Modeling other agents' behaviors plays an important role in decision models for interactions among multiple agents. To optimize its own decisions, a subject agent needs to model what other agents act simultaneously in an uncertain environment. However, modeling insufficiency occurs when the agents are competitive and the subject agent cannot get full knowledge about other agents. Even when the agents are collaborative, they may not share their true behaviors due to their privacy concerns. Most of the recent research still assumes that the agents have common knowledge about their environments and a subject agent has the true behavior of other agents in its mind. Consequently, the resulting techniques are not applicable in many practical problem domains. In this article, we investigate into diversifying behaviors of other agents in the subject agent's decision model before their interactions. The challenges lie in generating and measuring new behaviors of other agents. Starting with prior knowledge about other agents' behaviors, we use a linear reduction technique to extract representative behavioral features from the known behaviors. We subsequently generate their new behaviors by expanding the features and propose two diversity measurements to select top-$K$ behaviors. We demonstrate the performance of the new techniques in two well-studied problem domains. The top-$K$ behavior selection embarks the study of unknown behaviors in multiagent decision making and inspires investigation of diversifying agents' behaviors in competitive agent interactions. This study will contribute to intelligent systems dealing with unknown unknowns in an open artificial intelligence world.

中文翻译：

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在交互式决策模型中使代理的行为多样化

对其他代理的行为建模在多个代理之间交互的决策模型中起着重要作用。为了优化自己的决策，主体代理需要对其他代理在不确定环境中同时采取的行动进行建模。然而，当代理具有竞争性并且主题代理无法充分了解其他代理时，就会出现建模不足。即使代理是协作的，由于他们的隐私问题，他们也可能不会分享他们的真实行为。大多数最近的研究仍然假设代理人对他们的环境有共同的了解，并且主体代理人在它的脑海中有其他代理人的真实行为。因此，由此产生的技术不适用于许多实际问题领域。在本文中，我们在主体代理的决策模型中调查其他代理在交互之前的多样化行为。挑战在于生成和测量其他代理的新行为。从关于其他主体行为的先验知识开始，我们使用线性缩减技术从已知行为中提取具有代表性的行为特征。我们随后通过扩展特征来生成他们的新行为，并提出两种多样性测量来选择 top-$钾$行为。我们在两个经过充分研究的问题领域中展示了新技术的性能。顶端-$钾$行为选择着手研究多主体决策中的未知行为，并激发了对主体在竞争主体交互中的行为多样化的调查。这项研究将有助于智能系统在开放的人工智能世界中处理未知的未知数。