Multi-agent artificial intelligence research promises a path to develop
intelligent technologies that are more human-like and more human-compatible
than those produced by "solipsistic" approaches, which do not consider
interactions between agents. Melting Pot is a research tool developed to
facilitate work on multi-agent artificial intelligence, and provides an
evaluation protocol that measures generalization to novel social partners in a
set of canonical test scenarios. Each scenario pairs a physical environment (a
"substrate") with a reference set of co-players (a "background population"), to
create a social situation with substantial interdependence between the
individuals involved. For instance, some scenarios were inspired by
institutional-economics-based accounts of natural resource management and
public-good-provision dilemmas. Others were inspired by considerations from
evolutionary biology, game theory, and artificial life. Melting Pot aims to
cover a maximally diverse set of interdependencies and incentives. It includes
the commonly-studied extreme cases of perfectly-competitive (zero-sum)
motivations and perfectly-cooperative (shared-reward) motivations, but does not
stop with them. As in real-life, a clear majority of scenarios in Melting Pot
have mixed incentives. They are neither purely competitive nor purely
cooperative and thus demand successful agents be able to navigate the resulting
ambiguity. Here we describe Melting Pot 2.0, which revises and expands on
Melting Pot. We also introduce support for scenarios with asymmetric roles, and
explain how to integrate them into the evaluation protocol. This report also
contains: (1) details of all substrates and scenarios; (2) a complete
description of all baseline algorithms and results. Our intention is for it to
serve as a reference for researchers using Melting Pot 2.0.

Melting Pot 2.0 是一种用于研究多智能体人工智能的工具，它提供了一种评估协议，用于测量对小组社交环境的泛化能力，并支持具有不对称角色的情景。