Machine Recognition of Plan Typologies： Shotgun and Foursquare

The evolution of expert and knowledge-based systems in architecture requires the gradual population of building specific databases. Often these databases are slow to evolve due to the time consuming nature of effectively categorizing building features in a meaningful way that allows for retrieval and reuse. New advances in artificial intelligence such as Hierarchical Temporal Memory （HTM） have the potential to make the construction of these databases more realistic in the near future. Based on an emerging theory of human neurological function, HTMs excel at ambiguous pattern recognition. This paper includes a first experiment using HTMs for learning and recognizing patterns in the form of two distinct American house plan typologies, and further tests the relationship of HTM＇s recognition tendencies in alternate house plan types. Results from the experiment indicate that HTMs develop a similar storage of quality to humans and are therefore a promising option for capturing multi-modal information in future design automation efforts.