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The provability problem in intuitionistic propositional second-order logic with existential quantifier and implication $ (\exists,\to) $ (∃ , →) is proved to be undecidable in presence of free type variables (constants). This contrasts with the result that inutitionistic propositional second-order logic with existential quantifier, conjunction and negation is decidable. [ABSTRACT FROM AUTHOR]

The increased complexity of modern system designs and demands for quicker time to market have made safety-related verification and validation of such systems more challenging. Incorporating safety and risk considerations at the early stages of design is one way to acquire a more robust initial design for novel systems. Inductive fault analysis has its significance at final stages of design, e.g., verification and validation. However, to preclude certain system failure states—especially for the systems with high failure consequences, a designer would innately prefer to trace back and remedy the causes of failure, as compared to a more cumbersome activity of identifying the faults individually and sifting the combinations that lead to the failure of interest. The work presented in this paper is aimed at the development of a backward failure propagation methodology for analyzing the origins of functional failures in a conceptual design of systems including but not limited to nuclear, mechanical, aerospace, process, electrical/electronics, telecommunication, automotive, etc. This method allows the designer to achieve a robust early design based on the analyses of the system's functional dependencies before proceeding to the detailed design and testing stages. The insights provided by the analysis at the conceptual design stage also reduce redesign efforts, testing costs, and project delays. The proposed method is a functional analysis approach that extends the Integrated System Failure Analysis for backward failure propagation. When provided with an abstract system configuration, a system's functional model, and a system's behavioral model, it utilizes a known functional state (typically a failure) to acquire system component modes and the states of other functions. The method includes inversion of the functional failure logic and component behavioral rules using propositional logic and deductive analysis to assess valid states of a system that satisfy the given initial conditions. To test the method's scalability, we applied the proposed method to a simplified representation of the secondary loop of a typical pressurized water reactor. [ABSTRACT FROM AUTHOR]

Natural language does not express all connectives definable in classical logic as simple lexical items. Coordination in English is expressed by conjunction and, disjunction or, and negated disjunction nor. Other languages pattern similarly. Non-lexicalized connectives are typically expressed compositionally: in English, negated conjunction is typically expressed by combining negation and conjunction (not both). This is surprising: if ∧ and ∨ are duals, and the negation of the latter can be expressed lexically (nor), why not the negation of the former? I present a two-tiered model of the semantics of the binary connectives. The first tier captures the expressive power of the lexicon: it is a bilateral state-based semantics that, under a restriction, can express all and only the distinctions that can be expressed by the lexicon of natural language (and, or, nor). This first tier is characterized by rejection as non-assertion and a Neglect Zero assumption. The second tier is obtained by dropping the Neglect Zero assumption and enforcing a stronger notion of rejection, thereby recovering classical logic and thus definitions for all Boolean connectives. On the two-tiered model, we distinguish the limited expressive resources of the lexicon and the greater combinatorial expressive power of the language as a whole. This gives us a logic-based account of compositionality for the Boolean fragment of the language. [ABSTRACT FROM AUTHOR]

Rhetorical structure theory (RST) and relational propositions have been shown useful in analyzing texts as expressions in propositional logic. Because these expressions are systematically derived, they may be expected to model discursive reasoning as articulated in the text. If this is the case, it would follow that logical operations performed on the expressions would be reflected in the texts. In this paper the logic of relational propositions is used to demonstrate the applicability of transitive inference to discourse. Starting with a selection of RST analyses from the research literature, analyses of the logic of relational propositions are performed to identify their corresponding logical expressions and within each expression to identify the inference path implicit within the text. By eliminating intermediary relational propositions, transitivity is then used to progressively compress the expression. The resulting compressions are applied to the corresponding texts and their compressed RST analyses. The application of transitive inference to logical expressions results in abridged texts that are intuitively coherent and logically compatible with their originals. This indicates an underlying isomorphism between the inferential structure of logical expressions and discursive coherence, and it confirms that these expressions function as logical models of the text. Potential areas for application include knowledge representation, logic and argumentation, and RST validation. [ABSTRACT FROM AUTHOR]

This paper proposes a deletion algorithm for the marginal problem in propositional logic. The algorithm is based on the general Davis and Putnam deletion algorithm DP, expressed as a bucket elimination algorithm, representing sets of clauses with the same set of variables employing a Boolean array. The main contribution is the development of alternative procedures when deleting a variable which allow more efficient computations. In particular, it takes advantage of the case in which the variable to delete is determined by a subset of the rest of the variables. It also provides a set of useful results and tools for reasoning with Boolean tables. The algorithms are implemented using Python and the NumPy library. Experiments show that this procedure is feasible for intermediate problems and for difficult problems from hard Bayesian networks cases [ABSTRACT FROM AUTHOR]

The increasing significance of theorem proving-based formalization in mathematics and computer science highlights the necessity for formalizing foundational mathematical theories. In this work, we employ the Coq interactive theorem prover to methodically formalize the language, semantics, and syntax of propositional logic, a fundamental aspect of mathematical reasoning and proof construction. We construct four Hilbert-style axiom systems and a natural deduction system for propositional logic, and establish their equivalences through meticulous proofs. Moreover, we provide formal proofs for essential meta-theorems in propositional logic, including the Deduction Theorem, Soundness Theorem, Completeness Theorem, and Compactness Theorem. Importantly, we present an exhaustive formal proof of the Completeness Theorem in this paper. To bolster the proof of the Completeness Theorem, we also formalize concepts related to mappings and countability, and deliver a formal proof of the Cantor–Bernstein–Schröder theorem. Additionally, we devise automated Coq tactics explicitly designed for the propositional logic inference system delineated in this study, enabling the automatic verification of all tautologies, all internal theorems, and the majority of syntactic and semantic inferences within the system. This research contributes a versatile and reusable Coq library for propositional logic, presenting a solid foundation for numerous applications in mathematics, such as the accurate expression and verification of properties in software programs and digital circuits. This work holds particular importance in the domains of mathematical formalization, verification of software and hardware security, and in enhancing comprehension of the principles of logical reasoning. [ABSTRACT FROM AUTHOR]

Definition: In deep learning, large language models are typically trained on data from a corpus as representative of current knowledge. However, natural language is not an ideal form for the reliable communication of concepts. Instead, formal logical statements are preferable since they are subject to verifiability, reliability, and applicability. Another reason for this preference is that natural language is not designed for an efficient and reliable flow of information and knowledge, but is instead designed as an evolutionary adaptation as formed from a prior set of natural constraints. As a formally structured language, logical statements are also more interpretable. They may be informally constructed in the form of a natural language statement, but a formalized logical statement is expected to follow a stricter set of rules, such as with the use of symbols for representing the logic-based operators that connect multiple simple statements and form verifiable propositions. [ABSTRACT FROM AUTHOR]

Logic and logical thinking are present and play an important role in most of the disciplines at the university level but in different ways. In our research, which has been ongoing for several years, we are investigating the use of propositional logic among university students in different study programmes. Our current study evaluated data from 1,429 respondents involving students from 15 universities. The non-standardised knowledge test was previously pilot-tested and consisted of 15 tasks from selected elements of propositional logic in a different natural science subject-specific context. Significant differences in average results were found in terms of students’ gender, age, type of secondary school leaving exam and parents’ highest education level. Our research mainly aimed to compare students’ test scores by students’ fields of study. On average, mathematics-informatics students had the highest success rate of 67.4%, compared to students in engineering (61.0%), economics (57.9%), education (56.6%), science (56.5%) and humanities (54.7%). The result is significant (F = 13.521, p-value < 0.001). Furthermore, we found that the students performed differently in three selected areas of formal logic (F = 1108, df = 2, p < 0.001), with the lowest performance on statement negation tasks. The difference in means across groups of tasks is significant by the gender of the students and by their secondary education level.

In deep learning, large language models are typically trained on data from a corpus as representative of current knowledge. However, natural language is not an ideal form for the reliable communication of concepts. Instead, formal logical statements are preferable since they are subject to verifiability, reliability, and applicability. Another reason for this preference is that natural language is not designed for an efficient and reliable flow of information and knowledge, but is instead designed as an evolutionary adaptation as formed from a prior set of natural constraints. As a formally structured language, logical statements are also more interpretable. They may be informally constructed in the form of a natural language statement, but a formalized logical statement is expected to follow a stricter set of rules, such as with the use of symbols for representing the logic-based operators that connect multiple simple statements and form verifiable propositions.

Evaluating students' academic performance is crucial for assessing the quality of education and educational strategies. However, it can be challenging to predict and evaluate academic performance under uncertain and imprecise conditions. To address this issue, many research works have employed fuzzy concepts to analyze, predict, and make decisions about students' academic performance. This paper investigates the use of fuzzy concepts in research related to evaluating, analyzing, predicting, or making decisions about student academic performance. The paper proposes a fuzzy model, called FPM (Fuzzy Propositional Model), for reasoning and predicting students' academic performance. FPM aims to address the limitations of previous studies by incorporating propositional logic with fuzzy sets concept, which allows for the representation of uncertainty and imprecision in the data. FPM integrates and transforms if-then rules into weighted fuzzy production rules to predict and evaluate academic performance. This paper tests and evaluates the FPM in two scenarios. In the first scenario, the model predicts and examines the impact of absenteeism on academic performance where there is no clear relation between the two parts of the dataset. In the second scenario, the model predicts the final exam results using the lab exam results, where the data are more related. The FPM provides good results in both scenarios, demonstrating its effectiveness in predicting and evaluating students' academic performance. A comparison study of the FPM's results with a linear regression model and previous work showed that the FPM performs better in predicting academic performance and provides more insights into the underlying factors affecting it. Therefore, the FPM could be useful in educational institutions to predict and evaluate students' academic performance, identify underlying factors affecting it, and improve educational strategies. [ABSTRACT FROM AUTHOR]

Research on intelligent tutoring systems has been exploring data-driven methods to deliver effective adaptive assistance. While much work has been done to provide adaptive assistance when students seek help, they may not seek help optimally. This had led to the growing interest in proactive adaptive assistance, where the tutor provides unsolicited assistance upon predictions of struggle or unproductivity. Determining when and whether to provide personalized support is a well-known challenge called the assistance dilemma. Addressing this dilemma is particularly challenging in open-ended domains, where there can be several ways to solve problems. Researchers have explored methods to determine when to proactively help students, but few of these methods have taken prior hint usage into account. In this paper, we present a novel data-driven approach to incorporate students' hint usage in predicting their need for help. We explore its impact in an intelligent tutor that deals with the open-ended and well-structured domain of logic proofs. We present a controlled study to investigate the impact of an adaptive hint policy based on predictions of HelpNeed that incorporate students' hint usage. We show empirical evidence to support that such a policy can save students a significant amount of time in training and lead to improved posttest results, when compared to a control without proactive interventions. We also show that incorporating students' hint usage significantly improves the adaptive hint policy's efficacy in predicting students' HelpNeed, thereby reducing training unproductivity, reducing possible help avoidance, and increasing possible help appropriateness (a higher chance of receiving help when it was likely to be needed). We conclude with suggestions on the domains that can benefit from this approach as well as the requirements for adoption. [ABSTRACT FROM AUTHOR]

The study of coherent association rules based on propositional logic is an important area of association rule mining. Users may get a large number of itemsets for low minsup and lose valuable itemsets for high minsup. Mining without minsup may cause itemset explosions that contain spurious itemsets with low correlations and take a long time to mine. For mining coherence rules, existing approaches consider only the frequent itemsets, ignoring rare itemsets. Moreover, all items in the database are regarded equally important, which is not practical in real-world applications. By using the confidence-lift specified multiple minimum supports combined with propositional logic, we propose an efficient approach called rare correlated coherent association rule mining that addresses all of the problems stated above. We define and incorporate termination bound of support (⁠|${s}_{TB}$|⁠) and termination bound of dissociation (⁠|${d}_{TB}$|⁠) for early pruning of the candidate itemsets. In the proposed approach, support thresholds are automatically applied to the itemsets and coherent association rules are derived from the frequent and rare itemsets with high correlation and confidence. Experimental results obtained from real-life datasets show the effectiveness of the proposed approach in terms of itemsets and rule generation, correlation, confidence, runtime and scalability. [ABSTRACT FROM AUTHOR]

Developing a suggestion of Wittgenstein, I provide an account of truth tables as formulas of a formal language. I define the syntax and semantics of TPL (the language of Tabular Propositional Logic) and develop its proof theory. Single formulas of TPL, and finite groups of formulas with the same top row and TF matrix (depiction of possible valuations), are able to serve as their own proofs with respect to metalogical properties of interest. The situation is different, however, for groups of formulas whose top rows differ. [ABSTRACT FROM AUTHOR]

The Tsetlin Machine (TM) is a recent machine learning algorithm with several distinct properties, such as interpretability, simplicity, and hardware-friendliness. Although numerous empirical evaluations report on its performance, the mathematical analysis of its convergence is still open. In this article, we analyze the convergence of the TM with only one clause involved for classification. More specifically, we examine two basic logical operators, namely, the “IDENTITY”- and “NOT” operators. Our analysis reveals that the TM, with just one clause, can converge correctly to the intended logical operator, learning from training data over an infinite time horizon. Besides, it can capture arbitrarily rare patterns and select the most accurate one when two candidate patterns are incompatible, by configuring a granularity parameter. The analysis of the convergence of the two basic operators lays the foundation for analyzing other logical operators. These analyses altogether, from a mathematical perspective, provide new insights on why TMs have obtained state-of-the-art performance on several pattern recognition problems. [ABSTRACT FROM AUTHOR]

This paper presents the multi-adjoint lattice logic (MLL) and its completeness and soundness. Specifically, the proposed many-valued propositional logic framework is defined on a multi-adjoint algebra whose underlying algebraic structure is a bounded lattice, which embeds the well-known basic logic (BL) given by Hájek on residuated lattices. The consideration of truth-stressing hedges in the multi-adjoint algebra is also studied and, as a consequence, two new logics extensions of MLL arise: the multi-adjoint lattice logic very true intensified (MLL v t) and the multi-adjoint lattice logic ∨-very true intensified (MLL ∨ − v t). Finally, the soundness and completeness of the aforementioned logics are also proven. [ABSTRACT FROM AUTHOR]

This paper proposes a deletion algorithm for the marginal problem in propositional logic. The algorithm is based on the general Davis and Putnam deletion algorithm DP, expressed as a bucket elimination algorithm, representing sets of clauses with the same set of variables employing a Boolean array. The main contribution is the development of alternative procedures when deleting a variable which allow more efficient computations. In particular, it takes advantage of the case in which the variable to delete is determined by a subset of the rest of the variables. It also provides a set of useful results and tools for reasoning with Boolean tables. The algorithms are implemented using Python and the NumPy library. Experiments show that this procedure is feasible for intermediate problems and for difficult problems from hard Bayesian networks cases

Knowledge compilation is an alternative solution to address demanding reasoning tasks with high complexity via converting knowledge bases into a suitable target language. The notion of logical separability, proposed by Levesque, offers a general explanation for the tractability of clausal entailment for two remarkable languages: decomposable negation normal form and prime implicates. It is interesting to explore what role logical separability plays in problem tractability. In this paper, we apply the notion of logical separability to a number of reasoning problems within the context of propositional logic: satisfiability checking (CO), clausal entailment checking (CE), model counting (CT), model enumeration (ME) and forgetting (FO), as well as their dual tasks, contributing to several recursive procedures. We provide the corresponding logical separability based properties: CO-logical separability, CE-logical separability, CT-logical separability, ME-logical separability and their duals. Based on these properties, we then identify four novel normal forms: CO - LSNNF , CE - LSNNF , CT - LSNNF and ME - LSNNF , as well as their dual languages. We show that each of them is the necessary and sufficient condition under which the corresponding procedure is correct. We finally integrate the above normal forms into the knowledge compilation map. [ABSTRACT FROM AUTHOR]

The increasing significance of theorem proving-based formalization in mathematics and computer science highlights the necessity for formalizing foundational mathematical theories. In this work, we employ the Coq interactive theorem prover to methodically formalize the language, semantics, and syntax of propositional logic, a fundamental aspect of mathematical reasoning and proof construction. We construct four Hilbert-style axiom systems and a natural deduction system for propositional logic, and establish their equivalences through meticulous proofs. Moreover, we provide formal proofs for essential meta-theorems in propositional logic, including the Deduction Theorem, Soundness Theorem, Completeness Theorem, and Compactness Theorem. Importantly, we present an exhaustive formal proof of the Completeness Theorem in this paper. To bolster the proof of the Completeness Theorem, we also formalize concepts related to mappings and countability, and deliver a formal proof of the Cantor–Bernstein–Schröder theorem. Additionally, we devise automated Coq tactics explicitly designed for the propositional logic inference system delineated in this study, enabling the automatic verification of all tautologies, all internal theorems, and the majority of syntactic and semantic inferences within the system. This research contributes a versatile and reusable Coq library for propositional logic, presenting a solid foundation for numerous applications in mathematics, such as the accurate expression and verification of properties in software programs and digital circuits. This work holds particular importance in the domains of mathematical formalization, verification of software and hardware security, and in enhancing comprehension of the principles of logical reasoning.

Evaluating students’ academic performance is crucial for assessing the quality of education and educational strategies. However, it can be challenging to predict and evaluate academic performance under uncertain and imprecise conditions. To address this issue, many research works have employed fuzzy concepts to analyze, predict, and make decisions about students’ academic performance. This paper investigates the use of fuzzy concepts in research related to evaluating, analyzing, predicting, or making decisions about student academic performance. The paper proposes a fuzzy model, called FPM (Fuzzy Propositional Model), for reasoning and predicting students’ academic performance. FPM aims to address the limitations of previous studies by incorporating propositional logic with fuzzy sets concept, which allows for the representation of uncertainty and imprecision in the data. FPM integrates and transforms if-then rules into weighted fuzzy production rules to predict and evaluate academic performance. This paper tests and evaluates the FPM in two scenarios. In the first scenario, the model predicts and examines the impact of absenteeism on academic performance where there is no clear relation between the two parts of the dataset. In the second scenario, the model predicts the final exam results using the lab exam results, where the data are more related. The FPM provides good results in both scenarios, demonstrating its effectiveness in predicting and evaluating students’ academic performance. A comparison study of the FPM’s results with a linear regression model and previous work showed that the FPM performs better in predicting academic performance and provides more insights into the underlying factors affecting it. Therefore, the FPM could be useful in educational institutions to predict and evaluate students’ academic performance, identify underlying factors affecting it, and improve educational strategies.

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When people seek to understand concepts from an incomplete set of examples and counterexamples, there is usually an exponentially large number of classification rules that can correctly classify the observed data, depending on which features of the examples are used to construct these rules. A mechanistic approximation of human concept-learning should help to explain how humans prefer some rules over others when there are many that can be used to correctly classify the observed data. Here, we exploit the tools of propositional logic to develop an experimental framework that controls the minimal rules that are simultaneously consistent with the presented examples. For example, our framework allows us to present participants with concepts consistent with a disjunction and also with a conjunction, depending on which features are used to build the rule. Similarly, it allows us to present concepts that are simultaneously consistent with two or more rules of different complexity and using different features. Importantly, our framework fully controls which minimal rules compete to explain the examples and is able to recover the features used by the participant to build the classification rule, without relying on supplementary attention-tracking mechanisms (e.g. eye-tracking). We exploit our framework in an experiment with a sequence of such competitive trials, illustrating the emergence of various transfer effects that bias participants' prior attention to specific sets of features during learning. [ABSTRACT FROM AUTHOR]

Minimizing the cost function that corresponds to propositional logic is vital to ensure the learning phase of HNN can occur optimally. In that regard, optimal and non-biased algorithm is required to ensure HNN will always converge to global solution. Ant Colony Optimization (ACO) is a population-based and nature-inspired algorithm to solve various combinatorial optimization problems. ACO simulates the behaviour of the real ants that forage for food and communication of ants through pheromone density. In this work, ACO will be used to minimize the cost function that corresponds to the logical rule in Hopfield Neural Network. ACO will utilize pheromone density to find the optimal path that leads to zero cost function without consuming more learning iteration. Performance for all learning models will be evaluated based on various performance metrics. Results collected from computer simulation implies that ACO outperformed conventional learning model in minimizing the logical cost function. [ABSTRACT FROM AUTHOR]

Mathematical thinking is an integral part of the skills needed to solve not only problems in science subjects but also in everyday life. Propositional logic as a mathematical discipline forms the basic apparatus for logical reasoning that fundamentally supports scientific thinking. The current study presents the results of empirical research, which focused on analyses of the solution of tasks containing selected elements of propositional logic formulated in various subject-specific contexts. The focus group of our interest was students in the post-formal cognitive developmental stage. Our research was aimed at finding out how successfully the respondents can solve the given types of tasks; how respondents evaluate the difficulty of the tasks, and whether there are differences in the success rates of solving individual tasks according to the subject-specific context. [ABSTRACT FROM AUTHOR]

The class UMU consists of propositional formulas which are a union of minimal unsatisfiable formulas (MU formulas). The structural complexity of UMU formulas depends essentially on the type and degree of intertwining of the MU subformulas. Starting from class of formulas that consist only of clause (or variable) disjoint minimal unsatisfiable subformulas, we study various UMU subclasses given by weakening and these conditions. Generalizing an idea from [6] , we investigate a characterization of UMU formulas by whether they allow transformation into a MU formula by adding literals and/or clauses. It can be shown that simplicity in constructing of such extensions correlates with the degree of intertwining of the MU subformulas. For UMU formulas, however, we can give only extensions that have an exponential size in the worst case. The question of the existence of short MU combinations of MU-formulas by adding literals and clauses remains open. [ABSTRACT FROM AUTHOR]

Basic results of the paper are that any four-valued expansion L4 of Dunn-Belnap's logic DB4 is de_ned by a unique (up to isomorphism) conjunctive matrix ℳ4 with exactly two distinguished values over an expansion 𝔄4 of a De Morgan non-Boolean four-valued diamond, but by no matrix with either less than four values or a single [non-]distinguished value, and has no proper extension satisfying Variable Sharing Property (VSP). We then characterize L4's having a theorem / inconsistent formula, satisfying VSP and being [inferentially] maximal / subclassical / maximally paraconsistent, in particular, algebraically through ℳ4|𝔄4's (not) having certain submatrices|subalebras. Likewise, [providing 𝔄4 is regular / has no three-element subalgebra] L4 has a proper consistent axiomatic extension if[f] ℳ4 has a proper paraconsistent / two-valued submatrix [in which case the logic of this submatrix is the only proper consistent axiomatic extension of L4 and is relatively axiomatized by the Excluded Middle law axiom]. As a generic tool (applicable, in particular, to both classically-negative and implicative expansions of DB4), we also prove that the lattice of axiomatic extensions of the logic of an implicative matrix ℳ with equality determinant is dual to the distributive lattice of lower cones of the set of all submatrices of ℳ with non-distinguished values.

In this paper authors research the problem of traceability of assumptions in logical derivation. The essence of this task is to trace which assumptions from the available knowledge base of assumptions are necessary to derive a certain conclusion. The paper presents a new derivation procedure for propositional logic, which ensures traceability feature. For the derivable conclusion formula derivation procedure also returns the smallest set of assumptions those are enough to get derivation of the conclusion formula. Verification of the procedure were performed using authors implementation.

A great number of monitoring technologies have been developed especially for complex systems within the critical zones such as electric power substations, nuclear energy systems. But also, there is no single instruction or standardization in fault-focused on-line/off-line monitoring applications due to the acceleration of technological developments. Field experts have difficulty in choosing which test and measurement systems should be used in which stage of the complex systems. In this study, the propositional logic-based concept is presented, which field experts can use to manage this process. In this concept, test and measurement systems can be grouped according to the priority-order. According to the results of the graded groups on this concept, the suspected fault is verified by the cause of the occurrence. The applicability of the proposed concept has been tried to be explained by creating possible failure scenarios on the transformer. The theoretically validated concept can be used for even more fault situations. This concept can also be used in another complex systems with a large number of T&M systems where very different fault conditions can occur. 2009 Elsevier Ltd. All rights reserved.