Journal articles: 'Rule based systems'

1

Hayes-Roth, Frederick. "Rule-based systems." Communications of the ACM 28, no. 9 (September 1985): 921–32. http://dx.doi.org/10.1145/4284.4286.

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G, Manju. "Rule-based Cognitive Modelling for Multimodal Intelligent Tutoring Systems." International Journal of Psychosocial Rehabilitation 24, no. 1 (January 20, 2020): 1754–60. http://dx.doi.org/10.37200/ijpr/v24i1/pr200275.

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Jokste, Lauma, and Janis Grabis. "RULE BASED ADAPTATION: LITERATURE REVIEW." Environment. Technology. Resources. Proceedings of the International Scientific and Practical Conference 2 (June 15, 2017): 42. http://dx.doi.org/10.17770/etr2017vol2.2592.

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Rule based adaptive systems are growing in popularity and rules have been considered as an effective and elastic way to adapt systems. A rule based approach allows transparent monitoring of performed adaptation actions and gives an important advantage of easily modifiable adaptation process. The goal of this paper is to summarize literature review on rule based adaptation systems. The emphasis is put on rule types, semantics used for defining rules and measurement of effectiveness and correctness of rule based adaptation systems. The literature review has been done following a systematic approach consisting of three steps: planning, reviewing and analysis. Targeted research questions have been used to guide the review process. The review results are to be used for conducting further research in the area of rule based context-aware adaptive systems. This paper accents the potential of using rules as means to perform adaptive actions in enterprise applications taking into account contextual factors as well as points challenges, difficulties and open issues for planning, developing, implementing and running of such systems.

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HALAVATI, RAMIN, SAEED BAGHERI SHOURAKI, SIMA LOTFI, and POOYA ESFANDIAR. "SYMBIOTIC EVOLUTION OF RULE BASED CLASSIFIER SYSTEMS." International Journal on Artificial Intelligence Tools 18, no. 01 (February 2009): 1–16. http://dx.doi.org/10.1142/s0218213009000019.

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Evolutionary Algorithms are vastly used in development of rule based classifier systems in data mining where the rule base is usually a set of If-Then rules and an evolutionary trait develops and optimizes these rules. Genetic Algorithm is usually a favorite solution for such tasks as it globally searches for good rule-sets without any prior bias or greedy force, but it is usually slow. Also, designing a good genetic algorithm for rule base evolution requires the design of a recombination operator that merges two rule bases without disrupting the functionalities of each of them. To overcome the speed problem and the need to design recombination operator, this paper presents a novel algorithm for rule base evolution based on natural process of symbiogenesis. The algorithm uses symbiotic combination operator instead of traditional sexual recombination operator of genetic algorithms. This operator takes two chromosomes with different number of genes (rules here) and merges them by combining all the information content of both chromosomes. Using this operator results in two major advantages: First, it totally removes the need to design the recombination operator and therefore is easier to use; second, it outperforms traditional genetic algorithm both in emergence speed and classification rate, this is tested and presented on some globally used benchmarks.

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Finlay, P. N., Malcolm King, and A. Burnett. "Administering Rule Development in Rule-Based Expert Systems." Journal of the Operational Research Society 40, no. 2 (February 1989): 193. http://dx.doi.org/10.2307/2583238.

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Finlay, P. N., Malcolm King, and A. Burnett. "Administering Rule Development in Rule-Based Expert Systems." Journal of the Operational Research Society 40, no. 2 (February 1989): 193–98. http://dx.doi.org/10.1057/jors.1989.24.

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Lotfi, A., and M. Howarth. "Noninteractive fuzzy rule-based systems." Information Sciences 99, no. 3-4 (July 1997): 219–34. http://dx.doi.org/10.1016/s0020-0255(96)00271-x.

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Kogge, Peter, John Oldfield, Mark Brule, and Charles Stormon. "VLSI and rule-based systems." ACM SIGARCH Computer Architecture News 16, no. 5 (December 1988): 52–65. http://dx.doi.org/10.1145/65755.65761.

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Lotfi, A., M. Howarth, and J. B. Hull. "Orthogonal Fuzzy Rule-Based Systems: Selection of Optimum Rules." Neural Computing & Applications 9, no. 1 (May 30, 2000): 4–11. http://dx.doi.org/10.1007/s005210070029.

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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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Xie, Xuanfeng, Jianliang Zhou, Xuehai Fu, Ruoyi Zhang, Hui Zhu, and Quanxi Bao. "Automated Rule Checking for MEP Systems Based on BIM and KBMS." Buildings 12, no. 7 (July 1, 2022): 934. http://dx.doi.org/10.3390/buildings12070934.

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Due to the growing complexity of mechanical, electrical, and plumbing (MEP) designs and the rules that govern them, performing rule checks manually has become expensive. However, MEP-based rule checking has not received adequate attention compared to automated rule checking in other domains. Based on Knowledge Management and Building Information Modeling (BIM), an automated rule checking system integrated knowledge base management system (KBMS) for model information expansion, information extraction, system integrity checking, and element spacing checking was developed. MEP rules for automated rule checking were collected, optimized, and stored in the MEP knowledge base. The KBMS facilitates the management of MEP rules in the knowledge base. A Revit plug-in of MEP rule checking system was developed including functions of KBMS, Model Integrity Checking, Elements Space Checking, and Locating the non-compliant element in model view. This study integrated both KBMS and BIM technologies to achieve automated rule checking for MEP. This simplifies the process of rule checking of MEP systems in an automated manner.

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WALDINGER, RICHARD J., and MARK E. STICKEL. "PROVING PROPERTIES OF RULE-BASED SYSTEMS." International Journal of Software Engineering and Knowledge Engineering 02, no. 01 (March 1992): 121–44. http://dx.doi.org/10.1142/s0218194092000075.

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Rule-based systems are being applied to tasks of increasing responsibility. Deductive methods are being applied to their validation, to detect flaws in these systems and to enable us to use them with more confidence. Each system of rules is encoded as a set of axioms that define the system theory. The operation of the rule language and information about the subject domain are also described in the system theory. Validation tasks, such as establishing termination, unreachability, or consistency, or verifying properties of the system, are all phrased as conjectures. If we succeed in establishing the validity of the conjecture in the system theory, we have carried out the corresponding validation task. If the proof is restricted to be sufficiently constructive, we may extract from it information other than a simple yes/no answer. For example, we may obtain a description of a situation in which an error or anomaly may occur. A method for the gradual formulation of specifications based on the attempted proof of a series of conjectures has been found to be suitable for rule-based systems. Such a specification can serve as the basis for a reengineering of the system using conventional software technology. Validation conjectures are proved or disproved by a new theorem-proving system, SNARK, which implements (nonclausal) resolution and paramodulation, an optional constructive restriction, and some facilities for proof by induction. The system has already been applied to prove properties of a number of simple rule-based systems.

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Judge, William. "Relation-based Versus Rule-Based Governance Systems." Corporate Governance: An International Review 20, no. 5 (September 2012): 411–12. http://dx.doi.org/10.1111/j.1467-8683.2012.00929.x.

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Preece, A. D., C. Grossner, P. G. Chander, and T. Radhakrishnan. "Structure-based validation of rule-based systems." Data & Knowledge Engineering 26, no. 2 (June 1998): 161–89. http://dx.doi.org/10.1016/s0169-023x(97)00019-0.

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15

Ishibuchi, H., and T. Yamamoto. "Rule weight specification in fuzzy rule-based classification systems." IEEE Transactions on Fuzzy Systems 13, no. 4 (August 2005): 428–35. http://dx.doi.org/10.1109/tfuzz.2004.841738.

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TOMIĆ, BOJAN, BORIS HORVAT, and NEMANJA JOVANOVIĆ. "AN EXPLANATION FACILITY FRAMEWORK FOR RULE-BASED SYSTEMS." International Journal on Artificial Intelligence Tools 21, no. 04 (August 2012): 1250013. http://dx.doi.org/10.1142/s0218213012500133.

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Rule engines, business rule management systems and other rule-based systems used today widely utilize methods, techniques and technologies from the era of expert systems. Unfortunately, this doesn't seem to be the case when it comes to explanation facilities. Nowadays, the use of explanation facilities seems more important than ever. Business rule management systems control or constrain the behavior of business processes through business rules and an explanation of the inference process intended for the end user would be more than welcome. An explanation facility framework which was created in order to remedy this situation is presented in this paper. It is written in Java and is supposed to be a generic solution for modern rule-based systems. Besides being free and open-source, it is simple to use and can generate explanations in the form of natural language like sentences. Internationalization is also supported and explanations can be saved as textual, XML or PDF reports.

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17

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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Feret, Jerome, Thomas Henzinger, Heinz Koeppl, and Tatjana Petrov. "Lumpability Abstractions of Rule-based Systems." Electronic Proceedings in Theoretical Computer Science 40 (October 30, 2010): 142–61. http://dx.doi.org/10.4204/eptcs.40.10.

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19

Avritzer, A., J. P. Ros, and E. J. Weyuker. "Reliability testing of rule-based systems." IEEE Software 13, no. 5 (1996): 76–82. http://dx.doi.org/10.1109/52.536461.

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Feret, Jerome, Thomas Henzinger, Heinz Koeppl, and Tatjana Petrov. "Lumpability abstractions of rule-based systems." Theoretical Computer Science 431 (May 2012): 137–64. http://dx.doi.org/10.1016/j.tcs.2011.12.059.

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21

Perfilieva, Irina. "Logical foundations of rule-based systems." Fuzzy Sets and Systems 157, no. 5 (March 2006): 615–21. http://dx.doi.org/10.1016/j.fss.2005.10.006.

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22

Vestli, M., I. Nordbo, and A. Solvberg. "Modeling control in rule-based systems." IEEE Software 11, no. 2 (March 1994): 77–81. http://dx.doi.org/10.1109/52.268961.

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23

Nozaki, K., H. Ishibuchi, and H. Tanaka. "Adaptive fuzzy rule-based classification systems." IEEE Transactions on Fuzzy Systems 4, no. 3 (1996): 238–50. http://dx.doi.org/10.1109/91.531768.

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24

Wang, H., and A. H. Jones. "Rule based identifier for unknown systems." IEE Proceedings D Control Theory and Applications 138, no. 5 (1991): 500. http://dx.doi.org/10.1049/ip-d.1991.0069.

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25

Mahabala, H. N., and G. Ravi Prakash. "Auxiliary Tools for Rule-Based Systems." IETE Journal of Research 34, no. 3 (May 1988): 192–98. http://dx.doi.org/10.1080/03772063.1988.11436729.

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26

Giakoumakis, E., G. Papaconstantinou, and E. Skordalakis. "Rule-based systems and pattern recognition." Pattern Recognition Letters 5, no. 4 (April 1987): 267–72. http://dx.doi.org/10.1016/0167-8655(87)90056-0.

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27

Murrell, S., and R. Plant. "Formal semantics for rule-based systems." Journal of Systems and Software 29, no. 3 (June 1995): 251–59. http://dx.doi.org/10.1016/0164-1212(94)00108-y.

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28

Wood, William T., and Elaine N. Frankowski. "Verification of rule-based expert systems." Expert Systems with Applications 1, no. 3 (January 1990): 317–22. http://dx.doi.org/10.1016/0957-4174(90)90010-r.

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Matsuura, Kenichi, and Yukinori Kakazu. "Acquiring Objective Functions in Distributed Rule-Based Systems from Examples." Journal of Robotics and Mechatronics 8, no. 5 (October 20, 1996): 454–58. http://dx.doi.org/10.20965/jrm.1996.p0454.

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There are some great features in distributed problem solving systems, such as fault tolerance, robustness and so on. This system performs problem solving with search depending on an objective function. Distributed rulebased problem systems are considered to be of the same type. That is to say, the set of rules and the objective function exist separately within the system. However, in distributed rule-based systems, a set of rules should hold the objective function. The system should have a set of rules only, and the objective function should exist within that set of rules. In this paper, our objective is to acquire the objective function of a distributed rule-based system. A rule generation mechanism analyzes some given examples and acquires strategies for problem solving to a set of rules. In this way, the set of rules of the examples class in the domain represents the objective function of that class in the domain. Therefore, a solution using those rules keeps the same features as the examples if the problem belongs to the examples class that generates the set of rules. The system implemented by this theory has been applied to the domain of traveling salesman problem. This system has generated a set of rules that has held the objective function of its domain.

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YAMAMOTO, Takashi, and Hisao ISHIBUCHI. "Heuristic Rule Weight Specification for Fuzzy Rule-Based Classification Systems." Journal of Japan Society for Fuzzy Theory and Intelligent Informatics 16, no. 5 (2004): 441–51. http://dx.doi.org/10.3156/jsoft.16.441.

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Riid, Andri, and Ennu Rüstern. "Adaptability, interpretability and rule weights in fuzzy rule-based systems." Information Sciences 257 (February 2014): 301–12. http://dx.doi.org/10.1016/j.ins.2012.12.048.

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Ishibuchi, H., and T. Nakashima. "Effect of rule weights in fuzzy rule-based classification systems." IEEE Transactions on Fuzzy Systems 9, no. 4 (2001): 506–15. http://dx.doi.org/10.1109/91.940964.

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Plinere, Darya, and Arkady Borisov. "SWRL: Rule Acquisition Using Ontology." Scientific Journal of Riga Technical University. Computer Sciences 40, no. 1 (January 1, 2009): 117–22. http://dx.doi.org/10.2478/v10143-010-0016-8.

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SWRL: Rule Acquisition Using Ontology Nowadays rule-based systems are very common. The use of ontology-based systems is becoming ever more popular, especially in addition to the rule-based one. The most widely used ontology development platform is Protégé. Protégé provides a knowledge acquisition tool, but still the main issue of the ontology-based rule system is rule acquisition. This paper presents an approach to using SWRL rules Tab, a plug-in to Protégé, for rule acquisition. SWRL rules Tab transforms conjunctive rules to Jess rules in IF…THEN form.

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Eick, C. F., and P. Werstein. "Rule-based consistency enforcement for knowledge-based systems." IEEE Transactions on Knowledge and Data Engineering 5, no. 1 (1993): 52–64. http://dx.doi.org/10.1109/69.204091.

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Wang, De Xing, Hong Yan Lu, and Hong Wei Lu. "Rule Acquisition Based on Inconsistent Decision-Making Information System." Applied Mechanics and Materials 411-414 (September 2013): 1975–78. http://dx.doi.org/10.4028/www.scientific.net/amm.411-414.1975.

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Rule acquisition is a hot topic in the field of data mining. And the inconsistent information systems are widespread nowadays. However, rules acquisition methods are always the difficulty of rough set theory application in inconsistent decision information systems; So the paper proposes a new rule acquisition method. Firstly, we use maximum distribution reduction method for knowledge reduction in single decision-making inconsistent information system and then we use decision-making resolution matrix and decision-making matrix function to get the decision rules. Finally, we mine the rules from inconsistent decision-making information systems.

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Yang, Long-Hao, Ying-Ming Wang, and Yang-Geng Fu. "A consistency analysis-based rule activation method for extended belief-rule-based systems." Information Sciences 445-446 (June 2018): 50–65. http://dx.doi.org/10.1016/j.ins.2018.02.059.

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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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Artiemjew, Piotr, Lada Rudikova, and Oleg Myslivets. "About Rule-Based Systems: Single Database Queries for Decision Making." Future Internet 12, no. 12 (November 27, 2020): 212. http://dx.doi.org/10.3390/fi12120212.

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One of the developmental directions of Future Internet technologies is the implementation of artificial intelligence systems for manipulating data and the surrounding world in a more complex way. Rule-based systems, very accessible for people’s decision-making, play an important role in the family of computational intelligence methods. The use of decision-making rules along with decision trees are one of the simplest forms of presenting complex decision-making processes. Decision support systems, according to the cross-industry standard process for data mining (CRISP-DM) framework, require final embedding of the learned model in a given computer infrastructure, integrated circuits, etc. In this work, we deal with the topic concerning placing the learned rule-based model of decision support in the database environment-exactly in the SQL database tables. Our main goal is to place the previously trained model in the database and apply it by means of single queries. In our work we assume that the decision-making rules applied are mutually consistent and additionally the Minimal Description Length (MDL) rule is introduced. We propose a universal solution for any IF THEN rule induction algorithm.

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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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Jiang, Keyu, Hanyi Zhang, Weiting Zhang, Liming Fang, Chunpeng Ge, Yuan Yuan, and Zhe Liu. "TapChain: A Rule Chain Recognition Model Based on Multiple Features." Security and Communication Networks 2021 (December 23, 2021): 1–11. http://dx.doi.org/10.1155/2021/6568602.

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Trigger-action programming (TAP) is an intelligent tool, which makes it easy for users to make intelligent rules for IoT devices and applications. Unfortunately, with the popularization of TAP and more and more rules, the rule chain from multiple rules appears gradually and brings more and more threats. Previous work pays more attention to the construction of the security model, but few people focus on how to accurately identify the rule chain from multiple rules. Inaccurate identification of rule chains will lead to the omission of rule chains with threats. This paper proposes a rule chain recognition model based on multiple features, TapChain, which can more accurately identify the rule chain without source code. We design a correction algorithm for TapChain to help us get the correct NLP analysis results. We extract 12 features from 5 aspects of the rules to make the recognition of the rule chain more accurate. According to the evaluation, compared with the previous work, the accuracy rate of TapChain is increased by 3.1%, the recall rate is increased by 1.4%, and the precision rate can reach 88.2%. More accurate identification of the rule chain can help to better implement the security policies and better balance security and availability. What’s more, according to the rule chain that TapChain can recognize, there is a new kind of rule chain with threats. We give the relevant case studies in the evaluation.

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Cherniack, Mitch, and Stanley B. Zdonik. "Rule languages and internal algebras for rule-based optimizers." ACM SIGMOD Record 25, no. 2 (June 1996): 401–12. http://dx.doi.org/10.1145/235968.233356.

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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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Kim, Jun, Yoo, and Kim. "A Framework for Improving Reliability of Water Distribution Systems Based on A Segment-Based Minimum Cut-Set Approach." Water 11, no. 7 (July 23, 2019): 1524. http://dx.doi.org/10.3390/w11071524.

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A framework is presented to effectively improve the water distribution system (WDS) reliability. The proposed framework first classifies pipes in a WDS into three reinforcement types through topological analyses and hydraulic simulations over the WDS; type 1: no reinforcement, type 2: increasing pipe durability, and type 3: installing valve(s) at both ends. Then two rules, rules 1 and 2, are implemented, in which rule 1 first reinforces the pipe with the lowest reliability but rule 2 preferentially reduces damage size by pipe failures represented as the expected number of customers out of service (EN). The proposed method was applied to Cherry Hill network, and considerably improved the WDS reliability from 0.137 to 0.483. Both approaches showed notable differences in changes of the EN at each step of reinforcement, where rule 2 showed a better capability to reduce the EN and detect vulnerable areas in the WDS than rule 1. In addition, a practical approach, maximizing valve installations and minimizing pipe replacements according to the reinforcement types in the system, provided an improved WDS reliability (0.423) close to that of the rule-based approach (0.483) using only 21% of the construction cost by the rule-based approach. The proposed framework can be guidelines for improving the WDS reliability under restricted budget and site conditions.

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SANZOGN, LOUIS, FRANCIS SURAWEERA, and GEOFFREY I. WEBB. "Improving the efficiency of rule-based expert systems by rule activation." Journal of Experimental & Theoretical Artificial Intelligence 2, no. 4 (October 1990): 369–80. http://dx.doi.org/10.1080/09528139008953732.

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BENDAVID, A. "Rule effectiveness in rule-based systems: A credit scoring case study." Expert Systems with Applications 34, no. 4 (May 2008): 2783–88. http://dx.doi.org/10.1016/j.eswa.2007.05.003.

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Li, Meng, and Yong Chen. "Designing of power system stabilizer based on neural-like P systems." Proceedings of the Institution of Mechanical Engineers, Part I: Journal of Systems and Control Engineering 234, no. 2 (May 27, 2019): 199–210. http://dx.doi.org/10.1177/0959651819851668.

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Power system stabilizers play an important role in reducing the low-frequency oscillation. In this article, the problem of robustly selecting the parameters of the power system stabilizers is studied. A new neural-like P systems optimization algorithm is proposed in order to optimize the power system stabilizer parameters. First, the structure of the neural-like P systems is established. Then, the operation rules, including forgetting rule, spiking rule, evolving rule, and transferring rule, are designed. Furthermore, a new objective function is constructed on the eigenvalues and damping ratio. Finally, the proposed algorithm is tested on the 16-machine and 68-bus system. The simulation results show the effectiveness and robustness of the proposed methods to select the optimal power system stabilizer parameters for damping out the low f oscillation.

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Ravi, Chandrasekar, and Neelu Khare. "Review of Fuzzy Rule Based Classification systems." Research Journal of Pharmacy and Technology 9, no. 8 (2016): 1299. http://dx.doi.org/10.5958/0974-360x.2016.00247.x.

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48

Lee, Dong-Woo. "Rule-Based Cooperation of Distributed EC Systems." International Journal of Contents 5, no. 3 (September 28, 2009): 79–85. http://dx.doi.org/10.5392/ijoc.2009.5.3.079.

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Yun-Hong Lee and A. M. K. Cheng. "Optimizing real-time equational rule-based systems." IEEE Transactions on Software Engineering 30, no. 2 (February 2004): 112–25. http://dx.doi.org/10.1109/tse.2004.1265816.

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50

Gupta, A., C. Forgy, and A. Newell. "High-speed implementations of rule-based systems." ACM Transactions on Computer Systems 7, no. 2 (May 1989): 119–46. http://dx.doi.org/10.1145/63404.63405.

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Journal articles on the topic 'Rule based systems'

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