Intuitionistic modal logic and set theory

Intuitionistic Zermelo-Fraenkel set theory, which we call ZFI, was introduced by Friedman and Myhill in [3] in 1970. The idea was to have a theory with the same axioms as ordinary classical ZF, but with Heyting's predicate calculus HPC as the underlying logic. Since some classically equivalent statements are intuitionistically inequivalent, however, it was not always obvious which form of a classical axiom to take. For example, the usual formulation of the axiom of foundation had to be replaced with a principle of transfinite induction on the membership relation in order to avoid having excluded middle provable and thus making the logic classical. In [3] the replacement axiom is taken in its most common classical form:In the presence of the separation axiom,this is equivalent to the axiomIt is this schema Rep that we shall call the replacement axiom.Friedman and Myhill were able to show in [3] that ZFI has a number of desirable “constructive” properties, including the existence property for both numbers and sets. They were not able to determine, however, whether ZFI is proof-theoretically as strong as ZF. This is still open.Three years later, in [2], Friedman introduced a theory ZF− which is like ZFI except that the replacement axiom is changed to the classically equivalent collection axiom:He showed that ZF− is proof-theoretically of the same strength as ZF, and he asked whether ZF− is equivalent to ZFI.

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.

We perform a proof-theoretical investigation of two modal predicate logics: global intuitionistic logic GI and global intuitionistic fuzzy logic GIF. These logics were introduced by Takeuti and Titani to formulate an intuitionistic set theory and an intuitionistic fuzzy set theory together with their metatheories. Here we define analytic Gentzen style calculi for GI and GIF. Among other things, these calculi allows one to prove Herbrand’s theorem for suitable fragments of GI and GIF.

<p>The article presents the results of modeling the solution to the problem of determining the reliability of the components of a multiservice communication network (MCN) based on the theory of fuzzy sets. The main characteristics of the equipment that affect the reliability parameters of the MCN are given. To solve the problem of determining the reliability of MCN components based on the theory of fuzzy sets, a multiservice network is presented in the form of a hierarchical diagram, which shows the main components of each network level. A multiservice network is presented as a parameter of the U function. The reliable state of the MCN depends on the state of the equipment at the corresponding levels. The results of modeling the solution to the problem of determining the reliability of MCN components based on the theory of fuzzy sets are presented using the mathematical apparatus of the theory of fuzzy sets and fuzzy logic in MATLAB fuzzy logic toolbox, fuzzyTECH.</p>

Researchers in science and engineering face various obstacles due to a lack of specific and full data. Many different approaches have been devised to deal with these restrictive requirements, but two notable schools of thought are the fuzzy set (FS) theory and the rough set (RS) theory, both of which have spawned many extensions and hybridizations. Although RS theory originated from an indiscernibility relation (also known as an equivalence relation in mathematics), emphasis rapidly shifted to similarity or coverings (and their fuzzy analogues). Many other hybrid schemes were suggested with this goal in mind. The gap between those concepts shrank because to this thorough analysis. Fuzzy set theory is a legitimate way to convey the ambiguity of assessment data, yet it is still inadequate for dealing with certain intricate problems in the actual world. In reality, decision makers will undoubtedly provide different kinds of ambiguous and nuanced assessments. Atanassov’s intuitionistic fuzzy set theory broadened the application of fuzzy set theory by imbuing it with an element of uncertainty. Sometimes in real life, you have to deal with a neutral element on top of the indeterminate one. Picture fuzzy sets were developed specifically for this purpose. Membership roles may be positive, neutral, or negative/refusal. In contrast, hesitant fuzzy sets and its hybrid models are useful when decision makers are on the fence about which option to choose. As a binary relation on a set, a graph is symmetric. It is a staple in mathematical modelling and is used in almost every scientific and technological discipline. Graph theory has been essential in the mathematical modelling and subsequent resolution of several real-world situations. Information about connections between things is often best represented using graph theory, which uses vertices to stand in for the items and edges for the relationships between them. The suggested dynamic algorithm is better to the static approach in dealing with the multidimensional dynamic changes of the hybrid incomplete decision system, according to a series of experiments carried out on nine UCI datasets.

A new fuzzy set theory, C-fuzzy set theory, is introduced in this paper. It is a particular case of the classical set theory and satisfies all formulas of the classical set theory. To add a limitation to C-fuzzy set system, in which all fuzzy sets must be “non-uniform inclusive” to each other, then it forms a family of sub-systems, the Z-fuzzy set family. It can be proved that the Z0-fuzzy set system, one of Z-fuzzy set systems, is equivalent to Zadeh’s fuzzy set system. Analysis shows that 1) Zadeh’s fuzzy set system defines the relations A = B and A ⊆ B between two fuzzy sets A and B as “∀u ∈ U, (μA ∈ (u) = μB(u))” and “∀u ∈ U, (μA(u) ≤ μB(u))” respectively is inappropriate, because it makes all fuzzy sets be “non-uniformly inclusive”; 2) it is also inappropriate to define two fuzzy sets’ union and intersection operations as the max and min of their grades of membership, because this prevents fuzzy set’s ability to correctly reflect different kinds of fuzzy phenomenon in the natural world. Then it has to work around the problem by invent unnatural functions that are hard to understand, such as augmenting max and min for union and intersection to min{a + b, 1} and max{a + b – 1, 0}, but these functions are incorrect on inclusive case. If both pairs of definitions are used together, not only are they unnatural, but also they are still unable to cover all possible set relationships in the natural world; and 3) it is incorrect to define the set complement as 1 – μA(u), because it can be proved that set complement cannot exist in Zadeh’s fuzzy set, and it causes confusion in logic and thinking. And it is seriously mistaken to believe that logics of fuzzy sets necessarily go against classical and normal thinking, logic, and conception. The C-fuzzy set theory proposed in this paper overcomes all of the above errors and shortcomings, and more reasonably reflects fuzzy phenomenon in the natural world. It satisfies all relations, formulas, and operations of the classical set theory. It is consistent with normal, natural, and classical thinking, logic, and concepts.

Constructive toposes with countable sums as models of constructive set theory

Large sets in intuitionistic set theory

We are surrounded by a spate of data generating from various sources. To extract some relevant information from these data sets, many pre-processing techniques have been proposed, in which feature selection technique is widely used. However, most of the feature selection approaches focus on supervised learning, which operates on labelled data only. In real-world applications, such as medical diagnosis, forensic science, both labelled and unlabelled data instances are available. Semi-supervised learning handles these types of situations. Some of the researchers have presented rough set as well as fuzzy-rough set-based methods for feature selection of semi-supervised data sets but these approaches have their own limitations. Intuitionistic fuzzy sets maintain a stronger potency of exhibiting information and better drawing and representing intricate ambiguities of the uncertain character of the objective world when compared with fuzzy sets, as it considers positive, the negative and hesitancy degree simultaneously. In this paper, we have proposed a novel feature selection technique for partially labelled data set based on intuitionistic fuzzy-rough set theory. Moreover, we have presented supporting theorems and proposed a novel algorithm to compute reduct based on our method. Finally, we have presented supremacy of our approach over fuzzy-rough technique by considering a partially labelled information system.

Soft rough fuzzy sets and soft fuzzy rough sets

In this study, we establish a connection between rough soft set (Shabir et al., Knowl Base Syst 40:72–80, 2013) and fuzzy set. Based on the novel granulation structure called modified soft rough approximation space, fuzzy rough soft set is introduced. The important basic properties of fuzzy rough soft set are studied and supported by illustrative examples. Moreover lattice theory is studied on fuzzy rough soft set. The definitions and propositions presented in this paper enrich the soft set theory, rough set theory and fuzzy set theory, and also extend their application scopes. The paper ends with conclusions having future investigations of the study.

Granular structure plays a very important role in the model construction, theoretical analysis and algorithm design of a granular computing method. The granular structures of classical rough sets and fuzzy rough sets have been proven to be clear. In classical rough set theory, equivalence classes are basic granules, and the lower and upper approximations of a set can be computed by those basic granules. In the theory of fuzzy rough set, granular fuzzy sets can be used to describe the lower and upper approximations of a fuzzy set. This paper discusses the granular structure of type-2 fuzzy rough sets over two universes. Definitions of type-2 fuzzy rough sets over two universes are given based on a wavy-slice representation of type-2 fuzzy sets. Two granular type-2 fuzzy sets are deduced and then proven to be basic granules of type-2 fuzzy rough sets over two universes. Then, the properties of lower and upper approximation operators and these two granular type-2 fuzzy sets are investigated. At last, several examples are given to show the applications of type-2 fuzzy rough sets over two universes.

Soft set theory was first introduced by D. A. Molodtsov [D. Molodtsov, Soft set theory first results, Computers & Mathematics with Applications, 37(4–5) (1999) 19–31] in 1999 as an effective approach to deal with uncertainty. Over time, fuzzy soft set theory has evolved significantly, integrating concepts from both fuzzy set theory and soft set theory to enhance decision-making processes. Acharjee and Medhi [S. Acharjee and S. Medhi, Logical connectives of fuzzy soft set theory, New Mathematics and Natural Computation, 21(1) (2025) 339–352] introduced several advanced concepts related to logical connectives in fuzzy soft set theory. Building on Molodtsov’s foundational work, this paper explores new developments in fuzzy soft logic, extending the findings of Acharjee and Medhi. Specifically, we introduce the concepts of [Formula: see text]-fuzzy soft implication and [Formula: see text]-fuzzy soft bi-implication, along with their related results within the framework of fuzzy soft logic.

Fuzzy logic and fuzzy set theory can be interesting tools for engineers when solving image processing problems. They provide a mathematical model for approximate reasoning, which mimics human reasoning and therefore can be very powerfull. In this paper we review the basic concepts of fuzzy logic and fuzzy set theory, and use these tools for the construction of a filter for random impulse noise removal in image sequences. Experimental results and a comparative study demonstrate the excellent performance of the filter and hence the power of the used tools.

The research aimed to identify the reality of using artificial intelligence in educational research from the point of view of graduate students at the College of Education at Taif University. The study sample consisted of 32.8% of the study population consisting of (128 male and female students) enrolled in the master’s degree- at the College of Education- Taif University In the specializations of (educational leadership, pedagogy, educational policies, special education, curricula, teaching methods and educational techniques) in the second semester of the academic year 1444-1445 AH, and using the questionnaire tool, the research concluded the following: 1- That students use artificial intelligence to a high degree in all stages of research. 2- One of the most important problems in using artificial intelligence in research is related to reliability and security, and the lack of a clear policy for applying artificial intelligence in educational research. 3- One of the most important controls that students believed should be available (formulation The Artificial Intelligence Ethics Document, the establishment of the University Center for Artificial Intelligence Ethics, and the enhancement of cyber security skills for the educational researcher, while there are no statistically significant differences between the sample members in their responses to the questionnaire axes due to specialization and level While the most widely used artificial intelligence program in educational research was Chat GPT, it affected the reality axis without the rest of the axes. Among the most important recommendations reached by this study: establishing a university center for the ethics of artificial intelligence, drafting a document on the ethics of artificial intelligence in educational research, and adding it as a course for students. Undergraduate and postgraduate studies