Classification of artificial intelligence tooling in architecture. The information-algorithmic dimension (Collection of scientific works «Scientific Problems of Architecture and Urban Planning» | ODABA, 2026)
The article presents an information–algorithmic axis of classification forarchitectural AI tools and shows how AI-driven systems, platforms, and workflows can besystematically organised by two parameters: the information dimension (types and sources of dataused and produced) and the algorithmic dimension (families of AI/algorithmic methods). On theinformation side, the framework distinguishes geometric and spatial data, environmental andperformance data, programmatic and regulatory information, user- and sensor-derived streams, andhigher-level domain knowledge. On the algorithmic side, it spans rule-based and parametricapproaches, evolutionary optimisation, agent-based models, classical machine learning, deep neuralnetworks, generative models, reinforcement learning, and large foundation models.Using a matrix of I–A pairings, the article maps a set of contemporary tools and researchprototypes in architecture and urbanism, covering generative design platforms, performanceorientedsurrogates, code-compliance assistants, immersive evaluation workflows, structural designagents, and city-scale GeoAI systems. The analysis reveals dense clusters such as geometry-drivengenerative design and performance prediction, as well as under-explored combinations, includingcontext-aware reinforcement learning and programme-to-geometry generative pipelines. Thediscussion highlights typical trade-offs between data requirements, algorithmic complexity,interpretability, and the role of human oversight.The information–algorithmic axis complements functional and lifecycle classifications andprovides a practical lens for understanding data needs and computational behaviour of architecturalAI tools. It can inform the selection and chaining of tools in digital workflows, the design ofcurricula that prepare architects to work with data and algorithms, and the identification ofpromising gaps for future research and development.
Keywords: classification; artificial intelligence in architecture; information–algorithmic dimension; design; AI tooling