{"title":"Foundations of Programmable Process Structures for the unified modeling and simulation of agricultural and aquacultural systems","authors":"Monika Varga, Bela Csukas","doi":"10.1016/j.inpa.2022.10.001","DOIUrl":null,"url":null,"abstract":"<div><p>This research paper defines the theoretical foundations and computational implementation of a non-conventional modeling and simulation methodology, inspired by the needs of problem solving for biological, agricultural, aquacultural and environmental systems. The challenging practical problem is to develop a framework for automatic generation of causally right and balance-based, unified models that can also be applied for the effective coupling amongst the various (sophisticated field-specific, sensor data processing-based, upper level optimization-driven, etc.) models. The scientific problem addressed in this innovation is to develop Programmable Process Structures (PPS) by combining functional basis of systems theory, structural approach of net theory and computational principles of agent based modeling. PPS offers a novel framework for the automatic generation of easily extensible and connectible, unified models for the underlying complex systems. PPS models can be generated from one state and one transition meta-prototypes and from the transition oriented description of process structure. The models consist of unified state and transition elements. The local program containing prototype elements, derived also from the meta-prototypes, are responsible for the case-specific calculations. The integrity and consistency of PPS architecture are based on the meta-prototypes, prepared to distinguish between the conservation-laws-based measures and the signals. The simulation is based on data flows amongst the state and transition elements, as well as on the unification based data transfer between these elements and their calculating prototypes. This architecture and its AI language-based (Prolog) implementation support the integration of various field- and task-specific models, conveniently. The better understanding is helped by a simple example. The capabilities of the recently consolidated general methodology are discussed on the basis of some preliminary applications, focusing on the recently studied agricultural and aquacultural cases.</p></div>","PeriodicalId":53443,"journal":{"name":"Information Processing in Agriculture","volume":"11 1","pages":"Pages 91-108"},"PeriodicalIF":7.7000,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2214317322000737/pdfft?md5=d9d3dbf2df68ae15a8175599e80f60b2&pid=1-s2.0-S2214317322000737-main.pdf","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Information Processing in Agriculture","FirstCategoryId":"1091","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2214317322000737","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"AGRICULTURE, MULTIDISCIPLINARY","Score":null,"Total":0}
引用次数: 0
Abstract
This research paper defines the theoretical foundations and computational implementation of a non-conventional modeling and simulation methodology, inspired by the needs of problem solving for biological, agricultural, aquacultural and environmental systems. The challenging practical problem is to develop a framework for automatic generation of causally right and balance-based, unified models that can also be applied for the effective coupling amongst the various (sophisticated field-specific, sensor data processing-based, upper level optimization-driven, etc.) models. The scientific problem addressed in this innovation is to develop Programmable Process Structures (PPS) by combining functional basis of systems theory, structural approach of net theory and computational principles of agent based modeling. PPS offers a novel framework for the automatic generation of easily extensible and connectible, unified models for the underlying complex systems. PPS models can be generated from one state and one transition meta-prototypes and from the transition oriented description of process structure. The models consist of unified state and transition elements. The local program containing prototype elements, derived also from the meta-prototypes, are responsible for the case-specific calculations. The integrity and consistency of PPS architecture are based on the meta-prototypes, prepared to distinguish between the conservation-laws-based measures and the signals. The simulation is based on data flows amongst the state and transition elements, as well as on the unification based data transfer between these elements and their calculating prototypes. This architecture and its AI language-based (Prolog) implementation support the integration of various field- and task-specific models, conveniently. The better understanding is helped by a simple example. The capabilities of the recently consolidated general methodology are discussed on the basis of some preliminary applications, focusing on the recently studied agricultural and aquacultural cases.
期刊介绍:
Information Processing in Agriculture (IPA) was established in 2013 and it encourages the development towards a science and technology of information processing in agriculture, through the following aims: • Promote the use of knowledge and methods from the information processing technologies in the agriculture; • Illustrate the experiences and publications of the institutes, universities and government, and also the profitable technologies on agriculture; • Provide opportunities and platform for exchanging knowledge, strategies and experiences among the researchers in information processing worldwide; • Promote and encourage interactions among agriculture Scientists, Meteorologists, Biologists (Pathologists/Entomologists) with IT Professionals and other stakeholders to develop and implement methods, techniques, tools, and issues related to information processing technology in agriculture; • Create and promote expert groups for development of agro-meteorological databases, crop and livestock modelling and applications for development of crop performance based decision support system. Topics of interest include, but are not limited to: • Smart Sensor and Wireless Sensor Network • Remote Sensing • Simulation, Optimization, Modeling and Automatic Control • Decision Support Systems, Intelligent Systems and Artificial Intelligence • Computer Vision and Image Processing • Inspection and Traceability for Food Quality • Precision Agriculture and Intelligent Instrument • The Internet of Things and Cloud Computing • Big Data and Data Mining