Logical Rules Research Articles

Nothingness and Paraconsistent Logic

This paper explores the concept of “Nothingness” and its connection to Graham Priest’s paraconsistent logic, with a critical focus on Heidegger's ontological perspective. Heidegger argues that logic and ontology are incompatible, and truth extends beyond mere propositions, tied to the indescribable experience of “Nothing.” He contends that logical rules are not essential for ontological truth, leading to two conceptions of truth: fundamental and propositional. The study delves into this profound examination, considering the implications for understanding truth and the limitations of logic in grasping the elusive aspects of existence.

Legal Reasoning and Logic

Abstract This paper investigates the basis arguments of so-called legal logic and their relation to logic in its standard meaning. There is no doubt that legal arguments belong to logic in the wide sense (sensu largo), but their reduction to schemes of formal logic (logica sensu stricto) is a controversial issue. It can be demonstrated that only some legal arguments fall under explicit rules of formal logic, that is, having a deductive character. Most such reasoning is fallible, and its correctness depends on appealing to extra-logical principles taken from legal norms. For instance, if we say, “If it is permitted more, then it is permitted less” (argumentum a maiori ad minus), we assume that the concepts expressed by the words “more” and “less” are already defined.

Deep convolutional generative adversarial network for generation of computed tomography images of discontinuously carbon fiber reinforced polymer microstructures

Computed tomography images are of utmost importance when characterizing the heterogeneous and complex microstructure of discontinuously fiber reinforced polymers. However, the devices are expensive and the scans are time- and energy-intensive. Through recent advances in generative adversarial networks, the instantaneous generation of endless numbers of images that are representative of the input images and hold physical significance becomes possible. Hence, this work presents a deep convolutional generative adversarial network trained on approximately 30,000 input images from carbon fiber reinforced polyamide 6 computed tomography scans. The challenge lies in the low contrast between the two constituents caused by the close proximity of the density of polyamide 6 and carbon fibers as well as the small fiber diameter compared to the necessary resolution of the images. In addition, the stochastic, heterogeneous microstructure does not follow any logical or predictable rules exacerbating their generation. The quality of the images generated by the trained network of 256 pixel ×\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\ imes$$\\end{document} 256 pixel was investigated through the Fréchet inception distance and nearest neighbor considerations based on Euclidean distance and structural similarity index measure. Additional visual qualitative assessment ensured the realistic depiction of the complex mixed single fiber and fiber bundle structure alongside flow-related physically feasible positioning of the fibers in the polymer. The authors foresee additionally huge potential in creating three-dimensional representative volume elements typically used in composites homogenization.

J-type random 2,3 satisfiability: a higher-order logical rule in discrete hopfield neural network

J-type random 2,3 satisfiability: a higher-order logical rule in discrete hopfield neural network

Methodology for forming a digital farming management system

The paper presents methodological approaches for the creation of a digital farming management system (DFMS). A convergent approach, based on cognitive (conceptual) analysis methods, is employed in the research and applied to the formation of adaptive landscape farming systems. The fundamental principles of organizing DFMS include crop and environmental monitoring (in situ, remote sensing); the formation of farming system archetypes based on the analysis of long-term field experiments; spatial object modeling and land typology using GIS; planning and support for agrotechnologies to adapt to natural and economic conditions; modeling ecosystem services and biodiversity; assessing the impact on the sustainability and economics of crop production. The system is implemented using geoinformation models in a specific geographic coordinate. DFMS involves conducting a "inventory" of natural and production resources, as well as identifying limits of climatic, soil, and agrolandscape parameters at different levels of land use intensity. At each stage of organizing system blocks, methods of intelligent data analysis and machine learning are used, with the core of the system relying on the use of knowledge bases and logical rules of the subject area. A key element of the system is the scaling of the results of long-term field experiments and accumulated knowledge in different management areas based on the parameterization of the multi-level variability of farming systems and the formation of their archetypes. The practical implementation of the main provisions of DFMS allows approaching the solution of key issues related to reducing the level of uncertainty and associated risks in agriculture. This is achieved through scientifically justified organization of rational land use, increasing the resilience of crop production in different land use conditions, and providing information support to rural producers.

Learning explanatory logical rules in non-linear domains: a neuro-symbolic approach

AbstractDeep neural networks, despite their capabilities, are constrained by the need for large-scale training data, and often fall short in generalisation and interpretability. Inductive logic programming (ILP) presents an intriguing solution with its data-efficient learning of first-order logic rules. However, ILP grapples with challenges, notably the handling of non-linearity in continuous domains. With the ascent of neuro-symbolic ILP, there’s a drive to mitigate these challenges, synergising deep learning with relational ILP models to enhance interpretability and create logical decision boundaries. In this research, we introduce a neuro-symbolic ILP framework, grounded on differentiable Neural Logic networks, tailored for non-linear rule extraction in mixed discrete-continuous spaces. Our methodology consists of a neuro-symbolic approach, emphasising the extraction of non-linear functions from mixed domain data. Our preliminary findings showcase our architecture’s capability to identify non-linear functions from continuous data, offering a new perspective in neural-symbolic research and underlining the adaptability of ILP-based frameworks for regression challenges in continuous scenarios.

MLD Modeling and MPC-Based Energy Management Strategy for Hydrogen Fuel Cell/Supercapacitor Hybrid Electric Vehicles

Energy management strategies for hydrogen fuel cell hybrid electric vehicles (FCHEVs) are a key factor in achieving real-time vehicle energy optimization control, vehicle driving economy, and fuel cell durability. In this paper, for an FCHEV equipped with a fuel cell and supercapacitor, the quantitative information, logic rules, and operational constraints are transformed into linear integer inequalities according to its different operating modes, and the Hysdel language is used to establish its mixed logic dynamic model (MLD). Then, the energy management strategy based on model predictive control (MPC) is developed using the MLD model as the prediction model and the equivalent hydrogen consumption and the performance degradation of the fuel cell as the optimization performance indexes. Finally, under the World Light Vehicle Test Cycle, a joint simulation was carried out with Advisor and Simulink to verify the proposed strategy’s superiority by comparing it with the power following control strategy (PFCS) and the compound fuzzy control strategy (CFCS). The results show that the strategy not only ensures real-time FCHEV energy control, but also reduces hydrogen consumption by 10.98% and 1.98% and the number of start/stop times of a fuel cell by six and four, compared to PFCS and CFCS, respectively, which improves the economy of the whole vehicle as well as the durability of the fuel cell.

MEDYCZNY ROZMYTY SYSTEM EKSPERCKI DO PRZEWIDYWANIA STOPNIA WSZCZEPIENIA IMPLANTÓW DENTYSTYCZNYCH U PACJENTÓW Z PRZEWLEKŁĄ CHOROBĄ WĄTROBY

The paper presents an information technology for assessing the degree of engraftment of dental implants in the event of a pathology violation through the use of fuzzy sets, which allows using this method for medical diagnostic tasks. Main scientific results: developed algorithms and mathematical models that formalize the process supporting diagnostic decisions based on fuzzy logic; developed mathematical models of membership functions that formalize the presentation of qualitative and qualitative informational features based on the rules of fuzzy logic, which can be used in information expert systems when assessing the degree of engraftment of dental implants in case of disease with pathological diseases.

РАЗРАБОТКА МОДЕЛИ BP-СХЕМЫ НА ОСНОВЕ ТЕОРИИ АВТОМАТОВ И С ПРИМЕНЕНИЕМ МЕТОДОВ КВАНТОВОГО ПРОГРАММИРОВАНИЯ

The paper describes the concept of a new type of abstract automata “BP-scheme” (Business Process) that effectively distributes management commands between entities with their permanent growth in the paradigm of complexly modelled systems. The paper investigates the proposed model of the BP-scheme implementation, which is an artificial neural network, functioning based on fuzzy logic rules using the Takagi-Sugeno algorithm, for deriving rules in the form of functional dependencies. The model uses QPU (Quantum Processing Unit) in computing system behaviour, which implies a quick response to changes in the system entities. QPU used taking into account the developed rules of fuzzy neural network allows improving scenarios and predictive models of a complex system behaviour.

Automated compliance checking for BIM models based on Chinese-NLP and knowledge graph: an integrative conceptual framework

PurposeThe compliance checking of Building Information Modeling (BIM) models is crucial throughout the lifecycle of construction. The increasing amount and complexity of information carried by BIM models have made compliance checking more challenging, and manual methods are prone to errors. Therefore, this study aims to propose an integrative conceptual framework for automated compliance checking of BIM models, allowing for the identification of errors within BIM models.Design/methodology/approachThis study first analyzed the typical building standards in the field of architecture and fire protection, and then the ontology of these elements is developed. Based on this, a building standard corpus is built, and deep learning models are trained to automatically label the building standard texts. The Neo4j is utilized for knowledge graph construction and storage, and a data extraction method based on the Dynamo is designed to obtain checking data files. After that, a matching algorithm is devised to express the logical rules of knowledge graph triples, resulting in automated compliance checking for BIM models.FindingsCase validation results showed that this theoretical framework can achieve the automatic construction of domain knowledge graphs and automatic checking of BIM model compliance. Compared with traditional methods, this method has a higher degree of automation and portability.Originality/valueThis study introduces knowledge graphs and natural language processing technology into the field of BIM model checking and completes the automated process of constructing domain knowledge graphs and checking BIM model data. The validation of its functionality and usability through two case studies on a self-developed BIM checking platform.

Legal Education Aspects Among the youth in the Republic of Kazakhstan

The main problem: The development of theoretical aspects of legal education, legal awareness. Organizational forms of legal education. Forms and methods of education of legal awareness, legal culture. Features of the formation of legal culture in the modern period. Problems of improving legal culture and legal education. Legal education forms a sense of confidence and independence in the legal sphere among the population of the state, thereby stimulating the legal activity of an individual, which presupposes voluntary, conscious, proactive, socially and morally responsible human behavior. Purpose: The article deals with the legal education of modern youth. The means of forming a legal culture are the promotion of law, the development of legal knowledge among citizens, the practical strengthening of the rule of law, the presence of a strong legal science, the improvement of the system of legal acts, which is achieved due to the presence in the state of a democratic, effective constitution and high legal and technical-legal quality of laws and by-laws. Methods: Methodology of the study. The methodological basis of the study was made up of: generally recognized methods of scientific research of the phenomena of reality in their interrelation and interdependence both at the theoretical level (systemic, comparative legal, formal legal, historical methods of analysis, etc.) and at the empirical level (statistical analysis, questionnaires, research of documents, printed publications); the main provisions of the theory of criminal law, ethics, philosophy, psychology; rules of formal logic. The results and their significance: In this regard, there is a need to create a Republican Center for Legal Education at the Institute of State and Law, which will provide scientific research on the problems of legal education and upbringing, the development of the content and methodology of legal education at all levels of continuing education of students.

Criminal Legal Characteristics of Subjects of Crimes Against Morality

The main problem is the relevance of the research topic. The history of the development of civilization shows that law and morality as components of the spiritual culture of society are organically linked to each other. The legal system of any state enshrines the moral requirements and moral principles that are vital for the whole society. When creating norms or improving them, the legislator must take into account the state of the public moral culture of the population so that the laws being developed are fair and perceived by members of society. Purpose: The purpose of the study is the further theoretical development of a set of criminal law issues related to determining the place of morality in the system of objects of criminal law protection, their role in the system of measures to combat crime, as well as the development on this basis of proposals and recommendations for improving criminal legislation regulating liability for attacks on public morality. Methods: Methodology and methodology of the study. The methodological basis of the study was made up of: generally recognized methods of scientific research of the phenomena of reality in their interrelation and interdependence both at the theoretical level (systemic, comparative legal, formal legal, historical methods of analysis, etc.) and at the empirical level (statistical analysis, questionnaires, research of documents, printed publications); the main provisions of the theory of criminal law, ethics, philosophy, psychology; rules of formal logic. The results and their significance: The stated circumstances, as well as the lack of elaboration and discussion of most of the provisions related to the modern legal regulation of crimes against public morality and their qualifications, determined the choice of the research topic. Crimes against morality are mostly committed intentionally. For example, involvement in prostitution, the organization of brothels for prostitution or maintenance, as well as other types of criminal acts directed against humanity (morality), are committed with direct intent. A person who organizes and maintains brothels for prostitution is aware that he is doing this for profit. Among the crimes committed against humanity (morality), animal abuse is committed mainly out of hooligan motives.

LR-XFL: Logical Reasoning-Based Explainable Federated Learning

Federated learning (FL) is an emerging approach for training machine learning models collaboratively while preserving data privacy. The need for privacy protection makes it difficult for FL models to achieve global transparency and explainability. To address this limitation, we incorporate logic-based explanations into FL by proposing the Logical Reasoning-based eXplainable Federated Learning (LR-XFL) approach. Under LR-XFL, FL clients create local logic rules based on their local data and send them, along with model updates, to the FL server. The FL server connects the local logic rules through a proper logical connector that is derived based on properties of client data, without requiring access to the raw data. In addition, the server also aggregates the local model updates with weight values determined by the quality of the clients’ local data as reflected by their uploaded logic rules. The results show that LR-XFL outperforms the most relevant baseline by 1.19%, 5.81% and 5.41% in terms of classification accuracy, rule accuracy and rule fidelity, respectively. The explicit rule evaluation and expression under LR-XFL enable human experts to validate and correct the rules on the server side, hence improving the global FL model’s robustness to errors. It has the potential to enhance the transparency of FL models for areas like healthcare and finance where both data privacy and explainability are important.

Safe Abductive Learning in the Presence of Inaccurate Rules

Integrating complementary strengths of raw data and logical rules to improve the learning generalization has been recently shown promising and effective, e.g., abductive learning is one generic framework that can learn the perception model from data and reason between rules simultaneously. However, the performance would be seriously decreased when inaccurate logical rules appear, which may be even worse than baselines using only raw data. Efforts on this issue are highly desired while remain to be limited. This paper proposes a simple and effective safe abductive learning method to alleviate the harm caused by inaccurate rules. Unlike the existing methods which directly use all rules without correctness checks, it utilizes them selectively by constructing a graphical model with an adaptive reasoning process to prevent performance hazards. Theoretically, we show that induction and abduction are mutually beneficial, and can be rigorously justified from a classical maximum likelihood estimation perspective. Experiments on diverse tasks show that our method can tolerate at least twice as many inaccurate rules as accurate ones and achieve highly competitive performance while other methods can't. Moreover, the proposal can refine inaccurate rules and works well in extended weakly supervised scenarios.

Paths, Proofs, and Perfection: Developing a Human-Interpretable Proof System for Constrained Shortest Paths

People want to rely on optimization algorithms for complex decisions but verifying the optimality of the solutions can then become a valid concern, particularly for critical decisions taken by non-experts in optimization. One example is the shortest-path problem on a network, occurring in many contexts from transportation to logistics to telecommunications. While the standard shortest-path problem is both solvable in polynomial time and certifiable by duality, introducing side constraints makes solving and certifying the solutions much harder. We propose a proof system for constrained shortest-path problems, which gives a set of logical rules to derive new facts about feasible solutions. The key trait of the proposed proof system is that it specifically includes high-level graph concepts within its reasoning steps (such as connectivity or path structure), in contrast to, e.g., using linear combinations of model constraints. Thus, using our proof system, we can provide a step-by-step, human-auditable explanation showing that the path given by an external solver cannot be improved. Additionally, to maximize the advantages of this setup, we propose a proof search procedure that specifically aims to find small proofs of this form using a procedure similar to A* search. We evaluate our proof system on constrained shortest path instances generated from real-world road networks and experimentally show that we may indeed derive more interpretable proofs compared to an integer programming approach, in some cases leading to much smaller proofs.

Unveiling Implicit Deceptive Patterns in Multi-Modal Fake News via Neuro-Symbolic Reasoning

In the current Internet landscape, the rampant spread of fake news, particularly in the form of multi-modal content, poses a great social threat. While automatic multi-modal fake news detection methods have shown promising results, the lack of explainability remains a significant challenge. Existing approaches provide superficial explainability by displaying learned important components or views from well-trained networks, but they often fail to uncover the implicit deceptive patterns that reveal how fake news is fabricated. To address this limitation, we begin by predefining three typical deceptive patterns, namely image manipulation, cross-modal inconsistency, and image repurposing, which shed light on the mechanisms underlying fake news fabrication. Then, we propose a novel Neuro-Symbolic Latent Model called NSLM, that not only derives accurate judgments on the veracity of news but also uncovers the implicit deceptive patterns as explanations. Specifically, the existence of each deceptive pattern is expressed as a two-valued learnable latent variable, which is acquired through amortized variational inference and weak supervision based on symbolic logic rules. Additionally, we devise pseudo-siamese networks to capture distinct deceptive patterns effectively. Experimental results on two real-world datasets demonstrate that our NSLM achieves the best performance in fake news detection while providing insightful explanations of deceptive patterns.

TEILP: Time Prediction over Knowledge Graphs via Logical Reasoning

Conventional embedding-based models approach event time prediction in temporal knowledge graphs (TKGs) as a ranking problem. However, they often fall short in capturing essential temporal relationships such as order and distance. In this paper, we propose TEILP, a logical reasoning framework that naturaly integrates such temporal elements into knowledge graph predictions. We first convert TKGs into a temporal event knowledge graph (TEKG) which has a more explicit representation of time in term of nodes of the graph. The TEKG equips us to develop a differentiable random walk approach to time prediction. Finally, we introduce conditional probability density functions, associated with the logical rules involving the query interval, using which we arrive at the time prediction. We compare TEILP with state-of-the-art methods on five benchmark datasets. We show that our model achieves a significant improvement over baselines while providing interpretable explanations. In particular, we consider several scenarios where training samples are limited, event types are imbalanced, and forecasting the time of future events based on only past events is desired. In all these cases, TEILP outperforms state-of-the-art methods in terms of robustness.

A Fuzzy Based Intelligent Traffic Light Control (ITLC) Method: An Implementation in Ankara City

The escalating global population and increased vehicle usage have worsened traffic congestion in metropolitan areas, a significant urban challenge. Addressing this, adaptive traffic light control methods, especially at intersections, are being developed to improve traffic flow and reduce waiting times. This study significantly contributes to this field by implementing Fuzzy Logic in intelligent traffic light systems, focusing on Ankara's Polatlı Refik Cesur intersection. Using the SUMO simulation platform and Python programming, it analyzed waiting times and queue lengths. The initial phase used queue length for each intersection arm as an input. Fuzzy logic rules then determined the output, prioritizing street or phase order for optimal flow. The study further proposed an Adaptive Neuro-Fuzzy Inference System (ANFIS)-based control plan. ANFIS merges neural network capabilities with fuzzy logic, using waiting time and queue length as inputs to regulate the green light duration. Compared to existing traffic systems, this model showed a substantial improvement. It achieved a 36.5% reduction in waiting times, underlining the efficiency of the Fuzzy Logic-based method. This approach not only enhances traffic management but also contributes significantly to the literature on intelligent traffic light control systems. By addressing key urban traffic issues, the study paves the way for future advancements in traffic management technologies. The findings highlight the potential of combining advanced computational methods, like ANFIS, with traditional traffic control techniques to optimize urban traffic flow, offering a blueprint for similar challenges in other metropolitan areas.

To the question of the feasibility of using legal propaganda as a form of legal nurturing

The article attempts to clarify the term "legal propaganda” on the basis of clarification the essence of propaganda and justify the inappropriateness of using this phrase in relation to the form of legal education in a modern democratic state.
 It was found that both in the legislation and at the doctrinal level, the term "propaganda” is given a mostly negative meaning and is considered as a tool for spreading mostly anti-social, harmful, socially dangerous and frankly illegal phenomena and processes.
 It has been shown that most definitions of propaganda contain the words "manipulation”, "pressure”, "deception”, which clearly demonstrate that any propaganda implicitly includes false or distorted information, a deliberate violation of the rules of formal logic and is a hidden manipulation. It is argued that propaganda is rightly opposed to pluralism, and this makes this term inconsistent with the general principles (fundamentals of the constitutional system) of a modern democratic legal state.
 The given "systematic signs of propaganda” prove the impracticality of using the term propaganda in relation to positive phenomena and processes, such as "legal propaganda”:
 It is assumed that the use of the term "legal propaganda” can be avoided due to the development of doctrinal thought regarding legal influence and the method of persuasion.
 The possibility of a limited application of legal propaganda as a special-legal aspect of the operation of law in society, for example, some legal and educational areas in the activities of the police ("propaganda for compliance with traffic rules”), is assumed.
 The opinion that any coercion, suggestion, manipulation, imposition in law is inappropriate in view of the prevailing anthropocentrism is defended. It was concluded that the method of action through the system of values is more relevant and effective.

Preponderance of generalized chain functions in reconstructed Boolean models of biological networks

Boolean networks (BNs) have been extensively used to model gene regulatory networks (GRNs). The dynamics of BNs depend on the network architecture and regulatory logic rules (Boolean functions (BFs)) associated with nodes. Nested canalyzing functions (NCFs) have been shown to be enriched among the BFs in the large-scale studies of reconstructed Boolean models. The central question we address here is whether that enrichment is due to certain sub-types of NCFs. We build on one sub-type of NCFs, the chain functions (or chain-0 functions) proposed by Gat-Viks and Shamir. First, we propose two other sub-types of NCFs, namely, the class of chain-1 functions and generalized chain functions, the union of the chain-0 and chain-1 types. Next, we find that the fraction of NCFs that are chain-0 (also holds for chain-1) functions decreases exponentially with the number of inputs. We provide analytical treatment for this and other observations on BFs. Then, by analyzing three different datasets of reconstructed Boolean models we find that generalized chain functions are significantly enriched within the NCFs. Lastly we illustrate that upon imposing the constraints of generalized chain functions on three different GRNs we are able to obtain biologically viable Boolean models.