摘要翻译：
本文分析了在多Agent系统中包含不同智能程度的Agent对系统性能的影响。目标是更好地理解我们如何开发能够评估社会智力的智力测验。我们分析了几种合作和竞争环境下的增强算法。我们的实验环境受到最近发展起来的达尔文-华莱士分布的启发。
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英文标题：
《On the influence of intelligence in (social) intelligence testing
  environments》
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作者：
Javier Insa-Cabrera, Jose-Luis Benacloch-Ayuso, Jose Hernandez-Orallo
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最新提交年份：
2012
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分类信息：

一级分类：Computer Science        计算机科学
二级分类：Artificial Intelligence        人工智能
分类描述：Covers all areas of AI except Vision, Robotics, Machine Learning, Multiagent Systems, and Computation and Language (Natural Language Processing), which have separate subject areas. In particular, includes Expert Systems, Theorem Proving (although this may overlap with Logic in Computer Science), Knowledge Representation, Planning, and Uncertainty in AI. Roughly includes material in ACM Subject Classes I.2.0, I.2.1, I.2.3, I.2.4, I.2.8, and I.2.11.
涵盖了人工智能的所有领域，除了视觉、机器人、机器学习、多智能体系统以及计算和语言（自然语言处理），这些领域有独立的学科领域。特别地，包括专家系统，定理证明（尽管这可能与计算机科学中的逻辑重叠），知识表示，规划，和人工智能中的不确定性。大致包括ACM学科类I.2.0、I.2.1、I.2.3、I.2.4、I.2.8和I.2.11中的材料。
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英文摘要：
  This paper analyses the influence of including agents of different degrees of intelligence in a multiagent system. The goal is to better understand how we can develop intelligence tests that can evaluate social intelligence. We analyse several reinforcement algorithms in several contexts of cooperation and competition. Our experimental setting is inspired by the recently developed Darwin-Wallace distribution.
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PDF链接：
https://arxiv.org/pdf/1202.0837