Learning From Humans: Agent Modeling With Individual Human Behaviors
Learning From Humans: Agent Modeling With Individual Human Behaviors
复制标题
DOI：
10.1109/tsmca.2010.2055152
复制
发表时间：
2011-01-01
影响因子：
--
通讯作者：
Ishida, Toru
中科院分区：
文献类型：
--
作者：
Hattori, Hiromitsu;Nakajima, Yuu;Ishida, Toru
基于多智能体的仿真(MABS)是一个非常活跃的交叉学科领域，是多智能体研究和社会科学之间的桥梁。进行真正有用的MABS的关键技术是为再现真实行为而进行的代理建模。为了使代理模型逼真，从现实世界中的人类行为中学习似乎是很自然的。本文提出的挑战是在交通领域中使用参与式建模来获得个体行为模型。我们给出了一种能够获取先验知识的方法来解释特定环境下的人类驾驶行为，并基于先验知识集构建了驾驶行为模型。在现实世界中，人类司机经常做出无心之举，偶尔，他们的行为没有逻辑理由。在这些情况下，我们不能依靠先验知识来解释它们。我们被迫构建了一个行为模型，而我们的知识不足，无法重现驾驶行为。为了构建这样的个体驾驶行为模型，我们采取了利用他人的知识来补充目标知识的不足的方法。为了阐明包含他人先验知识的行为模型在驾驶行为中提供了个性，我们通过实验证实了混合模型复制的驾驶行为与人类行为具有较好的相关性。
Multiagent-based simulation (MABS) is a very active interdisciplinary area bridging multiagent research and social science. The key technology to conduct truly useful MABS is agent modeling for reproducing realistic behaviors. In order to make agent models realistic, it seems natural to learn from human behavior in the real world. The challenge presented in this paper is to obtain an individual behavior model by using participatory modeling in the traffic domain. We show a methodology that can elicit prior knowledge for explaining human driving behavior in specific environments, and then construct a driving behavior model based on the set of prior knowledge. In the real world, human drivers often perform unintentional actions, and occasionally, they have no logical reason for their actions. In these cases, we cannot rely on prior knowledge to explain them. We are forced to construct a behavior model with an insufficient amount of knowledge to reproduce the driving behavior. To construct such individual driving behavior model, we take the approach of using knowledge from others to complement the lack of knowledge from the target. To clarify that the behavior model including prior knowledge from others offers individuality in driving behavior, we experimentally confirm that the driving behaviors reproduced by the hybrid model correlate reasonably well with human behavior.