Dynamic Coalition Structure Detection in Natural Language-based Interactions

Andy Liu, Abhishek Ninad Kulkarni, Jean-Raphaël Gaglione, Daniel Fried, ufuk topcu

Abstract:

In strategic multi-agent sequential interactions, detecting dynamic
coalition structures is crucial for understanding how self-interested
agents coordinate to influence outcomes. However, natural-language-
based interactions introduce unique challenges to coalition detec-
tion due to ambiguity over intents and difficulty in modeling players’
subjective perspectives. We propose a new method that leverages
recent advancements in large language models and game theory to
predict dynamic multilateral coalition formation in Diplomacy, a
strategic multi-agent game where agents negotiate coalitions using
natural language. The method consists of two stages. The first stage
extracts the set of agreements discussed by two agents in their
private dialogue, by combining a parsing-based filtering function
with a fine-tuned language model trained to predict player intents.
In the second stage, we define a new metric using the concept of
subjective rationalizability from hypergame theory to evaluate the
expected value of an agreement for each player. We then compute
this metric for each agreement identified in the first stage by as-
sessing the strategic value of the agreement for both players and
taking into account the subjective belief of one player that the
second player would honor the agreement. We demonstrate that
our method effectively detects potential coalition structures in on-
line Diplomacy gameplay by assigning high values to agreements
likely to be honored and low values to those likely to be violated.
The proposed method provides foundational insights into coalition
formation in multi-agent environments with language-based nego-
tiation and offers key directions for future research on the analysis
of complex natural language-based interactions between agents.