This $213,247 Project Grant from the National Science Foundation's Division of Behavioral and Cognitive Sciences under the Social, Behavioral, and Economic Sciences program (CFDA 47.075) will fund research to advance understanding of how wear patterns form on stone tools and facilitate identification of worked materials. An interdisciplinary team of archaeologists and engineers will conduct controlled tribological experiments rubbing surrogate and organic materials against stone bits to generate a large database of wear patterns. They will also use a force-controlled robot and natural materials to produce more realistic wear patterns. All data will be used to train computers to classify archaeological wear traces by the worked materials. The researchers, including New York University, will compare human expert and AI classification algorithms and engage high school students in lab experiments to address diversity issues. Results aim to solve a long-standing problem in identifying worked materials from stone tool wear traces and enable large-scale archaeological studies.