ABSTRACT

Traditional agent-based modelling is mostly rule-based. For many systems, this approach is extremely successful, since the rules are well understood. However, for a large class of systems it is difficult to find rules that adequately describe the behaviour of the agents. A simple example would be two agents playing chess: Here, it is impossible to find simple rules. To solve this problem, we introduce a framework for agent-based modelling that incorporates machine learning. In a process closely related to reinforcement learning, the agents learn rules. As a trade-off, a utility function needs to be defined, which is much simpler in most cases. We test this framework to replicate the results of the prominent Sugarscape model as a proof of principle. Furthermore, we investigate a more complicated version of the Sugarscape model, that exceeds the scope of the original framework. By expanding the framework we also find satisfying results there.

摘要

传统的基于代理的建模大部分是基于规则的。对于许多系统来说，这种方法是非常成功的，因为人们已经很好地理解了规则。但是，对于一大类系统，很难找到足以描述代理行为的规则。一个简单的例子是两个代理下棋：在这里，不可能找到简单的规则。为了解决此问题，我们引入了一个基于机器学习的基于代理的建模框架。在与强化学习密切相关的过程中，代理人学习规则。作为一种折衷，需要定义一个效用函数，这在大多数情况下要简单得多。我们测试此框架，以复制著名的Sugarscape模型的结果作为原理证明。此外，我们研究了Sugarscape模型的更复杂版本，超出了原始框架的范围。通过扩展框架，我们在那也找到了令人满意的结果。