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Journal articles on the topic 'Rule based programming'

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Published: 4 June 2021

Last updated: 25 May 2024

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1

APT, KRZYSZTOF R., and ERIC MONFROY. "Constraint programming viewed as rule-based programming." Theory and Practice of Logic Programming 1, no. 6 (November 2001): 713–50. http://dx.doi.org/10.1017/s1471068401000072.

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We study here a natural situation when constraint programming can be entirely reduced to rule-based programming. To this end we explain first how one can compute on constraint satisfaction problems using rules represented by simple first-order formulas. Then we consider constraint satisfaction problems that are based on predefined, explicitly given constraints. To solve them we first derive rules from these explicitly given constraints and limit the computation process to a repeated application of these rules, combined with labeling. We consider two types of rule here. The first type, that we call equality rules, leads to a new notion of local consistency, called rule consistency that turns out to be weaker than arc consistency for constraints of arbitrary arity (called hyper-arc consistency in Marriott & Stuckey (1998)). For Boolean constraints rule consistency coincides with the closure under the well-known propagation rules for Boolean constraints. The second type of rules, that we call membership rules, yields a rule-based characterization of arc consistency. To show feasibility of this rule-based approach to constraint programming, we show how both types of rules can be automatically generated, as CHR rules of Frühwirth (1995). This yields an implementation of this approach to programming by means of constraint logic programming. We illustrate the usefulness of this approach to constraint programming by discussing various examples, including Boolean constraints, two typical examples of many valued logics, constraints dealing with Waltz's language for describing polyhedral scenes, and Allen's qualitative approach to temporal logic.

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2

Kirchner, Claude, and Christophe Ringeissen. "Rule-Based Constraint Programming." Fundamenta Informaticae 34, no. 3 (1998): 225–62. http://dx.doi.org/10.3233/fi-1998-34302.

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BROWNE, JAMES C., ALLEN EMERSON, MOHAMED G. GOUDA, DANIEL P. MIRANKER, ALOYSIUS MOK, LANCE OBERMEYER, FURMAN HADDIX, RWO-HSI WANG, and SARAH CHODROW. "MODULARITY AND RULE-BASED PROGRAMMING." International Journal on Artificial Intelligence Tools 04, no. 01n02 (June 1995): 201–18. http://dx.doi.org/10.1142/s0218213095000115.

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In this paper we describe a purely declarative method for introducing modularity into forward-chaining, rule-based languages. The method is enforced by the syntax of the language and includes the ability to parameterize the rule groups. We also describe the Venus programming environment, which implements the presented ideas. Drawing from two of three Venus applications developed to date, we illustrate how this form of modularity contributes directly to the resolution of certain software engineering problems associated with rule languages. We also discuss key implementation details and present performance data.

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Drewes, Frank, Berthold Hoffmann, Raimund Klein, and Mark Minas. "Rule-Based Programming with Diaplan." Electronic Notes in Theoretical Computer Science 127, no. 1 (March 2005): 15–26. http://dx.doi.org/10.1016/j.entcs.2004.12.027.

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5

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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Sanford, David L., and J. W. Roach. "Using rule‐based computer programming to unify communication rules research." Research on Language & Social Interaction 21, no. 1-4 (January 1987): 63–92. http://dx.doi.org/10.1080/08351818709389285.

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7

Xu, Yuzhao, Yanjing Sun, Zhanguo Ma, Hongjie Zhao, Yanfen Wang, and Nannan Lu. "Attribute Selection Based Genetic Network Programming for Intrusion Detection System." Journal of Advanced Computational Intelligence and Intelligent Informatics 26, no. 5 (September 20, 2022): 671–83. http://dx.doi.org/10.20965/jaciii.2022.p0671.

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Intrusion detection, as a technology used to monitor abnormal behavior and maintain network security, has attracted many researchers’ attention in recent years. Thereinto, association rule mining is one of the mainstream methods to construct intrusion detection systems (IDS). However, the existing association rule algorithms face the challenges of high false positive rate and low detection rate. Meanwhile, too many rules might lead to the uncertainty increase that affects the performance of IDS. In order to tackle the above problems, a modified genetic network programming (GNP) is proposed for class association rule mining. Specifically, based on the property that node connections in the directed graph structure of GNP can be used to construct attribute associations, we propose to introduce information gain into GNP node selection. The most important attributes are thus selected, and the irrelevant attributes are removed before the rule is extracted. Moreover, not only the uncertainty among the class association rules is alleviated and also time consumption is reduced. The extracted rules can be applied to any classifier without affecting the detection performance. Experiment results based on NSL-KDD and KDDCup99 verify the performance of our proposed algorithm.

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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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9

Lamma, E., L. Maestrami, P. Mello, F. Riguzzi, and S. Storari. "Rule-based Programming for Building Expert Systems." Electronic Notes in Theoretical Computer Science 59, no. 4 (November 2001): 397–411. http://dx.doi.org/10.1016/s1571-0661(04)00299-3.

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Olmedo-Aguirre, José Oscar, and Marisol Vázquez-Tzompantzi. "Flexible Rule-Based Programming for Autonomic Computing." Research in Computing Science 105, no. 1 (December 31, 2015): 63–73. http://dx.doi.org/10.13053/rcs-105-1-6.

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Herrera, Juan Felix Avila. "Integer Programming Applied to Rule Based Systems." Procedia Computer Science 9 (2012): 1553–62. http://dx.doi.org/10.1016/j.procs.2012.04.170.

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Michel, Olivier, Antoine Spicher, and Jean-Louis Giavitto. "Rule-based programming for integrative biological modeling." Natural Computing 8, no. 4 (November 12, 2008): 865–89. http://dx.doi.org/10.1007/s11047-008-9105-9.

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13

Vesonder, Gregg T. "Rule-Based Programming in theUnix® System." AT&T Technical Journal 67, no. 1 (January 2, 1988): 69–80. http://dx.doi.org/10.1002/j.1538-7305.1988.tb00235.x.

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14

Asghar, M., Aamir I. Bhatti, and Tahir Izhar. "Benchmark fuel economy for a parallel hybrid electric three-wheeler vehicle (rickshaw)." Advances in Mechanical Engineering 10, no. 12 (December 2018): 168781401880865. http://dx.doi.org/10.1177/1687814018808657.

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The core contribution to this work is the development of benchmark fuel economy for a three-wheeler hybrid electric rickshaw and its comparison with heuristics controllers designed with optimal and non-optimal rules. Dynamic programming is used as a feasible technique for powertrain benchmark analysis. A parallel hybrid electric three-wheeler vehicle is modeled in MATLAB/Simulink through forward facing simulator. The dynamic programming technique is employed through the backward facing simulator, ensuring optimal power sharing between two energy sources (engine and motor) while keeping the battery state of charge in the charge-sustaining mode. The extracted rules from dynamic programming forming near-optimal control strategies are playing a vital role in deciding overall fuel consumption. Unlike the dynamic programming control actions, these extracted rules are implementable through the forward facing simulator. From the simulation results, it can be concluded that a substantial improvement of fuel economy is achieved through the application of dynamic programming. Rule-based (near-optimal) strategy using dynamic programming results shows about 9% more fuel consumption as compared with the dynamic programming (benchmark solution), which is then compared with non-optimal rule-based heuristics controller. It is shown that non-optimal rule-based controller has 18% more fuel consumption than dynamic programming results.

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Lu, Jianjun, Shozo Tokinaga, and Yoshikazu Ikeda. "EXPLANATORY RULE EXTRACTION BASED ON THE TRAINED NEURAL NETWORK AND THE GENETIC PROGRAMMING." Journal of the Operations Research Society of Japan 49, no. 1 (2006): 66–82. http://dx.doi.org/10.15807/jorsj.49.66.

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16

Du, Changqing, Shiyang Huang, Yuyao Jiang, Dongmei Wu, and Yang Li. "Optimization of Energy Management Strategy for Fuel Cell Hybrid Electric Vehicles Based on Dynamic Programming." Energies 15, no. 12 (June 13, 2022): 4325. http://dx.doi.org/10.3390/en15124325.

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Fuel cell hybrid electric vehicles have attracted a large amount of attention in recent years owing to their advantages of zero emissions, high efficiency and low noise. To improve the fuel economy and system durability of vehicles, this paper proposes an energy management strategy optimization method for fuel cell hybrid electric vehicles based on dynamic programming. Rule-based and dynamic-programming-based strategies are developed based on building a fuel cell/battery hybrid system model. The rule-based strategy is improved with a power distribution scheme of dynamic programming strategy to improve the fuel economy of the vehicle. Furthermore, a limit on the rate of change of the output power of the fuel cell system is added to the rule-based strategy to avoid large load changes to improve the durability of the fuel cell. The simulation results show that the equivalent 100 km hydrogen consumption of the strategy based on the dynamic programming optimization rules is reduced by 6.46% compared with that before the improvement, and by limiting the rate of change of the output power of the fuel cell system, the times of large load changes are reduced. Therefore, the strategy based on the dynamic programming optimization rules effectively improves the fuel economy and system durability of vehicles.

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Fallah-Mehdipour, E., O. Bozorg Haddad, and M. A. Mariño. "Developing reservoir operational decision rule by genetic programming." Journal of Hydroinformatics 15, no. 1 (July 30, 2012): 103–19. http://dx.doi.org/10.2166/hydro.2012.140.

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The reservoir operational decision rule is an equation that can balance reservoir system parameters in each period by considering previous experiences of the system. That equation includes variables such as inflow, volume storage and released water from the reservoir that are commonly related to each other by some constant coefficients in predefined linear and nonlinear patterns. Although optimization tools have been extensively applied to develop an optimal operational decision rule, only optimal constant coefficients have been derived and the operational patterns are assumed to be fixed in that operational rule curve. Genetic programming (GP) is an evolutionary algorithm (EA), based on genetic algorithm (GA), which is capable of calculating an operational rule curve by considering optimal operational undefined patterns. In this paper, GP is used to extract optimal operational decision rules in two case studies by meeting downstream water demands and hydropower energy generation. The extracted rules are compared with common linear and nonlinear decision rules, LDR and NLDR, determined by a software package for interactive general optimization (LINGO) and GA. The GP rule improves the objective functions in the training and testing data sets by 2.48 and 8.53%, respectively, compared to the best rule by LINGO and GA in supplying downstream demand. Similarly, the hydropower energy generation improves by 48.03 and 44.21% in the training and testing data sets, respectively. Results show that the obtained objective function value is enhanced significantly for both the training and testing data using GP. They also indicate that the proposed rule, based on GP, is effective in determining optimal rule curves for reservoirs.

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18

Dash, Sanjeeb, and Joao Goncalves. "Rule Induction in Knowledge Graphs Using Linear Programming." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 4 (June 26, 2023): 4233–41. http://dx.doi.org/10.1609/aaai.v37i4.25541.

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We present a simple linear programming (LP) based method to learn compact and interpretable sets of rules encoding the facts in a knowledge graph (KG) and use these rules to solve the KG completion problem. Our LP model chooses a set of rules of bounded complexity from a list of candidate first-order logic rules and assigns weights to them. The complexity bound is enforced via explicit constraints. We combine simple rule generation heuristics with our rule selection LP to obtain predictions with accuracy comparable to state-of-the-art codes, even while generating much more compact rule sets. Furthermore, when we take as input rules generated by other codes, we often improve interpretability by reducing the number of chosen rules, while maintaining accuracy.

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Sumarudin, Sumarudin, Iryanto Iryanto, and Eka Ismantohadi. "Classification of Indramayu’s Mangoes Based on Shape Features Using Neural Network and Rule Based Programming." Proceeding International Conference on Science and Engineering 1 (October 31, 2017): 245–48. http://dx.doi.org/10.14421/icse.v1.310.

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Object classification using image processing simplifies the process. Many approaches have been used to classify the object. In general, classification of mangoes uses image of leaves. In this research, we do a slightly different approach using image of mango itself. Here, two kinds of method are used to classify the object. Implementations of deep learning using neural network and rule based programming are used in the process. Comparative study of the methods are presented in the article. Our result show that accuracy of deep learning approach is better than the rule based programming. The accuracy is 80% and 8% for neural network and rule based programming, respectively.

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20

Wu, Shuang. "FANUC CNC Lathe System Based on Macro Processing of Special Curves." Applied Mechanics and Materials 716-717 (December 2014): 717–19. http://dx.doi.org/10.4028/www.scientific.net/amm.716-717.717.

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Based on FANUC nc lathe catenary macro program, explores the rule curve macro program compiling method, using a simple example is given to illustrate the macro program programming method, the workpiece coordinate origin is not Z zero programming method, the continuous processing same shape graphics macro programming method, provides the programming method of the other special rule curve and reference .

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21

Erwig, Martin, and Delin Ren. "A rule-based language for programming software updates." ACM SIGPLAN Notices 37, no. 12 (December 2002): 88–97. http://dx.doi.org/10.1145/636517.636530.

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22

DASH, P. K., and A. C. LIEW. "A RULE BASED PROGRAMMING APPROACH FOR UNIT COMMITMENT." Electric Machines & Power Systems 23, no. 4 (July 1995): 469–82. http://dx.doi.org/10.1080/07313569508955637.

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23

Ftulis, S. G., M. Giordano, J. J. Plüss, and R. J. Vota. "Rule-based constraints programming: application to crew assignment." Expert Systems with Applications 15, no. 1 (July 1998): 77–85. http://dx.doi.org/10.1016/s0957-4174(98)00013-x.

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24

Greenberg, Harvey J. "Rule-based intelligence to support linear programming analysis." Decision Support Systems 9, no. 4 (June 1993): 425–47. http://dx.doi.org/10.1016/0167-9236(93)90051-4.

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Cirstea, Horatiu, Pierre-Etienne Moreau, and Antoine Reilles. "Rule-based Programming in Java For Protocol Verification." Electronic Notes in Theoretical Computer Science 117 (January 2005): 209–27. http://dx.doi.org/10.1016/j.entcs.2004.06.022.

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26

Gupta, Gopal, Huaduo Wang, Kinjal Basu, Farahad Shakerin, Parth Padalkar, Elmer Salazar, Sarat Chandra Varanasi, and Sopam Dasgupta. "Logic-Based Explainable and Incremental Machine Learning." Proceedings of the AAAI Symposium Series 2, no. 1 (January 22, 2024): 230–32. http://dx.doi.org/10.1609/aaaiss.v2i1.27678.

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Mainstream machine learning methods lack interpretability, explainability, incrementality, and data-economy. We propose using logic programming (LP) to rectify these problems. We discuss the FOLD family of rule-based machine learning algorithms that learn models from relational datasets as a set of default rules. These models are competitive with state-of-the-art machine learning systems in terms of accuracy and execution efficiency. We also motivate how logic programming can be useful for theory revision and explanation based learning.

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Yang, Guangfei, Kaoru Shimada, Shingo Mabu, Kotaro Hirasawa, and Jinglu Hu. "A Genetic Network Programming Based Method to Mine Generalized Association Rules with Ontology." Journal of Advanced Computational Intelligence and Intelligent Informatics 12, no. 1 (January 20, 2008): 63–76. http://dx.doi.org/10.20965/jaciii.2008.p0063.

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In this paper, we propose a Genetic Network Programming based method to mine equalized association rules in multi concept layers of ontology. We first introduce ontology to facilitate building the multi concept layers and propose Dynamic Threshold Approach (DTA) to equalize the different layers. We make use of an evolutionary computation method called Genetic Network Programming (GNP) to mine the rules and develop a new genetic operator to speed up searching the rule space.

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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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Ye, Fengming, Lu Yu, Shingo Mabu, Kaoru Shimada, and Kotaro Hirasawa. "Genetic Network Programming with Rules." Journal of Advanced Computational Intelligence and Intelligent Informatics 13, no. 1 (January 20, 2009): 16–24. http://dx.doi.org/10.20965/jaciii.2009.p0016.

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Genetic Network Programming (GNP) is an evolutionary approach which can evolve itself and find the optimal solutions. It is based on the idea of Genetic Algorithm and uses the data structure of directed graphs. Many papers have demonstrated that GNP can deal with complex problems in the dynamic environments very efficiently and effectively. As a result, recently, GNP is getting more and more attentions and is being used in many different areas such as data mining, extracting trading rules of stock markets, elevator systems, etc and GNP has obtained some outstanding results. In order to improve GNP's performance further, this paper proposes a new method called GNP with Rules. The aim of the proposed method is to balance exploitation and exploration of GNP, that is, to strengthen exploitation ability by using the exploited information extensively during the evolution process of GNP. The proposed method consists of 4 steps: rule extraction, rule selection, individual reconstruction and individual replacement. These 4 steps are added to the conventional algorithm of GNP. In order to measure the performance of the proposed method, the tileworld was used as the simulation environment. The simulation results show some advantages of GNP with Rules over conventional GNPs.

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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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Ladas, Nektarios, Florian Borchert, Stefan Franz, Alina Rehberg, Natalia Strauch, Kim Katrin Sommer, Michael Marschollek, and Matthias Gietzelt. "Programming techniques for improving rule readability for rule-based information extraction natural language processing pipelines of unstructured and semi-structured medical texts." Health Informatics Journal 29, no. 2 (April 2023): 146045822311646. http://dx.doi.org/10.1177/14604582231164696.

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Background Extraction of medical terms and their corresponding values from semi-structured and unstructured texts of medical reports can be a time-consuming and error-prone process. Methods of natural language processing (NLP) can help define an extraction pipeline for accomplishing a structured format transformation strategy. Objectives In this paper, we build an NLP pipeline to extract values of the classification of malignant tumors (TNM) from unstructured and semi-structured pathology reports and import them further to a structured data source for a clinical study. Our research interest is not focused on standard performance metrics like precision, recall, and F-measure on the test and validation data. We discuss how with the help of software programming techniques the readability of rule-based (RB) information extraction (IE) pipelines can be improved, and therefore minimize the time to correct or update the rules, and efficiently import them to another programming language. Methods The extract rules were manually programmed with training data of TNM classification and tested in two separate pipelines based on design specifications from domain experts and data curators. Firstly we implemented each rule directly in one line for each extraction item. Secondly, we reprogrammed them in a readable fashion through decomposition and intention-revealing names for the variable declaration. To measure the impact of both methods we measure the time for the fine-tuning and programming of the extractions through test data of semi-structured and unstructured texts. Results We analyze the benefits of improving through readability of the writing of rules, through parallel programming with regular expressions (REGEX), and the Apache Uima Ruta language (AURL). The time for correcting the readable rules in AURL and REGEX was significantly reduced. Complicated rules in REGEX are decomposed and intention-revealing declarations were reprogrammed in AURL in 5 min. Conclusion We discuss the importance of factor readability and how can it be improved when programming RB text IE pipelines. Independent of the features of the programming language and the tools applied, a readable coding strategy can be proven beneficial for future maintenance and offer an interpretable solution for understanding the extraction and for transferring the rules to other domains and NLP pipelines.

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Li, Tao. "A rule-based and object-oriented AI programming language." ACM SIGPLAN Notices 30, no. 12 (December 1995): 17–24. http://dx.doi.org/10.1145/219726.219736.

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Li, X. "Quality time-What's so bad about rule-based programming?" IEEE Software 8, no. 5 (1991): 103, 105. http://dx.doi.org/10.1109/52.84226.

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Yang, Jae Dong. "F_MP: A fuzzy match framework for rule-based programming." Data & Knowledge Engineering 24, no. 2 (November 1997): 183–203. http://dx.doi.org/10.1016/s0169-023x(97)00005-0.

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35

Raatz, Stan, and George Drastal. "Patterns of interaction in rule-based expert system programming." Computational Intelligence 3, no. 1 (February 1987): 107–16. http://dx.doi.org/10.1111/j.1467-8640.1987.tb00179.x.

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del Río, A., and P. Mariño. "A rule based programming language for real-time applications." Microprocessing and Microprogramming 39, no. 2-5 (December 1993): 139–42. http://dx.doi.org/10.1016/0165-6074(93)90074-u.

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37

García-Camino, Andrés, Juan A. Rodríguez-Aguilar, Carles Sierra, and Wamberto Vasconcelos. "Constraint rule-based programming of norms for electronic institutions." Autonomous Agents and Multi-Agent Systems 18, no. 1 (September 3, 2008): 186–217. http://dx.doi.org/10.1007/s10458-008-9059-4.

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Bocionek, Siegfried, and Klaus Fischer. "Task-oriented programming with co-operating rule-based modules." Engineering Applications of Artificial Intelligence 2, no. 3 (September 1989): 207–13. http://dx.doi.org/10.1016/0952-1976(89)90004-3.

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RAMAKRISHNAN, C. R., I. V. RAMAKRISHNAN, and DAVID S. WARREN. "XcelLog: a deductive spreadsheet system." Knowledge Engineering Review 22, no. 3 (September 2007): 269–79. http://dx.doi.org/10.1017/s026988890700118x.

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AbstractThe promise of rule-based computing was to allow end-users to create, modify, and maintain applications without the need to engage programmers. But experience has shown that rule sets often interact in subtle ways, making them difficult to understand and reason about. This has impeded the widespread adoption of rule-based computing. This paper describes the design and implementation of XcelLog, a user-centered deductive spreadsheet system, to empower non-programmers to specify and manipulate rule-based systems. The driving idea underlying the system is to treat sets as the fundamental data type and rules as specifying relationships among sets, and use the spreadsheet metaphor to create and view the materialized sets. The fundamental feature that makes XcelLog suitable for non-programmers is that the user mainly sees the effect of the rules; when rules or basic facts change, the user sees the impact of the change immediately. This enables the user to gain confidence in the rules and their modification, and also experiment with what-if scenarios without any programming. Preliminary experience with using XcelLog indicates that it is indeed feasible to put the power of deductive spreadsheets for doing rule-based computing into the hands of end-users and do so without the requirement of programming or the constraints of canned application packages.

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POGGI, AGOSTINO. "INTEGRATING OBJECT-ORIENTED AND RULE-BASED PROGRAMMING FOR BUILDING AGENT SYSTEMS." International Journal on Artificial Intelligence Tools 07, no. 02 (June 1998): 221–38. http://dx.doi.org/10.1142/s0218213098000123.

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To facilitate the success of agent technology, we need suitable tools both to reduce the effort in the development of agent systems and to obtain efficient implementations of them. This paper presents an agent-oriented language for the development of multi-agent systems, called ALL (Agent Level Language), that should offer both the two features. In fact, on the one hand, ALL integrates object-oriented and rule based programming to simplify the definition of agent systems and the management of interactions between agents. On the other hand, ALL can be translated into C++, Common Lisp and Java code and can encapsulate objects written in these three languages allowing the reuse of a large amount of pre-existent software and the realization of efficient applications in a large set of (different) domains. ALL rules derive from YACC rules and can simplify the management of agents interaction because they are suitable for the parsing of messages and because they can be reused thanks to object-oriented inheritance.

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B., Suma, and Shobha G. "Association rule hiding using integer linear programming." International Journal of Electrical and Computer Engineering (IJECE) 11, no. 4 (August 1, 2021): 3451. http://dx.doi.org/10.11591/ijece.v11i4.pp3451-3458.

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<span>Privacy preserving data mining has become the focus of attention of government statistical agencies and database security research community who are concerned with preventing privacy disclosure during data mining. Repositories of large datasets include sensitive rules that need to be concealed from unauthorized access. Hence, association rule hiding emerged as one of the powerful techniques for hiding sensitive knowledge that exists in data before it is published. In this paper, we present a constraint-based optimization approach for hiding a set of sensitive association rules, using a well-structured integer linear program formulation. The proposed approach reduces the database sanitization problem to an instance of the integer linear programming problem. The solution of the integer linear program determines the transactions that need to be sanitized in order to conceal the sensitive rules while minimizing the impact of sanitization on the non-sensitive rules. We also present a heuristic sanitization algorithm that performs hiding by reducing the support or the confidence of the sensitive rules. The results of the experimental evaluation of the proposed approach on real-life datasets indicate the promising performance of the approach in terms of side effects on the original database.</span>

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Caluza, Las Johansen B. "Fuzzy Unordered Rule Induction Algorithm Application Basic Programming Language Competence: A Rule-Based Model." Indian Journal of Science and Technology 12, no. 12 (March 1, 2019): 1–10. http://dx.doi.org/10.17485/ijst/2019/v12i12/142575.

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Satoh, Ken. "Statutory Interpretation by Case-Based Reasoning through Abductive Logic Programming." Journal of Advanced Computational Intelligence and Intelligent Informatics 1, no. 2 (December 20, 1997): 94–103. http://dx.doi.org/10.20965/jaciii.1997.p0094.

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This paper presents a method for solving statutory interpretation problem in the legal domain by using abductive logic programming. To interpret a rule, we must make an interpretation for legal predicates and terms in the rule; this interpretation is subject to change according to context. In this paper, we solve this problem byomparing and contrasting cases such as the CABARET system. This is done by translating case-based reasoning into abductive logic programming so that similarity between cases with respect to every undefined proposition in the rule is regarded as a set of abducibles that can be changed according to context. Our method is sound and complete in the sense that every possible interpretation is computed exactly.

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VOLIOTIS, C., N. M. SGOUROS, and G. PAPAKONSTANTINOU. "ATTRIBUTE GRAMMAR BASED MODELING OF CONCURRENT CONSTRAINT LOGIC PROGRAMMING." International Journal on Artificial Intelligence Tools 04, no. 03 (September 1995): 383–411. http://dx.doi.org/10.1142/s021821309500019x.

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The development of more powerful Concurrent Constraint Logic Programming (CCLP) languages depends largely on the development of environments that facilitate the specification and integration of constraints in the semantics of a logic program and automatically extract the inherent parallelism of Logic Programming. This paper presents a novel method for automating the parallel AND/OR execution of CCLP applications. This method consists of two stages. The first stage translates a logic program into an equivalent Attribute Grammar providing a common language in which the specifications of constraints and the semantics of a logic program can be expressed. The second stage evaluates this Attribute Grammar using MPAGE, a Multipass Parallel Attribute Grammar Evaluation algorithm. Support for the processing of constraints in the logic program is provided with the creation of a dependency graph between the variables of a logic rule during the first stage. Using this dependency graph MPAGE delays, if necessary, the evaluation of variables in a constraint until they become bound at some later point in processing. As a result our method supports rule representations in CLP languages with no restrictions on the ordering of subgoals. Furthermore, during execution MPAGE allows single rules in a CLP program to deal with multiple assignments of bindings, thus generating a minimal number of processes. Finally, this method is independent from the underlying hardware and its memory organization, therefore it can be implemented on both shared and distributed memory systems.

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Wang, Wenwei, Hong Pan, and Lin Cheng. "Energy management of a dual-motor driven city bus based on reformed dynamic programming." E3S Web of Conferences 236 (2021): 02020. http://dx.doi.org/10.1051/e3sconf/202123602020.

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This paper proposes a reformed dynamic programming (DP) based energy management strategy for a city bus driven by dual-motor coupling propulsion system(DMCPS). An instantaneous optimal problem of DMCPS’s total energy loss is constructed to solve the torque allocation between two motors. Taking the results as extra constraints, a reformed DP architecture aimed at optimal energy consumption is established, where the state variables are the battery’s SOC and operating modes of DMCPS, with a sole decision variable of mode switching action. The optimization results show a close performance to the original method, with the calculation efficiency greatly improved and the calculation time reduced by nearly 97%. To obtain practical rules for real-time application, the mode switching schedule is extracted based on a RBF-SVM classifier, and the torque allocation is ruled by linear function. Simulation results demonstrate that the extracted rules can be executed through an on-board processor, with energy consumption reduced by 2.19% compared to the original rule-based strategy.

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Zhou, Haicheng, Zhaoping Xu, Liang Liu, Dong Liu, and Lingling Zhang. "A Rule-Based Energy Management Strategy Based on Dynamic Programming for Hydraulic Hybrid Vehicles." Mathematical Problems in Engineering 2018 (October 22, 2018): 1–10. http://dx.doi.org/10.1155/2018/9492026.

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Energy management strategy is very important for hydraulic hybrid vehicles to improve fuel economy. The rule-based energy management strategies are widely used in engineering practice due to their simplicity and practicality. However, their performances differ a lot from different parameters and control actions. A rule-based energy management strategy is designed in this paper to realize real-time control of a novel hydraulic hybrid vehicle, and a control parameter selection method based on dynamic programming is proposed to optimize its performance. Firstly, the simulation model of the hydraulic hybrid vehicle is built and validated by the data tested from prototype experimental platform. Based on the simulation model, the optimization method of dynamic programming is used to find the global optimal solution of the engine control for the UDDS drive cycle. Then, the engine control parameters of the rule-based energy management strategy are selected according to the engine control trajectory of the global optimal solution. The simulation results show that the 100 km fuel consumption of the proposed rule-based energy management strategy is 12.7L, which is very close to the global optimal value of 12.4L and is suboptimal.

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Syahidah Sufi Haris, and Nazlia Omar. "Determining Cognitive Category of Programming Question with Rule-based Approach." International Journal of Information Processing and Management 4, no. 3 (May 31, 2013): 86–95. http://dx.doi.org/10.4156/ijipm.vol4.issue3.9.

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O'Leary, Daniel E., and Joanne Schaffer. "The use of mathematical programming to verify rule-based knowledge." Annals of Operations Research 65, no. 1 (August 1996): 181–93. http://dx.doi.org/10.1007/bf02187331.

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Derhami, Shahab, and Alice E. Smith. "An integer programming approach for fuzzy rule-based classification systems." European Journal of Operational Research 256, no. 3 (February 2017): 924–34. http://dx.doi.org/10.1016/j.ejor.2016.06.065.

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Eleyedath, Abhary, and Aravind Krishna Swamy. "Gene expression programming-based viscosity-mixing rule for asphalt blends." Petroleum Science and Technology 35, no. 14 (July 18, 2017): 1508–14. http://dx.doi.org/10.1080/10916466.2017.1347682.

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