AI agents imitate engineers to construct effective new designs using visual cues like humans do
Trained AI agents can adopt human design strategies to solve problems, according to findings published in the ASME Journal of Mechanical Design.
Big design problems require creative and exploratory decision making, a skill in which humans excel. When engineers use artificial intelligence (AI), they have traditionally applied it to a problem within a defined set of rules rather than having it generally follow human strategies to create something new.
This novel research considers an AI framework that learns human design strategies through observation of human data to generate new designs without explicit goal information, bias, or guidance.
The study was co-authored by Jonathan Cagan, professor of mechanical engineering and interim dean of Carnegie Mellon University's College of Engineering, Ayush Raina, a Ph.D. candidate in mechanical engineering at Carnegie Mellon, and Chris McComb, an assistant professor of engineering design at the Pennsylvania State University.
"The AI is not just mimicking or regurgitating solutions that already exist," said Cagan. "It's learning how people solve a specific type of problem and creating new design solutions from scratch." How good can AI be? "The answer is quite good."
The study focuses on truss problems because they represent complex engineering design challenges. Commonly seen in bridges, a truss is an assembly of rods forming a complete structure. The AI agents were trained to observe the progression in design modification sequences that had been followed in creating a truss based on the same visual information that engineers use--pixels on a screen--but without further context.
When it was the agents' turn to design, they imagined design progressions that were similar to those used by humans and then generated design moves to realize them. The researchers emphasized visualization in the process because vision is an integral part of how humans perceive the world and go about solving problems.
The framework was made up of multiple deep neural networks which worked together in a prediction-based situation. Using a neural network, the AI looked through a set of five sequential images and predicted the next design using the information it gathered from these images.
"We were trying to have the agents create designs similar to how humans do it, imitating the process they use: how they look at the design, how they take the next action, and then create a new design, step by step," said Raina.
The researchers tested the AI agents on similar problems and found that on average, they performed better than humans. Yet, this success came without many of the advantages humans have available when they are solving problems.
Unlike humans, the agents were not working with a specific goal (like making something lightweight) and did not receive feedback on how well they were doing. Instead, they only used the vision-based human strategy techniques they had been trained to use.
"It's tempting to think that this AI will replace engineers, but that's simply not true," said McComb. "Instead, it can fundamentally change how engineers work. If we can offload boring, time-consuming tasks to an AI, like we did in the work, then we free engineers up to think big and solve problems creatively."
This paper is part of a larger research project sponsored by the Defense Advanced Research Projects Agency (DARPA) about the role of AI in human/computer hybrid teams, specifically how humans and AI can work together. With the results from this project, the researchers are considering how AI could be used as a partner or guide to improve human processes to achieve results that are better than humans or AI on their own.
For more information: Raina A, McComb C, Cagan J. Learning to design from humans: imitating human designers through deep learning. ASME. J. Mech. Des. 2019;():1-24. doi:10.1115/1.4044256.
About the College of Engineering at Carnegie Mellon University: The College of Engineering at Carnegie Mellon University is a top-ranked engineering college that is known for our intentional focus on cross-disciplinary collaboration in research. The College is well-known for working on problems of both scientific and practical importance. Our "maker" culture is ingrained in all that we do, leading to novel approaches and transformative results. Our acclaimed faculty have a focus on innovation management and engineering to yield transformative results that will drive the intellectual and economic vitality of our community, nation and world.
Lisa Kulick | EurekAlert!
Multifunctional e-glasses monitor health, protect eyes, control video game
28.05.2020 | American Chemical Society
Researchers incorporate computer vision and uncertainty into AI for robotic prosthetics
28.05.2020 | North Carolina State University
In living cells, enzymes drive biochemical metabolic processes enabling reactions to take place efficiently. It is this very ability which allows them to be used as catalysts in biotechnology, for example to create chemical products such as pharmaceutics. Researchers now identified an enzyme that, when illuminated with blue light, becomes catalytically active and initiates a reaction that was previously unknown in enzymatics. The study was published in "Nature Communications".
Enzymes: they are the central drivers for biochemical metabolic processes in every living cell, enabling reactions to take place efficiently. It is this very...
Early detection of tumors is extremely important in treating cancer. A new technique developed by researchers at the University of California, Davis offers a significant advance in using magnetic resonance imaging to pick out even very small tumors from normal tissue. The work is published May 25 in the journal Nature Nanotechnology.
researchers at the University of California, Davis offers a significant advance in using magnetic resonance imaging to pick out even very small tumors from...
Microelectronics as a key technology enables numerous innovations in the field of intelligent medical technology. The Fraunhofer Institute for Biomedical Engineering IBMT coordinates the BMBF cooperative project "I-call" realizing the first electronic system for ultrasound-based, safe and interference-resistant data transmission between implants in the human body.
When microelectronic systems are used for medical applications, they have to meet high requirements in terms of biocompatibility, reliability, energy...
Thomas Heine, Professor of Theoretical Chemistry at TU Dresden, together with his team, first predicted a topological 2D polymer in 2019. Only one year later, an international team led by Italian researchers was able to synthesize these materials and experimentally prove their topological properties. For the renowned journal Nature Materials, this was the occasion to invite Thomas Heine to a News and Views article, which was published this week. Under the title "Making 2D Topological Polymers a reality" Prof. Heine describes how his theory became a reality.
Ultrathin materials are extremely interesting as building blocks for next generation nano electronic devices, as it is much easier to make circuits and other...
Scientists took a leukocyte as the blueprint and developed a microrobot that has the size, shape and moving capabilities of a white blood cell. Simulating a blood vessel in a laboratory setting, they succeeded in magnetically navigating the ball-shaped microroller through this dynamic and dense environment. The drug-delivery vehicle withstood the simulated blood flow, pushing the developments in targeted drug delivery a step further: inside the body, there is no better access route to all tissues and organs than the circulatory system. A robot that could actually travel through this finely woven web would revolutionize the minimally-invasive treatment of illnesses.
A team of scientists from the Max Planck Institute for Intelligent Systems (MPI-IS) in Stuttgart invented a tiny microrobot that resembles a white blood cell...
19.05.2020 | Event News
07.04.2020 | Event News
06.04.2020 | Event News
28.05.2020 | Transportation and Logistics
28.05.2020 | Physics and Astronomy
28.05.2020 | Power and Electrical Engineering