README.md

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<h1>Australian Research Council (ARC) Discovery Project DP220101925 — Deakin University</h1>

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<b>Summary:</b>

An intelligent machine modelling assistant for combinatorial optimisation. This project aims to discover key fundamental technologies for automating assistance to non-expert users in the formulation of mathematical models. Through automating the modelling of combinatorial optimization problems, this research will generate new knowledge to address the fundamental challenges of automatic mathematical modelling. This intelligent assistant will enable synthesis of new mathematical models through the utilisation of pioneering natural language processing components and novel custom-made machine-readable knowledge bases. The outcome of this research will broaden access to high-quality models by non-expert workforce and alleviate the shortage of expert mathematicians, bringing significant social and economic benefits.


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<b>National Interest Test Statement:</b>

Mathematical modelling has an important role in science, business, civic services, and government operations and is traditionally conducted by expert mathematicians. However, there is a shortage of trained expert mathematicians in Australia that has a direct impact on quality and timely mathematical modelling. Optimisation modelling is a prime example of mathematical modelling that has improved business processes by saving resources or increasing efficiency for optimal outcomes. This research will make it possible for non-mathematician users to develop models tailored to their requirements through interacting with a computer. Our prototype will assist non-experts in formulating optimisation models for a range of planning, scheduling, resource allocation, timetabling problems and will benefit businesses and not-for-profit organisations. In doing so, this project will utilise a natural language processing-based agent with knowledge bases and Artificial Intelligence solutions to deliver economic and societal benefits according to Australia’s Tech Future report 2018.

  
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![image/png](https://cdn-uploads.huggingface.co/production/uploads/65b046a825c7e48fd02fb839/OCiVGKwTQAfnS5p30kO1p.png)

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<b>Publications:</b>

<a href="https://arxiv.org/abs/2401.17461" target="_blank" rel="noopener noreferrer"> Yelaman Abdullin, Diego Molla-Aliod, Bahadorreza Ofoghi, John Yearwood, Qingyang Li: <b style="color:blue;">Synthetic Dialogue Dataset Generation using LLM Agents [2024]</b> </a>

<a href="https://www.sciencedirect.com/science/article/abs/pii/S095070512300730X" target="_blank" rel="noopener noreferrer"> Bahadorreza Ofoghi, John Yearwood: <b style="color:blue;">Knowledge representation of mathematical optimization problems and constructs for modeling [2023]</b> </a>

<a href="https://arxiv.org/abs/2311.15271" target="_blank" rel="noopener noreferrer"> Qingyang Li, Lele Zhang, Vicky Mak-Hau: <b style="color:blue;">Synthesizing mixed-integer linear programming models from natural language descriptions [2023]</b> </a>

<a href="https://arxiv.org/abs/2011.06300" target="_blank" rel="noopener noreferrer"> Bahadorreza Ofoghi, Vicky Mak-Hau, John Yearwood: <b style="color:blue;">A Knowledge Representation Approach to Automated Mathematical Modelling [2021]</b> </a>

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