作为社交智力测试评估多智能体环境的定义和属性
Definition and properties to assess multi-agent environments as social intelligence tests
摘要：
自然和人工系统中的社会智能通常通过评估被认为代表社会行为某些方面的相关特征或任务来衡量。这些特征的融合，然后被用来配置社会智力的直观概念。相反，在本文中，我们从社交智能的参数化定义开始，将其定义为在一组具有多个代理的环境中的预期表现，并从中评估和导出测试。这个定义明确了几个依赖关系：（1）定义取决于选择（和重量）的环境和代理，（2）该定义可以包括竞争行为和合作行为，这取决于代理和奖励如何被安排到团队中，（3）定义主要取决于其他主体的能力，（4）社会智力和一般智力（或其他能力）之间的实际差异取决于这些选择。因此，我们解决这个定义转换成一个更精确的一些基本属性，确保社会行为（如行动和奖励的依赖性和竞争/合作行为的预期）满足以及其他一些更工具性的属性（如secernment，有界性，对称性，有效性，可靠性，效率），这是方便的定义转换成一个实际的测试。从定义和形式化属性出发，我们来看看几个有代表性的多智能体环境、测试和游戏，看看它们是否满足这些属性。
英文摘要：
Social intelligence in natural and artificial systems is usually measured by the evaluation of associated traits or tasks that are deemed to represent some facets of social behaviour. The amalgamation of these traits is then used to configure the intuitive notion of social intelligence. Instead, in this paper we start from a parametrised definition of social intelligence as the expected performance in a set of environments with several agents, and we assess and derive tests from it. This definition makes several dependencies explicit: (1) the definition depends on the choice (and weight) of environments and agents, (2) the definition may include both competitive and cooperative behaviours depending on how agents and rewards are arranged into teams, (3) the definition mostly depends on the abilities of other agents, and (4) the actual difference between social intelligence and general intelligence (or other abilities) depends on these choices. As a result, we address the problem of converting this definition into a more precise one where some fundamental properties ensuring social behaviour (such as action and reward dependency and anticipation on competitive/cooperative behaviours) are met as well as some other more instrumental properties (such as secernment, boundedness, symmetry, validity, reliability, efficiency), which are convenient to convert the definition into a practical test. From the definition and the formalised properties, we take a look at several representative multi-agent environments, tests and games to see whether they meet these properties.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
