更新概率：一个基于复杂Agent的示例
Updating Probabilities: A Complex Agent Based Example
摘要：
它已被证明，一个可以容纳数据（贝叶斯）和约束（最大熵）在一个方法，最大（相对）熵（ME）的方法（Giffin 2007）。在本文中，我们展示了一个复杂的代理推理的例子与两种不同形式的信息;时刻和数据。在这个例子中，几个代理每个都以数据的形式接收关于系统的部分信息。此外，每个代理都同意或被告知，系统上存在某些全局约束，这些约束始终为真。然后，代理被要求对整个系统进行推断。当我们添加代理并允许它们共享信息时，系统变得更加复杂。该系统可以具有几何形式，例如晶体结构。形状可以指示代理如何能够共享信息，例如与最近的邻居共享。这种方法可以用来模拟许多系统中的代理或细胞有本地或部分的信息，但必须遵守一些全球性的规则。这也可以说明智能体是如何进化的，并可以说明系统的紧急行为。
英文摘要：
It has been shown that one can accommodate data (Bayes) and constraints (MaxEnt) in one method, the method of Maximum (relative) Entropy (ME) (Giffin 2007). In this paper we show a complex agent based example of inference with two different forms of information; moments and data. In this example, several agents each receive partial information about a system in the form of data. In addition, each agent agrees or is informed that there are certain global constraints on the system that are always true. The agents are then asked to make inferences about the entire system. The system becomes more complex as we add agents and allow them to share information. This system can have a geometrical form, such as a crystal structure. The shape may dictate how the agents are able to share information, such as sharing with nearest neighbors. This method can be used to model many systems where the agents or cells have local or partial information but must adhere to some global rules. This could also illustrate how the agents evolve and could illuminate emergent behavior of the system.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
