Artificial Intelligence for Predictive and Evidence Based Architecture Design
Authors: Mehul Bhatt, Jakob Suchan, Carl Schultz, Vasiliki Kondyli, Saurabh Goyal
AAAI 2016 | Conference PDF | Archive PDF | Plain Text | 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. |