A process algebraic framework for multi-agent dynamic epistemic systems
- URL: http://arxiv.org/abs/2407.17537v1
- Date: Wed, 24 Jul 2024 08:35:50 GMT
- Title: A process algebraic framework for multi-agent dynamic epistemic systems
- Authors: Alessandro Aldini,
- Abstract summary: We propose a unifying framework for modeling and analyzing multi-agent, knowledge-based, dynamic systems.
On the modeling side, we propose a process algebraic, agent-oriented specification language that makes such a framework easy to use for practical purposes.
- Score: 55.2480439325792
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: This paper combines the classical model of labeled transition systems with the epistemic model for reasoning about knowledge. The result is a unifying framework for modeling and analyzing multi-agent, knowledge-based, dynamic systems. On the modeling side, we propose a process algebraic, agent-oriented specification language that makes such a framework easy to use for practical purposes. On the verification side, we define a modal logic encompassing temporal and epistemic operators.
Related papers
- No Equations Needed: Learning System Dynamics Without Relying on Closed-Form ODEs [56.78271181959529]
This paper proposes a conceptual shift to modeling low-dimensional dynamical systems by departing from the traditional two-step modeling process.
Instead of first discovering a closed-form equation and then analyzing it, our approach, direct semantic modeling, predicts the semantic representation of the dynamical system.
Our approach not only simplifies the modeling pipeline but also enhances the transparency and flexibility of the resulting models.
arXiv Detail & Related papers (2025-01-30T18:36:48Z) - On the Reasoning Capacity of AI Models and How to Quantify It [0.0]
Large Language Models (LLMs) have intensified the debate surrounding the fundamental nature of their reasoning capabilities.
While achieving high performance on benchmarks such as GPQA and MMLU, these models exhibit limitations in more complex reasoning tasks.
We propose a novel phenomenological approach that goes beyond traditional accuracy metrics to probe the underlying mechanisms of model behavior.
arXiv Detail & Related papers (2025-01-23T16:58:18Z) - Process Modeling With Large Language Models [42.0652924091318]
This paper explores the integration of Large Language Models (LLMs) into process modeling.
We propose a framework that leverages LLMs for the automated generation and iterative refinement of process models.
Preliminary results demonstrate the framework's ability to streamline process modeling tasks.
arXiv Detail & Related papers (2024-03-12T11:27:47Z) - OSM: Leveraging Model Checking for Observing Dynamic 1 behaviors in
Aspect-Oriented Applications [0.0]
observe-based statistical model-checking (OSM) framework devised to craft executable formal models directly from foundational system code.
This ensures the unyielding performance of electronic health record systems amidst shifting preconditions.
arXiv Detail & Related papers (2024-03-03T00:03:34Z) - A Transition System Abstraction Framework for Neural Network Dynamical
System Models [2.414910571475855]
This paper proposes a transition system abstraction framework for neural network dynamical system models.
The framework is able to abstract a data-driven neural network model into a transition system, making the neural network model interpretable.
arXiv Detail & Related papers (2024-02-18T23:49:18Z) - Benchmarking formalisms for dynamic structure system Modeling and Simulation [3.2268447897914943]
We identify criteria for a smooth flow from model creation to its simulation for dynamic structure systems.
We benchmark the existing modeling formalisms focusing more on DEVS extensions and use the results to identify approaches gaps and discuss them.
arXiv Detail & Related papers (2024-01-25T09:13:40Z) - Discrete, compositional, and symbolic representations through attractor dynamics [51.20712945239422]
We introduce a novel neural systems model that integrates attractor dynamics with symbolic representations to model cognitive processes akin to the probabilistic language of thought (PLoT)
Our model segments the continuous representational space into discrete basins, with attractor states corresponding to symbolic sequences, that reflect the semanticity and compositionality characteristic of symbolic systems through unsupervised learning, rather than relying on pre-defined primitives.
This approach establishes a unified framework that integrates both symbolic and sub-symbolic processing through neural dynamics, a neuroplausible substrate with proven expressivity in AI, offering a more comprehensive model that mirrors the complex duality of cognitive operations
arXiv Detail & Related papers (2023-10-03T05:40:56Z) - Constructing Neural Network-Based Models for Simulating Dynamical
Systems [59.0861954179401]
Data-driven modeling is an alternative paradigm that seeks to learn an approximation of the dynamics of a system using observations of the true system.
This paper provides a survey of the different ways to construct models of dynamical systems using neural networks.
In addition to the basic overview, we review the related literature and outline the most significant challenges from numerical simulations that this modeling paradigm must overcome.
arXiv Detail & Related papers (2021-11-02T10:51:42Z) - Towards a Predictive Processing Implementation of the Common Model of
Cognition [79.63867412771461]
We describe an implementation of the common model of cognition grounded in neural generative coding and holographic associative memory.
The proposed system creates the groundwork for developing agents that learn continually from diverse tasks as well as model human performance at larger scales.
arXiv Detail & Related papers (2021-05-15T22:55:23Z) - S2RMs: Spatially Structured Recurrent Modules [105.0377129434636]
We take a step towards exploiting dynamic structure that are capable of simultaneously exploiting both modular andtemporal structures.
We find our models to be robust to the number of available views and better capable of generalization to novel tasks without additional training.
arXiv Detail & Related papers (2020-07-13T17:44:30Z)
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.