach against variations lacking in these critical elements. As shown in Table 5, the ablation study conducted on our text-to-terrain generation method underscores the indispensable role each component plays in crafting high-quality,  
Figure 7: This image illustrates another significant role of the multiagent update strategy besides optimizing existing maps: continuously updating the terrain to simulate changes in the landscape over time. realistic, and complex terrains from textual descriptions. By systematically removing key components and observing the impact on terrain generation, we have gained valuable insights into how each part contributes to the overall efficacy of the method. This analysis highlights not only the individual importance of these components but also their synergistic effect, which is crucial for achieving the best results. The Gaussian-Voronoi map, in particular, stands out as a critical element for simulating the randomness and complexity inherent in natural terrains. This component effectively elevates low-frequency Gaussian data to high-frequency Voronoi data, capturing the intricate details and variations found in real-world landscapes. The absence of the Gaussian-Voronoi map in our experiments led to generated terrains that were notably less detailed and more uniform, lacking the natural variability and complexity that characterize genuine terrains. This demonstrates that the Gaussian-Voronoi map is essential for adding depth and realism to the generated terrains, making them more believable and engaging. Figure 8 illustrates the stark contrasts between terrains generated with and without the Gaussian-Voronoi map. Figure 8: In the first scenario, we directly converted the recognition results of the chain-of-thought behavior tree into depth maps.