# A cooperative game for automated learning of elasto-plasticity knowledge graphs and models with AI-guided experimentation

## TL;DR

This paper presents a multi-agent AI framework that automates the discovery and optimization of elasto-plastic material models through graph-based modeling, reinforcement learning, and AI-guided experimentation, emulating scientific collaboration.

## Contribution

It introduces a novel graph-theoretic approach combined with reinforcement learning for automated model selection and experiment design in material science.

## Key findings

- Successful automatic generation of constitutive models
- Effective optimization of experimental design
- Demonstration of AI-guided collaboration in modeling

## Abstract

We introduce a multi-agent meta-modeling game to generate data, knowledge, and models that make predictions on constitutive responses of elasto-plastic materials. We introduce a new concept from graph theory where a modeler agent is tasked with evaluating all the modeling options recast as a directed multigraph and find the optimal path that links the source of the directed graph (e.g. strain history) to the target (e.g. stress) measured by an objective function. Meanwhile, the data agent, which is tasked with generating data from real or virtual experiments (e.g. molecular dynamics, discrete element simulations), interacts with the modeling agent sequentially and uses reinforcement learning to design new experiments to optimize the prediction capacity. Consequently, this treatment enables us to emulate an idealized scientific collaboration as selections of the optimal choices in a decision tree search done automatically via deep reinforcement learning.