论文标题
在基于代理的模型中找到代理的最大伴侣行为
Finding the maximum-a-posteriori behaviour of agents in an agent-based model
论文作者
论文摘要
在本文中，我们考虑了找到最可能导致一组部分动态系统的部分嘈杂观察的最可能事件集的问题。特别是，我们考虑了一个具有离散状态空间的（可能是随机的）基于时间步长的代理模型的动力系统的情况，（可能是嘈杂的）观察结果是具有一些给定属性的代理的数量，并且我们感兴趣的事件是代理商（他们的“表达行为''）的决定，是模型的代理人所做的决定。 我们表明，可以将此问题简化为整数线性编程问题，随后可以使用标准的分支和切割算法来数值解决。我们描述了两种实现，一种``离线''算法，它在有限的时间窗口上找到了一组观察结果的最大a-posteriori表达的行为，以及一种``在线''算法'''在线'算法，从而逐渐构建了一组可行的行为，这些观察表中可能没有自然的开始或结束。 我们在32x32网格上的空间捕食者模型上展示了这两种算法，初始群体为100个试剂。
In this paper we consider the problem of finding the most probable set of events that could have led to a set of partial, noisy observations of some dynamical system. In particular, we consider the case of a dynamical system that is a (possibly stochastic) time-stepping agent-based model with a discrete state space, the (possibly noisy) observations are the number of agents that have some given property and the events we're interested in are the decisions made by the agents (their ``expressed behaviours'') as the model evolves. We show that this problem can be reduced to an integer linear programming problem which can subsequently be solved numerically using a standard branch-and-cut algorithm. We describe two implementations, an ``offline'' algorithm that finds the maximum-a-posteriori expressed behaviours given a set of observations over a finite time window, and an ``online'' algorithm that incrementally builds a feasible set of behaviours from a stream of observations that may have no natural beginning or end. We demonstrate both algorithms on a spatial predator-prey model on a 32x32 grid with an initial population of 100 agents.
