Dynamic Coalition Structure Detection in Natural Language-based Interactions

TL;DR

This paper introduces a novel two-stage method combining large language models and game theory to detect dynamic coalition structures in natural language negotiations within multi-agent strategic interactions, exemplified by the game Diplomacy.

Contribution

It presents a new approach that integrates language understanding and subjective rationalizability to identify likely coalitions from natural language dialogues, advancing coalition detection in complex multi-agent environments.

Findings

Effective detection of potential coalitions in Diplomacy gameplay

High accuracy in predicting agreements likely to be honored

Provides a foundation for analyzing natural language negotiations in multi-agent systems

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 detection 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 assessing 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 online 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 negotiation and offers key directions for future research on the analysis of complex natural language-based interactions between agents.