Definition and properties to assess multi-agent environments as social intelligence tests

by   Javier Insa-Cabrera, et al.

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.


page 31

page 34

page 37

page 39


Balancing Rational and Other-Regarding Preferences in Cooperative-Competitive Environments

Recent reinforcement learning studies extensively explore the interplay ...

Feasibility Study: Moving Non-Homogeneous Teams in Congested Video Game Environments

Multi-agent path finding (MAPF) is a well-studied problem in artificial ...

Learning Reward Machines in Cooperative Multi-Agent Tasks

This paper presents a novel approach to Multi-Agent Reinforcement Learni...

On the influence of intelligence in (social) intelligence testing environments

This paper analyses the influence of including agents of different degre...

Analysis of Algorithms and Partial Algorithms

We present an alternative methodology for the analysis of algorithms, ba...

Melting Pot 2.0

Multi-agent artificial intelligence research promises a path to develop ...

Please sign up or login with your details

Forgot password? Click here to reset