ArcheoAI

Advances in Machine Learning Applications in Prehistoric Archaeology. Classification and Spatial-Temporal Modeling of Archaeological and Archaeobotanical Data.

The project focuses on applying artificial intelligence techniques, machine learning, and computational simulation in archaeology. In addition to the development of new theories, techniques, and technologies for building archaeological classifications, chronological models, and the study of spatial-temporal dynamics, the project focuses on the study of the adoption of agriculture and animal husbandry in the Neolithic. Various computational simulations of different prehistoric scenarios will be developed with the aim of reconstructing the possible migratory mechanisms and/or cultural transmission involved in the transformation of hunter-gatherer societies and the implementation of new social and economic forms in prehistory.

The subproject aims to explore the most advanced theories, techniques, and technologies within the domain of Artificial Intelligence to develop new methods that may be useful in archaeological practice, with an emphasis on the construction of typologies, functional classification, reverse engineering, chronological modeling, and advanced spatial modeling and geostatistics. Different approaches to the computational simulation of historical mechanisms will be explored, in relation to understanding the transition from hunting-gathering to agriculture, and the study of the Neolithic.