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Journal articles on the topic 'Rule Representation'

Author: Grafiati

Published: 9 March 2023

Last updated: 31 July 2025

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1

Raden, Megan J., and Andrew F. Jarosz. "Knowledge Representations: Individual Differences in Novel Problem Solving." Journal of Intelligence 11, no. 4 (2023): 77. http://dx.doi.org/10.3390/jintelligence11040077.

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The present study investigates how the quality of knowledge representations contributes to rule transfer in a problem-solving context and how working memory capacity (WMC) might contribute to the subsequent failure or success in transferring the relevant information. Participants were trained on individual figural analogy rules and then asked to rate the subjective similarity of the rules to determine how abstract their rule representations were. This rule representation score, along with other measures (WMC and fluid intelligence measures), was used to predict accuracy on a set of novel figur

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Branke, Jürgen, Torsten Hildebrandt, and Bernd Scholz-Reiter. "Hyper-heuristic Evolution of Dispatching Rules: A Comparison of Rule Representations." Evolutionary Computation 23, no. 2 (2015): 249–77. http://dx.doi.org/10.1162/evco_a_00131.

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Dispatching rules are frequently used for real-time, online scheduling in complex manufacturing systems. Design of such rules is usually done by experts in a time consuming trial-and-error process. Recently, evolutionary algorithms have been proposed to automate the design process. There are several possibilities to represent rules for this hyper-heuristic search. Because the representation determines the search neighborhood and the complexity of the rules that can be evolved, a suitable choice of representation is key for a successful evolutionary algorithm. In this paper we empirically compa

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Mao, Yanying, and Honghui Chen. "Rule-Guided Compositional Representation Learning on Knowledge Graphs with Hierarchical Types." Mathematics 9, no. 16 (2021): 1978. http://dx.doi.org/10.3390/math9161978.

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The representation learning of the knowledge graph projects the entities and relationships in the triples into a low-dimensional continuous vector space. Early representation learning mostly focused on the information contained in the triplet itself but ignored other useful information. Since entities have different types of representations in different scenarios, the rich information in the types of entity levels is helpful for obtaining a more complete knowledge representation. In this paper, a new knowledge representation frame (TRKRL) combining rule path information and entity hierarchical

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Doğan, Battal, Serhat Doğan, and Kemal Yıldız. "On Capacity-Filling and Substitutable Choice Rules." Mathematics of Operations Research 46, no. 3 (2021): 856–68. http://dx.doi.org/10.1287/moor.2021.1128.

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Each capacity-filling and substitutable choice rule is known to have a maximizer-collecting representation: There exists a list of priority orderings such that from each choice set that includes more alternatives than the capacity, the choice is the union of the priority orderings’ maximizers. We introduce the notion of a critical set and constructively prove that the number of critical sets for a choice rule determines its smallest-size maximizer-collecting representation. We show that responsive choice rules require the maximal number of priority orderings in their smallest-size maximizer-co

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Niu, Guanglin, Yongfei Zhang, Bo Li, et al. "Rule-Guided Compositional Representation Learning on Knowledge Graphs." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 03 (2020): 2950–58. http://dx.doi.org/10.1609/aaai.v34i03.5687.

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Representation learning on a knowledge graph (KG) is to embed entities and relations of a KG into low-dimensional continuous vector spaces. Early KG embedding methods only pay attention to structured information encoded in triples, which would cause limited performance due to the structure sparseness of KGs. Some recent attempts consider paths information to expand the structure of KGs but lack explainability in the process of obtaining the path representations. In this paper, we propose a novel Rule and Path-based Joint Embedding (RPJE) scheme, which takes full advantage of the explainability

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Keck, Janis, Caswell Barry, Christian F. Doeller, and Jürgen Jost. "Impact of symmetry in local learning rules on predictive neural representations and generalization in spatial navigation." PLOS Computational Biology 21, no. 6 (2025): e1013056. https://doi.org/10.1371/journal.pcbi.1013056.

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In spatial cognition, the Successor Representation (SR) from reinforcement learning provides a compelling candidate of how predictive representations are used to encode space. In particular, hippocampal place cells are hypothesized to encode the SR. Here, we investigate how varying the temporal symmetry in learning rules influences those representations. To this end, we use a simple local learning rule which can be made insensitive to the temporal order. We analytically find that a symmetric learning rule results in a successor representation under a symmetrized version of the experienced tran

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Prakash, R. Vijaya, S. S. V. N. Sarma, and M. Sheshikala. "Generating Non-redundant Multilevel Association Rules Using Min-max Exact Rules." International Journal of Electrical and Computer Engineering (IJECE) 8, no. 6 (2018): 4568–76. https://doi.org/10.11591/ijece.v8i6.pp4568-4576.

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Association Rule mining plays an important role in the discovery of knowledge and information. Association Rule mining discovers huge number of rules for any dataset for different support and confidence values, among this many of them are redundant, especially in the case of multi-level datasets. Mining non-redundant Association Rules in multi-level dataset is a big concern in field of Data mining. In this paper, we present a definition for redundancy and a concise representation called Reliable Exact basis for representing non-redundant Association Rules from multi-level datasets. The given n

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Lee, Sang-Yong. "Rule-Following & Perspicuous Representation." Cogito 99 (February 28, 2023): 251–82. http://dx.doi.org/10.48115/cogito.2023.02.99.251.

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

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

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Hardgrove, Anne. "Agrarian Environments: Resources, Representation, and Rule in India:Agrarian Environments: Resources, Representation, and Rule in India." Culture Agriculture 24, no. 2 (2002): 64–65. http://dx.doi.org/10.1525/cag.2002.24.2.64.

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11

Greenberg, Gabriel. "The Iconic-Symbolic Spectrum." Philosophical Review 132, no. 4 (2023): 579–627. http://dx.doi.org/10.1215/00318108-10697558.

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It is common to distinguish two great families of representation. Symbolic representations include logical and mathematical symbols, words, and complex linguistic expressions. Iconic representations include dials, diagrams, maps, pictures, 3-dimensional models, and depictive gestures. This essay describes and motivates a new way of distinguishing iconic from symbolic representation. It locates the difference not in the signs themselves, nor in the contents they express, but in the semantic rules by which signs are associated with contents. The two kinds of rule have divergent forms, occupying

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Sevian, H., S. Bernholt, G. A. Szteinberg, S. Auguste, and L. C. Pérez. "Use of representation mapping to capture abstraction in problem solving in different courses in chemistry." Chemistry Education Research and Practice 16, no. 3 (2015): 429–46. http://dx.doi.org/10.1039/c5rp00030k.

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A perspective is presented on how the representation mapping framework by Hahn and Chater (1998) may be used to characterize reasoning during problem solving in chemistry. To provide examples for testing the framework, an exploratory study was conducted with students and professors from three different courses in the middle of the undergraduate chemistry curriculum. Each participant's reasoning while solving exam problems was characterized by comparing the stored knowledge representation used as a resource and the new instance representation associated with the problem being solved. Doing so r

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NALEPA, GRZEGORZ J., and KRZYSZTOF KLUZA. "UML REPRESENTATION FOR RULE-BASED APPLICATION MODELS WITH XTT2-BASED BUSINESS RULES." International Journal of Software Engineering and Knowledge Engineering 22, no. 04 (2012): 485–524. http://dx.doi.org/10.1142/s021819401250012x.

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In this paper challenges of using Knowledge Engineering methods in the Software Engineering practice, with focus on the visual design methods for software applications, are discussed. The paper concerns practical design issues of rule-based models integrated with business applications built using the Model-View-Controller (MVC), or equivalent architectural pattern. The Unified Modeling Language (UML) constitutes a standardized notation among software engineers. Various attempts has been made to design the model in a way alternative to UML. Recently, the Business Rules approach has been propose

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Betzler, N., A. Slinko, and J. Uhlmann. "On the Computation of Fully Proportional Representation." Journal of Artificial Intelligence Research 47 (July 22, 2013): 475–519. http://dx.doi.org/10.1613/jair.3896.

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We investigate two systems of fully proportional representation suggested by Chamberlin Courant and Monroe. Both systems assign a representative to each voter so that the "sum of misrepresentations" is minimized. The winner determination problem for both systems is known to be NP-hard, hence this work aims at investigating whether there are variants of the proposed rules and/or specific electorates for which these problems can be solved efficiently. As a variation of these rules, instead of minimizing the sum of misrepresentations, we considered minimizing the maximal misrepresentation introdu

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Liu, Han, Alexander Gegov, and Mihaela Cocea. "Rule Based Networks: An Efficient and Interpretable Representation of Computational Models." Journal of Artificial Intelligence and Soft Computing Research 7, no. 2 (2017): 111–23. http://dx.doi.org/10.1515/jaiscr-2017-0008.

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Abstract Due to the vast and rapid increase in the size of data, data mining has been an increasingly important tool for the purpose of knowledge discovery to prevent the presence of rich data but poor knowledge. In this context, machine learning can be seen as a powerful approach to achieve intelligent data mining. In practice, machine learning is also an intelligent approach for predictive modelling. Rule learning methods, a special type of machine learning methods, can be used to build a rule based system as a special type of expert systems for both knowledge discovery and predictive modell

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Bello Hutt, Donald. "Rule of Law and Political Representation." Hague Journal on the Rule of Law 14, no. 1 (2021): 1–25. http://dx.doi.org/10.1007/s40803-021-00163-5.

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AbstractHow do the rule of law and political representation relate to each other? I answer this question, hitherto neglected by rule-of-law scholars, taking my cue from Joseph Raz’s revision of his conception of the rule of law and by relying on a distinction between preferences and interests, which pervades discussions of political representation. I argue that political representatives’ attention to their constituents’ preferences, and not just their interests, is a necessary feature of a conception of representation that expresses a robust allegiance to the rule of law. More specifically, th

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Jarke, M., and M. Jeusfeld. "Rule representation and management in conceptbase." ACM SIGMOD Record 18, no. 3 (1989): 46–51. http://dx.doi.org/10.1145/71031.71037.

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Ergun, Selim Jürgen. "From plurality rule to proportional representation." Economics of Governance 11, no. 4 (2010): 373–408. http://dx.doi.org/10.1007/s10101-010-0081-z.

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Prakash, R. Vijaya, S. S. V. N. Sarma, and M. Sheshikala. "Generating Non-redundant Multilevel Association Rules Using Min-max Exact Rules." International Journal of Electrical and Computer Engineering (IJECE) 8, no. 6 (2018): 4568. http://dx.doi.org/10.11591/ijece.v8i6.pp4568-4576.

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Association Rule mining plays an important role in the discovery of knowledge and information. Association Rule mining discovers huge number of rules for any dataset for different support and confidence values, among this many of them are redundant, especially in the case of multi-level datasets. Mining non-redundant Association Rules in multi-level dataset is a big concern in field of Data mining. In this paper, we present a definition for redundancy and a concise representation called Reliable Exact basis for representing non-redundant Association Rules from multi-level datasets. The given n

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NALEPA, GRZEGORZ J., ANTONI LIGĘZA, and KRZYSZTOF KACZOR. "FORMALIZATION AND MODELING OF RULES USING THE XTT2 METHOD." International Journal on Artificial Intelligence Tools 20, no. 06 (2011): 1107–25. http://dx.doi.org/10.1142/s0218213011000541.

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The paper discusses a new knowledge representation for rule-based systems called XTT2. It combines decision trees and decision tables forming a transparent and hierarchical visual representation of the decision units linked into a workflow-like structure. There are two levels of abstraction in the XTT2 model: the lower level, where a single knowledge component defined by a set of rules working in the same context is represented by a single decision table, and the higher level, where the structure of the whole knowledge base is considered. This model has a concise formalization which opens up p

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Hanson, Stephen José, and David J. Burr. "What connectionist models learn: Learning and representation in connectionist networks." Behavioral and Brain Sciences 13, no. 3 (1990): 471–89. http://dx.doi.org/10.1017/s0140525x00079760.

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AbstractConnectionist models provide a promising alternative to the traditional computational approach that has for several decades dominated cognitive science and artificial intelligence, although the nature of connectionist models and their relation to symbol processing remains controversial. Connectionist models can be characterized by three general computational features: distinct layers of interconnected units, recursive rules for updating the strengths of the connections during learning, and “simple” homogeneous computing elements. Using just these three features one can construct surpri

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Nalepa, Grzegorz, and Antoni Ligęza. "The HeKatE methodology. Hybrid engineering of intelligent systems." International Journal of Applied Mathematics and Computer Science 20, no. 1 (2010): 35–53. http://dx.doi.org/10.2478/v10006-010-0003-9.

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The HeKatE methodology. Hybrid engineering of intelligent systemsThis paper describes a new approach, the HeKatE methodology, to the design and development of complex rule-based systems for control and decision support. The main paradigm for rule representation, namely, eXtended Tabular Trees (XTT), ensures high density and transparency of visual knowledge representation. Contrary to traditional, flat rule-based systems, the XTT approach is focused on groups of similar rules rather than on single rules. Such groups form decision tables which are connected into a network for inference. Efficien

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Deisig, Nina, Martin Giurfa, and Jean Christophe Sandoz. "Antennal Lobe Processing Increases Separability of Odor Mixture Representations in the Honeybee." Journal of Neurophysiology 103, no. 4 (2010): 2185–94. http://dx.doi.org/10.1152/jn.00342.2009.

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Local networks within the primary olfactory centers reformat odor representations from olfactory receptor neurons to second-order neurons. By studying the rules underlying mixture representation at the input to the antennal lobe (AL), the primary olfactory center of the insect brain, we recently found that mixture representation follows a strict elemental rule in honeybees: the more a component activates the AL when presented alone, the more it is represented in a mixture. We now studied mixture representation at the output of the AL by imaging a population of second-order neurons, which conve

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Kikumoto, Atsushi, and Ulrich Mayr. "Conjunctive representations that integrate stimuli, responses, and rules are critical for action selection." Proceedings of the National Academy of Sciences 117, no. 19 (2020): 10603–8. http://dx.doi.org/10.1073/pnas.1922166117.

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People can use abstract rules to flexibly configure and select actions for specific situations, yet how exactly rules shape actions toward specific sensory and/or motor requirements remains unclear. Both research from animal models and human-level theories of action control point to the role of highly integrated, conjunctive representations, sometimes referred to as event files. These representations are thought to combine rules with other, goal-relevant sensory and motor features in a nonlinear manner and represent a necessary condition for action selection. However, so far, no methods exist

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Gill, Amaninder Singh, Joshua D. Summers, and Cameron J. Turner. "Comparing function structures and pruned function structures for market price prediction: An approach to benchmarking representation inferencing value." Artificial Intelligence for Engineering Design, Analysis and Manufacturing 31, no. 4 (2017): 550–66. http://dx.doi.org/10.1017/s0890060417000543.

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AbstractBenchmarking function modeling and representation approaches requires a direct comparison, including the inferencing support by the different approaches. To this end, this paper explores the value of a representation by comparing the ability of a representation to support reasoning based on varying amounts of information stored in the representational components of a function structure: vocabulary, grammar, and topology. This is done by classifying the previously developed functional pruning rules into vocabulary, grammatical, and topological classes and applying them to function struc

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Pinker, Steven. "Representations and decision rules in the theory of self-deception." Behavioral and Brain Sciences 34, no. 1 (2011): 35–37. http://dx.doi.org/10.1017/s0140525x1000261x.

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AbstractSelf-deception is a powerful but overapplied theory. It is adaptive only when a deception-detecting audience is in the loop, not when an inaccurate representation is invoked as an internal motivator. First, an inaccurate representation cannot be equated with self-deception, which entails two representations, one inaccurate and the other accurate. Second, any motivational advantages are best achieved with an adjustment to the decision rule on when to act, not with a systematic error in an internal representation.

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BOCCARA, NINO, and HENRYK FUKŚ. "MOTION REPRESENTATION OF ONE-DIMENSIONAL CELLULAR AUTOMATON RULES." International Journal of Modern Physics C 17, no. 11 (2006): 1605–11. http://dx.doi.org/10.1142/s0129183106010029.

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Generalizing the motion representation we introduced for number-conserving rules, we give a systematic way to construct a generalized motion representation valid for non-conservative rules using the expression of the current, which appears in the discrete version of the continuity equation, completed by the discrete analogue of the source term. This new representation is general, but not unique, and can be used to represent, in a more visual way, any one-dimensional cellular automaton rule. A few illustrative examples are presented.

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Pugh, Zachary H., and Douglas J. Gillan. "Nodes Afford Connection: A Pilot Study Examining the Design and Use of a Graphical Modeling Language." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 65, no. 1 (2021): 1024–28. http://dx.doi.org/10.1177/1071181321651150.

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External representations such as diagrams facilitate reasoning. Many diagramming systems and notations are amenable to manipulation by actual or imagined intervention (e.g., transposing terms in an equation). Such manipulation is constrained by user-enforced constraints, including rules of syntax and semantics which help preserve the representation’s validity. We argue that the concepts of affordances and signifiers can be applied to understand such representations, and we suggest the term graphical affordance to refer to rule-constrained syntactic manipulation of an external representation. F

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Hernández, Alexis R., Carlos Gracia-Lázaro, Edgardo Brigatti, and Yamir Moreno. "A networked voting rule for democratic representation." Royal Society Open Science 5, no. 3 (2018): 172265. http://dx.doi.org/10.1098/rsos.172265.

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We introduce a general framework for exploring the problem of selecting a committee of representatives with the aim of studying a networked voting rule based on a decentralized large-scale platform, which can assure a strong accountability of the elected. The results of our simulations suggest that this algorithm-based approach is able to obtain a high representativeness for relatively small committees, performing even better than a classical voting rule based on a closed list of candidates. We show that a general relation between committee size and representatives exists in the form of an inv

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Kurz, Sascha, Nicola Maaser, and Stefan Napel. "Fair representation and a linear Shapley rule." Games and Economic Behavior 108 (March 2018): 152–61. http://dx.doi.org/10.1016/j.geb.2017.10.002.

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Saad, D. "TRAINING RECURRENT NEURAL NETWORKS — THE MINIMAL TRAJECTORY ALGORITHM." International Journal of Neural Systems 03, no. 01 (1992): 83–101. http://dx.doi.org/10.1142/s0129065792000097.

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The Minimal Trajectory (MINT) algorithm for training recurrent neural networks with a stable end point is based on an algorithmic search for the systems’ representations in the neighbourhood of the minimal trajectory connecting the input-output representations. The said representations appear to be the most probable set for solving the global perceptron problem related to the common weight matrix, connecting all representations of successive time steps in a recurrent discrete neural networks. The search for a proper set of system representations is aided by representation modification rules si

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Craven, Mark W., and Jude W. Shavlik. "Understanding Time-Series Networks: A Case Study in Rule Extraction." International Journal of Neural Systems 08, no. 04 (1997): 373–84. http://dx.doi.org/10.1142/s0129065797000380.

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A significant limitation of neural networks is that the representation they learn are usually incomprehensible to humans. We have developed an algorithm, called TREPAN, for extracting comprehensible, symbolic representations from trained neural networks. Given a trained network, TREPAN produces a decision tree that approximates the concept represented by the network. In this article, we discuss the application of TREPAN to a neural network trained on a noisy time series task: predicting the Dollar–Mark exchange rate. We present experiments that show that TREPAN is able to extract a decision tr

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Jokste, Lauma. "COMPARATIVE EVALUATION OF THE RULE BASED APPROACH TO REPRESENTATION OF ADAPTATION LOGICS." ENVIRONMENT. TECHNOLOGIES. RESOURCES. Proceedings of the International Scientific and Practical Conference 2 (June 20, 2019): 65. http://dx.doi.org/10.17770/etr2019vol2.4156.

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Due to the rapid growth of business processes digitalization, enterprise applications cover more and more business and daily life functions thus becoming more complex. Complex enterprise applications often deal with low users’ satisfaction of usability. This problem can be solved by implementing adaptation algorithms in enterprise applications, so they can be adjusted for specific context situations and specific users’ needs. Some adaptation logics representation techniques are complex and require specific knowledge and skills to manage and modify adaptation process. In this paper rule based a

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Soodak, Robert E. "Reverse-Hebb plasticity leads to optimization and association in a simulated visual cortex." Visual Neuroscience 6, no. 5 (1991): 507–18. http://dx.doi.org/10.1017/s0952523800001358.

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AbstractThe effects of a variable-phase synaptic modification rule on the experience-dependent development of a simulated visual cortex were investigated. Of interest were the process of optimization of the internal representation with respect to orientation, through which the weakly tuned neurons of visually inexperienced animals attain their tightly tuned adult characteristics, and the process of association by which identical stimuli presented to either eye come to evoke identical cortical representations. In its general form, the synaptic modification rule was Hebbian. However, it was not

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Dwivedi, Sanjay K., and Pramod P. Sukhadeve. "English to Hindi Paraphrase Convention for Translating Homoeopathy Literature." International Journal of Artificial Life Research 3, no. 4 (2012): 66–74. http://dx.doi.org/10.4018/ijalr.2012100105.

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The rule based approach to machine translation (MT) confines grammatical rules between the source and the target language with the goal of constructing grammatical translation between the language pair. In this paper, we describe the structural representation of English stemmer, POS tagging and design transfer rules which can generate Hindi sentence from the structural representation of the English sentence. Due to the specific terminology of homoeopathic sentences and the linguistic gap between the two languages the translation of these literatures form English to Hindi is a challenging task.

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Mendelberg, Tali, Christopher F. Karpowitz, and J. Baxter Oliphant. "Gender Inequality in Deliberation: Unpacking the Black Box of Interaction." Perspectives on Politics 12, no. 1 (2014): 18–44. http://dx.doi.org/10.1017/s1537592713003691.

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When and why do women gain from increased descriptive representation in deliberating bodies? Using a large randomized experiment, and linking individual-level speech with assessments of speaker authority, we find that decision rules interact with the number of women in the group to shape the conversation dynamics and deliberative authority, an important form of influence. With majority rule and few women, women experience a negative balance of interruptions when speaking, and these women then lose influence in their own eyes and in others’. But when the group is assigned to unanimous rule, or

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Wang, Ping, and Tai Shan Yan. "A Knowledge Rule Mining Method for the Evaluation of Library Service Quality Based on Genetic Algorithm." Advanced Materials Research 532-533 (June 2012): 1588–92. http://dx.doi.org/10.4028/www.scientific.net/amr.532-533.1588.

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In this study, the evaluation index system of library service quality is established and the representation method of knowledge rule is analyzed firstly. Then, a knowledge rule mining method for the evaluation of library service quality based on an improved genetic algorithm is proposed. In the algorithm, selection operator, help operator, crossover operator and mutation operator are used to generate new knowledge rules. Knowledge rules are evaluated by their accuracy, coverage and reliability. Experimental results show that this knowledge rule mining method is feasible and valid. It is helpfu

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Biletskiy, Yevgen, Harold Boley, Girish R. Ranganathan, and Harold Boley. "RuleML‐based learning object interoperability on the Semantic Web." Interactive Technology and Smart Education 5, no. 1 (2008): 39–58. http://dx.doi.org/10.1108/17415650810871574.

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PurposeThe present paper aims to describe an approach for building the Semantic Web rules for interoperation between heterogeneous learning objects, namely course outlines from different universities, and one of the rule uses: identifying (in)compatibilities between course descriptions.Design/methodology/approachAs proof of concept, a rule set is implemented using the rule markup language (RuleML), a member of XML‐based languages. This representation in RuleML allows the rule base to be platform‐independent, flexibly extensible and executable.FindingsThe RuleML source representation is easily

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Qian, Chen, Shiwei Liang, Zhaoyang Wang, and Yin Lou. "Fanglue: An Interactive System for Decision Rule Crafting." Proceedings of the VLDB Endowment 16, no. 12 (2023): 4062–65. http://dx.doi.org/10.14778/3611540.3611621.

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In many applications the training data do not always contain sufficient information to produce high-quality decision rules for standard (end-to-end) rule mining algorithms, and human experts have to incorporate domain knowledge during rule induction in order to get meaningful results. In this work we present Fanglue, a home-grown system inside Alipay, for interactive decision rule crafting. Fanglue is a distributed in-memory system and is highly responsive when processing large-scale datasets. In addition, Fanglue extends the standard representation of a decision rule by introducing disjunctiv

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Jiang, Dihua, Baiying Liu, and Bin Xu. "A reciprocal branching problem for automorphic representations and global Vogan packets." Journal für die reine und angewandte Mathematik (Crelles Journal) 2020, no. 765 (2020): 249–77. http://dx.doi.org/10.1515/crelle-2019-0016.

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AbstractLet G be a group and let H be a subgroup of G. The classical branching rule (or symmetry breaking) asks: For an irreducible representation π of G, determine the occurrence of an irreducible representation σ of H in the restriction of π to H. The reciprocal branching problem of this classical branching problem is to ask: For an irreducible representation σ of H, find an irreducible representation π of G such that σ occurs in the restriction of π to H. For automorphic representations of classical groups, the branching problem has been addressed by the well-known global Gan–Gross–Prasad c

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CHEN, Yin, Si-Qing SHAN, Lu LIU, and Yan LI. "Minimum-redundant and Lossless Association Rule-set Representation." Acta Automatica Sinica 34, no. 12 (2009): 1490–96. http://dx.doi.org/10.3724/sp.j.1004.2008.01490.

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Moon, Suzanne, Arun Agrawal, and K. Sivaramakrishnan. "Agrarian Environments: Resources, Representation, and Rule in India." Environmental History 7, no. 2 (2002): 337. http://dx.doi.org/10.2307/3985701.

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Moya, Lisa Jean, Frederic D. McKenzie, and Quynh-Anh H. Nguyen. "Visualization and rule validation in human-behavior representation." Simulation & Gaming 39, no. 1 (2007): 101–17. http://dx.doi.org/10.1177/1046878107308096.

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CARPENTER, GAIL A., and AH-HWEE TAN. "Rule Extraction: From Neural Architecture to Symbolic Representation." Connection Science 7, no. 1 (1995): 3–27. http://dx.doi.org/10.1080/09540099508915655.

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TORRE, MASSIMO LA. "Democracy and Tensions. Representation, Majority Rule, Fundamental Rights." Ratio Juris 8, no. 3 (1995): 373–96. http://dx.doi.org/10.1111/j.1467-9337.1995.tb00221.x.

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Seebohm, Thomas, and William Wallace. "Rule-based representation of design in architectural practice." Automation in Construction 8, no. 1 (1998): 73–85. http://dx.doi.org/10.1016/s0926-5805(98)00066-1.

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Ralescu, Anca L. "A note on rule representation in expert systems." Information Sciences 38, no. 2 (1986): 193–203. http://dx.doi.org/10.1016/0020-0255(86)90020-4.

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Segev, Bilha. "Fermi s golden rule in the Wigner representation." Journal of Optics B: Quantum and Semiclassical Optics 5, no. 3 (2003): S381—S387. http://dx.doi.org/10.1088/1464-4266/5/3/373.

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Subramanian, Ajantha. "Agrarian Environments: Resources, Representation, and Rule in India." American Ethnologist 30, no. 3 (2003): 454–55. http://dx.doi.org/10.1525/ae.2003.30.3.454.

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Waldron, Peter. "Wortman, Richard. Russian Monarchy: Representation and Rule (review)." Slavonic and East European Review 93, no. 2 (2015): 371–73. http://dx.doi.org/10.1353/see.2015.0037.

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