Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Artificial Intelligence for Predictive and Evidence Based Architecture Design
Authors: Mehul Bhatt, Jakob Suchan, Carl Schultz, Vasiliki Kondyli, Saurabh Goyal
AAAI 2016 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our research has addressed the representation of space from a formal modelling and computational viewpoint, i.e., space, as it is interpreted within the disciplines of artificial intelligence and knowledge representation (KR) in general, and logic-based geometric and qualitative spatial representation and reasoning, applied ontology & formal semantics, and spatial computing for design in particular. Our key research methodology and deliverables have been along three dimensions (C1 C3):... The applied perspectives offered by our AI for design computing agenda have resulted in the declarative spatial reasoning paradigm within a KR context. Particularly, methods for commonsense spatial reasoning with constraint logic programming (Bhatt et al.(2011)Bhatt, Lee, and Schultz) and answer set programming (Walega et al.(2015)Walega, Bhatt, and Schultz) have been developed. Figure 1: An eye-tracking experiment involving a wayfinding task at the New Parkland Hospital in Dallas, Texas (USA)... We employ a range of sensors for measuring the embodied visuo-locomotive experience of building users: eye-tracking, egocentric gaze analysis (from video), external camera based visual analysis to interpret fine-grained behaviour (e.g., stopping, looking around, interacting with other people), and also manual observations made by human experimenters. |
| Researcher Affiliation | Academia | Mehul Bhatt, Jakob Suchan, Carl Schultz, Vasiliki Kondyli, Saurabh Goyal The Design Space Group., www.design-space.org/Next University of Bremen, Germany |
| Pseudocode | No | The paper does not contain any pseudocode or algorithm blocks. |
| Open Source Code | No | The paper describes developed methods and tools but does not provide any links to open-source code or explicit statements about code availability. |
| Open Datasets | Yes | Figure 1: An eye-tracking experiment involving a wayfinding task at the New Parkland Hospital in Dallas, Texas (USA) (Bhatt et al.(2014a)Bhatt, Schultz, Mc Gilberry, Agosta, and English). |
| Dataset Splits | No | The paper describes experimental approaches and data analysis but does not provide specific details on dataset splits (e.g., training, validation, test percentages or counts). |
| Hardware Specification | No | The paper does not specify any hardware details (e.g., CPU, GPU models, memory, or specific computing environments) used for running experiments or developing systems. |
| Software Dependencies | No | The paper mentions computational paradigms like 'constraint logic programming' and 'answer set programming' and cites related works, but does not list specific software names with version numbers. |
| Experiment Setup | No | The paper describes general research methods and observations but does not provide specific details about experimental setup, such as hyperparameters, training configurations, or model initialization. |