Agent-based modeling is a paradigm of modeling dynamic systems of interacting agents that are individually governed by specified behavioral rules. Training a model of such agents to produce an emergent behavior by specification of the emergent (as opposed to agent) behavior is easier from a demonstration perspective. While many approaches involve manual behavior specification via code or reliance on a defined taxonomy of possible behaviors, the meta-modeling framework in Miner [2010] generates mapping functions between agent-level parameters and swarm-level parameters, which are re-usable once generated. This work builds on that framework by integrating demonstration by image or video. The demonstrator specifies spatial motion of the agents over time and retrieves agent-level parameters required to execute that motion. The framework, at its core, uses computationally cheap image-processing algorithms. Our work is tested with a combination of primitive visual feature extraction methods (contour area and shape) and features generated using a pre-trained deep neural network in different stages of image featurization. The framework is also evaluated for its potential using complex visual features for all image featurization stages. Experimental results show significant coherence between demonstrated behavior and predicted behavior based on estimated agent-level parameters specific to the spatial arrangement of agents.

中文翻译：

---

视觉演示控制群的特征构建

基于代理的建模是对交互代理的动态系统进行建模的范例，这些代理由指定的行为规则单独管理。从演示的角度来看，通过规范紧急（与代理相反）行为来训练此类代理的模型以产生紧急行为更容易。虽然许多方法涉及通过代码手动行为规范或依赖于可能行为的定义分类，但 Miner [2010] 中的元建模框架生成代理级参数和群级参数之间的映射函数，这些函数一旦生成就可以重用. 这项工作通过集成图像或视频演示建立在该框架之上。演示器指定代理随时间的空间运动，并检索执行该运动所需的代理级参数。该框架的核心是，使用计算成本低廉的图像处理算法。我们的工作结合原始视觉特征提取方法（轮廓区域和形状）和使用预训练的深度神经网络在图像特征化的不同阶段生成的特征进行了测试。该框架还使用所有图像特征化阶段的复杂视觉特征评估其潜力。实验结果表明，基于特定于代理空间排列的估计代理级参数，所证明的行为和预测的行为之间存在显着的一致性。该框架还使用所有图像特征化阶段的复杂视觉特征评估其潜力。实验结果表明，基于特定于代理空间排列的估计代理级参数，所证明的行为和预测的行为之间存在显着的一致性。该框架还使用所有图像特征化阶段的复杂视觉特征评估其潜力。实验结果表明，基于特定于代理空间排列的估计代理级参数，所证明的行为和预测的行为之间存在显着的一致性。