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Articles de revues sur le sujet « Rule Representation »

Auteur : Grafiati

Publié le 9 mars 2023

Mis à jour le 10 mars 2023

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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 compare three different representations, both numeric and symbolic, for automated rule design: A linear combination of attributes, a representation based on artificial neural networks, and a tree representation. Using appropriate evolutionary algorithms (CMA-ES for the neural network and linear representations, genetic programming for the tree representation), we empirically investigate the suitability of each representation in a dynamic stochastic job shop scenario. We also examine the robustness of the evolved dispatching rules against variations in the underlying job shop scenario, and visualize what the rules do, in order to get an intuitive understanding of their inner workings. Results indicate that the tree representation using an improved version of genetic programming gives the best results if many candidate rules can be evaluated, closely followed by the neural network representation that already leads to good results for small to moderate computational budgets. The linear representation is found to be competitive only for extremely small computational budgets.

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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 type information is proposed to exploit interpretability of logical rules and the advantages of entity hierarchical types. Specifically, for entity hierarchical type information, we consider that entities have multiple representations of different types, as well as treat it as the projection matrix of entities, using the type encoder to model entity hierarchical types. For rule path information, we mine Horn rules from the knowledge graph to guide the synthesis of relations in paths. Experimental results show that TRKRL outperforms baselines on the knowledge graph completion task, which indicates that our model is capable of using entity hierarchical type information, relation paths information, and logic rules information for representation learning.

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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 and accuracy of logic rules, the generalization of KG embedding as well as the supplementary semantic structure of paths. Specifically, logic rules of different lengths (the number of relations in rule body) in the form of Horn clauses are first mined from the KG and elaborately encoded for representation learning. Then, the rules of length 2 are applied to compose paths accurately while the rules of length 1 are explicitly employed to create semantic associations among relations and constrain relation embeddings. Moreover, the confidence level of each rule is also considered in optimization to guarantee the availability of applying the rule to representation learning. Extensive experimental results illustrate that RPJE outperforms other state-of-the-art baselines on KG completion task, which also demonstrate the superiority of utilizing logic rules as well as paths for improving the accuracy and explainability of representation learning.

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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-collecting representations among all capacity-filling and substitutable choice rules. We also analyze maximizer-collecting choice rules in which the number of priority orderings equals the capacity. We show that if the capacity is greater than three and the number of alternatives exceeds the capacity by at least two, then no capacity-filling and substitutable choice rule has a maximizer-collecting representation of the size equal to the capacity.

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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. Following this argument, we examine a graphical modeling language in terms of these graphical affordances, and we present a pilot study examining how participants interact with the modeling language. Preliminary results suggest that using the modeling language, as opposed to prose representation, influences user behavior in a manner aligned with the graphical affordances and signifiers of the modeling language.

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Arawi, Fadia Amellia. "Perbandingan Representasi Perempuan di Parlemen Indonesia dan Filipina." Politeia: Jurnal Ilmu Politik 14, no. 2 (2022): 74–85. http://dx.doi.org/10.32734/politeia.v14i2.8600.

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The purpose of this article is to examine the contrasts between the Philippines and Indonesia between the two countries. Both countries share the same democracy system, but its democratic value has not been fully operational, and there is still an imbalance for women in the parliament. The problem focused on the disparity of right to politically correct between women and men who were not equally equal. To analyze the problem using the theory of representation according to Hanna pitkin in (niron & seda, 2020), representational representation is divided into 4 of them: formal descriptive, substantiating/resonant and symbolic. Formal representation focuses on the rule of law on the procedure of how a representative is chosen or replaced. Descriptive reliability is focused on compositions that are proportionately composed between those represented and represented. Descriptive adherence is focused on composition in parliament. Subthanctive/clergy awareness focuses on a representative who should be in line with the person it represents. The last time of symbolic representation, it focused on how strongly the symbol was viewed by the viewer. Data was collected through literature studies from previous studies and qualitative analysis. Research has concluded that, based on ipu data in the 2019 elections in Indonesia, women's representations in parliament are only 17.39% while the Philippine parliament is at 27.96%. But in second representation

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Nowak-Brzezińska, Agnieszka. "Enhancing the Efficiency of a Decision Support System through the Clustering of Complex Rule-Based Knowledge Bases and Modification of the Inference Algorithm." Complexity 2018 (December 6, 2018): 1–14. http://dx.doi.org/10.1155/2018/2065491.

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Decision support systems founded on rule-based knowledge representation should be equipped with rule management mechanisms. Effective exploration of new knowledge in every domain of human life requires new algorithms of knowledge organization and a thorough search of the created data structures. In this work, the author introduces an optimization of both the knowledge base structure and the inference algorithm. Hence, a new, hierarchically organized knowledge base structure is proposed as it draws on the cluster analysis method and a new forward-chaining inference algorithm which searches only the so-called representatives of rule clusters. Making use of the similarity approach, the algorithm tries to discover new facts (new knowledge) from rules and facts already known. The author defines and analyses four various representative generation methods for rule clusters. Experimental results contain the analysis of the impact of the proposed methods on the efficiency of a decision support system with such knowledge representation. In order to do this, four representative generation methods and various types of clustering parameters (similarity measure, clustering methods, etc.) were examined. As can be seen, the proposed modification of both the structure of knowledge base and the inference algorithm has yielded satisfactory results.

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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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Reshetnikova, Irina V. "Professional Representation in Russian Courts: The Application Practice." Arbitrazh-civil procedure 11 (October 29, 2020): 19–20. http://dx.doi.org/10.18572/1812-383x-2020-11-19-20.

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The article reveals the exceptions to professional representation in court (the rules on the mandatory presence of a representative in the court of higher legal education or a degree in law). This requirement does not apply to patent attorneys in disputes related to the legal protection of intellectual property results and means of individualization, arbitration managers in the performance of their duties in a bankruptcy case. An exception to the General rule is also legal representation, representation of organizations by bodies of legal entities. However, the head of a representative office (branch) of a legal entity must submit to the court documents on higher legal education or academic degree in a legal specialty. An auditor or a member of the chamber of Commerce and industry must have a higher legal education or a degree in law in the case of representation in court.

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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, that such allegiance is better honoured when representatives are responsive to preferences warranted by public interests. I offer two groups of rule-of-law reasons for that claim. First, because respect for preferences by representatives facilitates the conditions for the law to be obeyed. Second, respect for those preferences through the justification of the representatives’ collective decisions allows for accountability and for non-arbitrary creation and application of the law. I finish addressing a threefold objection to my reliance on preferences as objects that representatives should consider when making their decisions.

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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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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, designed to accommodate fuzzy information processing, is presented in consideration of the proposed methods.

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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 required consideration of two ways in which abstraction occurs: abstractness of representations, and abstracting while using representations. The representation mapping framework facilitates comparison of the representations and how they were used. This resulted in characterization of reasoning as memory-bank or rule-based (rules processes), or similarity-based or prototype (similarity processes). Rules processes were observed in all three courses. Similarity-based reasoning seldom occurred in students, but was common to all of the professors’ problem solving, though with higher abstractness than in students. Examples from the data illustrate how representation mapping can be used to examine abstraction in problem solving across different kinds of problems and in participants with different levels of expertise. Such utility could permit identifying barriers to abstraction capacity and may facilitate faculty assessment development.

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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 introducing effectively two new rules. In the general case these "minimax" versions of classical rules appeared to be still NP-hard. We investigated the parameterized complexity of winner determination of the two classical and two new rules with respect to several parameters. Here we have a mixture of positive and negative results: e.g., we proved fixed-parameter tractability for the parameter the number of candidates but fixed-parameter intractability for the number of winners. For single-peaked electorates our results are overwhelmingly positive: we provide polynomial-time algorithms for most of the considered problems. The only rule that remains NP-hard for single-peaked electorates is the classical Monroe rule.

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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 proposed as a new way of capturing the functional requirements in a designer-friendly fashion. However, concepts on which the rule-based representation is based cannot be directly modeled in UML, because the semantics of UML diagrams does not correspond to rule semantics. Our research considers a method of designing rule bases which will be proper and consistent with the UML design. The approach has several important features: the core logic of the application is clearly identified and separated, and it is built in a declarative way, which makes the design transparent, easier to follow and develop. When rules are used to model the application logic, one has to keep in mind some common and well-known limitations of the existing rule tools. Our solution to these problems consists of using an expressive visual rule design formalism called XTT2, defining a direct translation between XTT2 and selected UML diagrams, implementing practical translators between the logical model and MOF-based UML model, and using the verification features provided with the XTT2 framework to assure the quality of the rule base. This solution seems to be superior both to existing visual UML rule notations, as well as visual design tools for rules. It does not extend any custom UML artifacts and can be used with standard UML tools.

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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 surprisingly elegant and powerful models of memory, perception, motor control, categorization, and reasoning. What makes the connectionist approach unique is not its variety of representational possibilities (including “distributed representations”) or its departure from explicit rule-based models, or even its preoccupation with the brain metaphor. Rather, it is that connectionist models can be used to explore systematically the complex interaction between learning and representation, as we try to demonstrate through the analysis of several large networks.

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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 modelling. A rule based system may be represented through different structures. The techniques for representing rules are known as rule representation, which is significant for knowledge discovery in relation to the interpretability of the model, as well as for predictive modelling with regard to efficiency in predicting unseen instances. This paper justifies the significance of rule representation and presents several existing representation techniques. Two types of novel networked topologies for rule representation are developed against existing techniques. This paper also includes complexity analysis of the networked topologies in order to show their advantages comparing with the existing techniques in terms of model interpretability and computational efficiency.

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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 non-redundant Association Rules are loss less representation for any datasets.

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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 possibility of well-defined, structured design and verification of formal characteristics. Based on the visual XTT2 model, a textual representation of the rule base is generated. A dedicated engine provides a unified run-time environment for the XTT2 rule bases. The focus of the paper is on the formalization of the presented approach. It is based on the so-called ALSV(FD) logic that provides an expressive calculus for rules.

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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. Efficient inference is assured as only the rules necessary for achieving the goal, identified by the context of inference and partial order among tables, are fired. In the paper a new version of the language—XTT22—is presented. It is based on ALSV(FD) logic, also described in the paper. Another distinctive feature of the presented approach is a top-down design methodology based on successive refinement of the project. It starts with Attribute Relationship Diagram (ARD) development. Such a diagram represents relationships between system variables. Based on the ARD scheme, XTT tables and links between them are generated. The tables are filled with expert-provided constraints on values of the attributes. The code for rule representation is generated in a humanreadable representation called HMR and interpreted with a provided inference engine called HeaRT. A set of software tools supporting the visual design and development stages is described in brief.

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Spitzer, Aaron John. "Reconciling Shared Rule: Liberal Theory, Electoral-Districting Law and “National Group” Representation in Canada." Canadian Journal of Political Science 51, no. 2 (2018): 447–66. http://dx.doi.org/10.1017/s0008423918000033.

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AbstractCanada, like all representative democracies, apportions representation to individuals; also, like all federal states, it accords polity-based representation to federal subunits. But Canada is additionally a consociational state, comprising three constitutionally recognized “national groups”: anglophones, francophones and Indigenous peoples. These groups share power and bear rights beyond the bounds of the federal system. In recent decades, Indigenous peoples and francophones have appealed for representation as “national groups,” leading to constitutional challenges. Courts have either failed to address the constitutionality of “national group” representation or have rejected it as irreconcilable with individual voting rights. I suggest the former is unnecessary and the latter procedurally illogical. Drawing on the liberal principles of individualism, egalitarianism and universalism, I develop a framework contextualizing such representation within liberal theory. I then deploy this framework to analyze recent Canadian case law. I show that appeals for “national group” representation should be approached not through the lens of individual rights, but rather through the “constitutionally prior” lens of universalism.

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Alsberg, Bjørn K., William G. Wade, and Royston Goodacre. "Chemometric Analysis of Diffuse Reflectance-Absorbance Fourier Transform Infrared Spectra Using Rule Induction Methods: Application to the Classification of Eubacterium Species." Applied Spectroscopy 52, no. 6 (1998): 823–32. http://dx.doi.org/10.1366/0003702981944562.

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Strains representative of four Eubacterium species were analyzed by using diffuse reflectance-absorbance Fourier transform infrared spectroscopy. To identify important wavenumber regions for the classification of these 22 bacterial isolates, we investigated three rule induction methods and various spectral preprocessing regimes. In this study both univariate and multivariate classification and regression trees (CART) methods and the fuzzy multivariate rule-building expert system (FuRES) method were exploited. It was found that the FuRES method was superior in terms of prediction, whereas the rules proposed by the univariate CART method were easier to interpret in terms of which wavenumbers in the IR spectra were important for bacterial class separation. Scaled and detrended FT-IR spectra and first-order numerical differentiation preprocessing steps were necessary to obtain optimal classification models. Finally, a reduction in the classification error for the CART-based methods was observed by analyzing the compressed B-spline coefficients rather than the original spectra representation. The spectral interpretation of these rules is in agreement with analyses using the uncompressed representation.

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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 convey AL processed odor information to higher brain centers. We systematically measured odor-evoked activity in 22 identified glomeruli in response to four single odorants and all their possible binary, ternary and quaternary mixtures. By comparing input and output responses, we determined how the AL network reformats mixture representation and what advantage this confers for odor discrimination. We show that increased inhibition within the AL leads to more synthetic, less elemental, mixture representation at the output level than that at the input level. As a result, mixture representations become more separable in the olfactory space, thus allowing better differentiation among floral blends in nature.

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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 inverse square root law and that the normalized committee size approximately scales with the inverse of the community size, allowing the scalability to very large populations. These findings are not strongly influenced by the different networks used to describe the individuals’ interactions, except for the presence of few individuals with very high connectivity which can have a marginal negative effect in the committee selection process.

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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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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 to track such representations in humans during action selection with adequate temporal resolution. Here, we applied time-resolved representational similarity analysis to the spectral-temporal profiles of electroencephalography signals while participants performed a cued, rule-based action selection task. In two experiments, we found that conjunctive representations were active throughout the entire selection period and were functionally dissociable from the representation of constituent features. Specifically, the strength of conjunctions was a highly robust predictor of trial-by-trial variability in response times and was selectively related to an important behavioral indicator of conjunctive representations, the so-called partial-overlap priming pattern. These results provide direct evidence for conjunctive representations as critical precursors of action selection in humans.

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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 structures available from an external design repository. The original and pruned function structures of electromechanical devices are then evaluated for how accurately market values can be predicted using the graph complexity connectivity method. The accuracy is found to be inversely related to the amount of information and level of detail. Applying the topological rule does not significantly impact the predictive power of the models, while applying the vocabulary rules and the grammar rules reduces the accuracy of the predictions. Finally, the least predictive model set is that which had all rules applied. In this manner, the value of a representation to predict or answer questions is quantified.

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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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Landemore, Hélène. "Deliberative Democracy as Open, Not (Just) Representative Democracy." Daedalus 146, no. 3 (2017): 51–63. http://dx.doi.org/10.1162/daed_a_00446.

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Deliberative democracy is at risk of becoming collateral damage of the current crisis of representative democracy. If deliberative democracy is necessarily representative and if representation betrays the true meaning of democracy as rule of, by, and for the people, then how can deliberative democracy retain any validity as a theory of political legitimacy? Any tight connection between deliberative democracy and representative democracy thus risks making deliberative democracy obsolete: a dated paradigm fit for a precrisis order, but maladjusted to the world of Occupy, the Pirate Party, the Zapatistas, and other antirepresentative movements. This essay argues that the problem comes from a particular and historically situated understanding of representative democracy as rule by elected elites. I argue that in order to retain its normative appeal and political relevance, deliberative democracy should dissociate itself from representative democracy thus understood and reinvent itself as the core of a more truly democratic paradigm, which I call “open democracy.” In open democracy, popular rule means the mediated but real exercise of power by ordinary citizens. This new paradigm privileges nonelectoral forms of representation and in it, power is meant to remain constantly inclusive of and accessible–in other words open–to ordinary citizens.

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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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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 tree from this network that equals the network in terms of predictive accuracy, yet provides a comprehensible concept representation. Moreover, our experiments indicate that decision trees induced directly from the training data using conventional algorithms do not match the accuracy nor the comprehensibility of the tree extracted by TREPAN.

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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 similar to those presented in our former paper,1 aimed to support contributing hidden and non-end-point representations while supressing non-contributing ones. Similar representation modification rules were used in other training methods for feed-forward networks,2–4 based on modification of the internal representations. A feed-forward version of the MINT algorithm will be presented in another paper.5 Once a proper set of system representations is chosen, the weight matrix is then modified accordingly, via the Perceptron Learning Rule (PLR) to obtain the proper input-output relation. Computer simulations carried out for the restricted cases of parity and teacher-net problems show rapid convergence of the algorithm in comparison with other existing algorithms, together with modest memory requirements.

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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 adaptation approach is introduced where rules are used as means to manage and modify adaptation process. Rules are easy to read and understand, thereby rule based adaptation should ensure elastic, transparent and easy administrable adaptation process. The goal of this paper is to test this statement by carrying out a comparative adaptation logics representation evaluation experiment. During the experiment participants are required to complete tasks which include different forms of adaptation logics representation (code, rules and models). Experiment results are analyzed by qualitative and quantitative measures such as users’ understandability of applications behavior when adaptation case occurs and users’ satisfaction with adaptation logics representation. Experiment results are summarized and are to be used for further development of the study.

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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 assumed that positive correlation of presynaptic and postsynaptic activity would lead to an increase in synaptic weight. The relative phase of presynaptic vs. postsynaptic activity that would effect an increase in synaptic weight was a parameter of the modification rule. When this parameter was zero, synaptic modification conformed to the standard Hebbian type. With a value of 180 deg, or the reverse-Hebb condition, negative correlation between presynaptic and postsynaptic activity led to increased synaptic weight. It was found that a synaptic modification rule of the reverse-Hebb type not only optimized the cortical representation, and associated the representations from the two eyes, but was quite stable with respect to retaining the optimized state for long periods of learning.

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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. The rule sets are used to plug gap between the two languages. Further, rule sets are described for mapping preposition verbs, nouns, etc. Finally, a system architecture has been proposed for the translation of homoeopathy literature from English to Hindi Language.The system accuracy has been evaluated using Bleu score, which is found out to be 0.7501 and the accuracy percentage of the system is 82.23%.

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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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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 when women are many, women experience a positive balance of interruptions, mitigating the deleterious effect of small numbers. Men do not experience this pattern. We draw implications for a type of representation that we call authoritative representation, and for democratic deliberation.

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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 helpful for us to evaluate the library service quality fairly and objectively.

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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 converted to other XML‐based languages (such as RDF, OWL and XMI) as well as incorporated into, and extracted from, existing XML‐based repositories (such as IEEE LOM and CanLOM) using XSL Transformations (XSLT).Practical implicationsThe RuleML facts and rules represented in the positional slotted language are used by the OO jDREW reasoning engine to detect and map between semantically equivalent components of course outlines as the key step in their interoperation. In particular, this will enable the precise delivery of learning objects (e.g. course outlines) from repositories to a specific learner's context.Originality/valueAlthough the particular scenario is discussed in the present paper, the proposed approach can be applied to other tasks related to enabling semantic interoperability.

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Zimmerman, Joseph F. "Wilma L. Rule." PS: Political Science & Politics 37, no. 3 (2004): 508–9. http://dx.doi.org/10.1017/s1049096504004822.

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It is with the deepest regret that I report the death on January 15, 2004, of Wilma L. Rule who collaborated with me on a number of research projects relating to the election of women and members of certain minority groups to elective office in the United States and other nations. She was a long-time member and secretary-treasurer of the Section on Representation and Electoral Systems of The American Political Science Association whose members continue to appreciate her research and service to the profession.

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Markman, Arthur B., and Eric Dietrich. "Whither structured representation?" Behavioral and Brain Sciences 22, no. 4 (1999): 626–27. http://dx.doi.org/10.1017/s0140525x99412142.

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The perceptual symbol system view assumes that perceptual representations have a role-argument structure. A role-argument structure is often incorporated into amodal symbol systems in order to explain conceptual functions like abstraction and rule use. The power of perceptual symbol systems to support conceptual functions is likewise rooted in its use of structure. On Barsalou's account, this capacity to use structure (in the form of frames) must be innate.

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