Summary of Towards Next-generation Urban Decision Support Systems Through Ai-powered Construction Of Scientific Ontology Using Large Language Models — a Case in Optimizing Intermodal Freight Transportation, by Jose Tupayachi et al.
Towards Next-Generation Urban Decision Support Systems through AI-Powered Construction of Scientific Ontology using Large Language Models – A Case in Optimizing Intermodal Freight Transportation
by Jose Tupayachi, Haowen Xu, Olufemi A. Omitaomu, Mustafa Can Camur, Aliza Sharmin, Xueping Li
First submitted to arxiv on: 29 May 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Medium Difficulty summary: This paper investigates the potential of using Large Language Models (LLMs) like ChatGPT API as reasoning cores in optimization systems. The authors propose an integrated workflow that combines natural language processing, methontology-based prompt tuning, and transformers to automate the creation of scenario-based ontology for urban datasets and simulations. By leveraging pre-trained LLMs, the paper aims to enhance decision support systems by facilitating data and metadata modeling, integrating complex datasets, coupling multi-domain simulation models, and formulating decision-making metrics and workflow. The authors evaluate their methodology through a comparative analysis with the well-known Pizza Ontology and demonstrate its feasibility in a real-world case study of optimizing multi-modal freight transportation. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Low Difficulty summary: This paper explores how artificial intelligence (AI) can help solve complex problems related to urban planning and environmental management. The researchers use pre-trained AI models like ChatGPT API to create a new way of organizing data and simulations about cities. This helps experts make better decisions by providing more information and connections between different datasets and simulation models. The paper compares this new approach with an existing method called the Pizza Ontology, which is used in tutorials for popular software tools. Finally, the authors show how their approach can be applied to a real-world problem of optimizing transportation systems in cities. |
Keywords
» Artificial intelligence » Multi modal » Natural language processing » Optimization » Prompt
