Texas A&M University researchers have been awarded a National Science Foundation (NSF) grant to research data mining to optimize decision making in the software brain.
An interdisciplinary team of Texas A&M University researchers have been awarded a $1 million National Science Foundation (NSF) grant to research data mining to optimize decision making in the software brain.
The project introduces artificial intelligence and autonomy modules into an autonomous experimentation platform to mimic a human scientist’s ability to handle surprising observations, synthesize diverse bodies of knowledge and explore a large, complex design space. The key research components in this platform are organized around three capability themes: exploitation to efficiently determine the most promising regions of a design space, exploration to recognize and reason about surprises arising from unusual designs and the expansion of newly discovered design spaces based on mining new knowledge from literature and databases, while preferentially gaining knowledge in regions likely to contain superior material design solutions.
The proposed system is cognizant, adaptive, and able to interact with human scientists by way of simple commands and executing an autonomous discovery process with a minimal and appropriate degree of human intervention. The autonomous experimental testbed may have major impacts on engineering practice and revolutionize the material discovery and advanced manufacturing landscape.
“Once the machine is able to data mine past literature of the design space, its knowledge base will surpass that of a group of industry experts,” said Xia (Ben) Hu, assistant professor at the Texas A&M computer science and engineering department in a press release. “This will enhance the artificial intelligence decision making function of the software brain.”
– Edited by Chris Vavra, production editor, Control Engineering, CFE Media, [email protected].
