Can LLM-Augmented autonomous agents cooperate?, An evaluation of their
  cooperative capabilities through Melting Pot
Manuel Mosquera Juan Sebastian Pinzon Manuel Rios Yesid Fonseca Luis Felipe Giraldo Nicanor Quijano Ruben Manrique
As the field of AI continues to evolve, a significant dimension of this
progression is the development of Large Language Models and their potential to
enhance multi-agent artificial intelligence systems. This paper explores the
cooperative capabilities of Large Language Model-augmented Autonomous Agents
(LAAs) using the well-known Meltin Pot environments along with reference models
such as GPT4 and GPT3.5. Preliminary results suggest that while these agents
demonstrate a propensity for cooperation, they still struggle with effective
collaboration in given environments, emphasizing the need for more robust
architectures. The study's contributions include an abstraction layer to adapt
Melting Pot game scenarios for LLMs, the implementation of a reusable
architecture for LLM-mediated agent development - which includes short and
long-term memories and different cognitive modules, and the evaluation of
cooperation capabilities using a set of metrics tied to the Melting Pot's
"Commons Harvest" game. The paper closes, by discussing the limitations of the
current architectural framework and the potential of a new set of modules that
fosters better cooperation among LAAs.