In Pursuit of Unification of Conceptual Models: Sets as Machines
- URL: http://arxiv.org/abs/2306.13833v1
- Date: Sat, 24 Jun 2023 01:28:31 GMT
- Title: In Pursuit of Unification of Conceptual Models: Sets as Machines
- Authors: Sabah Al-Fedaghi
- Abstract summary: This manuscript is a sequel in a research venture that belongs to the second approach.
It uses a model called thinging machines founded on Stoic and Lupascian logic.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Conceptual models as representations of real-world systems are based on
diverse techniques in various disciplines but lack a framework that provides
multidisciplinary ontological understanding of real-world phenomena.
Concurrently, systems complexity has intensified, leading to a rise in
developing models using different formalisms and diverse representations even
within a single domain. Conceptual models have become larger; languages tend to
acquire more features, and it is not unusual to use different modeling
languages for different components. This diversity has caused problems with
consistency between models and incompatibly with designed systems. Two main
solutions have been adopted over the last few years: (1) A currently dominant
technology-based solution tries to harmonize or unify models, e.g., unifies EER
and UML. This solution would solidify modeling achievements, reaping benefits
from huge investments over the last thirty years. (2) A less prevalent solution
is to pursuit deeper roots that reveal unifying modeling principles and
apparatuses. An example of the second method is a category theory-based
approach that utilizes the strengths of the graph and set theory, along with
other topological tools. This manuscript is a sequel in a research venture that
belongs to the second approach and uses a model called thinging machines (TMs)
founded on Stoic ontology and Lupascian logic. TM modeling contests the thesis
that there is no universal approach that covers all aspects of an application,
and the paper demonstrates that pursuing such universality is anything but a
dead-end method. This paper continues in this direction, with emphasis on TM
foundation (e.g., existence and subsistence of things) and exemplifies this
pursuit by proposing an alternative representation of set theory.
Related papers
- Learnable & Interpretable Model Combination in Dynamic Systems Modeling [0.0]
We discuss which types of models are usually combined and propose a model interface that is capable of expressing a variety of mixed equation based models.
We propose a new wildcard topology, that is capable of describing the generic connection between two combined models in an easy to interpret fashion.
The contributions of this paper are highlighted at a proof of concept: Different connection topologies between two models are learned, interpreted and compared.
arXiv Detail & Related papers (2024-06-12T11:17:11Z) - Understanding the differences in Foundation Models: Attention, State Space Models, and Recurrent Neural Networks [50.29356570858905]
We introduce the Dynamical Systems Framework (DSF), which allows a principled investigation of all these architectures in a common representation.
We provide principled comparisons between softmax attention and other model classes, discussing the theoretical conditions under which softmax attention can be approximated.
This shows the DSF's potential to guide the systematic development of future more efficient and scalable foundation models.
arXiv Detail & Related papers (2024-05-24T17:19:57Z) - Fundamental Components of Deep Learning: A category-theoretic approach [0.0]
This thesis develops a novel mathematical foundation for deep learning based on the language of category theory.
We also systematise many existing approaches, placing many existing constructions and concepts under the same umbrella.
arXiv Detail & Related papers (2024-03-13T01:29:40Z) - Exploring Conceptual Modeling Metaphysics: Existence Containers, Leibniz's Monads and Avicenna's Essence [0.0]
Requirement specifications in software engineering involve developing a conceptual model of a target domain.
Much metaphysical work might best be understood as a model-building process.
The focus is on thimacs as a single category of TM modeling in the context of a two-phase world of staticity and dynamics.
arXiv Detail & Related papers (2024-02-20T22:25:20Z) - Learning Interpretable Concepts: Unifying Causal Representation Learning
and Foundation Models [51.43538150982291]
We study how to learn human-interpretable concepts from data.
Weaving together ideas from both fields, we show that concepts can be provably recovered from diverse data.
arXiv Detail & Related papers (2024-02-14T15:23:59Z) - Foundational Models Defining a New Era in Vision: A Survey and Outlook [151.49434496615427]
Vision systems to see and reason about the compositional nature of visual scenes are fundamental to understanding our world.
The models learned to bridge the gap between such modalities coupled with large-scale training data facilitate contextual reasoning, generalization, and prompt capabilities at test time.
The output of such models can be modified through human-provided prompts without retraining, e.g., segmenting a particular object by providing a bounding box, having interactive dialogues by asking questions about an image or video scene or manipulating the robot's behavior through language instructions.
arXiv Detail & Related papers (2023-07-25T17:59:18Z) - Foundation Models for Decision Making: Problems, Methods, and
Opportunities [124.79381732197649]
Foundation models pretrained on diverse data at scale have demonstrated extraordinary capabilities in a wide range of vision and language tasks.
New paradigms are emerging for training foundation models to interact with other agents and perform long-term reasoning.
Research at the intersection of foundation models and decision making holds tremendous promise for creating powerful new systems.
arXiv Detail & Related papers (2023-03-07T18:44:07Z) - Foundations and Recent Trends in Multimodal Machine Learning:
Principles, Challenges, and Open Questions [68.6358773622615]
This paper provides an overview of the computational and theoretical foundations of multimodal machine learning.
We propose a taxonomy of 6 core technical challenges: representation, alignment, reasoning, generation, transference, and quantification.
Recent technical achievements will be presented through the lens of this taxonomy, allowing researchers to understand the similarities and differences across new approaches.
arXiv Detail & Related papers (2022-09-07T19:21:19Z) - Language Models are General-Purpose Interfaces [109.45478241369655]
We propose to use language models as a general-purpose interface to various foundation models.
A collection of pretrained encoders perceive diverse modalities (such as vision, and language)
We propose a semi-causal language modeling objective to jointly pretrain the interface and the modular encoders.
arXiv Detail & Related papers (2022-06-13T17:34:22Z) - GENOME: A GENeric methodology for Ontological Modelling of Epics [0.0]
GENOME is the first dedicated methodology for iterative ontological modelling of epics.
It is grounded in transdisciplinary foundations of canonical norms for epics, knowledge modelling best practices, application satisfiability norms and cognitive generative questions.
It is also the first methodology to be flexible enough to integrate, in practice, the options of knowledge modelling via reuse or from scratch.
arXiv Detail & Related papers (2022-02-13T15:52:54Z)
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.