Relevant bibliographies by topics / Production rule-based systems

Academic literature on the topic 'Production rule-based systems'

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Published: 10 December 2022
Last updated: 28 January 2023

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

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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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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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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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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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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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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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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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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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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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Dissertations / Theses on the topic "Production rule-based systems"

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Moi, Havard. "Rule-based control of manufacturing systems." Thesis, Hong Kong : University of Hong Kong, 2000. http://sunzi.lib.hku.hk/hkuto/record.jsp?B22190168.

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Wadatkar, Ajit. "Process Selection for Hole Operations Using a Rule Based Approach." Ohio University / OhioLINK, 2004. http://www.ohiolink.edu/etd/view.cgi?ohiou1108480552.

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Noorwali, Ammar. "Investigating different types of variability in food production system." Thesis, De Montfort University, 2016. http://hdl.handle.net/2086/12264.

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A high level of competition in the food industry, specifically in the Middle East and the UK has forced companies to improve their processes by reducing lead time, waste, and costs and increasing production efficiency. The main challenge to the achievement of the process improvement objectives is the high level of process variability. Therefore, this research investigates the different types of variability in food production system and proposes a methodology to reduce the effect variability in food production system. The variability can be caused by several factors, for instance, in biscuit production lines variability can be induced due to short breakdown and long breakdown, variable processing times, variable temperature, etc. The proposed approach addresses process time variability issues associated with both make-to-stock (MTS) and make-to-order (MTO) manufacturing environments using an iterated approach. The proposed methodology integrates process mapping, (which is a lean tool for identifying value added and non-value added activities), discrete event simulation (to mirror the real production line), Taguchi orthogonal arrays (to generate different scenarios in order to investigate the effect of variability on the simulation model), correlation analysis (to identify the highest variability factors), and the rule based system (to improve food production system performance based on identified key performance indicators (KPIs)). The research uses a biscuit production line as a case study to validate the proposed methodology. The application of the proposed approach determines that the highest effected KPI is %working. The results showed that after implementation of the rule-based system, key performance improved in high variable areas. Results analysis based on before scenario shows that %working performance indicator is highly effected by variable temperature, speed, and breakdown factors for high variable areas such as baking, cooling, aligning, and packing. Based on identified factors and high variable areas, rules are developed by applying standardisation setting (SOP, WI, PP) in high variable areas and the results shows %working improved in baking by 4.78%, in cooling by 16.06%, in aligning by 0.35%, in packing machine1 by 2.5%, in packing machine2 by 2.37%, in packaging1 by 3.35%, and in packaging2 by 3.16%. The integrated method allow quick response , control the environment without production interruption, reduce number of experiments , and reducing variability in high variable areas, which narrowed the improvement in the required areas and increased its effectiveness.
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Yang, Tien-Hui, and 楊天惠. "Category-Based Dispatching Rule for Reentrant Flow Shop Production Systems." Thesis, 2009. http://ndltd.ncl.edu.tw/handle/t579q7.

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碩士
國立臺北科技大學
工業工程與管理研究所
97
Scheduling of production systems with reentrant characteristic is often more complicate and dynamic. Most of dispatching rules use numerical value as the priority value. In this study, we construct a simulation model for a experimental reentrant production system and a category-based dispatching rule is developed for the bottleneck station. The category-based dispatching rule is based on family-based rule. At first, family-based rule is applied and the range of accumulated cycle time of jobs at bottleneck work station and the WIP of each succeeding work station is collected. Then, we divided the range into three categories. The jobs in the queue of the bottleneck station are classified to one of the three urgent categories according to its accumulated cycle time. Each work station is classified to the three hungry categories according to its current WIP in queue. Each job is assigned one of three priority classes in the priority matrix which is based on the job’s urgent category and the hungry category of job’s succeeding work station. The result indicates that category-based dispatching rule is quite effective in minimizing the mean cycle time and variance of cycle time. We tried for different boundary setting of urgent categories and hungry categories and different product mix scenarios and different inter-arrival times. When the FB+CB(433,433) rule is applied to the experimental production system, the mean cycle time decreases about 7% while the variance of cycle time decreases about 31% to 78%.
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Yuan, Xin. "Multi-approaches to achieve an advanced cognitive agent in a new type of parallel processing computer." Thesis, 2020. http://hdl.handle.net/2440/130741.

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In this work, we addressed the problem of developing an agent-based artificial general intelligence that can be implemented in compact and power-efficient electronic hardware. We proposed an approach intended to show the feasibility of using this conceptual hardware-based architecture to replicate simple cognitive behaviours. This research started with surveys on cognitive behaviours and their decision-making architectures and compared them with a production rule-based parallel processing computation architecture. In order to demonstrate their potential, a sample case study of the homing behaviour of honey bees was undertaken to demonstrate the possibility of reproducing cognitive behaviours using a production rule-based cognitive architecture. We developed rule-based agents for a mobile platform which, under experimental conditions, made decisions to retrace its path back to a target position by comparison with the reference images. The agent made consistent overall cognitive decisions using fuzzified elements and guided the system reliably to target positions. Then, the research shifted to finding cognitive data representations and constructing cognitive decision-making structures in that production rule-based system. We introduced a new symbolic way of describing the significant features in an image, which is to use a collection of fuzzy symbolic elements to describe the characteristics of the current environmental information. It filtered out any unnecessary details, yet retained sufficient information describing the frame to enable reliable comparisons between images for the purposes of navigation. Numerical data were converted into fuzzy symbolic representations of the surrounding environment. The modified Fuzzy Inference System includes the reasoning rules used to support the cognitive decision-making process. One of the main disadvantages of a rule-based approach is the effort spent on developing rules. In order to reduce the workload of developing rules manually for agents, a modified Association Rules Mining (ARM) method was introduced to discover effective rules for agents autonomously, based on training data sets. This novel rule development method has been demonstrated through a trainable autonomous parking system, which can develop rules for autonomous parking agents.
Thesis (Ph.D.) -- University of Adelaide, School of Electrical and Electronic Engineering, 2021
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Lee, Tsung-Yu, and 李宗祐. "Applying Hierarchical Censored Production Rule(HCPR)-based system to fault diagnosis advisory system : A case study of pump fault of the slurry supply system at the semiconductor foundry." Thesis, 2007. http://ndltd.ncl.edu.tw/handle/19058032643336664329.

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碩士
國立成功大學
工業與資訊管理學系專班
95
Most of engineers who are employed at a semiconductor foundry only depend on the accumulation of maintain experience or consult manual books to find out trouble reasons and the staff's loyalty to the company not never wavered. Thus it is difficult to make enterprises toward staff's training. A fault diagnosis technology has been established in expert system. But knowledge engineer is difficult to construct knowledge and analyses reliability problem from knowledge data. In view of this issue, our research adopts hierarchical censored production rule (HCPR)-based system to help knowledge engineer build a systematic fault diagnosis advisory system. This system would strengthen personnel's ability of controlling to the system and reduce the risk of operating. In order to test and verify this fault diagnosis advisory system, we establish an application software by Visual Basic. We also arrange an experimental design to make an efficiency analysis. These results would prove that this fault diagnosis advisory system is suitable to made great progress for novices.
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Book chapters on the topic "Production rule-based systems"

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Rezk, Martín, and Michael Kifer. "Formalizing Production Systems with Rule-Based Ontologies." In Lecture Notes in Computer Science, 332–51. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-28472-4_19.

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Jankowska, Beata, and Magdalena Szymkowiak. "On Ranking Production Rules for Rule-Based Systems with Uncertainty." In Computational Collective Intelligence. Technologies and Applications, 546–56. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-23935-9_54.

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Artiba, A. "Loading and scheduling a hybrid multiproduct plant via a rule-based planning system." In The Planning and Scheduling of Production Systems, 132–62. Boston, MA: Springer US, 1997. http://dx.doi.org/10.1007/978-1-4613-1195-9_5.

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Bharadwaj, Kamal Kant, and Jose Demisio Simoes da Silva. "Towards Integrating Hierarchical Censored Production Rule(HCPR) Based Systems and Neural Networks." In Advances in Artificial Intelligence, 121–30. Berlin, Heidelberg: Springer Berlin Heidelberg, 1998. http://dx.doi.org/10.1007/10692710_13.

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Geng, Christoph, Natalia Moriz, Andreas Bunte, and Henning Trsek. "Concept for Rule-Based Information Aggregation in Modular Production Plants." In Technologien für die intelligente Automation, 77–89. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-662-64283-2_6.

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AbstractIn the context of Industrie 4.0, (Self-)adapting Cyber-Physical Production Systems (CPPS) offer a solution for production facilities to adapt to changing market requirements. The operation of a CPPS requires information of the entire plant at any time, which can usually be derived by aggregating the individual information of the production modules of a CPPS (information models). Even if the information models are designed following standardized guidelines, the aggregation of these individual information models often needs to be done manually, which requires a lot of effort and is error prone. To achieve an automated information aggregation, this paper presents a novel concept for modular production plants. The main aspect of the concept is the use of a Rule Engine for the processing of information models in order to create aggregated plant information. This Rule Engine uses a classification method for plant components in order to enable standardization. The challenge of this work is to design a Rule Engine that is suitable for different aspects of CPPS.
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de Bruijn, Jos, and Martín Rezk. "A Logic Based Approach to the Static Analysis of Production Systems." In Web Reasoning and Rule Systems, 254–68. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-05082-4_18.

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de Toni, A., G. Nassimbeni, and S. Tonchia. "Lot Production: A Rule-Based Expert System Approach." In Proceedings of the Thirtieth International MATADOR Conference, 315–22. London: Macmillan Education UK, 1993. http://dx.doi.org/10.1007/978-1-349-13255-3_41.

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Bharadwaj, K. K., Nabil M. Hewahi, and Maria Augusta Brandao. "Adaptive Hierarchical Censored Production Rule-based system: A genetic algorithm approach." In Advances in Artificial Intelligence, 81–90. Berlin, Heidelberg: Springer Berlin Heidelberg, 1996. http://dx.doi.org/10.1007/3-540-61859-7_9.

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Gupta, Sakshi, Sunita Kumawat, and Gajendra Pratap Singh. "Fuzzy Petri Net Representation of Fuzzy Production Propositions of a Rule Based System." In Communications in Computer and Information Science, 197–210. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-9939-8_18.

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Kim, Sangwoo, and Hojung Nam. "In Silico Simulation of Signal Cascades in Biomedical Networks Based on the Production Rule System." In Bioinformatics Research and Applications, 356–61. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-59575-7_34.

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Conference papers on the topic "Production rule-based systems"

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Wang, C. K., A. K. Mok, and A. M. K. Cheng. "MRL: a real-time rule-based production system." In [1990] Proceedings 11th Real-Time Systems Symposium. IEEE, 1990. http://dx.doi.org/10.1109/real.1990.128757.

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Bajwa, Imran Sarwar, M. Imran Siddique, and M. Abbas Choudhary. "Rule based Production Systems for Automatic Code Generation in Java." In 2006 1st International Conference on Digital Information Management. IEEE, 2007. http://dx.doi.org/10.1109/icdim.2007.369214.

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Yao, Jiaqi. "An efficient approach for rule matching in production system based on multi-agent." In 2016 3rd International Conference on Systems and Informatics (ICSAI). IEEE, 2016. http://dx.doi.org/10.1109/icsai.2016.7810984.

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Kiritsis, D., Michel Porchet, L. Boutzev, I. Zic, and P. Sourdin. "Knowledge Based Expert Systems for CAD/CAM Experience of an EDM Application." In ASME 1993 International Computers in Engineering Conference and Exposition. American Society of Mechanical Engineers, 1993. http://dx.doi.org/10.1115/cie1993-0035.

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Abstract In this paper we present our experience from the use of two different expert system development environments to Wire-EDM CAD/CAM knowledge based application. The two systems used follow two different AI approaches: the one is based on the constraint propagation theory and provides a natural language oriented programming environment, while the other is a production rule system with backward-forward chaining mechanisms and a conventional-like programming style. Our experience showed that the natural language programming style offers an easier and more productive environment for knowledge based CAD/CAM systems development.
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Li, Yunpeng, Utpal Roy, Y. Tina Lee, and Sudarsan Rachuri. "Integrating Rule-Based Systems and Data Analytics Tools Using Open Standard PMML." In ASME 2015 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2015. http://dx.doi.org/10.1115/detc2015-46412.

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Rule-based expert systems such as CLIPS (C Language Integrated Production System) are 1) based on inductive (if-then) rules to elicit domain knowledge and 2) designed to reason new knowledge based on existing knowledge and given inputs. Recently, data mining techniques have been advocated for discovering knowledge from massive historical or real-time sensor data. Combining top-down expert-driven rule models with bottom-up data-driven prediction models facilitates enrichment and improvement of the predefined knowledge in an expert system with data-driven insights. However, combining is possible only if there is a common and formal representation of these models so that they are capable of being exchanged, reused, and orchestrated among different authoring tools. This paper investigates the open standard PMML (Predictive Model Mockup Language) in integrating rule-based expert systems with data analytics tools, so that a decision maker would have access to powerful tools in dealing with both reasoning-intensive tasks and data-intensive tasks. We present a process planning use case in the manufacturing domain, which is originally implemented as a CLIPS-based expert system. Different paradigms in interpreting expert system facts and rules as PMML models (and vice versa), as well as challenges in representing and composing these models, have been explored. They will be discussed in detail.
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Xirouchakis, Paul C. "Production and Classification-Based Active Structural Design Databases." In ASME 1995 15th International Computers in Engineering Conference and the ASME 1995 9th Annual Engineering Database Symposium collocated with the ASME 1995 Design Engineering Technical Conferences. American Society of Mechanical Engineers, 1995. http://dx.doi.org/10.1115/edm1995-0844.

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Abstract We consider the beam section design problem domain and its representation using the LOOM knowledge representation and reasoning database system. We use LOOM’s concept classification capabilities to built a taxonomy of terms describing the beam section design process. This allows LOOM to automatically classify concepts and instances of objects in the proper location in the taxonomy of terms. This classification-based reasoning is what distinguishes LOOM from other knowledge or object-oriented database systems. We also use LOOM’s two behavioral programming features: object-oriented and rule-based in our beam problem domain. This way we define the beam concepts of flat bar, exactly one flange, exactly two flanges and box type. We also define three versions of a section area calculation method that fires depending on the type of the beam section. Finally, we define a production that fires when the type of an object changes to tank boundary. As a result the applicable beam design pressure is changed accordingly.
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Lin, Chang-Pin, and Hsin-Yuan Chuang. "Adaptive Process Control and Optimization in Intelligent Manufacturing Systems." In ASME 2000 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2000. http://dx.doi.org/10.1115/detc2000/cie-14619.

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Abstract Modern manufacturing systems have incorporated various “intelligent” decision support techniques and algorithms in the form of rules-based systems which have been promoted extensively because of the recent development in fuzzy inference, artificial neural network, machine learning and optimization algorithms, and etc. The basic elements of a modern manufacturing system include design automation (CAD and CAPP), production automation (CAM and MRP II), and shop floor automation (SFC and FMS), which must then be integrated into a coherent system through information automation (distributed DBMS and workflow automation) to form a fully automated factory. Therefore, considerable amount of rule-based systems have been developed and implemented fully or partially into each of the above areas of automation. To build a robust rule-based system requires a suitable modeling and analysis tool and a systematic design methodology. By incorporating neural network concept and associated learning algorithm, we present in this paper a Petri net based modeling methodology which can be used to design and implement adaptive rule-based systems for control and optimization in intelligent manufacturing systems. Demonstration is done on parametric design and optimization in manufacturing processes.
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Yu, Jin, F. W. Liou, Zhongfang Tong, and Yueming Sun. "A Knowledge Based System for Intelligent Process Planning." In ASME 1992 Design Technical Conferences. American Society of Mechanical Engineers, 1992. http://dx.doi.org/10.1115/detc1992-0117.

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Abstract Knowledge extraction and representation are the two most important problems to build up expert systems. This paper describes an example of a knowledge based prototype Hi-KJBIPP, which is designed in a hierarchical architecture to express different levels of machining knowledge for complex box-type workpieces. Production systems and frames are both used as knowledge representation methods in Hi-KBIPP. A production rule system is used to represent top levels of knowledge, in which the control strategies that consist of priority classification, priority execution, and context restriction are designed to decide the first rule to be executed when several rules are of the same qualification. Frames are used to represent the information of machining procedures. A particular frame system is used in Hi-KBIPP which provide multi-dimension frame, similarity network, and associate-network to handle and compile knowledge.
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Yang, Hao, and Wen F. Lu. "PROCASE: A Prototype of Intelligent Case-Based Process Planning System With Simulation Environment." In ASME 1993 International Computers in Engineering Conference and Exposition. American Society of Mechanical Engineers, 1993. http://dx.doi.org/10.1115/cie1993-0069.

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Abstract An intelligent case-based process planning system with interactive graphic simulation environment, PROCASE, is developed to demonstrate an integrated methodology of case-based process planning system. In PROCASE, both the mechanical part features and the machining operations are represented with a frame based scheme. PROCASE contains a retriever, a modifier, a simulator and a repairer. It distinguishes itself from traditional rule-based process planning systems by representing the process planning knowledge through previous process planning cases instead of production rules. It therefore can overcome some problems in the traditional rule-based expert systems. PROCASE currently resides in IRIS Indigo workstation. With a user friendly graphic environment, the generated process plans can be demonstrated vividly. This simulation environment not only serves as a good assistance in debugging, but also helps the user to be convinced of the outcomes of the reasoning of PROCASE.
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Wang, Weida, Xinhua Liu, Wenjian Liu, and Yang Luo. "A XML-Based Shop Floor Adaptive Scheduler Design." In ASME 2007 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2007. http://dx.doi.org/10.1115/detc2007-35130.

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Due to the complex, uncertainty and dynamic in the modern manufacturing environment, a flexible and robust shop floor scheduler is essential to achieve the system production goals. The paper proposes a design framework of shop floor scheduler. The details of its function modules were discussed. An adaptive scheduling strategy and a XML-based process plan representation were utilized in the scheduler, which acquires the dispatching rule from the knowledge base and uses the build-in on-line simulator to evaluate the rule. These technologies enable the scheduler has a good fine-tune ability and transfers effectively process information to other heterogeneous information systems in a shop floor. The framework introduced in the paper has been demonstrated with a manufacturing case.
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