Predicting Opponent Actions by Observation
Predicting Opponent Actions by Observation
复制标题
通过观察预测对手的行动
DOI：
10.1007/978-3-540-32256-6_23
复制
发表时间：
2004
期刊：
影响因子：
--
通讯作者：
D. Borrajo
中科院分区：
文献类型：
--
作者：
Agapito Ledezma;R. Aler;A. Sanchis;D. Borrajo
在竞争领域，对对手的了解可以给玩家带来明显的优势。这一想法使我们在过去提出了一种方法来获取对手的模型，只基于对他们的输入-输出行为的观察。如果对手的输出可以直接访问，则可以通过向机器学习方法提供对手的踪迹来构建模型。然而，在Robocup域中情况并非如此。为了克服这一问题，本文提出了一个三阶段的方法来建模单个对手代理的低层行为。首先，我们构建了一个分类器来基于观察来标记对手的动作。其次，我们的代理观察对手，并使用前一个分类器标记其操作。根据这些观察结果，构建了一个模型来预测对手的行动。最后，代理使用该模型来预测对手的反应。在本文中，我们提出了一种方法的原理证明，称为Ombo(基于观察的对手建模)，以便前锋代理可以预测守门员。结果表明，使用获得的对手的动作模型，得分明显更高。
In competitive domains, the knowledge about the opponent can give players a clear advantage. This idea lead us in the past to propose an approach to acquire models of opponents, based only on the observation of their input-output behavior. If opponent outputs could be accessed directly, a model can be constructed by feeding a machine learning method with traces of the opponent. However, that is not the case in the Robocup domain. To overcome this problem, in this paper we present a three phases approach to model low-level behavior of individual opponent agents. First, we build a classifier to label opponent actions based on observation. Second, our agent observes an opponent and labels its actions using the previous classifier. From these observations, a model is constructed to predict the opponent actions. Finally, the agent uses the model to anticipate opponent reactions. In this paper, we have presented a proof-of-principle of our approach, termed OMBO (Opponent Modeling Based on Observation), so that a striker agent can anticipate a goalie. Results show that scores are significantly higher using the acquired opponent's model of actions.
通过随机梯度下降的阶次统计进行故障安全拓扑优化
DOI：
--
发表时间：
2020
期刊：
Proc. of the Asian Congress of Structural and Multidisciplinary Optimization 2020
影响因子：
--
作者：
Makoto Yamakawa;Makoto Ohsaki
通讯作者：
Makoto Ohsaki