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Journal articles on the topic 'Production rule-based systems'

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Published: 10 December 2022
Last updated: 28 January 2023

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1

Tsatsoulis, Costas, and King-Sun Fu. "Modeling rule-based systems by stochastic programmed production systems." Information Sciences 36, no. 3 (September 1985): 207–30. http://dx.doi.org/10.1016/0020-0255(85)90054-4.

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2

Aiello, Aldo, Ernesto Burattini, and Guglielmo Tamburrini. "Purely neural, rule-based diagnostic systems. I. Production rules." International Journal of Intelligent Systems 10, no. 8 (1995): 735–49. http://dx.doi.org/10.1002/int.4550100804.

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3

Hewahi, Nabil M. "Concept Based Censor Production Rules." International Journal of Decision Support System Technology 10, no. 1 (January 2018): 59–67. http://dx.doi.org/10.4018/ijdsst.2018010104.

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This paper presents a rule structure called Concept Based Censor Production Rule (CBCPR) that deals with real time cases. CBCPR is an extension of a rule structure called Censored Production Rule (CPR). CPR is a standard rule structure with UNLESS slot, which contains various censor conditions that might rarely happen and prevent the action of the rule to be taken. The more time one has, the more censor conditions one can check. The major extension of CPR is by concentrating on what is called concept. The concept is what about the user needs the decision. Each rule will have a certain concept title that specifies its job. In addition, in every CBCPR structure, at least one slot related to UNLESS part in the rule is existing, where each UNLESS slot is related to a certain category having censor conditions concerned with the concept. The structure will help the system to give more certain answers within the given time for the real-time systems instead of keep checking unnecessary censor conditions for the same concept of different UNLESS categories.
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4

Sadiq, Ahmed. "Premises Reduction of Rule-Based Expert Systems Using Association Rules Technique." Journal of Al-Rafidain University College For Sciences ( Print ISSN: 1681-6870 ,Online ISSN: 2790-2293 ), no. 1 (October 27, 2021): 132–50. http://dx.doi.org/10.55562/jrucs.v22i1.495.

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The heart of expert system is the knowledge base that determines the power of expert system and search engine space. The one important form of this knowledge is the production rule. The premises of these rules are the heart of production rules, therefore, the reduction of these premises is very useful to reduce the time and space in search engine. In this paper an approach will be presented to reduce the premises of production rules using association rules algorithm especially Apriori algorithm. Applying this approach gives a logical premise reduction which plays a good role in the search engine.
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5

Stoeva, Stefka P. "On Processing of Fuzzy Production Systems." Fundamenta Informaticae 14, no. 3 (March 1, 1991): 301–12. http://dx.doi.org/10.3233/fi-1991-14304.

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Knowledge bases in the form of fuzzy production systems with weighting coefficients, i.e. sets of weighted fuzzy rules, are studied. A software implementation of a rule-firing algorithm. based on Zadeh’s generalized modus ponens. is presented. The time complexity of the algorithm proposed is determined. Different types of parallelism in processing of fuzzy production systems are explored.
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6

Paschke, Adrian. "A Report on the Ninth International Web Rule Symposium." AI Magazine 37, no. 1 (April 13, 2016): 105–6. http://dx.doi.org/10.1609/aimag.v37i1.2634.

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The annual International Web Rule Symposium (RuleML) is an international conference on research, applications, languages and standards for rule technologies. RuleML is a leading conference to build bridges between academe and industry in the field of rules and its applications, especially as part of the semantic technology stack. It is devoted to rule-based programming and rule-based systems including production rules systems, logic programming rule engines, and business rule engines/business rule management systems; semantic web rule languages and rule standards; rule-based event processing languages (EPLs) and technologies; and research on inference rules, transformation rules, decision rules, production rules, and ECA rules. The 9th International Web Rule Symposium (RuleML 2015) was held in Berlin, Germany, August 2-5. This report summarizes the events of that conference.
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Abdel Rahman, Sami Salah, and M. I. Shukri. "COMPUTERIZING A RULE-BASED EXPERT SYSTEMS FOR DYNAMIC SCHEDULING IN BATCH PRODUCTION." International Journal of Engineering Applied Sciences and Technology 5, no. 2 (June 30, 2020): 78–86. http://dx.doi.org/10.33564/ijeast.2020.v05i02.012.

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8

Febriani, Risky Ayu, Hong-Seok Park, and Chang-Myung Lee. "A Rule-Based System for Quality Control in Brake Disc Production Lines." Applied Sciences 10, no. 18 (September 20, 2020): 6565. http://dx.doi.org/10.3390/app10186565.

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Currently, challenges in quality improvement have driven various enterprises to create quality management systems in smart factories. The development of quality management systems enables quality control for reviewing product quality, identification, and eliminating product failures. However, process adjustment in quality control decisions may be hard to determine when failures are detected. To overcome this problem, an expert system (ES) that applies the failure mode and effects analysis (FMEA) method for developing quality control systems in brake disc production lines is considered. This quality control system concentrates on analyzing product defects that occur frequently in the production line and will lead to an improved performance of the braking system; the selected product defects are disc thickness variation (DTV), runout, and parallelism. This quality control system developed two modules, the designed FMEA (DFMEA) and component FMEA, which apply a rule-based algorithm for selecting actions. We propose the rules of configuration into the expert system code. The results indicate that the operator can carry out a quality control system with decision-making that can be supported by intelligent searching and reasoning in an expert system.
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9

BIKAKIS, ANTONIS, PAUL FODOR, ADRIAN GIURCA, and LEORA MORGENSTERN. "Introduction to the special issue on the International Web Rule Symposia 2012–2014." Theory and Practice of Logic Programming 16, no. 3 (March 7, 2016): 243–46. http://dx.doi.org/10.1017/s1471068416000028.

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The annual International Web Rule Symposium (RuleML) is an international conference on research, applications, languages, and standards for rule technologies. It has evolved from an annual series of international workshops since 2002, international conferences in 2005 and 2006, and international symposia since 2007. It is the flagship event of the Rule Markup and Modeling Initiative (RuleML, http://ruleml.org), a nonprofit umbrella organization of several technical groups from academia, industry, and government working on rule technology and its applications. RuleML is the leading conference to build bridges between academia and industry in the field of rules and its applications, especially as part of the semantic technology stack. It is devoted to rule-based programming and rule-based systems including production rules systems, logic programming rule engines, and business rules engines/business rules management systems; Semantic Web rule languages and rule standards (e.g., RuleML, SWRL, RIF, PRR, SBVR, DMN, CL, Prolog); rule-based event processing languages and technologies; and research on inference rules, transformation rules, decision rules, production rules, and ECA rules.
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10

Ou-Yang, Chao, Chandrawati Putri Wulandari, Mohammad Iqbal, Han-Cheng Wang, and Chiehfeng Chen. "Extracting Production Rules for Cerebrovascular Examination Dataset through Mining of Non-Anomalous Association Rules." Applied Sciences 9, no. 22 (November 18, 2019): 4962. http://dx.doi.org/10.3390/app9224962.

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Today, patients generate a massive amount of health records through electronic health records (EHRs). Extracting usable knowledge of patients’ pathological conditions or diagnoses is essential for the reasoning process in rule-based systems to support the process of clinical decision making. Association rule mining is capable of discovering hidden interesting knowledge and relations among attributes in datasets, including medical datasets, yet is more likely to produce many anomalous rules (i.e., subsumption and circular redundancy) depends on the predefined threshold, which lead to logical errors and affects the reasoning process of rule-based systems. Therefore, the challenge is to develop a method to extract concise rule bases and improve the coverage of non-anomalous rule bases, i.e., one that not only reduces anomalous rules but also finds the most comprehensive rules from the dataset. In this study, we generated non-anomalous association rules (NAARs) from a cerebrovascular examination dataset through several steps: obtaining a frequent closed itemset, generating association rule bases, subsumption checking, and circularity checking, to fit production rules (PRs) in rule-based systems. Toward the end, the rule inferencing part was performed by PROLOG to obtain possible conclusions toward a specific query given by a user. The experiment shows that compared with the traditional method, the proposed method eliminated a significant number of anomalous rules while improving computational time.
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11

LEE, KEON-MYUNG, and HYUNG LEE-KWANG. "FUZZY INFORMATION PROCESSING FOR EXPERT SYSTEMS." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 03, no. 01 (March 1995): 93–109. http://dx.doi.org/10.1142/s0218488595000098.

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This paper investigates the problems incurred when fuzzy values and certainty factors are used in rule-based knowledge representation. It proposes several measures for evaluating the satisfaction degree of fuzzy matching, fuzzy comparison and interval inclusion occurring in the course of inference for such knowledge representation. It introduces an inference method for such knowledge representation. In addition, it suggests a strategy for flexibly using and managing both conventional rules and fuzzy production rules in rule-based systems. Finally a fuzzy expert system shell, called FOPS5, designed to accommodate fuzzy information processing, is presented in consideration of the proposed methods.
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12

Lee, C. K. H., C. C. Luk, K. L. Choy, H. Y. Lam, C. K. M. Lee, Y. P. Tsang, and G. T. S. Ho. "Using fuzzy-based association rule mining to improve production systems for chemical product development." International Journal of Productivity and Quality Management 26, no. 4 (2019): 446. http://dx.doi.org/10.1504/ijpqm.2019.099624.

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13

Ho, G. T. S., Y. P. Tsang, C. K. M. Lee, H. Y. Lam, K. L. Choy, C. C. Luk, and C. K. H. Lee. "Using fuzzy-based association rule mining to improve production systems for chemical product development." International Journal of Productivity and Quality Management 26, no. 4 (2019): 446. http://dx.doi.org/10.1504/ijpqm.2019.10020991.

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14

Chang, Te-Chuan, C. William Ibbs, and Keith C. Crandall. "A fuzzy logic system for expert systems." Artificial Intelligence for Engineering Design, Analysis and Manufacturing 2, no. 3 (August 1988): 183–93. http://dx.doi.org/10.1017/s0890060400000640.

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Using the theory of fuzzy sets, this paper develops a fuzzy logic reasoning system as an augmentation to a rule-based expert system to deal with fuzzy information. First, fuzzy set theorems and fuzzy logic principles are briefly reviewed and organized to form a basis for the proposed fuzzy logic system. These theorems and principles are then extended for reasoning based on knowledge base with fuzzy production rules. When an expert system is augmented with the fuzzy logic system, the inference capability of the expert system is greatly expanded; and the establishment of a rule-based knowledge base becomes much easier and more economical. Interpretations of the system’s power and possible future research directions conclude the paper.
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15

Cañadas, Joaquín, José Palma, and Samuel Túnez. "Defining the semantics of rule-based Web applications through model-driven development." International Journal of Applied Mathematics and Computer Science 21, no. 1 (March 1, 2011): 41–55. http://dx.doi.org/10.2478/v10006-011-0003-4.

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Defining the semantics of rule-based Web applications through model-driven developmentRule languages and inference engines incorporate reasoning capabilities to Web information systems. This paper presents an approach for the specification and development of Web applications performing the usual functionalities of data management and incorporating a rule engine for reasoning capabilities. The proposed approach is based on the definition of a high-level representation of the semantics of rule-based applications through a formalism for conceptual modeling combining lightweight ontologies and production rules. These models are used as the source for a model-driven method that applies several transformations to conceptual models generating the rule-based Web application code in an automatic process. As a result, the rule-based Web application embeds a rule engine suitable for deducing information by applying an inference process. The structure of the information managed by the Web application is based on ontology classes, whereas the logical expressions applied in reasoning are obtained from production rules of the model. A rule-based Web application has been developed and evaluated using a supporting tool that implements the ideas presented in this paper.
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16

Dong, Tianyang, Qiang Cheng, Bin Cao, and Jianwei Shi. "A Novel Approach to Distributed Rule Matching and Multiple Firing Based on MapReduce." Journal of Database Management 29, no. 2 (April 2018): 62–84. http://dx.doi.org/10.4018/jdm.2018040104.

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In order to solve the poor performance problem of massive rules reasoning, as well as the inconsistency problem of working memory in distributed rule matching, this article presents the formal definition of interference relations between rules, and proposes a novel approach to distributed rule matching and multiple firing based on MapReduce. This approach adopts the way of access request control to detect and exclude interference rules, then selects several rule instantiations to perform multiple firing and concurrent execution, thus reducing the number of inference cycles effectively. By detecting the interferences between rules, this method selects and executes compatible rule sets, and avoids the inconsistency problem of system working memory. In order to verify the validity of the authors' approach, this article developes a production system based on MapReduce, and applied this approach in the master server of a distributed production system. The experimental results show that their method can promote the performance of massive rules reasoning effectively.
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17

Kuo, C. T., J. T. Lim, and S. M. Meerkov. "Bottlenecks in serial production lines: A system-theoretic approach." Mathematical Problems in Engineering 2, no. 3 (1996): 233–76. http://dx.doi.org/10.1155/s1024123x96000348.

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In this work, a new definition of production systems bottlenecks is formulated and analyzed. Specifically, a machine is defined as the bottleneck if the sensitivity of the system's performance index to this machine's production rate in isolation is the largest. Although appealing from the systems point of view, this definition suffers a deficiency due to the fact that the sensitivities involved cannot be either measured on-line or efficiently calculated off-line. To avoid this, the paper develops a method based on indirect but real-time data. From this point of view, the main result of the work is as follows: The bottleneck machine in a serial production line can be identified by analyzing relationships between the so-called manufacturing blockage and manufacturing starvation of each machine. This leads to a simple rule for bottleneck identification. The rule requires neither the calculation of the production rate sensitivities nor the production rate itself. When the probabilities of manufacturing blockages and starvations are not available from on-line measurements, the paper presents their analytical estimates which, under certain conditions, can be used for bottleneck identification. Finally, a case study at an automotive component plant is described.
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18

PATKOS, THEODORE, DIMITRIS PLEXOUSAKIS, ABDELGHANI CHIBANI, and YACINE AMIRAT. "An event calculus production rule system for reasoning in dynamic and uncertain domains." Theory and Practice of Logic Programming 16, no. 3 (March 7, 2016): 325–52. http://dx.doi.org/10.1017/s1471068416000065.

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AbstractAction languages have emerged as an important field of knowledge representation for reasoning about change and causality in dynamic domains. This paper presents Cerbere, a production system designed to perform online causal, temporal and epistemic reasoning based on the Event Calculus. The framework implements the declarative semantics of the underlying logic theories in a forward-chaining rule-based reasoning system, coupling the high expressiveness of its formalisms with the efficiency of rule-based systems. To illustrate its applicability, we present both the modeling of benchmark problems in the field, as well as its utilization in the challenging domain of smart spaces. A hybrid framework that combines logic-based with probabilistic reasoning has been developed, that aims to accommodate activity recognition and monitoring tasks in smart spaces.
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19

Lee, Carmen K. H., Kris M. Y. Law, and Andrew W. H. Ip. "A Rule-Based Quality Analytics System for the Global Wine Industry." Journal of Global Information Management 29, no. 3 (May 2021): 256–73. http://dx.doi.org/10.4018/jgim.20210501.oa1.

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The global wine-making industry has faced challenges due to the increasing demands of consumers, particularly in emerging markets such as China, Brazil, India, and Russia. Controlling the quality during wine production is one of the key challenges faced by global winemakers to produce wine with appropriate sensorial properties tailored to specific markets. The wine production quality is constituted from a number of environmental factors such as climate, soil, and temperature, which affect the sensorial properties and the overall quality. This paper proposed a rule-based quality analytics system (RBQAS) to capture physicochemical data during wine production and to investigate the hidden patterns from the data for quality prediction. It consists of IoT for data capture on a real-time basis, followed by association rule mining to identify relationships between sensorial and physicochemical properties of wine.
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Lee, Carmen K. H., Kris M. Y. Law, and Andrew W. H. Ip. "A Rule-Based Quality Analytics System for the Global Wine Industry." Journal of Global Information Management 29, no. 3 (July 2021): 1–18. http://dx.doi.org/10.4018/jgim.20210701.oa1.

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The global wine-making industry has faced challenges due to the increasing demands of consumers, particularly in emerging markets such as China, Brazil, India, and Russia. Controlling the quality during wine production is one of the key challenges faced by global winemakers to produce wine with appropriate sensorial properties tailored to specific markets. The wine production quality is constituted from a number of environmental factors such as climate, soil, and temperature, which affect the sensorial properties and the overall quality. This paper proposed a rule-based quality analytics system (RBQAS) to capture physicochemical data during wine production and to investigate the hidden patterns from the data for quality prediction. It consists of IoT for data capture on a real-time basis, followed by association rule mining to identify relationships between sensorial and physicochemical properties of wine.
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BESNARD, PHILIPPE. "A LOGICAL ANALYSIS OF RULE INCONSISTENCY." International Journal of Semantic Computing 05, no. 03 (September 2011): 271–80. http://dx.doi.org/10.1142/s1793351x11001250.

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Representing knowledge in a rule-based system takes place by means of "if…then…" statements. These are called production rules for the reason that new information is produced when the rule fires. The logic attached to rule-based systems is taken to be classical inasmuch as "if…then…" is encoded by material implication. However, it appears that the notion of triggering "if…then…" amounts to different logical definitions. The paper investigates the matter, with an emphasis upon consistency because reading "if… then…" statements as rules calls for a notion of rule consistency that does not conform with consistency in the classical sense. Natural deduction is used to explore entailment and equivalence among various formulations and properties.
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Ikken, Naima, Nour-Eddine Tariba, Abdelhadi Bouknadel, Ahmed Haddou, Hafsa El Omari, and Hamid El Omari. "A fuzzy rule based approach for islanding detection in grid connected inverter systems." International Journal of Electrical and Computer Engineering (IJECE) 11, no. 6 (December 1, 2021): 4759. http://dx.doi.org/10.11591/ijece.v11i6.pp4759-4766.

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<span lang="EN-US">Islanding is when an area of the electrical distribution system is isolated from the electrical system while being powered by distributed generators. An important condition for the interconnection of power plants and distribution systems is the ability of the power plant to detect islands. The presented and proposed method is a combination of best active sandia frequency shift (SFS) method with the intelligent fuzzy logic controller, which has been tested in distributed production using the island detection function. And the choice to improve the method by fuzzy logic control (FLC) is retained, as this process is more effective in decreasing the non-</span><span lang="EN-US">detection zone (NDZ) and in further improving the efficiency of the islanding detection system. This paper proposes a new active islanding detection technique controlled by a fuzzy logic controller, for grid connected photovoltaic (PV) inverters. In addition, the efficiency and performance of the proposed method strategy for islanding detection has been analyzed and tested in the various situations of the network. In addition, the results of the simulations with the <span lang="EN-US">power </span><span lang="EN-US">simulation (</span>PSIM) software will be provided to illustrate the main conclusions and the development of the control. Thus, will be used to show the feasibility and validity of the proposed new algorithm.</span>
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23

Chiadamrong, Navee, and Chayanan Tangchaisuk. "Hybrid Simulation-Based Optimization for Production Planning of a Dedicated Remanufacturing System." International Journal of Knowledge and Systems Science 12, no. 3 (July 2021): 53–79. http://dx.doi.org/10.4018/ijkss.2021070103.

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This paper presents a comparative simulation study of a dedicated remanufacturing system. The production line of a dedicated remanufacturing system producing multiple products under uncertain environment is improved through the simulation-based optimization approach. Appropriate inventory capacity of buffers, a proper switching rule, and a suitable run size of each product should be optimally set to yield the highest system's profit. Then, hybrid simulation-based optimization algorithms with two hybrid optimization forms using a Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) as complementary to each other in relation to their standard algorithms are proposed and compared. A case study is used to demonstrate and compare the performances among the algorithms to show the advantages of the proposed algorithms. This approach can assist in decision making for the planning and management of dedicated remanufacturing systems that are required to operate with various decision variables under uncertainties.
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24

Ksystra, Katerina, and Petros Stefaneas. "Formal analysis and verification support for reactive rule-based Web agents." International Journal of Web Information Systems 12, no. 4 (November 7, 2016): 418–47. http://dx.doi.org/10.1108/ijwis-04-2016-0024.

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Purpose Reactive rules are used for programming rule-based Web agents, which have the ability to detect events and respond to them automatically and can have complex structure and unpredictable behavior. The aim of this paper is to provide an appropriate formal framework for analyzing such rules. Design/methodology/approach To achieve this goal, the authors give two alternative semantics for the basic reactive rules’ families which allow us to specify reactive rule-based agents and verify their intended behavior. The first approach expresses the functionality of production and event condition action rules in terms of equations, whereas the second methodology is based in the formalism of rewriting logic. Both semantics can be expressed within the framework of CafeOBJ algebraic specification language, which then offers the verification support and have their advantages and downsides. Findings The authors report on experiences gained by applying those methodologies in a reactive rule-based system and compare the two methodologies. Originality/value Finally, the authors demonstrate a tool that translates a set of reactive rules into CafeOBJ rewrite rules, thus making the verification of reactive rules possible for inexperienced users.
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Bates, Maynard E. "MANAGING ENVIRONMENT TO OPTIMIZE PRODUCTION AND MINIMIZE COSTS." HortScience 25, no. 9 (September 1990): 1182b—1182. http://dx.doi.org/10.21273/hortsci.25.9.1182b.

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Increased production and reduced costs are goals of all plant growers. As a rule, introduction of computer-based control of the plant environment in a well-designed greenhouse will result in yield increases of thirty percent (30%) over other control techniques. A simple model will show how these changes impact profitability.New technologies in sensors, interfaces, computers, software, and plant growth knowledge are being applied to management of the crop environment. Examples of direct canopy temperature measurement, online plant weight measurement, integration and control of photosynthetic photon flux, and nutrition control will be presented. Integrated process control is replacing setpoint maintenance. Models are being developed for incorporation into environmental control systems. Examples for solar irradiance and crop growth will be demonstrated.Ultimately expert systems based on artificial intelligence technology will manage crop production in controlled environments. These systems will incorporate information on crop genome, local climate, cultural practices, pests and diseases, economics, and markets, in addition to environmental control. A possible configuration of the hardware and software for such a system will be discussed.
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Li, Jianxiang, Ling Li, Lixin Tang, and Huijiang Wu. "A case of rule-based heuristics for scheduling hot rolling seamless steel tube production." Expert Systems 23, no. 3 (July 2006): 145–58. http://dx.doi.org/10.1111/j.1468-0394.2006.00330.x.

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Fang, Xi Feng, T. X. Lan, Sheng Wen Zhang, W. Jia, and Tong Yue Wang. "Study on Intelligent Tool Optimal Selection System in Milling." Materials Science Forum 626-627 (August 2009): 605–10. http://dx.doi.org/10.4028/www.scientific.net/msf.626-627.605.

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Rule-based reasoning (RBR) and weight decision making have been widely used in a lot of decision support systems. According to the waste phenomenon of manufacturing corporations due to the reasonless use of tools during production process, and considering the characteristics of former tool selection systems, develop a tool optimal selection system with Visual Basic 6.0 as development tool and SQL Server 2000 as database developing platform. The system adopts the rule-based reasoning and weight decision-making theory, combines fuzzy theory, artificial intelligent technology and production conditions of corporations. According to the information of workpiece and processing conditions imported by users, the system can select the reasonable tools for users. The system overcomes the limitation of single theory tool selection system, simplifies reasoning mechanism and structure of knowledge base, makes programmer easy to realize, promotes operation efficiency of system, and raises the accuracy and efficiency of tool selection in actual production. A developed prototype system and an example have verified some presented techniques and the research results are the basis of the future development.
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Červeňanská, Zuzana, Pavel Važan, Martin Juhás, and Bohuslava Juhásová. "Multi-Criteria Optimization in Operations Scheduling Applying Selected Priority Rules." Applied Sciences 11, no. 6 (March 19, 2021): 2783. http://dx.doi.org/10.3390/app11062783.

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The utilization of a specific priority rule in scheduling operations in flexible job shop systems strongly influences production goals. In a context of production control in real practice, production performance indicators are evaluated always en bloc. This paper addresses the multi-criteria evaluating five selected conflicting production objectives via scalar simulation-based optimization related to applied priority rule. It is connected to the discrete-event simulation model of a flexible job shop system with partially interchangeable workplaces, and it investigates the impact of three selected priority rules—FIFO (First In First Out), EDD (Earliest Due Date), and STR (Slack Time Remaining). In the definition of the multi-criteria objective function, two scalarization methods—Weighted Sum Method and Weighted Product Method—are employed in the optimization model. According to the observations, EDD and STR priority rules outperformed the FIFO rule regardless of the type of applied multi-criteria method for the investigated flexible job shop system. The results of the optimization experiments also indicate that the evaluation via applying multi-criteria optimization is relevant for identifying effective solutions in the design space when the specific priority rule is applied in the scheduling operations.
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BOUZIANE, M’HAMED, and CHENG HSU. "A RULEBASE MODEL FOR DATA AND KNOWLEDGE INTEGRATION IN MULTIPLE SYSTEMS ENVIRONMENTS." International Journal on Artificial Intelligence Tools 02, no. 04 (December 1993): 485–509. http://dx.doi.org/10.1142/s0218213093000230.

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There are three aspects of integration that a computerized enterprise using multiple systems must address: (1) data integration (among, e.g., product design databases, shop floor control databases, and production planning databases), (2) knowledge integration (across various knowledge-based systems that share common enterprise facts, operating rules, or decision logic), and (3) data and knowledge integration (as as to, at least, calibrate databases with factbases and data models with knowledge models). A key issue in this integration problem is the global representation of knowledge (including data semantic constraints and application processing logic) in a form compatible with that of data resources. This paper develops a rulebase model to address this need. In particular, the model combines production rule and relation into a structure that allows (1) full factbase management, (2) inclusion of complete logic (both condition and action), and (3) efficient rulebase processing. It contributes directly to the problem of trigger management (or more generally, rule and event management) in both single-site and multiple-site data and knowledge systems. The model is implemented as a part of the Metadatabase system at Rensselaer.
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Park, Kyu Tae, Yoo Ho Son, Sang Wook Ko, and Sang Do Noh. "Digital Twin and Reinforcement Learning-Based Resilient Production Control for Micro Smart Factory." Applied Sciences 11, no. 7 (March 26, 2021): 2977. http://dx.doi.org/10.3390/app11072977.

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To achieve efficient personalized production at an affordable cost, a modular manufacturing system (MMS) can be utilized. MMS enables restructuring of its configuration to accommodate product changes and is thus an efficient solution to reduce the costs involved in personalized production. A micro smart factory (MSF) is an MMS with heterogeneous production processes to enable personalized production. Similar to MMS, MSF also enables the restructuring of production configuration; additionally, it comprises cyber-physical production systems (CPPSs) that help achieve resilience. However, MSFs need to overcome performance hurdles with respect to production control. Therefore, this paper proposes a digital twin (DT) and reinforcement learning (RL)-based production control method. This method replaces the existing dispatching rule in the type and instance phases of the MSF. In this method, the RL policy network is learned and evaluated by coordination between DT and RL. The DT provides virtual event logs that include states, actions, and rewards to support learning. These virtual event logs are returned based on vertical integration with the MSF. As a result, the proposed method provides a resilient solution to the CPPS architectural framework and achieves appropriate actions to the dynamic situation of MSF. Additionally, applying DT with RL helps decide what-next/where-next in the production cycle. Moreover, the proposed concept can be extended to various manufacturing domains because the priority rule concept is frequently applied.
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Jafarova, Sh M. "Development of information security model with application of production model." Herald of Dagestan State Technical University. Technical Sciences 48, no. 3 (November 9, 2021): 68–72. http://dx.doi.org/10.21822/2073-6185-2021-48-3-68-72.

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Objective. The purpose of the study is to find methods for solving the problem of ensuring a reliable level of security for cloud services. Production models can be considered the most common models that provide simplicity of knowledge representation and inference organization.Method. The research is based on the application of methods of fuzzy logic and mathematical modeling.Result. It is proposed to use fuzzy models that provide a flexible strategy for processing heterogeneous dynamic interacting processes that represent data and knowledge in an essentially fuzzy state space of objects of analysis.Conclusion. A production model of information security management is proposed, which is used to create data processing and storage centers. The production system includes a rule base, a global database, and a rule interpreter. The effective use of large information resources operating in an uncertain environment on the basis of a production model makes it possible to model and ensure information security of systems.
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Lee, G.-C., Y.-D. Kim, J.-G. Kim, and S.-H. Choi. "A dispatching rule-based approach to production scheduling in a printed circuit board manufacturing system." Journal of the Operational Research Society 54, no. 10 (October 2003): 1038–49. http://dx.doi.org/10.1057/palgrave.jors.2601601.

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Sugimura, Nobuhiro, Koji Iwamura, and Tomohiko Maeda. "Special Issue on Production Planning and Scheduling." International Journal of Automation Technology 9, no. 3 (May 5, 2015): 209. http://dx.doi.org/10.20965/ijat.2015.p0209.

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This issue focuses on production planning and scheduling for production system and the related problems that have arisen in these areas in the last half century as digital computer systems developed. These problems relate to production management, production planning, shop floor control, product design and process planning. In the first stage of production planning and scheduling systems R&D, optimization is a key issue that has been widely discussed and many theories and optimization algorithms proposed. Rule-based methods are discussed as potential solutions to these problems. With rapid advances in computer and information processing technologies and performance, tremendous progress has been made in the areas of production systems such as production planning, production scheduling, advances production systems (APS), enterprise resource planning (ERP), just-in time (JIT) processes, the theory of constraint (TOC), product data management (PDM) and computer-aided design / manufacturing / engineering (CAD/CAM/CAE). This special issue addresses the latest research advances, applications, and case studies in production planning and scheduling covering such as decentralized and autonomous production systems, distributed simulation models, robust capacity planning models, wireless sensor networks for production systems and applications to automotive component and steel production. We hope that learning about these advances will enable readers to share their own experience and knowledge in technology, new developments and the potential applications of production planning and scheduling methods and solutions.
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CHAN, CHIEN-CHUNG. "INCREMENTAL LEARNING OF PRODUCTION RULES FROM EXAMPLES UNDER UNCERTAINTY: A ROUGH SET APPROACH." International Journal of Software Engineering and Knowledge Engineering 01, no. 04 (December 1991): 439–61. http://dx.doi.org/10.1142/s0218194091000299.

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This paper introduces an algorithm, LEM3, for incremental learning of production rules from examples. Based on the concept of rough sets introduced by Pawlak, LEM3 is capable of learning rules from consistent as well as inconsistent examples. In LEM3, rules are generated by using the rule-generating procedure implemented in a nonincremental learning program LEM2. Consequently, the rules learned by LEM3 do not use redundant attribute-value pairs. One major feature of LEM3 is the incorporation of a separate global data structure for storing information learned from new examples. The global data structure is updated on an example by example basis, and it provides all the essential information for the incremental updating of lower and upper approximations of a concept and the generating of rules. This separation of learned knowledge from rule-generating procedures provides a more modular design of learning systems.
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Mende, Hendrik, Alexander Peters, Faruk Ibrahim, and Robert H. Schmitt. "Integrating deep learning and rule-based systems into a smart devices decision support system for visual inspection in production." Procedia CIRP 109 (2022): 305–10. http://dx.doi.org/10.1016/j.procir.2022.05.254.

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36

Zhang, Yinsheng, Guoming Zhang, and Qian Shang. "Computer-Aided Clinical Trial Recruitment Based on Domain-Specific Language Translation: A Case Study of Retinopathy of Prematurity." Journal of Healthcare Engineering 2017 (2017): 1–9. http://dx.doi.org/10.1155/2017/7862672.

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Reusing the data from healthcare information systems can effectively facilitate clinical trials (CTs). How to select candidate patients eligible for CT recruitment criteria is a central task. Related work either depends on DBA (database administrator) to convert the recruitment criteria to native SQL queries or involves the data mapping between a standard ontology/information model and individual data source schema. This paper proposes an alternative computer-aided CT recruitment paradigm, based on syntax translation between different DSLs (domain-specific languages). In this paradigm, the CT recruitment criteria are first formally represented as production rules. The referenced rule variables are all from the underlying database schema. Then the production rule is translated to an intermediate query-oriented DSL (e.g., LINQ). Finally, the intermediate DSL is directly mapped to native database queries (e.g., SQL) automated by ORM (object-relational mapping).
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Hu, Kai-Mo, Bin Wang, Yong Liu, Jing Huang, and Jun-Hai Yong. "An extended schema and its production rule-based algorithms for assembly data exchange using IGES." International Journal of Advanced Manufacturing Technology 58, no. 9-12 (July 10, 2011): 1155–70. http://dx.doi.org/10.1007/s00170-011-3434-z.

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Yang, Zong Xiao, Tian Xiao, Lei Song, and Guan Qiang Dong. "Expressway Event Management Expert System Based on Ontology and Rule Reasoning." Advanced Materials Research 601 (December 2012): 361–68. http://dx.doi.org/10.4028/www.scientific.net/amr.601.361.

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Response and decision-making support in expressway emergency management and rescue greatly affects some key factors like emergency rescue plan, emergency duration time, effect range, economy and time loss. Experts system is a kind of intelligent program, using knowledge and reasoning to solve the complex problem that only experts can work out, it is a popular and efficient decision-support means, has some practical features that manual way cannot compare with and offers a modem scientific measure in incident management. Expert system can provide high-efficiency, rapid, exact and powerful assistance in decision support. In order to deal with various possible emergencies promptly and efficiently and therefore ensure the safety and unblocked state of the expressway, develop the expert system has become a trend of emergency rescue and management. In expert system,The knowledge representation is the key basic issues has the important influence to construction of the knowledge base and the reasoning of the expert system. So there has the practical significance to further research on knowledge representation of expressway accident management system, According to features of different knowledge, in exsiting expert systems different knowledge representation methods were adopted such as frame, production rule, procedure, first-order predicate logic, etc. This paper focus on the ontology’s application in the field of expressway emergency rescue and propose a expressway event managemnet expert system based on ontology and rule reasoning.
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Ju, Yuchen, Joakim Lindholm, Moritz Verbeck, Juha Jokisalo, Risto Kosonen, Philipp Janßenc, Yantong Li, Hans Schäfers, and Natasa Nord. "Simulation of demand response on buildings and district heating production." E3S Web of Conferences 362 (2022): 13002. http://dx.doi.org/10.1051/e3sconf/202236213002.

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Demand response (DR) has effectively maximized renewable energies integrated into energy supply systems. This paper investigated DR benefits on three building types and the district heating (DH) production of a community consisted by these buildings in German conditions. Firstly, the buildings and the DH production were simulated without DR by tools IDA-ICE and HGSO, separately. Secondly, the three buildings were simulated by a rule-based DR control. After that, the tool HGSO calculated the total production costs and CO2 emissions based on the power demand with DR. The results show 2.8%-4.8% heating cost savings by DR for different building types. For DH producers, DR application reduces the total DH demand and CO2 emissions by 3.8% and 32.3 %, respectively.
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Yaseen, Zaher, Mohammad Ehteram, Md Hossain, Chow Fai, Suhana Binti Koting, Nuruol Mohd, Wan Binti Jaafar, et al. "A Novel Hybrid Evolutionary Data-Intelligence Algorithm for Irrigation and Power Production Management: Application to Multi-Purpose Reservoir Systems." Sustainability 11, no. 7 (April 2, 2019): 1953. http://dx.doi.org/10.3390/su11071953.

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Multi-purpose advanced systems are considered a complex problem in water resource management, and the use of data-intelligence methodologies in operating such systems provides major advantages for decision-makers. The current research is devoted to the implementation of hybrid novel meta-heuristic algorithms (e.g., the bat algorithm (BA) and particle swarm optimization (PSO) algorithm) to formulate multi-purpose systems for power production and irrigation supply. The proposed hybrid modelling method was applied for the multi-purpose reservoir system of Bhadra Dam, which is located in the state of Karnataka, India. The average monthly demand for irrigation is 142.14 (106 m3), and the amount of released water based on the new hybrid algorithm (NHA) is 141.25 (106 m3). Compared with the shark algorithm (SA), BA, weed algorithm (WA), PSO algorithm, and genetic algorithm (GA), the NHA decreased the computation time by 28%, 36%, 39%, 82%, and 88%, respectively, which represents an excellent enhancement result. The amount of released water based on the proposed hybrid method attains a more reliable index for the volumetric percentage and provides a more effective operation rule for supplying the irrigation demand. Additionally, the average demand for power production is 18.90 (106 kwh), whereas the NHA produces 18.09 (106 kwh) of power. Power production utilizing the NHA’s operation rule achieved a sufficient magnitude relative to that of stand-alone models, such as the BA, PSO, WA, SA, and GA. The excellent proficiency of the developed intelligence expert system is the result of the hybrid structure of the BA and PSO algorithm and the substitution of weaker solutions in each algorithm with better solutions from other algorithms. The main advantage of the proposed NHA is its ability to increase the diversity of solutions and hence avoid the worst possible solutions obtained using BA, that is, preventing a decrease in local optima. In addition, the NHA enhances the convergence rate obtained using the PSO algorithm. Hence, the proposed NHA as an intelligence model could contribute to providing reliable solutions for complex multi-purpose reservoir systems to optimize the operation rule for similar reservoir systems worldwide.
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Pogodaev, Anatoly, Elena Khabibullina, and Dmitriy Inyutin. "APPLYING NEURAL NETWORK MODELS TO THE CONSTRUCTION OF PRODUCTION RULES EXPERT SYSTEMS." Applied Mathematics and Control Sciences, no. 2 (August 16, 2021): 73–92. http://dx.doi.org/10.15593/2499-9873/2021.2.05.

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The development of urbanization, the dramatic increase in the population of large cities and, as a consequence, the increased tempo of life, have had a direct impact on the number of vehicles. That is why intelligent methods of traffic flow control to optimize traffic capacity are growing in popularity. The application of these methods makes it possible to use existing roads and highways with high efficiency without the need to build additional lanes, interchanges, ring roads and so on, which in particular is postulated by the strategy of the National Technological Initiative of the Russian Federation. The purpose of this study is to develop an algorithm, the idea of which is to form productive rules for the traffic flow control by directing the impact on its parameters. In this way, experts can assess the impact of certain characteristics on those that can be manipulated. The construction of the algorithm is done by forming neural network models followed by using the method of the most dominant rule and Garson's algorithm. Since the use of the algorithm involves expert group evaluation, it must have a logical (verbal) rule output in the following form, e.g: "if the slope is minimum, the width of the roadway is maximum, ... then the percentage of heavy vehicles is minimum". This transformation is carried out through the use of the membership function, which allows to fully describe the degree to which a certain parameter belongs to a certain fuzzy subset. In the problems of traffic flow control, it is recommended to use a triangular-shaped membership function. The study contains the results of computational experiments to determine the optimal partitioning of input parameters into fuzzy values to generate satisfying real-world conditions rules. When constructing the algorithm, special attention should be paid to assessing the quality of the neural network model. For this purpose such methods of estimation as root mean square error (RMSE) and logistic error function (LogLoss) are used. Data from loop and radar detectors describing the capacity in long-term operating areas on sections of transportation corridors were used as the initial data set for the numerical study. Sensitivity analysis based on the application of the finite increment formula was used to determine the most significant traffic flow parameters.
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Ara, Ismail, and Mutlu Yasar. "Optimization of the Operation Rule Curves for Cascade Reservoirs Using the Cuckoo Search Algorithm." Advances in Civil Engineering 2022 (April 6, 2022): 1–11. http://dx.doi.org/10.1155/2022/8463358.

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Reservoir operation optimization models are based on the hydrological cycle principle. These models determine the inflow and outflow necessary to meet the demands for drinking water, irrigation water, etc. Rather than optimizing each reservoir separately in cascaded reservoir systems, more efficient results are obtained if the reservoirs are optimized as a whole. In this study, the optimal operation rule curves for cascaded reservoirs were obtained by using the cuckoo search algorithm, which is a soft computing method. Both the irrigation and flood control constraints of the Adiguzel and Cindere dams in Denizli (Turkey) were satisfied by utilizing these rule curves, and the total energy production was maximized. In addition, these rule curves considered turbine efficiency, which significantly contributes to published literature. The total energy obtained with the proposed operation rule curves was 14% higher than that currently produced by the Adiguzel and Cindere dams.
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43

Musen, M. A. "Scalable Software Architectures for Decision Support." Methods of Information in Medicine 38, no. 04/05 (1999): 229–38. http://dx.doi.org/10.1055/s-0038-1634422.

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AbstractInterest in decision-support programs for clinical medicine soared in the 1970s. Since that time, workers in medical informatics have been particularly attracted to rule-based systems as a means of providing clinical decision support. Although developers have built many successful applications using production rules, they also have discovered that creation and maintenance of large rule bases is quite problematic. In the 1980s, several groups of investigators began to explore alternative programming abstractions that can be used to build decision-support systems. As a result, the notions of “generic tasks” and of reusable problem-solving methods became extremely influential. By the 1990s, academic centers were experimenting with architectures for intelligent systems based on two classes of reusable components: (1) problem-solving methods – domain-independent algorithms for automating stereotypical tasks – and (2) domain ontologies that captured the essential concepts (and relationships among those concepts) in particular application areas. This paper highlights how developers can construct large, maintainable decision-support systems using these kinds of building blocks. The creation of domain ontologies and problem-solving methods is the fundamental end product of basic research in medical informatics. Consequently, these concepts need more attention by our scientific community.
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TSAI, JEFFREY J. J. P., and HUNG-CHIN JANG. "A KNOWLEDGE-BASED APPROACH FOR THE SPECIFICATION AND ANALYSIS OF REAL-TIME SOFTWARE SYSTEMS." International Journal on Artificial Intelligence Tools 01, no. 01 (March 1992): 1–35. http://dx.doi.org/10.1142/s0218213092000119.

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FRORL (Frame-and-Rule Oriented Requirements specification Language) was proposed to exploit knowledge representation techniques as an aid in the specification, analysis, and development of a software system. With the real-time software systems as our target systems, the merely mechanisms provided by FRORL cannot meet the specific demands on real-time software systems. As a result, in this paper, we propose RT-FRORL (Real-Time FRORL) as an extension of FRORL. RT-FRORL not only inherits FRORL's basic structure but also includes those language constructs needed to support the specification of real-time systems. The syntax of RT-FRORL is based on frames and production rules. The semantics of RT-FRORL is defined through the integration of first order logic and temporal logic extension. Using RT-FRORL, concurrent and absolute time properties of real-time systems can be easily specified.
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45

Smedley, Georgia, and Steve G. Sutton. "The Effect of Alternative Procedural Explanation Types on Procedural Knowledge Acquisition during Knowledge-Based Systems Use." Journal of Information Systems 21, no. 1 (March 1, 2007): 27–51. http://dx.doi.org/10.2308/jis.2007.21.1.27.

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This study applies Adaptive Character of Thought-Rational (ACT-R), a theory of cognitive skill acquisition, to identify two techniques theorized to provide learners with a simplified and situation-responsive set of production rules to use in a problemsolving context. The two techniques are abstraction (an optimization technique that produces a generalized rule set) and goal structuring (another optimization technique that produces a differentiated rule set). Accordingly, abstraction and goal structuring explanations were provided to users through a knowledge-based system (KBS). Due to cognitive effort constraints on procedural learning, a subset of volunteer participants was extracted for analysis based on an exhibition of attentive learning behavior. Results of the study found that while intermediary stages of development were not detectable, participants receiving goal-structuring explanations exhibited better problem solving performance, and the joint presentation of abstraction explanations led to further problem solving improvements. Abstraction explanations did not lead to improved problem solving, however, when provided in absence of goal-structuring explanations. These findings extend ACT-R to a new venue, increase understanding of ACT-R theory, and provide developers of KBS with more substantive knowledge on optimization of KBS explanation design when knowledge transfer to less expert users is an objective.
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46

Ciancarini, Paolo. "An overview of declarative process modelling using logic programming." Knowledge Engineering Review 11, no. 4 (December 1996): 303–16. http://dx.doi.org/10.1017/s0269888900008006.

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AbstractSoftware process modelling is the activity of formalising the production lifecycle of large software systems. Its aim is to formally describe a software development process, which is then effectively used and possibly enacted by an environment able to support the geographically distributed and coordinated activities involved in the process itself. I show that rule-based languages, especially logic programming languages, are an important technology for the specification, modelling, enactment and coordination of software processes. This is because most routine activities in any development process can be defined by rules. Some initial proposals aimed at simply simulating the software process by a Prolog-like program embedding some development rules. A further step toward the integration of rule-based languages in the software process has been taken using a dynamic knowledge base as project database, and a number of special primitives have been introduced to support process programs. Currently there is a trend toward more complex programming environments, called process-centred development environments. I show how some rule-based coordination language have been used to build an environment of this kind.
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Zhuang, Zilong, Yue Li, Yanning Sun, Wei Qin, and Zhao-Hui Sun. "Network-based dynamic dispatching rule generation mechanism for real-time production scheduling problems with dynamic job arrivals." Robotics and Computer-Integrated Manufacturing 73 (February 2022): 102261. http://dx.doi.org/10.1016/j.rcim.2021.102261.

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48

CASTRO, J. L., M. DELGADO, and C. J. MANTAS. "FUZZY GRAMMAR FOR HANDLING FUZZY ALGORITHMS." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 07, no. 03 (June 1999): 277–86. http://dx.doi.org/10.1142/s0218488599000210.

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This paper presents a new procedure for handling fuzzy algorithm. It consists of transforming the steps of a fuzzy algorithm into production rules of a fuzzy grammar, executing it using the dynamic of this grammar and adjusting its execution by means of the learning capability of the fuzzy formal languages. With this method, some flexible criterial about the goodness of the execution of a fuzzy algorithm are only necessary for adjusting it. Finally, this procedure is applied for executing and adjusting a fuzzy rule-based system.
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Fohlmeister, Silas, Berend Denkena, and André Dittrich. "Selbstoptimierende Reihenfolgebildung in der Fertigung/Intelligent order sequencing in manufacturing." wt Werkstattstechnik online 111, no. 04 (2021): 212–16. http://dx.doi.org/10.37544/1436-4980-2021-04-34.

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Konventionelle Ansätze der Reihenfolgebildung basieren zumeist auf regelbasierten Heuristiken und erfordern bei Systemänderungen manuelle Anpassungen. Dieser Beitrag stellt einen Ansatz zur dezentralen Reihenfolgebildung mittels „Deep Q-Learning“ vor. Durch die Berücksichtigung verschiedener Fertigungskennzahlen für die Bewertung wird die automatisierte Anpassung der Reihenfolgebildung an das Fertigungssystem und eine Verringerung der Durchlaufzeit erreicht. &nbsp; Conventional approaches for order sequencing are usually put into practice by rule-based heuristics, requiring manual adjustments if changes to the production system occur. This article presents an approach for decentralized sequencing using deep q-learning. By considering different production key figures for evaluation, the sequencing can be adapted automatically to changes of the production system, thus achieving a reduction of the cycle time.
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Stanovska, Iraida, Vasyl Duhanets, Lada Prokopovych, and Serhiy Yakhin. "CLASSIFICATION RULE FOR DETERMINING THE TEMPERATURE REGIME OF INDUCTION GRAY CAST IRON." EUREKA: Physics and Engineering, no. 1 (January 29, 2021): 60–66. http://dx.doi.org/10.21303/2461-4262.2021.001604.

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The complexity of using instruments for measuring the technological parameters of induction melting in a continuous mode, and sometimes the impossibility of this, requires the creation of reliable indirect methods for assessing the numerical values of these parameters. This is especially important for quality control of control systems that ensure a given melting temperature regime. The paper proposes a classification rule based on parametric classification methods, which makes it possible to determine the temperature regime of induction melting based on the SiO2 content in the slag and the distribution coefficient. Checking the classifying ability of the obtained rule showed that it is high, since for all the numerical data of the factor-signs, both the high-temperature and low-temperature modes were classified correctly. The restrictions on the application of the classification rule are shown, among them: the restrictions imposed by the range of variation of the values of the attribute factors, and the restrictions imposed by the small sample of the initial data, as well as the arbitrary area of their distribution in the space of the factor-attributes. The rule is presented in a normalized form, and also converted to natural form for ease of practical use. Application of the rule can be recommended to technologists of metallurgical production of foundries to check the compliance of the technological process operations with the specified melting regulations. It can also be used to diagnose processes or temperature control systems that determine the quality of the resulting cast iron. To do this, it is enough to substitute the actual values of SiO2, and Kd into the classification rule. The value of the distribution coefficient Кd is calculated according to the actual data on the content of FeO and MnO in the slag
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