Tool interoperability for model-based systems engineering
- URL: http://arxiv.org/abs/2302.03503v2
- Date: Fri, 22 Sep 2023 13:34:00 GMT
- Title: Tool interoperability for model-based systems engineering
- Authors: Sander Thuijsman, G\"okhan Kahraman, Alireza Mohamadkhani, Ferry
Timmers, Loek Cleophas, Marc Geilen, Jan Friso Groote, Michel Reniers, Ramon
Schiffelers, Jeroen Voeten
- Abstract summary: We discuss several tools, each state-of-the-art in its own discipline, offering functionality such as specification, synthesis, and verification.
We present Analytics as a Service, built on the Arrowhead framework, to connect these tools and make them interoperable.
- Score: 0.7182467727359453
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Supervisory control design of cyber-physical systems has many challenges.
Model-based systems engineering can address these, with solutions originating
from various disciplines. We discuss several tools, each state-of-the-art in
its own discipline, offering functionality such as specification, synthesis,
and verification. Integrating such mono-disciplinary tools in a
multi-disciplinary workflow is a major challenge. We present Analytics as a
Service, built on the Arrowhead framework, to connect these tools and make them
interoperable. A seamless integration of the tools has been established through
a service-oriented architecture: The engineer can easily access the
functionality of the tools from a single interface, as translation steps
between equivalent models for the respective tools are automated.
Related papers
- Asynchronous Tool Usage for Real-Time Agents [61.3041983544042]
We introduce asynchronous AI agents capable of parallel processing and real-time tool-use.
Our key contribution is an event-driven finite-state machine architecture for agent execution and prompting.
This work presents both a conceptual framework and practical tools for creating AI agents capable of fluid, multitasking interactions.
arXiv Detail & Related papers (2024-10-28T23:57:19Z) - Towards Completeness-Oriented Tool Retrieval for Large Language Models [60.733557487886635]
Real-world systems often incorporate a wide array of tools, making it impractical to input all tools into Large Language Models.
Existing tool retrieval methods primarily focus on semantic matching between user queries and tool descriptions.
We propose a novel modelagnostic COllaborative Learning-based Tool Retrieval approach, COLT, which captures not only the semantic similarities between user queries and tool descriptions but also takes into account the collaborative information of tools.
arXiv Detail & Related papers (2024-05-25T06:41:23Z) - 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) - An Architecture for Software Engineering Gamification [44.17758641654784]
Gamification has been applied in software engineering to improve quality and results by increasing people's motivation and engagement.
Most existing gamified tools are custom developments or prototypes.
We propose a software architecture that allows us to transform the work environment of a software organization into an integrated gamified environment.
arXiv Detail & Related papers (2024-01-31T23:23:52Z) - Learning Reusable Manipulation Strategies [86.07442931141634]
Humans demonstrate an impressive ability to acquire and generalize manipulation "tricks"
We present a framework that enables machines to acquire such manipulation skills through a single demonstration and self-play.
These learned mechanisms and samplers can be seamlessly integrated into standard task and motion planners.
arXiv Detail & Related papers (2023-11-06T17:35:42Z) - ControlLLM: Augment Language Models with Tools by Searching on Graphs [97.62758830255002]
We present ControlLLM, a novel framework that enables large language models (LLMs) to utilize multi-modal tools for solving real-world tasks.
Our framework comprises three key components: (1) a textittask decomposer that breaks down a complex task into clear subtasks with well-defined inputs and outputs; (2) a textitThoughts-on-Graph (ToG) paradigm that searches the optimal solution path on a pre-built tool graph; and (3) an textitexecution engine with a rich toolbox that interprets the solution path and runs the
arXiv Detail & Related papers (2023-10-26T21:57:21Z) - Learning Generalizable Tool-use Skills through Trajectory Generation [13.879860388944214]
We train a single model on four different deformable object manipulation tasks.
The model generalizes to various novel tools, significantly outperforming baselines.
We further test our trained policy in the real world with unseen tools, where it achieves the performance comparable to human.
arXiv Detail & Related papers (2023-09-29T21:32:42Z) - Tool Learning with Foundation Models [158.8640687353623]
With the advent of foundation models, AI systems have the potential to be equally adept in tool use as humans.
Despite its immense potential, there is still a lack of a comprehensive understanding of key challenges, opportunities, and future endeavors in this field.
arXiv Detail & Related papers (2023-04-17T15:16:10Z) - Driving Digital Engineering Integration and Interoperability Through
Semantic Integration of Models with Ontologies [0.0]
This paper introduces the Digital Engineering Framework for Integration and.
DEFII, for incorporating SWT into engineering design and analysis tasks.
The framework includes three notional interfaces for interacting with ontology-aligned data.
Use of the framework results in a tool-agnostic authoritative source of truth spanning the entire project, system, or mission.
arXiv Detail & Related papers (2022-06-08T14:58:09Z) - Tools and Practices for Responsible AI Engineering [0.5249805590164901]
We present two new software libraries that address critical needs for responsible AI engineering.
hydra-zen dramatically simplifies the process of making complex AI applications, and their behaviors reproducible.
The rAI-toolbox is designed to enable methods for evaluating and enhancing the robustness of AI-models.
arXiv Detail & Related papers (2022-01-14T19:47:46Z) - Workflow Automation for Cyber Physical System Development Processes [1.6735240552964108]
Development of Cyber Physical Systems (CPSs) requires close interaction between developers with expertise in many domains.
We introduce a workflow modeling language for the automation of complex CPS development processes.
We implement a platform for execution of these models in the Assurance-based Learning-enabled CPS Toolchain.
arXiv Detail & Related papers (2020-04-12T17:32:05Z)
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.