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A Multi-Agent Formalism Based on Contextual Defeasible Logic for Healthcare Systems
In the last decade, smart computing has garnered much attention, particularly in ubiquitous environments, thus increasing the ease of everyday human life. Users can dynamically interact with the systems using different modalities in a smart computing environment. The literature discussed multiple me...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Frontiers Media S.A.
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8927623/ https://www.ncbi.nlm.nih.gov/pubmed/35309219 http://dx.doi.org/10.3389/fpubh.2022.849185 |
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author | Akhtar, Salwa Muhammad Nazir, Makia Saleem, Kiran Ahmad, Rana Zeeshan Javed, Abdul Rehman S. Band, Shahab Mosavi, Amir |
author_facet | Akhtar, Salwa Muhammad Nazir, Makia Saleem, Kiran Ahmad, Rana Zeeshan Javed, Abdul Rehman S. Band, Shahab Mosavi, Amir |
author_sort | Akhtar, Salwa Muhammad |
collection | PubMed |
description | In the last decade, smart computing has garnered much attention, particularly in ubiquitous environments, thus increasing the ease of everyday human life. Users can dynamically interact with the systems using different modalities in a smart computing environment. The literature discussed multiple mechanisms to enhance the modalities for communication using different knowledge sources. Among others, Multi-context System (MCS) has been proven quite significant to interlink various context domains dynamically to a distributed environment. MCS is a collection of different contexts (independent knowledge sources), and every context contains its own set of defined rules and facts and inference systems. These contexts are interlinked via bridge rules. However, the interaction among knowledge sources could have the consequences such as bringing out inconsistent results. These issues may report situations such as the system being unable to reach a conclusion or communication in different contexts becoming asynchronous. There is a need for a suitable framework to resolve inconsistencies. In this article, we provide a framework based on contextual defeasible reasoning and a formalism of multi-agent environment is to handle the issue of inconsistent information in MCS. Additionally, in this work, a prototypal simulation is designed using a simulation tool called NetLogo, and a formalism about a Parkinson's disease patient's case study is also developed. Both of these show the validity of the framework. |
format | Online Article Text |
id | pubmed-8927623 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-89276232022-03-18 A Multi-Agent Formalism Based on Contextual Defeasible Logic for Healthcare Systems Akhtar, Salwa Muhammad Nazir, Makia Saleem, Kiran Ahmad, Rana Zeeshan Javed, Abdul Rehman S. Band, Shahab Mosavi, Amir Front Public Health Public Health In the last decade, smart computing has garnered much attention, particularly in ubiquitous environments, thus increasing the ease of everyday human life. Users can dynamically interact with the systems using different modalities in a smart computing environment. The literature discussed multiple mechanisms to enhance the modalities for communication using different knowledge sources. Among others, Multi-context System (MCS) has been proven quite significant to interlink various context domains dynamically to a distributed environment. MCS is a collection of different contexts (independent knowledge sources), and every context contains its own set of defined rules and facts and inference systems. These contexts are interlinked via bridge rules. However, the interaction among knowledge sources could have the consequences such as bringing out inconsistent results. These issues may report situations such as the system being unable to reach a conclusion or communication in different contexts becoming asynchronous. There is a need for a suitable framework to resolve inconsistencies. In this article, we provide a framework based on contextual defeasible reasoning and a formalism of multi-agent environment is to handle the issue of inconsistent information in MCS. Additionally, in this work, a prototypal simulation is designed using a simulation tool called NetLogo, and a formalism about a Parkinson's disease patient's case study is also developed. Both of these show the validity of the framework. Frontiers Media S.A. 2022-03-03 /pmc/articles/PMC8927623/ /pubmed/35309219 http://dx.doi.org/10.3389/fpubh.2022.849185 Text en Copyright © 2022 Akhtar, Nazir, Saleem, Ahmad, Javed, S. Band and Mosavi. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Public Health Akhtar, Salwa Muhammad Nazir, Makia Saleem, Kiran Ahmad, Rana Zeeshan Javed, Abdul Rehman S. Band, Shahab Mosavi, Amir A Multi-Agent Formalism Based on Contextual Defeasible Logic for Healthcare Systems |
title | A Multi-Agent Formalism Based on Contextual Defeasible Logic for Healthcare Systems |
title_full | A Multi-Agent Formalism Based on Contextual Defeasible Logic for Healthcare Systems |
title_fullStr | A Multi-Agent Formalism Based on Contextual Defeasible Logic for Healthcare Systems |
title_full_unstemmed | A Multi-Agent Formalism Based on Contextual Defeasible Logic for Healthcare Systems |
title_short | A Multi-Agent Formalism Based on Contextual Defeasible Logic for Healthcare Systems |
title_sort | multi-agent formalism based on contextual defeasible logic for healthcare systems |
topic | Public Health |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8927623/ https://www.ncbi.nlm.nih.gov/pubmed/35309219 http://dx.doi.org/10.3389/fpubh.2022.849185 |
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