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Journal articles on the topic 'Approximate logics'

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Published: 4 June 2021

Last updated: 1 February 2022

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1

Biacino, Loredana, and Giangiacomo Gerla. "Logics with approximate premises." International Journal of Intelligent Systems 13, no. 1 (January 1998): 1–10. http://dx.doi.org/10.1002/(sici)1098-111x(199801)13:1<1::aid-int1>3.0.co;2-u.

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2

Dunin-Kęplicz, Barbara, Anh Nguyen, and Andrzej Szałas. "A layered rule-based architecture for approximate knowledge fusion?" Computer Science and Information Systems 7, no. 3 (2010): 617–42. http://dx.doi.org/10.2298/csis100209015d.

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In this paper we present a framework for fusing approximate knowledge obtained from various distributed, heterogenous knowledge sources. This issue is substantial in modeling multi-agent systems, where a group of loosely coupled heterogeneous agents cooperate in achieving a common goal. In paper [5] we have focused on defining general mechanism for knowledge fusion. Next, the techniques ensuring tractability of fusing knowledge expressed as a Horn subset of propositional dynamic logic were developed in [13,16]. Propositional logics may seem too weak to be useful in real-world applications. On the other hand, propositional languages may be viewed as sublanguages of first-order logics which serve as a natural tool to define concepts in the spirit of description logics [2]. These notions may be further used to define various ontologies, like e.g. those applicable in the Semantic Web. Taking this step, we propose a framework, in which our Horn subset of dynamic logic is combined with deductive database technology. This synthesis is formally implemented in the framework of HSPDL architecture. The resulting knowledge fusion rules are naturally applicable to real-world data.

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3

Esteva, Francesc, Lluís Godo, Ricardo O. Rodríguez, and Thomas Vetterlein. "Logics for approximate and strong entailments." Fuzzy Sets and Systems 197 (June 2012): 59–70. http://dx.doi.org/10.1016/j.fss.2011.09.005.

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4

Lemström, Kjell, and Lauri Hella. "Approximate pattern matching and transitive closure logics." Theoretical Computer Science 299, no. 1-3 (April 2003): 387–412. http://dx.doi.org/10.1016/s0304-3975(02)00484-x.

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5

Liau, Churn-Jung. "Possibilistic Residuated Implication Logics with Applications." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 06, no. 04 (August 1998): 365–85. http://dx.doi.org/10.1142/s0218488598000306.

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In this paper, we will develop a class of logics for reasoning about qualitative and quantitative uncertainty. The semantics of the logics is uniformly based on possibility theory. Each logic in the class is parameterized by a t-norm operation on [0,1], and we express the degree of implication between the possibilities of two formulas explicitly by using residuated implication with respect to the t-norm. The logics are then shown to be applicable to possibilistic reasoning, approximate reasoning, and nonmonotonic reasoning.

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6

Vetterlein, Thomas, Francesc Esteva, and Lluís Godo. "Logics for Approximate Entailment in ordered universes of discourse." International Journal of Approximate Reasoning 71 (April 2016): 50–63. http://dx.doi.org/10.1016/j.ijar.2016.02.001.

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7

Jiang, Yuncheng, Ju Wang, Suqin Tang, and Bao Xiao. "Reasoning with rough description logics: An approximate concepts approach." Information Sciences 179, no. 5 (February 2009): 600–612. http://dx.doi.org/10.1016/j.ins.2008.10.021.

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8

Krynicki, Michał. "A Note on Rough Concepts Logic." Fundamenta Informaticae 13, no. 2 (April 1, 1990): 227–35. http://dx.doi.org/10.3233/fi-1990-13206.

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In papers [4,5] Pawlak introduced the notion of a rough set and approximation space. In [6] Pawlak formulated some concept of rough logic. The notion of the approximate truth was considered by many philosophers and logicians and in the last time by computer scientists. This was motivated by some research in artificial intelligence as for example expert systems, approximate reasoning methods and information system with imprecise information. The concept of rough logic introduced in [6] based on the notion of approximate truth determined by rough sets. Following these ideas Rasiowa and Skowron in [7] proposed the apropriate first order logic for concepts of rough definability. We denote this logic by LR. In [9] Szczerba proposed some logic with additional quantifier as rough concepts logic. We denote this logic by L(QR). The aim of this paper is a comparizing of these two logics with respect to their expressive power and giving some propositions of some modificated versions of rough concepts logics. We use more or less standard notation. By [a]R we denote the equivalence class of the element a with respect to the equivalence relation R. We write L ⩽ L ′ if expressive power of the logic L is weaker then t.he expressive power of the logic L ′ (i.e. each class of models definable by a sentence from L is also definable by a sentence from L ′ ). If L ⩽ L ′ and L ′ ⩽ L then we say that L and L ′ are equivalent and denote this by L ≡ L ′ . If L ⩽ L ′ but L ≢ L ′ then we write L < L ′ .

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9

Whalen, Thomas, and Brian Schott. "Alternative logics for approximate reasoning in expert systems: a comparative study." International Journal of Man-Machine Studies 22, no. 3 (March 1985): 327–46. http://dx.doi.org/10.1016/s0020-7373(85)80007-9.

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10

Feng, Zhi-Qiang, and Cun-Gen Liu. "On vague logics and approximate reasoning based on vague linear transformation." International Journal of Systems Science 43, no. 9 (September 2012): 1591–602. http://dx.doi.org/10.1080/00207721.2010.549579.

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11

Thi-Minh-Tam, Nguyen, and Tran Duc-Khanh. "Linguistic-Valued Logics Based on Hedge Algebras and Applications to Approximate Reasoning." Applied Mathematics & Information Sciences 11, no. 5 (September 1, 2017): 1317–34. http://dx.doi.org/10.18576/amis/110509.

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12

BRAUER, ETHAN. "RELEVANCE FOR THE CLASSICAL LOGICIAN." Review of Symbolic Logic 13, no. 2 (November 6, 2018): 436–57. http://dx.doi.org/10.1017/s1755020318000382.

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AbstractAlthough much technical and philosophical attention has been given to relevance logics, the notion of relevance itself is generally left at an intuitive level. It is difficult to find in the literature an explicit account of relevance in formal reasoning. In this article I offer a formal explication of the notion of relevance in deductive logic and argue that this notion has an interesting place in the study of classical logic. The main idea is that a premise is relevant to an argument when it contributes to the validity of that argument. I then argue that the sequents which best embody this ideal of relevance are the so-called perfect sequents—that is, sequents which are valid but have no proper subsequents that are valid. Church’s theorem entails that there is no recursively axiomatizable proof-system that proves all and only the perfect sequents, so the project that emerges from studying perfection in classical logic is not one of finding a perfect subsystem of classical logic, but is rather a comparative study of classifying subsystems of classical logic according to how well they approximate the ideal of perfection.

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13

ESTEVA, FRANCESC, PERE GARCIA-CALVÉS, and LLUÍS GODO. "ENRICHED INTERVAL BILATTICES AND PARTIAL MANY-VALUED LOGICS: AN APPROACH TO DEAL WITH GRADED TRUTH AND IMPRECISION." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 02, no. 01 (March 1994): 37–54. http://dx.doi.org/10.1142/s0218488594000055.

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Within the many-valued approach for approximate reasoning, the aim of this paper is two-fold. First, to extend truth-values lattices to cope with the imprecision due to possible incompleteness of the available information. This is done by considering two bilattices of truth-value intervals corresponding to the so-called weak and strong truth orderings. Based on the use of interval bilattices, the second aim is to introduce what we call partial many-valued logics. The (partial) models of such logics may assign intervals of truth-values to formulas, and so they stand for representations of incomplete states of knowledge. Finally, the relation between partial and complete semantical entailment is studied, and it is provedtheir equivalence for a family of formulas, including the so-called free well formed formulas.

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14

Xiao, Han, and Ge Xu. "Neural Decision Tree Towards Fully Functional Neural Graph." Unmanned Systems 08, no. 03 (July 2020): 203–10. http://dx.doi.org/10.1142/s2301385020500132.

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Though the traditional algorithms could be embedded into neural architectures with the proposed principle of [H. Xiao, Hungarian layer: Logics empowered neural architecture, arXiv: 1712.02555], the variables that only occur in the condition of branch could not be updated as a special case. To tackle this issue, we multiply the conditioned branches with Dirac symbol (i.e., [Formula: see text]), then approximate Dirac symbol with the continuous functions (e.g., [Formula: see text]). In this way, the gradients of condition-specific variables could be worked out in the back-propagation process, approximately, making a fully functional neural graph. Within our novel principle, we propose the neural decision tree (NDT), which takes simplified neural networks as decision function in each branch and employs complex neural networks to generate the output in each leaf. Extensive experiments verify our theoretical analysis and demonstrate the effectiveness of our model.

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15

Perlovsky, Leonid, and Gary Kuvich. "Machine Learning and Cognitive Algorithms for Engineering Applications." International Journal of Cognitive Informatics and Natural Intelligence 7, no. 4 (October 2013): 64–82. http://dx.doi.org/10.4018/ijcini.2013100104.

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Mind is based on intelligent cognitive processes, which are not limited by language and logic only. The thought is a set of informational processes in the brain, and such processes have the same rationale as any other systematic informational processes. Their specifics are determined by the ways of how brain stores, structures, and process this information. Systematic approach allows representing them in a diagrammatic form that can be formalized. Semiotic approach allows for the universal representation of such diagrams. In that approach, logic is a way of synthesis of such structures, which is a small but clearly visible top of the iceberg. The most efforts were traditionally put into logics without paying much attention to the rest of the mechanisms that make the entire thought system working autonomously. Dynamic fuzzy logic is reviewed and its connections with semiotics are established. Dynamic fuzzy logic extends fuzzy logic in the direction of logic-processes, which include processes of fuzzification and defuzzification as parts of logic. The paper reviews basic cognitive mechanisms, including instinctual drives, emotional and conceptual mechanisms, perception, cognition, language, a model of interaction between language and cognition upon the new semiotic models. The model of interacting cognition and language is organized in an approximate hierarchy of mental representations from sensory percepts at the “bottom” to objects, contexts, situations, abstract concepts-representations, and to the most general representations at the “top” of mental hierarchy. Knowledge Instinct and emotions are driving feedbacks for these representations. Interactions of bottom-up and top-down processes in such hierarchical semiotic representation are essential for modeling cognition. Dynamic fuzzy logic is analyzed as a fundamental mechanism of these processes. Future research directions are discussed.

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16

KUVICH, GARY, and LEONID PERLOVSKY. "COGNITIVE MECHANISMS OF THE MIND." New Mathematics and Natural Computation 09, no. 03 (October 3, 2013): 301–23. http://dx.doi.org/10.1142/s1793005713400097.

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Successes of information and cognitive science brought a growing understanding that mind is based on intelligent cognitive processes, which are not limited by language and logic only. A nice overview can be found in the excellent work of Jeff Hawkins "On Intelligence." This view is that thought is a set of informational processes in the brain, and such processes have the same rationale as any other systematic informational processes. Their specifics are determined by the ways of how brain stores, structures and process this information. Systematic approach allows representing them in a diagrammatic form that can be formalized and programmed. Semiotic approach allows for the universal representation of such diagrams. In our approach, logic is just a way of synthesis of such structures, which is a small but clearly visible top of the iceberg. However, most of the efforts were traditionally put into logics without paying much attention to the rest of the mechanisms that make the entire thought system working autonomously. Dynamic fuzzy logic is reviewed and its connections with semiotics are established. Dynamic fuzzy logic extends fuzzy logic in the direction of logic-processes, which include processes of fuzzification and defuzzification as parts of logic. This extension of fuzzy logic is inspired by processes in the brain-mind. The paper reviews basic cognitive mechanisms, including instinctual drives, emotional and conceptual mechanisms, perception, cognition, language, a model of interaction between language and cognition upon the new semiotic models. The model of interacting cognition and language is organized in an approximate hierarchy of mental representations from sensory percepts at the "bottom" to objects, contexts, situations, abstract concepts-representations, and to the most general representations at the "top" of mental hierarchy. Knowledge instinct and emotions are driving feedbacks for these representations. Interactions of bottom-up and top-down processes in such hierarchical semiotic representation are essential for modeling cognition. Dynamic fuzzy logic is analyzed as a fundamental mechanism of these processes. In this paper we are trying to formalize cognitive processes of the human mind using approaches above, and provide interfaces that could allow for their practical realization in software and hardware. Future research directions are discussed.

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17

Vlaeminck, H., J. Vennekens, M. Denecker, and M. Bruynooghe. "An approximative inference method for solving ∃∀SO satisfiability problems." Journal of Artificial Intelligence Research 45 (September 25, 2012): 79–124. http://dx.doi.org/10.1613/jair.3658.

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This paper considers the fragment ∃∀SO of second-order logic. Many interesting problems, such as conformant planning, can be naturally expressed as finite domain satisfiability problems of this logic. Such satisfiability problems are computationally hard (ΣP2) and many of these problems are often solved approximately. In this paper, we develop a general approximative method, i.e., a sound but incomplete method, for solving ∃∀SO satisfiability problems. We use a syntactic representation of a constraint propagation method for first-order logic to transform such an ∃∀SO satisfiability problem to an ∃SO(ID) satisfiability problem (second-order logic, extended with inductive definitions). The finite domain satisfiability problem for the latter language is in NP and can be handled by several existing solvers. Inductive definitions are a powerful knowledge representation tool, and this moti- vates us to also approximate ∃∀SO(ID) problems. In order to do this, we first show how to perform propagation on such inductive definitions. Next, we use this to approximate ∃∀SO(ID) satisfiability problems. All this provides a general theoretical framework for a number of approximative methods in the literature. Moreover, we also show how we can use this framework for solving practical useful problems, such as conformant planning, in an effective way.

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18

Doherty, Patrick, Martin Magnusson, and Andrzej Szalas. "Approximate databases: a support tool for approximate reasoning." Journal of Applied Non-Classical Logics 16, no. 1-2 (January 2006): 87–117. http://dx.doi.org/10.3166/jancl.16.87-117.

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19

Ying, Mingsheng. "A logic for approximate reasoning." Journal of Symbolic Logic 59, no. 3 (September 1994): 830–37. http://dx.doi.org/10.2307/2275910.

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Classical logic is not adequate to face the essential vagueness of human reasoning, which is approximate rather than precise in nature. The logical treatment of the concepts of vagueness and approximation is of increasing importance in artificial intelligence and related research. Consequently, many logicians have proposed different systems of many-valued logic as a formalization of approximate reasoning (see, for example, Goguen [G], Gerla and Tortora [GT], Novak [No], Pavelka [P], and Takeuti and Titani [TT]). As far as we know, all the proposals are obtained by extending the range of truth values of propositions. In these logical systems reasoning is still exact and to make a conclusion the antecedent clause of its rule must match its premise exactly. In addition. Wang [W] pointed out: “If we compare calculation with proving,... Procedures of calculation... can be made so by fairly well-developed methods of approximation; whereas... we do not have a clear conception of approximate methods in theorem proving.... The concept of approximate proofs, though undeniably of another kind than approximations in numerical calculations, is not incapable of more exact formulation in terms of, say, sketches of and gradual improvements toward a correct proof” (see pp, 224–225). As far as the author is aware, however, no attempts have been made to give a conception of approximate methods in theorem proving.The purpose of this paper is. unlike all the previous proposals, to develop a propositional calculus, a predicate calculus in which the truth values of propositions are still true or false exactly and in which the reasoning may be approximate and allow the antecedent clause of a rule to match its premise only approximately. In a forthcoming paper we shall establish set theory, based on the logic introduced here, in which there are ∣L∣ binary predicates ∈λ, λ ∈ L such that R(∈, ∈λ) = λ where ∈ stands for ∈1 and 1 is the greatest element in L, and x ∈λy is interpreted as that x belongs to y in the degree of λ, and relate it to intuitionistic fuzzy set theory of Takeuti and Titani [TT] and intuitionistic modal set theory of Lano [L]. In another forthcoming paper we shall introduce the resolution principle under approximate match and illustrate its applications in production systems of artificial intelligence.

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20

Shi, Yi-Hua, and Setsuo Arikawa. "APPROXIMATE EXTENSIONS IN DEFAULT LOGIC." Bulletin of informatics and cybernetics 25, no. 1/2 (September 1991): 7–19. http://dx.doi.org/10.5109/3153.

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21

Scarabottolo, Ilaria, Giovanni Ansaloni, George A. Constantinides, Laura Pozzi, and Sherief Reda. "Approximate Logic Synthesis: A Survey." Proceedings of the IEEE 108, no. 12 (December 2020): 2195–213. http://dx.doi.org/10.1109/jproc.2020.3014430.

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22

Venkataramani, Swagath, Vivek J. Kozhikkottu, Amit Sabne, Kaushik Roy, and Anand Raghunathan. "Logic Synthesis of Approximate Circuits." IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems 39, no. 10 (October 2020): 2503–15. http://dx.doi.org/10.1109/tcad.2019.2940680.

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23

Goldbring, Isaac, and Henry Towsner. "An approximate logic for measures." Israel Journal of Mathematics 199, no. 2 (October 10, 2013): 867–913. http://dx.doi.org/10.1007/s11856-013-0054-3.

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24

Wu, Yi, and Weikang Qian. "ALFANS: Multilevel Approximate Logic Synthesis Framework by Approximate Node Simplification." IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems 39, no. 7 (July 2020): 1470–83. http://dx.doi.org/10.1109/tcad.2019.2915328.

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25

Gerla, Giangiacomo. "Approximate Similarities and Poincaré Paradox." Notre Dame Journal of Formal Logic 49, no. 2 (April 2008): 203–26. http://dx.doi.org/10.1215/00294527-2008-008.

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26

Krajíček, Jan. "Interpolation and Approximate Semantic Derivations." MLQ 48, no. 4 (November 2002): 602–6. http://dx.doi.org/10.1002/1521-3870(200211)48:4<602::aid-malq602>3.0.co;2-j.

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27

Biacino, Loredana, Giangiacomo Gerla, and Mingsheng Ying. "Approximate Reasoning Based on Similarity." MLQ 46, no. 1 (January 2000): 77–86. http://dx.doi.org/10.1002/(sici)1521-3870(200001)46:1<77::aid-malq77>3.0.co;2-x.

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28

Hemmerling, Armin. "Approximate decidability in euclidean spaces." MLQ 49, no. 1 (January 2003): 34–56. http://dx.doi.org/10.1002/malq.200310003.

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29

BUSS, SAMUEL R., LESZEK ALEKSANDER KOŁODZIEJCZYK, and NEIL THAPEN. "FRAGMENTS OF APPROXIMATE COUNTING." Journal of Symbolic Logic 79, no. 2 (June 2014): 496–525. http://dx.doi.org/10.1017/jsl.2013.37.

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AbstractWe study the long-standing open problem of giving $\forall {\rm{\Sigma }}_1^b$ separations for fragments of bounded arithmetic in the relativized setting. Rather than considering the usual fragments defined by the amount of induction they allow, we study Jeřábek’s theories for approximate counting and their subtheories. We show that the $\forall {\rm{\Sigma }}_1^b$ Herbrandized ordering principle is unprovable in a fragment of bounded arithmetic that includes the injective weak pigeonhole principle for polynomial time functions, and also in a fragment that includes the surjective weak pigeonhole principle for FPNP functions. We further give new propositional translations, in terms of random resolution refutations, for the consequences of $T_2^1$ augmented with the surjective weak pigeonhole principle for polynomial time functions.

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30

Koriche, Frédéric. "Approximate coherence-based reasoning." Journal of Applied Non-Classical Logics 12, no. 2 (January 2002): 239–58. http://dx.doi.org/10.3166/jancl.12.239-258.

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31

Decock, Lieven, Igor Douven, Christoph Kelp, and Sylvia Wenmackers. "Knowledge and Approximate Knowledge." Erkenntnis 79, S6 (November 1, 2013): 1129–50. http://dx.doi.org/10.1007/s10670-013-9544-2.

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32

Yang, Yu-Shen, Subarna Sinha, Andreas Veneris, and Robert K. Brayton. "Automating Logic Transformations With Approximate SPFDs." IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems 30, no. 5 (May 2011): 651–64. http://dx.doi.org/10.1109/tcad.2011.2110590.

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33

Weston, T. S. "Approximate truth and Ł ukasiewicz logic." Notre Dame Journal of Formal Logic 29, no. 2 (March 1988): 229–34. http://dx.doi.org/10.1305/ndjfl/1093637873.

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34

Barmpalias, George. "The approximation structure of a computably approximable real." Journal of Symbolic Logic 68, no. 3 (September 2003): 885–922. http://dx.doi.org/10.2178/jsl/1058448447.

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AbstractA new approach for a uniform classification of the computably approximable real numbers is introduced. This is an important class of reals, consisting of the limits of computable sequences of rationals, and it coincides with the 0′-computable reals. Unlike some of the existing approaches, this applies uniformly to all reals in this class: to each computably approximable real x we assign a degree structure, the structure of all possible ways available to approximate x. So the main criterion for such classification is the variety of the effective ways we have to approximate a real number. We exhibit extreme cases of such approximation structures and prove a number of related results.

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35

Jeřábek, Emil. "Approximate counting in bounded arithmetic." Journal of Symbolic Logic 72, no. 3 (September 2007): 959–93. http://dx.doi.org/10.2178/jsl/1191333850.

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AbstractWe develop approximate counting of sets definable by Boolean circuits in bounded arithmetic using the dual weak pigeonhole principle (dWPHP(PV)), as a generalization of results from [15]. We discuss applications to formalization of randomized complexity classes (such as BPP, APP, MA, AM) in PV1 + dWPHP(PV).

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36

Barrett, Jeffrey Alan. "Approximate Truth and Descriptive Nesting." Erkenntnis 68, no. 2 (October 24, 2007): 213–24. http://dx.doi.org/10.1007/s10670-007-9086-6.

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37

Cadoli, Marco, and Marco Schaerf. "APPROXIMATE INFERENCE IN DEFAULT LOGIC AND CIRCUMSCRIPTION." Fundamenta Informaticae 21, no. 1,2 (1994): 103–12. http://dx.doi.org/10.3233/fi-1994-21126.

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38

Cadoli, Marco, and Marco Schaerf. "APPROXIMATE INFERENCE IN DEFAULT LOGIC AND CIRCUMSCRIPTION." Fundamenta Informaticae 23, no. 1 (1995): 123–43. http://dx.doi.org/10.3233/fi-1995-2316.

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39

Zenzo, Silvano Di. "A many-valued logic for approximate reasoning." IBM Journal of Research and Development 32, no. 4 (July 1988): 552–65. http://dx.doi.org/10.1147/rd.324.0552.

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40

Pal, Sankar K., and Deba Prasad Mandal. "Fuzzy Logic and Approximate Reasoning: An Overview." IETE Journal of Research 37, no. 5-6 (September 1991): 548–60. http://dx.doi.org/10.1080/03772063.1991.11437008.

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41

Vetterlein, Thomas. "Logic of approximate entailment in quasimetric spaces." International Journal of Approximate Reasoning 64 (September 2015): 39–53. http://dx.doi.org/10.1016/j.ijar.2015.06.008.

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42

Sierawski, Brian D., Bharat L. Bhuva, and Lloyd W. Massengill. "Reducing Soft Error Rate in Logic Circuits Through Approximate Logic Functions." IEEE Transactions on Nuclear Science 53, no. 6 (December 2006): 3417–21. http://dx.doi.org/10.1109/tns.2006.884352.

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43

Hájek, Petr. "Giangiacomo Gerla. Fuzzy logic — Mathematical tools for approximate reasoning. Trends in Logic—Studia Logica Library 11. Kluwer Academic Publishers, 2001, xii + 269 pp." Bulletin of Symbolic Logic 9, no. 4 (December 2003): 510–11. http://dx.doi.org/10.1017/s1079898600004315.

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44

Graham, Ian. "Book review: Approximate reasoning models." Fuzzy Sets and Systems 37, no. 2 (September 1990): 262–63. http://dx.doi.org/10.1016/0165-0114(90)90054-a.

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45

Lano, Kevin. "Formal frameworks for approximate reasoning." Fuzzy Sets and Systems 51, no. 2 (October 1992): 131–46. http://dx.doi.org/10.1016/0165-0114(92)90186-8.

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46

Buckley, James J., and Yoichi Hayashi. "Can approximate reasoning be consistent?" Fuzzy Sets and Systems 65, no. 1 (July 1994): 13–18. http://dx.doi.org/10.1016/0165-0114(94)90243-7.

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47

Turksen, I. B. "Approximate reasoning for production planning." Fuzzy Sets and Systems 26, no. 1 (April 1988): 23–37. http://dx.doi.org/10.1016/0165-0114(88)90003-6.

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48

Castro Liñares, David. "Profanación, exhumación y violación de los enterramientos: arqueología penal de su regulación en el siglo XIX." Revista de Derecho Penal y Criminología, no. 23 (January 21, 2021): 13–44. http://dx.doi.org/10.5944/rdpc.23.2020.28126.

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Este trabajo tiene como finalidad analizar el tratamiento penal que durante el siglo XIX se dispensó a los actos indebidos para con el cuerpo y memoria de las personas fallecidas. Para ello, este texto se inicia con un recorrido normativo por los Códigos Penales españoles del siglo XIX (1822-1848-1850-1870) con el propósito de analizar la forma en que el Legislador penal fue incorporando esta cuestión en los distintos textos normativos. A continuación, y como forma de continuar este análisis, se estima adecuado detenerse en las razones político criminales subyacentes a la tipificación de estas conductas. De esta forma, se intenta realizar una aproximación a las lógicas punitivas decimonónicas inherentes a una esfera tan particular como el castigo penal a los actos irrespetuosos para con los difuntos. Por último, se incorpora un apartado conclusivo en el que abordar algunas ideas que, por razón de estructura narrativa no encontraban un acomodo idóneo en otras partes del texto pero que igualmente resultan de importancia para esta propuesta de análisis político-criminal histórico.This work aims to analyse the criminal law treatment that during the 19th century is dispensed to wrongdoing with the body and memory of deceased people. For that purpose, this text begins with a normative view of the Spanish Criminal Codes of the 19th century (1822-1848-1850-1870) in order to investigate how the Criminal Legislator incorporated this issue into the various normative texts. Hereunder, as a way to continue this analysis, it is considered appropriate to dwell on the criminal political reasons typification of these conducts. In this way, an attempt is made to approximate the decimonic punitive logics inherent in an area as particular as criminal punishment to disrespectful acts with the deceased. Finally, a concluding section is incorporated in order to address some ideas that, by reasons of narrative structure, did not find an appropriate accommodation in other parts of the text but which are also relevant for this proposal of historical political-criminal analysis.

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ZHAO, QINPING, and BO LI. "M: AN APPROXIMATE REASONING SYSTEM." International Journal of Pattern Recognition and Artificial Intelligence 07, no. 03 (June 1993): 431–40. http://dx.doi.org/10.1142/s0218001493000212.

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A system of multivalued logical equations and its solution algorithm are put forward in this paper. Based on this work we generalize SLD-resolution into multivalued logic and establish the corresponding truth value calculus. As a result, M, an approximate reasoning system, is built. We present the language and inference rules of M. Furthermore, we analyse inconsistency of assignments to truth degrees and give the solving strategies of M.

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Tserkovny, Alex. "A t-Norm Fuzzy Logic for Approximate Reasoning." Journal of Software Engineering and Applications 10, no. 07 (2017): 639–62. http://dx.doi.org/10.4236/jsea.2017.107035.

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