Game-Theoretic Multi-Agent Collaboration for AI-Driven Scientific Discovery

Sandeep Ravindra Tengali

Keywords: Multi-Agent Systems, Game Theory, Scientific AI, Nash Equilibrium, Cooperative Bargaining, AI for Science, HPC Scheduling, AI Collaboration

TL;DR: A game-theoretic AI framework for multi-agent scientific collaboration, dynamically switching between Nash Equilibrium and Cooperative Bargaining to optimize HPC scheduling, hypothesis validation, and AI-driven research workflows.

Abstract:

This paper introduces a game-theoretic multi-agent AI framework where autonomous AI agents negotiate and refine hypotheses in either a cooperative or competitive scientific environment. By leveraging tools from Nash equilibrium analysis and cooperative game theory, agents can independently validate scientific hypotheses, manage shared computational resources, and optimize discovery pathways.

Experimental results in climate modeling, astrophysics, and biomedical research show that this agentic AI approach significantly accelerates scientific exploration while providing robust conflict resolution among heterogeneous domain tasks. Our findings highlight both the theoretical foundations of multi-agent negotiation for scientific hypothesis generation and the practical potential to transform decentralized scientific collaborations.

Submission Number: 17