Why It's Important: Among the archaeological data generated from excavations, building remains are of the largest scale and the most complicated to understand and reconstruct. They include fragments of foundations, walls, and architectural details such as columns and beams. The mission of archaeology is to document their state of preservation in drawings, text and numerical measurements, reconstruct their initial form, and interpret them to understand the history, the societal and cultural values, and their evolution over time. The challenge is that data is always partial and fragmentary to various degrees. Archaeologists must identify the known (invariable) and the missing (variable) parts of the building. To reconstruct a building, they must look at the whole archaeological record and identify other parallel examples based on geography, chronology, typology, or patronage from which they can infer the variable parts. This process is very laborious and never conclusive as more than one hypothesis is likely, and at the same time, must be transparent about the distinction between evidence and hypothesis. In theory, Generative AI can tremendously simplify this process as it can generate multiple structured depictions ±hypotheses ± to fill in the gaps based on the input.
Our Approach: The long-term goal of our proposed direction of study is to build computational AI tools that will aid archaeologists, historians, and other scholars in analyzing a plethora of architectural records at scale simultaneously by reasoning about structured hypothetical plans based on the free form partial descriptions from disparate sources. As a first take, for the 12-month period of the project, we propose to investigate systematically initial approaches for leveraging generative AI to generate consistent hypothetical structured plans for buildings given their partial unstructured archaeological description.
- Dataset creation: Data collection from literature review of thousands of Greek and Roman buildings of different typology, scale and from different periods and regions, which will become the input to our AI methods. This will be the first such dataset for computational analysis of ancient architecture.
- Symbolic representation of visual plans: Because these plans are structured compositions of small architectural components, we will build upon prior work on shape grammars [5, 1, 2, 3, 4] and develop prototypical grammars for the orders for different types of buildings. This is not only useful for interpretation and analysis, but also crucial for the development, and evaluation of the AI methods that will generate these symbolic plans.
- Evaluation of AI output: Develop protocols, annotation strategies, and evaluation methodology.
- Development of AI techniques: We will a) investigate in-context learning methods for converting
descriptions into symbolic representations of plans via LLMs, b) develop structured inference training techniques to produce consistent plans and reduce hallucinations from LLMs, and c) tackle long-form
archaeological descriptions and semi-structured tabular archaeological data using retrieval and context compression techniques. - Expert human interaction with AI output: Develop AI techniques that also produce attribution to data and justification in addition to plan hypotheses for expert verification. We also aim to develop protocols and methods to collect and use expert feedback on AI output to improve performance.