Abstract Agent modeling has rightfully garnered much attention in the design and study of autonomous agents that interact with other agents. However, despite substantial progress to date, existing agent-modeling methods too often (a) have unrealistic computational requirements and data needs; (b) fail to properly generalize across environments, tasks, and associates; and (c) guide behavior toward inefficient (myopic) solutions. Can these challenges be overcome? Or are they just inherent to a very complex problem? In this reflection, I argue that some of these challenges may be reduced by, first, modeling alternative processes than what is often modeled by existing algorithms and, second, considering more deeply the role of non-binding communication signals. Additionally, I believe that progress in developing autonomous agents that effectively interact with other agents will be enhanced as we develop and utilize a more comprehensive set of measurement tools and benchmarks. I believe that further development of these areas is critical to creating autonomous agents that effectively model and interact with other agents.

中文翻译：

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当自主智能体模仿其他智能体时：呼吁改变判断力，加上嘴巴、耳朵和更多的胶带

摘要 代理建模在与其他代理交互的自主代理的设计和研究中受到了广泛关注。然而，尽管迄今为止取得了实质性进展，但现有的代理建模方法往往 (a) 具有不切实际的计算要求和数据需求；(b) 未能正确概括环境、任务和同事；(c) 引导行为走向低效（近视）解决方案。能否克服这些挑战？或者它们只是一个非常复杂的问题所固有的？在本次反思中，我认为可以通过以下方式减少其中一些挑战：首先，对替代过程进行建模，而不是通常由现有算法建模，其次，更深入地考虑非约束性通信信号的作用。此外，我相信，随着我们开发和利用一套更全面的测量工具和基准，开发与其他代理有效交互的自主代理的进展将会得到加强。我相信这些领域的进一步发展对于创建能够有效建模并与其他代理交互的自主代理至关重要。