Detecting aggressive agents in egress process by using conflict data in cellular automaton model
Detecting aggressive agents in egress process by using conflict data in cellular automaton model
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DOI：
10.1080/15472450.2021.1942869
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发表时间：
2021-07
影响因子：
3.6
通讯作者：
D. Yanagisawa;Keisuke Yamazaki
中科院分区：
文献类型：
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作者：
D. Yanagisawa;Keisuke Yamazaki
摘要出口处的攻击性行为会引起冲突并增加出口时间。为了阻止代理人的攻击行为，我们首先需要知道谁是攻击者。因此，我们开发了一种检测攻击性药物的方法。在本文中，我们只专注于使用细胞自动机模型进行出口模拟，因为我们希望深入研究我们方法的理论特性。仿真中的智能体分为正常智能体和攻击性智能体两类。攻击型代理人倾向于在冲突中排挤他人，并试图积极地向目标细胞移动。我们考虑了代理类型的所有可能组合，标记它们，并根据从出口模拟获得的冲突数据计算标签的联合概率。获得最大联合概率的标签被视为预测标签。我们的检测方法成功地检测侵略性代理完美的合理数量的观察。此外，没有任何虚假指控。我们还研究了冲突数据使用的限制如何影响结果。如果只使用成功解决冲突的冲突数据，当有许多攻击性代理时，准确率无法达到1.0。然而，如果有几个非常积极的代理，准确性的进展率增加的冲突数据的使用的限制。我们利用一个简单的概率计算从理论上阐明了这一违反直觉的现象。
Abstract Aggressive behaviors at exits cause conflicts and increase egress times. To deter agents from aggressive behaviors, we first need to know who the aggressive ones are. Therefore, we developed a method for detecting aggressive agents. We focused on only egress simulations with a cellular-automata model in this article since we would like to deeply investigate theoretical characteristics of our method. There are two types of agents, which are normal agents and aggressive agents in the simulations. Aggressive agents tend to push out others in conflicts and try to move to their target cell aggressively. We considered all the possible combinations of agent types, labeled them, and computed the joint probabilities of the labels from the conflict data obtained from the egress simulations. The label which achieved the maximum joint probability was regarded as the predicted label. Our detecting method succeeded in detecting the aggressive agents perfectly with the reasonable number of observations. Moreover, there were no false accusations. We have also investigated how the restriction of the usage of the conflict data affect the results. By only using the conflict data of successes in solving conflicts, the accuracy failed to achieve 1.0 when there are many aggressive agents. However, if there are a few very aggressive agents, the progress rate of the accuracy increases by the restriction of the usage of the conflict data. We elucidated this counterintuitive phenomenon theoretically by exploiting a simple probabilistic calculation.