A Self-Modeling Network Model Addressing Controlled Adaptive Mental Models for Analysis and Support Processes

IF 0.7 4区数学 Q4 MATHEMATICS, INTERDISCIPLINARY APPLICATIONS Advances in Complex Systems Pub Date : 2021-12-15 DOI:10.25088/complexsystems.30.4.483

J. Treur

引用次数: 1

Abstract

In this paper, a self-modeling mental network model is presented for cognitive analysis and support processes for a human. These cognitive analysis and support processes are modeled by internal mental models. At the base level, the model is able to perform the analysis and support processes based on these internal mental models. To obtain adaptation of these internal mental models, a first-order self-model is included in the network model. In addition, to obtain control of this adaptation, a second-order self-model is included. This makes the network model a second-order self-modeling network model. The adaptive network model is illustrated for a number of realistic scenarios for a supported car driver.

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一种自建模网络模型，用于分析和支持过程的受控自适应心理模型

本文提出了一种用于人的认知分析和支持过程的自建模心理网络模型。这些认知分析和支持过程是由内部心理模型建模的。在基础级别，模型能够基于这些内部心智模型执行分析和支持流程。为了获得这些内部心理模型的适应性，在网络模型中加入了一阶自我模型。此外，为了控制这种自适应，还引入了一个二阶自模型。这使得网络模型成为一个二阶自建模网络模型。该自适应网络模型是一个支持汽车驾驶员的一些现实场景。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Advances in Complex Systems 综合性期刊-数学跨学科应用

CiteScore

1.40

自引率

0.00%

发文量

121

审稿时长

6-12 weeks

期刊介绍： Advances in Complex Systems aims to provide a unique medium of communication for multidisciplinary approaches, either empirical or theoretical, to the study of complex systems. The latter are seen as systems comprised of multiple interacting components, or agents. Nonlinear feedback processes, stochastic influences, specific conditions for the supply of energy, matter, or information may lead to the emergence of new system qualities on the macroscopic scale that cannot be reduced to the dynamics of the agents. Quantitative approaches to the dynamics of complex systems have to consider a broad range of concepts, from analytical tools, statistical methods and computer simulations to distributed problem solving, learning and adaptation. This is an interdisciplinary enterprise.

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