Bridging MDE and AI: A Systematic Review of Domain-Specific Languages and Model-Driven Practices in AI Software Systems Engineering
- URL: http://arxiv.org/abs/2307.04599v2
- Date: Mon, 6 May 2024 09:19:07 GMT
- Title: Bridging MDE and AI: A Systematic Review of Domain-Specific Languages and Model-Driven Practices in AI Software Systems Engineering
- Authors: Simon Raedler, Luca Berardinelli, Karolin Winter, Abbas Rahimi, Stefanie Rinderle-Ma,
- Abstract summary: This study aims to investigate the existing model-driven approaches relying on DSL in support of the engineering of AI software systems.
The use of MDE for AI is still in its early stages, and there is no single tool or method that is widely used.
- Score: 1.4853133497896698
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Background:Technical systems are growing in complexity with more components and functions across various disciplines. Model-Driven Engineering (MDE) helps manage this complexity by using models as key artifacts. Domain-Specific Languages (DSL) supported by MDE facilitate modeling. As data generation in product development increases, there's a growing demand for AI algorithms, which can be challenging to implement. Integrating AI algorithms with DSL and MDE can streamline this process. Objective:This study aims to investigate the existing model-driven approaches relying on DSL in support of the engineering of AI software systems to sharpen future research further and define the current state of the art. Method:We conducted a Systemic Literature Review (SLR), collecting papers from five major databases resulting in 1335 candidate studies, eventually retaining 18 primary studies. Each primary study will be evaluated and discussed with respect to the adoption of MDE principles and practices and the phases of AI development support aligned with the stages of the CRISP-DM methodology. Results:The study's findings show that language workbenches are of paramount importance in dealing with all aspects of modeling language development and are leveraged to define DSL explicitly addressing AI concerns. The most prominent AI-related concerns are training and modeling of the AI algorithm, while minor emphasis is given to the time-consuming preparation of the data. Early project phases that support interdisciplinary communication of requirements, e.g., CRISP-DM Business Understanding phase, are rarely reflected. Conclusion:The study found that the use of MDE for AI is still in its early stages, and there is no single tool or method that is widely used. Additionally, current approaches tend to focus on specific stages of development rather than providing support for the entire development process.
Related papers
- Retrieval-Enhanced Machine Learning: Synthesis and Opportunities [60.34182805429511]
Retrieval-enhancement can be extended to a broader spectrum of machine learning (ML)
This work introduces a formal framework of this paradigm, Retrieval-Enhanced Machine Learning (REML), by synthesizing the literature in various domains in ML with consistent notations which is missing from the current literature.
The goal of this work is to equip researchers across various disciplines with a comprehensive, formally structured framework of retrieval-enhanced models, thereby fostering interdisciplinary future research.
arXiv Detail & Related papers (2024-07-17T20:01:21Z) - Towards Next-Generation Urban Decision Support Systems through AI-Powered Generation of Scientific Ontology using Large Language Models -- A Case in Optimizing Intermodal Freight Transportation [1.6230958216521798]
This study investigates the potential of leveraging the pre-trained Large Language Models (LLMs)
By adopting ChatGPT API as the reasoning core, we outline an integrated workflow that encompasses natural language processing, methontology-based prompt tuning, and transformers.
The outcomes of our methodology are knowledge graphs in widely adopted ontology languages (e.g., OWL, RDF, SPARQL)
arXiv Detail & Related papers (2024-05-29T16:40:31Z) - Generative Artificial Intelligence: A Systematic Review and Applications [7.729155237285151]
This paper documents the systematic review and analysis of recent advancements and techniques in Generative AI.
The major impact that generative AI has made to date, has been in language generation with the development of large language models.
The paper ends with a discussion of Responsible AI principles, and the necessary ethical considerations for the sustainability and growth of these generative models.
arXiv Detail & Related papers (2024-05-17T18:03:59Z) - Generative AI Agent for Next-Generation MIMO Design: Fundamentals, Challenges, and Vision [76.4345564864002]
Next-generation multiple input multiple output (MIMO) is expected to be intelligent and scalable.
We propose the concept of the generative AI agent, which is capable of generating tailored and specialized contents.
We present two compelling case studies that demonstrate the effectiveness of leveraging the generative AI agent for performance analysis.
arXiv Detail & Related papers (2024-04-13T02:39:36Z) - A Data-to-Product Multimodal Conceptual Framework to Achieve Automated Software Evolution for Context-rich Intelligent Applications [0.0]
This study proposes a conceptual framework for achieving automated software evolution.
A Selective Sequential Scope Model (3S) model is developed based on the conceptual framework.
Although the study is about intelligent applications, the framework and analysis methods may be adapted for other types of software as AI brings more intelligence into their life cycles.
arXiv Detail & Related papers (2024-04-07T06:05:25Z) - An Interactive Agent Foundation Model [49.77861810045509]
We propose an Interactive Agent Foundation Model that uses a novel multi-task agent training paradigm for training AI agents.
Our training paradigm unifies diverse pre-training strategies, including visual masked auto-encoders, language modeling, and next-action prediction.
We demonstrate the performance of our framework across three separate domains -- Robotics, Gaming AI, and Healthcare.
arXiv Detail & Related papers (2024-02-08T18:58:02Z) - LAMM: Language-Assisted Multi-Modal Instruction-Tuning Dataset,
Framework, and Benchmark [81.42376626294812]
We present Language-Assisted Multi-Modal instruction tuning dataset, framework, and benchmark.
Our aim is to establish LAMM as a growing ecosystem for training and evaluating MLLMs.
We present a comprehensive dataset and benchmark, which cover a wide range of vision tasks for 2D and 3D vision.
arXiv Detail & Related papers (2023-06-11T14:01:17Z) - Multimodal Explainable Artificial Intelligence: A Comprehensive Review of Methodological Advances and Future Research Directions [2.35574869517894]
This study focuses on analyzing the recent advances in the area of Multimodal XAI (MXAI)
MXAI comprises methods that involve multiple modalities in the primary prediction and explanation tasks.
arXiv Detail & Related papers (2023-06-09T07:51:50Z) - DIME: Fine-grained Interpretations of Multimodal Models via Disentangled
Local Explanations [119.1953397679783]
We focus on advancing the state-of-the-art in interpreting multimodal models.
Our proposed approach, DIME, enables accurate and fine-grained analysis of multimodal models.
arXiv Detail & Related papers (2022-03-03T20:52:47Z) - Quantitatively Assessing the Benefits of Model-driven Development in
Agent-based Modeling and Simulation [80.49040344355431]
This paper compares the use of MDD and ABMS platforms in terms of effort and developer mistakes.
The obtained results show that MDD4ABMS requires less effort to develop simulations with similar (sometimes better) design quality than NetLogo.
arXiv Detail & Related papers (2020-06-15T23:29:04Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.