Session: DAC-03-01-Novel AI or ML Frameworks for Design or Systems Science

Paper Number: 89740

89740 - Self Learning Design Agent (SLDA): Enabling Deep Learning and Tree Search in Complex Action Spaces 

Building an AI agent that can design on its own has been a goal since the 1980s. Recently, deep learning has shown the ability to learn from large-scale data, enabling significant advances in data-driven design. However, learning over prior data limits us only to solve problems that have been solved before and biases us towards existing solutions. The ultimate goal for a design agent is the ability to learn generalizable design behavior in a problem space without having seen it before. We introduce a self-learning agent framework in this work that achieves this goal. This framework integrates a deep policy network with a novel tree search algorithm, where the tree search explores the problem space, and the deep policy network learns strategies to guide the search further. This framework first demonstrates an ability to discover high-performing generative strategies without any prior data, and second, it illustrates a zero-shot generalization of generative strategies across various unseen boundary conditions. This work evaluates the effectiveness and versatility of the framework by solving multiple versions of the truss design problem without retraining. Overall, this paper presents a methodology to self-learn high-performing and generalizable problem-solving behavior in an arbitrary problem space, circumventing the needs for expert data, existing solutions, and problem-specific learning.

Presenting Author: Ayush Raina Carnegie Mellon University

Presenting Author Biography: Ayush Raina graduated from Carnegie Mellon University in 2022 and is currently a Post-Doctoral Associate at Carnegie Mellon University. His work focuses on developing agents for human-machine collaboration in design using deep learning and sampling-based search. Prior to his Ph.D. he received his B.Tech. from Indian Institute of Technology Jodhpur in 2017.

Authors:

Ayush Raina Carnegie Mellon University
Jonathan Cagan Carnegie Mellon University
Christopher Mccomb Carnegie Mellon University