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Cancer cells are known to express the Warburg effect—increased glycolysis and formation of lactic acid even in the presence of oxygen—as well as high glutamine uptake. In tumors, cancer cells are surrounded by collagen, immune cells, and neoangiogenesis. Whether collagen formation, neoangiogenesis, and inflammation in cancer are associated with the Warburg effect needs to be established. Metabolic modelling has proven to be a tool of choice to understand biological reality better and make in silico predictions. Elementary Flux Modes (EFMs) are essential for conducting an unbiased decomposition of a metabolic model into its minimal functional units. EFMs can be investigated using our tool, aspefm, an innovative approach based on logic programming where biological constraints can be incorporated. These constraints allow networks to be characterized regardless of their size. Using a metabolic model of the human cell containing collagen, neoangiogenesis, and inflammation markers, we derived a subset of EFMs of biological relevance to the Warburg effect. Within this model, EFMs analysis provided more adequate results than parsimonious flux balance analysis and flux sampling. Upon further inspection, the EFM with the best linear regression fit to cancer cell lines exometabolomics data was selected. The minimal pathway, presenting the Warburg effect, collagen synthesis, angiogenesis, and release of inflammation markers, showed that collagen production was possible directly de novo from glutamine uptake and without extracellular import of glycine and proline, collagen's main constituents. [ABSTRACT FROM AUTHOR]

This article proposes a framework for integrating ethical reasoning into AI systems through Continuous Logic Programming (CLP), emphasizing the improvement of transparency and accountability in automated decision-making. The study highlights requirements for AI that respects human values and societal norms by examining concerns such as algorithmic bias, data privacy, and ethical dilemmas in fields like healthcare and autonomous systems. The proposed CLP-based methodology offers a systematic, elucidative framework for ethical decision-making, allowing AI systems to balance operational efficiency with ethical principles. Important contributions include strategies for the integration of ethical frameworks, stakeholder engagement, and transparency, as well as discussion on artificial moral agents and their function in addressing ethical dilemmas in AI. The paper presents practical examples that illustrate the application of CLP in ethical reasoning, highlighting its ability to bring together AI performance with responsible AI practices. [ABSTRACT FROM AUTHOR]

We propose probabilistic logic factored Markov decision processes (PL-fMDPs) as a behavior selection scheme for self-driving cars. Probabilistic logic combines logic programming with probability theory to achieve clear, rule-based knowledge descriptions of multivariate probability distributions, and a flexible mixture of deductive and probabilistic inferences. Factored Markov decision processes (fMDPs) are widely used to generate reward-optimal action policies for stochastic sequential decision problems. For evaluation, we developed a simulated self-driving car with reliable modules for behavior selection, perception, and control. The behavior selection module is composed of a two-level structure of four action policies obtained from PL-fMDPs. Three main tests were conducted focused on the selection of the appropriate actions in specific driving scenarios, and the overtaking of static obstacle vehicles and dynamic obstacle vehicles. We performed 520 repetitions of these tests. The self-driving car completed its task without collisions in 99.2% of the repetitions. Results show the suitability of the overall self-driving strategy and PL-fMDPs to construct safe action policies for self-driving cars. [ABSTRACT FROM AUTHOR]

This paper proposes the development of a smart traffic light prototype based on vehicular traffic flow measurement in the stretch between two avenues in the city of Limeira, SP, Brazil, focusing on the stretch towards UNICAMP's School of Technology. To this end, we initially developed a Python code using the OpenCV library in order to detect and count vehicles. With the counting in operation, programming logic was inserted, aiming at preparing traffic light timers based on vehicular traffic. Finally, the traffic lights were added to display video via a code change to show the ongoing color changes, also obtaining a code for identifying vehicles and flow, in addition to the virtual traffic light system itself in the system. Vehicle counting accuracy was 75% for large vehicles, 90% for passenger cars, and 100% for motorcycles. The simulation of a smart traffic light implementation worked satisfactorily according to the post-processing of the video recorded for validation. [ABSTRACT FROM AUTHOR]

Automated legal reasoning and its application in smart contracts and automated decisions are increasingly attracting interest. In this context, ethical and legal concerns make it necessary for automated reasoners to justify in human-understandable terms the advice given. Logic Programming, specially Answer Set Programming, has a rich semantics and has been used to very concisely express complex knowledge. However, modelling discretionality to act and other vague concepts such as ambiguity cannot be expressed in top-down execution models based on Prolog, and in bottom-up execution models based on ASP the justifications are incomplete and/or not scalable. We propose to use s(CASP), a top-down execution model for predicate ASP, to model vague concepts following a set of patterns. We have implemented a framework, called s(LAW), to model, reason, and justify the applicable legislation and validate it by translating (and benchmarking) a representative use case, the criteria for the admission of students in the "Comunidad de Madrid". [ABSTRACT FROM AUTHOR]

With the increasing demand for autonomous driving, ensuring safe and efficient lane-changing behavior in multi-lane traffic scenarios has become a key challenge. This paper proposes an algorithm for active lane-changing decision-making and trajectory planning designed for intelligent vehicles in such environments. The lane-changing intent is evaluated using fuzzy logic, followed by an assessment of lane-changing feasibility based on a lane utility evaluation function. A hierarchical model for path and speed planning is established. Path clusters are generated using quintic polynomials. With a multi-objective cost function designed to ensure collision safety, smoothness, road boundaries, and trajectory continuity, dynamic programming (DP) and quadratic programming (QP) are employed to obtain the trajectory with the minimum cost among the trajectory set fitted by fifth-order polynomials, which is the optimal lane-changing trajectory. For speed planning, obstacles are projected onto the S–T coordinate system, which is a coordinate system with time as the horizontal axis and the distance(s) of the planned path as the vertical axis, and multi-objective cost functions for speed, acceleration, and speed continuity are designed. The speed curve is optimized using DP followed by QP under given constraints. Simulation results show that the proposed algorithm makes safe and effective lane-changing decisions based on traffic conditions, vehicle distances, and speeds. The model generates smooth and stable paths while ensuring the safe and efficient execution of lane changes. This process meets real-time requirements and verifies the reliability of the algorithm. [ABSTRACT FROM AUTHOR]

This study introduces an inventory model for manufacturing that prioritizes product quality and cost efficiency. Utilizing fuzzy logic and mathematical programming, the model integrates fuzzy numbers to describe uncertainties associated with manufacturing costs and quality control parameters. The model extends beyond conventional inventory systems by incorporating a dynamic mechanism to halt production, employing fuzzy decision variables to optimize the economic order quantity and minimize total costs. Key innovations include the application of approaches related to graded mean integration for defuzzification and the use of Kuhn–Tucker conditions to ensure optimal solutions under complex constraints. These approaches facilitate the precise management of production rates, inventory levels, and cost factors, which are essential in achieving a balance between supply and demand. A computational analysis validates the model's effectiveness, demonstrating cost reductions while maintaining optimal inventory levels. This underscores the potential of integrating fuzzy arithmetic with traditional optimization techniques to enhance decision making in inventory management. The model's adaptability and accuracy indicate its broad applicability across various sectors facing similar challenges, offering a valuable tool for operational managers and decision makers to improve efficiency and reduce waste in production cycles. [ABSTRACT FROM AUTHOR]

One of the foundational subjects in both artificial intelligence (AI) and database technologies is natural language interfaces for databases (NLIDB). The primary goal of NLIDB is to enable users to interact with databases using natural languages such as English, Arabic, and French. While many existing NLIDBs rely on linguistic operations to meet the challenges of user's ambiguity existing in natural language queries (NLQ), there is currently a growing emphasis on utilizing inductive logic programming (ILP) to develop natural language processing (NLP) applications. This is because ILP reduces the requirement for linguistic expertise in building NLP systems. This paper outlines a methodology for automating the construction of NLIDB. This method utilizes ILP to derive transfer rules that directly translate NLQ into a clear and unambiguous logical query, which subsequently translatable into database query languages (DQL). To acquire these rules, our system was trained within a corpus consisting of parallel examples of NLQs and their logical interpretations. The experimental results demonstrate the promise of this approach, as it enables the direct translation of all NLQs with grammatical structures similar to those already present in the trained corpus into a logical query. [ABSTRACT FROM AUTHOR]

Many autistic children have feeding problems, typically eating a limited range of foods. Feeding problems affect quality of life, health, and development. Research suggests that parents are often unsure when to or whether to seek help. When they do, local provision of help across the UK is often lacking. A toolkit could offer a tailored, accessible, and scalable early intervention to support parents. We aim to develop the blueprint of a toolkit to help parents/caregivers manage feeding problems in their autistic children. Medical Research Council guidance on developing complex interventions informs three successive work packages: Realist review: a literature search and analysis using realist theory of logic to construct programme theory(s) in line with RAMESES (Realist And Meta-narrative Evidence Syntheses: Evolving Standards) guidance. Realist evaluation: interviews of three groups: autistic children, young people and adults (experts), parents/caregivers of autistic children (parents), and professionals who help parents manage feeding problems (professionals) across the UK. Analysis of verbatim interview transcripts using realist theory of logic to refine programme theory(s). Co-design of the toolkit blueprint: behaviour change theory applied to the programme theory(s) will generate candidate components for the online tool. A blueprint (a detailed textual outline) will be co-designed. A participatory research team of experts, parents, and professionals will be involved in each work package. Where consensus is needed it will be reached by asynchronous nominal group technique. A PPI (public and patient involvement) advisory group of experts and parents will ensure the project is relevant, respectful, and accessible. Findings of each step will be disseminated via journal publications, conferences, social media, as well as PPI-co-produced webinars and a dissemination event. On completion, this project will provide the foundation for the subsequent development and refinement of the prototype toolkit. [ABSTRACT FROM AUTHOR]

Aims and method: This study aimed to develop and articulate a logic model and programme theories for implementing a new cognitive–behavioural suicide prevention intervention for men in prison who are perceived to be at risk of death by suicide. Semi-structured one-to-one interviews with key stakeholders and a combination of qualitative analysis techniques were used to develop programme theories. Results: Interviews with 28 stakeholders resulted in five programme theories, focusing on: trust, willingness and engagement; readiness and ability; assessment and formulation; practitioner delivering the 'change work' stage of the intervention face-to-face in a prison environment; and practitioner training, integrating the intervention and onward care. Each theory provides details of what contextual factors need to be considered at each stage, and what activities can facilitate achieving the intended outcomes of the intervention, both intermediate and long term. Clinical implications: The PROSPECT implementation strategy developed from the five theories can be adapted to different situations and environments. [ABSTRACT FROM AUTHOR]

The management of time is essential in most AI-related applications. In addition, we know that temporal information is often not precise. In fact, in most cases, it is necessary to deal with imprecision and/or uncertainty. On the other hand, there is the need to handle the implicit common-sense information present in many temporal statements. In this paper, we present FTCProlog, a logic programming language capable of handling fuzzy temporal constraints soundly and efficiently. The main difference of FTCProlog with respect to its predecessor, PROLogic, is its ability to associate a certainty index with deductions obtained through SLD-resolution. This resolution is based on a proposal within the theoretical logical framework FTCLogic. This model integrates a first-order logic based on possibilistic logic with the Fuzzy Temporal Constraint Networks (FTCNs) that allow efficient time management. The calculation of the certainty index can be useful in applications where one wants to verify the extent to which the times elapsed between certain events follow a given temporal pattern. In this paper, we demonstrate that the calculation of this index respects the properties of the theoretical model regarding its semantics. FTCProlog is implemented in Haskell. [ABSTRACT FROM AUTHOR]

This article explores the intertwining connections among artificial intelligence, machine learning, digital transformation, and computational sustainability, detailing how these elements jointly empower citizens within a smart city framework. As technological advancement accelerates, smart cities harness these innovations to improve residents' quality of life. Artificial intelligence and machine learning act as data analysis powerhouses, making urban living more personalized, efficient, and automated, and are pivotal in managing complex urban infrastructures, anticipating societal requirements, and averting potential crises. Digital transformation transforms city operations by weaving digital technology into every facet of urban life, enhancing value delivery to citizens. Computational sustainability, a fundamental goal for smart cities, harnesses artificial intelligence, machine learning, and digital resources to forge more environmentally responsible cities, minimize ecological impact, and nurture sustainable development. The synergy of these technologies empowers residents to make well-informed choices, actively engage in their communities, and adopt sustainable lifestyles. This discussion illuminates the mechanisms and implications of these interconnections for future urban existence, ultimately focusing on empowering citizens in smart cities. [ABSTRACT FROM AUTHOR]

Within the swiftly evolving domain of neural networks, the discrete Hopfield-SAT model, endowed with logical rules and the ability to achieve global minima of SAT problems, has emerged as a novel prototype for SAT solvers, capturing significant scientific interest. However, this model shows substantial sensitivity to network size and logical complexity. As the number of neurons and logical complexity increase, the solution space rapidly contracts, leading to a marked decline in the model's problem-solving performance. This paper introduces a novel discrete Hopfield-SAT model, enhanced by Crow search-guided fuzzy clustering hybrid optimization, effectively addressing this challenge and significantly boosting solving speed. The proposed model unveils a significant insight: its uniquely designed cost function for initial assignments introduces a quantification mechanism that measures the degree of inconsistency within its logical rules. Utilizing this for clustering, the model utilizes a Crow search-guided fuzzy clustering hybrid optimization to filter potential solutions from initial assignments, substantially narrowing the search space and enhancing retrieval efficiency. Experiments were conducted with both simulated and real datasets for 2SAT problems. The results indicate that the proposed model significantly surpasses traditional discrete Hopfield-SAT models and those enhanced by genetic-guided fuzzy clustering optimization across key performance metrics: Global minima ratio, Hamming distance, CPU time, retrieval rate of stable state, and retrieval rate of global minima, particularly showing statistically significant improvements in solving speed. These advantages play a pivotal role in advancing the discrete Hopfield-SAT model towards becoming an exemplary SAT solver. Additionally, the model features exceptional parallel computing capabilities and possesses the potential to integrate with other logical rules. In the future, this optimized model holds promise as an effective tool for solving more complex SAT problems.

Inductive Logic Programming (ILP) is a research field at the intersection between machine learning and logic programming, focusing on developing a formal framework for inductively learning relational descriptions in the form of logic programs from examples and background knowledge. As an emerging method of ILP, Meta-Interpretive Learning (MIL) leverages the specialization of a set of higher-order metarules to learn logic programs. In MIL, the input includes a set of examples, background knowledge, and a set of metarules, while the output is a logic program. MIL executes a depth-first traversal search, where its program search space expands polynomially with the number of predicates in the provided background knowledge and exponentially with the number of clauses in the program, sometimes even leading to search collapse. To address this challenge, this study introduces a strategy that employs the concept of reuse, specifically through the integration of auxiliary predicates, to reduce the number of clauses in programs and improve the learning efficiency. This approach focuses on the proactive identification and reuse of common program patterns. To operationalize this strategy, we introduce MILER, a novel method integrating a predicate generator, program learner, and program evaluator. MILER leverages frequent subgraph mining techniques to detect common patterns from a limited dataset of training samples, subsequently embedding these patterns as auxiliary predicates into the background knowledge. In our experiments involving two Visual Question Answering (VQA) tasks and one program synthesis task, we assessed MILER's approach to utilizing reusable program patterns as auxiliary predicates. The results indicate that, by incorporating these patterns, MILER identifies reusable program patterns, reduces program clauses, and directly decreases the likelihood of timeouts compared to traditional MIL. This leads to improved learning success rates by optimizing computational efforts. [ABSTRACT FROM AUTHOR]

Teaching programming logic to students who do not have a background in computer science is challenging, as the instructor has to awaken problem-solving, critical thinking, and logical reasoning skills Several programming tools have been created to teach coding concepts to computer science students of different ages. However, these tools are not well designed to meet the challenge of teaching programming to new developers who come with school training in other areas such as accounting, management, etc. Therefore, this research focused on analyzing the importance of the application of online programming tools to students starting college who come with a school background in an area other than technology. A pre/post experimental design was carried out with 82 first-level students of the Technical University of Ambato in the careers of Systems and Electronics. The results revealed that 45% of the students increased their levels of application and analysis in programming processes. In addition, the research revealed that students who come from a background other than computer science agree with the integration of online programming tools from the first level of university entrance since this method helps to improve their learning capacity. [ABSTRACT FROM AUTHOR]

In this paper, we define two particular forms of non-termination, namely loops and binary chains, in an abstract framework that encompasses term rewriting and logic programming. The definition of loops relies on the notion of compatibility of binary relations. We also present a syntactic criterion for the detection of a special case of binary chains. Moreover, we describe our implementation NTI and compare its results at the Termination Competition 2023 with those of leading analyzers. [ABSTRACT FROM AUTHOR]

DNA programming, which is based on the principle of base complementary pairing and Boolean operations, exhibits organizational structures and algorithms similar to those observed in machine language. Consequently, the practical implementation of DNA logic programming can be achieved through the utilization of programming techniques, enabling the discrimination and output generation. In recent years, DNA programming has witnessed a convergence with disciplines, such as life sciences, medicine, and other interdisciplinary areas, thereby giving rise to an advanced research system that yields valuable insights. This development has paved the way for multidisciplinary cutting‐edge research. Furthermore, the successful transition from conceptualization to the practical implementation of DNA programming has been accomplished. This review summarizes the recent advances in DNA logic programming within the biomedical fields, specifically emphasizing the conceptualization and execution of DNA logic programming constructs. The benefits and obstacles associated with the adoption of DNA programming in cutting‐edge research areas are also highlighted. [ABSTRACT FROM AUTHOR]

Customer demands for product quality are increasingly complex, requiring better inspection accuracy. It is not enough if done manually because it requires high costs and varying operator accuracy. Automatic vision inspect­ion was developed to check the product quality of terminal-type electronic components To solve this problem. Design intelligent inspection uses a conveyor driven by a stepper motor, a photosensor to calculate product distance, guides position to direct the product, a vision camera to detect product quality, cylinder ejection for product selection, and PLC as a control system. The process of detecting normal and abnormal product quality is carried out using computer logic control, then separating the ab­normal product into the reject box through the ejection cylinder. The machine speed is 60 pieces/minute. The system evaluation results are carried out on three parts of the system: the success rate on the vision camera is 100%, automatic product sorting through the cylinder ejection rate success is 100%, and the success rate for product positioning is 97.5%. This research provides a useful reference for developing intelligent automatic inspection technology in electronic components.

A crucial aspect of bioinformatics is sequence comparison, which entails matching recently discovered biological sequences with previously identified sequences kept in databases. To find similarities between two or more nucleotide or amino acid sequences, sequence alignment organizes the sequences. Understanding the functional, structural, and evolutionary links between the sequences is made easier by looking at these areas of commonality. This study highlighted types of alignment. Also, proposed an effective methodology for deciding which algorithm can be utilized and satisfying the objective. Hence, Multi-Criteria Decision-Making (MCDM) techniques have been harnessed with Neutrosophic theory as a supporter in uncertain situations. Herein Single Value Neutrosophic Sets (SVNSs) as a type of uncertainty theory-Neutrosophic. This process requires a set of criteria leveraged in judgment. Also, Tree Soft Sets (TrSS) are applied for the first time to model the required criteria to facilitate the decision process. The hybrid techniques are applied to support stakeholders in making optimal decisions for optimal alignment algorithms among various algorithms such as pairwise and sequence algorithms. The results of the implementation of this decision technique indicated that multiple sequence alignment is the best compared with pairwise algorithms. Thus, we implemented multiple sequences in our study and employed logic programming to perform sequence matching. To ensure optimal alignment, the approach is tested on different sets of 16S rRNA gene of Actinobacteria (Streptomyces) sequences taken from NCBI. Then, the results are compared with MEGA.

Abstract DNA programming, which is based on the principle of base complementary pairing and Boolean operations, exhibits organizational structures and algorithms similar to those observed in machine language. Consequently, the practical implementation of DNA logic programming can be achieved through the utilization of programming techniques, enabling the discrimination and output generation. In recent years, DNA programming has witnessed a convergence with disciplines, such as life sciences, medicine, and other interdisciplinary areas, thereby giving rise to an advanced research system that yields valuable insights. This development has paved the way for multidisciplinary cutting‐edge research. Furthermore, the successful transition from conceptualization to the practical implementation of DNA programming has been accomplished. This review summarizes the recent advances in DNA logic programming within the biomedical fields, specifically emphasizing the conceptualization and execution of DNA logic programming constructs. The benefits and obstacles associated with the adoption of DNA programming in cutting‐edge research areas are also highlighted.

Partially Observable Markov Decision Processes (POMDPs) are a powerful framework for planning under uncertainty. They allow to model state uncertainty as a belief probability distribution. Approximate solvers based on Monte Carlo sampling show great success to relax the computational demand and perform online planning. However, scaling to complex realistic domains with many actions and long planning horizons is still a major challenge, and a key point to achieve good performance is guiding the action-selection process with domain-dependent policy heuristics which are tailored for the specific application domain. We propose to learn high-quality heuristics from POMDP traces of executions generated by any solver. We convert the belief-action pairs to a logical semantics, and exploit data- and time-efficient Inductive Logic Programming (ILP) to generate interpretable belief-based policy specifications, which are then used as online heuristics. We evaluate thoroughly our methodology on two notoriously challenging POMDP problems, involving large action spaces and long planning horizons, namely, rocksample and pocman. Considering different state-of-the-art online POMDP solvers, including POMCP, DESPOT and AdaOPS, we show that learned heuristics expressed in Answer Set Programming (ASP) yield performance superior to neural networks and similar to optimal handcrafted taskspecific heuristics within lower computational time. Moreover, they well generalize to more challenging scenarios not experienced in the training phase (e.g., increasing rocks and grid size in rocksample, incrementing the size of the map and the aggressivity of ghosts in pocman). [ABSTRACT FROM AUTHOR]

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Copyright of Cogency: Journal of Reasoning & Argumentation is the property of Cogency Journal and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

A crucial aspect of bioinformatics is sequence comparison, which entails matching recently discovered biological sequences with previously identified sequences kept in databases. To find similarities between two or more nucleotide or amino acid sequences, sequence alignment organizes the sequences. Understanding the functional, structural, and evolutionary links between the sequences is made easier by looking at these areas of commonality. This study highlighted types of alignment. Also, proposed an effective methodology for deciding which algorithm can be utilized and satisfying the objective. Hence, Multi-Criteria Decision-Making (MCDM) techniques have been harnessed with Neutrosophic theory as a supporter in uncertain situations. Herein Single Value Neutrosophic Sets (SVNSs) as a type of uncertainty theory-Neutrosophic. This process requires a set of criteria leveraged in judgment. Also, Tree Soft Sets (TrSS) are applied for the first time to model the required criteria to facilitate the decision process. The hybrid techniques are applied to support stakeholders in making optimal decisions for optimal alignment algorithms among various algorithms such as pairwise and sequence algorithms. The results of the implementation of this decision technique indicated that multiple sequence alignment is the best compared with pairwise algorithms. Thus, we implemented multiple sequences in our study and employed logic programming to perform sequence matching. To ensure optimal alignment, the approach is tested on different sets of 16S rRNA gene of Actinobacteria (Streptomyces) sequences taken from NCBI. Then, the results are compared with MEGA. [ABSTRACT FROM AUTHOR]

Learning programming logic remains an obstacle for students from different academic fields. Considered one of the essential disciplines in the field of Science and Technology, it is vital to investigate the new tools or techniques used in the teaching and learning of Programming Language. This work presents a systematic literature review (SLR) on approaches using Mobile Learning methodology and the process of learning programming in introductory courses, including mobile applications and their evaluation and validation. We consulted three digital libraries, considering articles published from 2011 to 2022 related to Mobile Learning and Programming Learning. As a result, we found twelve mobile tools for learning or teaching programming logic. Most are free and used in universities. In addition, these tools positively affect the learning process, engagement, motivation, and retention, providing a better understanding, and improving content transmission. [ABSTRACT FROM AUTHOR]

The automatic controlling system of the production will increase the preciseness of the work and it helps the employees to meet the organizational targets. The programming logic controller (PLC) and software-based technologies controlled everything interrelated with the production processes. This reduces the risk of production and evaluates the quality of the tobacco used to manufacture cigarettes. This study is based on secondary sources as which helps to get adequate information about the study topic. The implementation cost of the PLC is quite high, and proper training of the employees is needed for the manipulation of the PLC system of production. This is a computerised technology that supports the production process of the organisation and reduces the cost of production in the long term. The term control technology is usually based on the control which is manual and on which the human operator has a general or primary role in following the procedures. These approaches are used for the decision-making and the execution of the programs and the operations, which are done manually. [ABSTRACT FROM AUTHOR]

Natural language annotations and manuals can provide useful procedural information and relations for the highly specialized scenario of autonomous robotic task planning. In this paper, we propose and publicly release AUTOMATE, a pipeline for automatic task knowledge extraction from expert-written domain texts. AUTOMATE integrates semantic sentence classification, semantic role labeling, and identification of procedural connectors, in order to extract templates of Linear Temporal Logic (LTL) relations that can be directly implemented in any sufficiently expressive logic programming formalism for autonomous reasoning, assuming some low-level commonsense and domain-independent knowledge is available. This is the first work that bridges natural language descriptions of complex LTL relations and the automation of full robotic tasks. Unlike most recent similar works that assume strict language constraints in substantially simplified domains, we test our pipeline on texts that reflect the expressiveness of natural language used in available textbooks and manuals. In fact, we test AUTOMATE in the surgical robotic scenario, defining realistic language constraints based on a publicly available dataset. In the context of two benchmark training tasks with texts constrained as above, we show that automatically extracted LTL templates, after translation to a suitable logic programming paradigm, achieve comparable planning success in reduced time, with respect to logic programs written by expert programmers. [ABSTRACT FROM AUTHOR]

With its powerful expressive capability and intuitive presentation, the knowledge graph has emerged as one of the primary forms of knowledge representation and management. However, the presence of biases in our cognitive and construction processes often leads to varying degrees of incompleteness and errors within knowledge graphs. To address this, reasoning becomes essential for supplementing and rectifying these shortcomings. Logical rule-based knowledge graph reasoning methods excel at performing inference by uncovering underlying logical rules, showcasing remarkable generalization ability and interpretability. Moreover, the flexibility of logical rules allows for seamless integration with diverse neural network models, thereby offering promising prospects for research and application. Despite the growing number of logical rule-based knowledge graph reasoning methods, a systematic classification and analysis of these approaches is lacking. In this review, we delve into the relevant research on logical rule-based knowledge graph reasoning, classifying them into four categories: methods based on inductive logic programming (ILP), methods that unify probabilistic graphical models and logical rules, methods that unify embedding techniques and logical rules, and methods that jointly use neural networks (NNs) and logical rules. We introduce and analyze the core concepts and key techniques, as well as the advantages and disadvantages associated with these methods, while also providing a comparative evaluation of their performance. Furthermore, we summarize the main problems and challenges, and offer insights into potential directions for future research. [ABSTRACT FROM AUTHOR]

Proof-theoretic semantics (P-tS) is the paradigm of semantics in which meaning in logic is based on proof (as opposed to truth). A particular instance of P-tS for intuitionistic propositional logic (IPL) is its base-extension semantics (B-eS). This semantics is given by a relation called support, explaining the meaning of the logical constants, which is parameterized by systems of rules called bases that provide the semantics of atomic propositions. In this paper, we interpret bases as collections of definite formulae and use the operational view of them as provided by uniform proof-search—the proof-theoretic foundation of logic programming (LP)—to establish the completeness of IPL for the B-eS. This perspective allows negation, a subtle issue in P-tS, to be understood in terms of the negation-as-failure protocol in LP. Specifically, while the denial of a proposition is traditionally understood as the assertion of its negation, in B-eS we may understand the denial of a proposition as the failure to find a proof of it. In this way, assertion and denial are both prime concepts in P-tS.

The Boolean satisfiability problem, a renowned NP-complete challenge in computer science, has recently garnered interest in the Discrete Hopfield Neural Network - Satisfiability model. This model adeptly integrates logical rules into Hopfield networks, excelling in locating global minima for traditional SAT problems. However, it faces efficiency challenges when dealing with SAT problems characterized by dynamic evolution constraints due to its static network architecture. During dynamic evolution iterations, there is a significant exponential increase in the computational costs due to redundant and repetitive computations.In order to address this challenge, this paper introduces a dynamic evolution variant of the Discrete Hopfield Neural Network - Satisfiability model. In extensive simulation experiments, we progressively augmented the number of constraint clauses from 1 to 1500, seeking global minima for CNF problems. The proposed model exhibited congruent performance with the traditional model, achieving a Global Minimum Ratio of 1 and a Hamming distance of 0. Crucially, the proposed model minimized CPU utilization and neared zero error metrics, while the traditional model experienced exponential CPU and error metric escalation. These outcomes affirm the proposed model's robust global search capabilities and high precision, aligning with the traditional model. Furthermore, owing to this model's incorporation of not only temporal constraint increment operator but also innovative real-time learning techniques and clever integration methods, along with the establishment of a novel real-time decision mechanism, the proposed model effectively addresses the issues of redundancy and repeated calculations inherent in traditional models. This results in a stable and significantly improved computational speed. Additionally, this model's dynamic evolution network architecture is specifically designed to accommodate arbitrary and efficient extensions of dynamic constraints, while also featuring a built-in preprocessing function to filter out duplicate variables. In the future, as a dynamic evolution processor, this model exhibits immense potential for solving large-scale SAT problems with dynamic evolution constraints and holds broad application prospects in emerging fields such as automated reasoning, intelligent recommendation systems, and machine learning.

The biological target identification process, a pivotal phase in the drug discovery workflow, becomes particularly challenging when mutations affect proteins' mechanisms of action. COVID-19 Spike glycoprotein mutations are known to modify the affinity toward the human angiotensin-converting enzyme ACE2 and several antibodies, compromising their neutralizing effect. Predicting new possible mutations would be an efficient way to develop specific and efficacious drugs, vaccines, and antibodies. In this work, we developed and applied a computational procedure, combining constrained logic programming and careful structural analysis based on the Structural Activity Relationship (SAR) approach, to predict and determine the structure and behavior of new future mutants. "Mutations rules" that would track statistical and functional types of substitutions for each residue or combination of residues were extracted from the GISAID database and used to define constraints for our software, having control of the process step by step. A careful molecular dynamics analysis of the predicted mutated structures was carried out after an energy evaluation of the intermolecular and intramolecular interactions using the HINT (Hydrophatic INTeraction) force field. Our approach successfully predicted, among others, known Spike mutants. [ABSTRACT FROM AUTHOR]

The teaching and learning of programming has proven to be a challenge for students of computer courses, since it presents challenges and requires complex skills for the good development of students. The traditional teaching model is not able to motivate students and arouse their interest in the topic. The tool proposed herein, the REA-LP, aims to facilitate the study and retention of content related to the discipline of programming logic at the technical level by presenting its content through various types of media, in addition to allowing students to actively participate in the construction of their knowledge, favoring engagement and motivation. From the results of an empirical study with 39 students, it can be concluded that the tool was very well accepted, being effective in facilitating and assisting participants in their learning, motivation, and interest in classes, mainly due to the way in which the content is presented by REA-LP. [ABSTRACT FROM AUTHOR]

Space syntax is a set of theories and techniques for analysing urban settlements and buildings. Here, we propose a new approach to perform syntactic analyses that requires only the declaration in a computer program of the connections between axial lines or convex spaces using Prolog, a logic programming language concerned with artificial intelligence. With this new tool, we found that the deep tree nature of modern estates can be mitigated with a concentric structure similar to the famous Bororo village. In fact, Portela, a high-rise settlement near Lisbon (Portugal), is structured around a central open space (green park) equipped with noninterchangeable facilities (mall, sports centre and church), which are highly synchronised with the surrounding buildings (towers and blocks). The transpatial relations between housing estates and the central zone are maximised either by a distributive ringy network or by a smart grid of pedestrian paths. The result is a compact and integrated settlement with a strong identity and sense of belonging. Nevertheless, this kind of concentric dual system is potentially unstable, a problem that was minimised by forcing a clear opposition with a popular neighbourhood at Portela's vicinity. With this case study, we show how logic programming is a useful tool to describe the patterns of discrete systems as social knowables due to its declarative nature. In fact, a Prolog program represents a certain amount of knowledge, namely, concerned with the structure of an urban settlement (or building), which could be used to answer queries about the social and economic consequences of certain spatial designs. [ABSTRACT FROM AUTHOR]

Over the last decade, the use of robots in production and daily life has increased. With increasingly complex tasks and interaction in different environments including humans, robots are required a higher level of autonomy for efficient deliberation. Task planning is a key element of deliberation. It combines elementary operations into a structured plan to satisfy a prescribed goal, given specifications on the robot and the environment. In this manuscript, we present a survey on recent advances in the application of logic programming to the problem of task planning. Logic programming offers several advantages compared to other approaches, including greater expressivity and interpretability which may aid in the development of safe and reliable robots. We analyze different planners and their suitability for specific robotic applications, based on expressivity in domain representation, computational efficiency and software implementation. In this way, we support the robotic designer in choosing the best tool for his application. [ABSTRACT FROM AUTHOR]

This paper presents BlockCode, a web application developed to create board games that support the learning of computer programming logic. The goal of the games created with BlockCode is to move a bunny around a board using block-based programming in order to collect all the carrots that are placed on the board. In every game, the bunny has to deal with obstacles, represented by rocks and holes, which have to be avoided using the commands available. BlockCode was designed for teachers and students: teachers can create games, specifying the arrangement of the boards; and students play the games, specifying the sequence of commands with the aim of collecting all the carrots. [ABSTRACT FROM AUTHOR]

Smart materials respond to environmental signals by changing their microstructure and physical properties. Programming multiple behaviors and functions into a single material could increase its utility and adaptability to ever‐changing environmental conditions. A swellable and stretchable metamaterial hydrogel or "metasponge" engineered to morph into customized sizes and shapes that dynamically tune its physical properties and functions is reported. Multiple case studies that take advantage of the morphing properties of the metasponge, including robotic actuation, light guidance, optical and sonic invisibility ("cloaking"), adaptation of propulsion mode, sampling, and multiple biomedical applications, are illustrated. Developing multifunctional smart materials in which logic is programmed into the materials rather than electronic components could pave a new path to autonomy and dynamic responses in soft robots, sensors, and actuators. [ABSTRACT FROM AUTHOR]

Day by day, malware as a service becomes more popular and easy to acquire, thus allowing anyone to start an attack without any technical background, which in turn introduces challenges for detecting such attacks. One of those challenges is the detection of malware activities early to prevent harm as much as possible. This paper presents a trusted dynamic analysis approach based on Answer Set Programming (ASP), a logic engine inference named Malware-Logic-Miner (MalpMiner). ASP is a nonmonotonic reasoning engine built on an open-world assumption, which allows MalpMiner to adopt commonsense reasoning when capturing malware activities of any given binary. Furthermore, MalpMiner requires no prior training; therefore, it can scale up quickly to include more malware-attack attributes. Moreover, MalpMiner considers the invoked application programming interfaces’ values, resulting in correct malware behaviour modelling. The baseline experiments prove the correctness of MalpMiner related to recognizing malware activities. Moreover, MalpMiner achieved a detection ratio of 99% with a false-positive rate of less than 1% while maintaining low computational costs and explaining the detection decision.

This study presents a new way of increasing 3SAT logic programming's efficiency in the Hopfield network. A new model of merging fuzzy logic with 3SAT in the Hopfield network is presented called HNN-3SATFuzzy. The hybridised dynamic model can avoid locally minimal solutions and lessen the computing burden by utilising fuzzification and defuzzification techniques in fuzzy logic. In addressing the 3SAT issue, the proposed hybrid approach can select neuron states between zero and one. Aside from that, unsatisfied neuron clauses will be changed using the alpha-cut method as a defuzzifier step until the correct neuron state is determined. The defuzzification process is a mapping stage that converts a fuzzy value into a crisp output. The corrected neuron state using alpha-cut in the defuzzification stage is either sharpening up to one or sharpening down to zero. A simulated data collection was utilised to evaluate the hybrid techniques' performance. In the training phase, the network for HNN-3SATFuzzy was weighed using RMSE, SSE, MAE and MAPE metrics. The energy analysis also considers the ratio of global minima and processing period to assess its robustness. The findings are significant because this model considerably impacts Hopfield networks' capacity to handle 3SAT problems with less complexity and speed. The new information and ideas will aid in developing innovative ways to gather knowledge for future research in logic programming. Furthermore, the breakthrough in dynamic learning is considered a significant step forward in neuro-symbolic integration. [ABSTRACT FROM AUTHOR]

Due to the rapid urbanization of China, residential areas and residents in small towns exhibit dual and complex urbanization patterns and characteristics. The "one-size-fits-all" approach to programming public service facilities has led to inefficient use of idle facilities and the frequent use of facilities that are not integrated into the public service system. Therefore, an investigation of programming logic based on residents' needs within the complex urbanization patterns and characteristics of small towns is urgently required. This study distinguishes between rural and nonrural facilities, using the Kano model to evaluate residents' hypothetical satisfaction with the provision of facilities or lack thereof. Based on the "rural and nonrural" facility need coupling model, four stages of urbanization were identified. Using the Better–Worse coefficient and the chi-square test, this study analyzes residents' cognitive needs and population attributes across various stages of urbanization. Moreover, the study examines the "residential area–resident facility" matching relationship. Ultimately, a "required + optional item" public service facility guidance-control system based on the matching of human–land urbanization is proposed to improve the current programming system for public service facilities. This system provides a theoretical basis for improving the public service level in small towns and ensuring optimally relevant regulations. [ABSTRACT FROM AUTHOR]

Proof-theoretic semantics (P-tS) is the paradigm of semantics in which meaning in logic is based on proof (as opposed to truth). A particular instance of P-tS for intuitionistic propositional logic (IPL) is its base-extension semantics (B-eS). This semantics is given by a relation called support, explaining the meaning of the logical constants, which is parameterized by systems of rules called bases that provide the semantics of atomic propositions. In this paper, we interpret bases as collections of definite formulae and use the operational view of them as provided by uniform proof-search|the proof-theoretic foundation of logic programming (LP)|to establish the completeness of IPL for the B-eS. This perspective allows negation, a subtle issue in P-tS, to be understood in terms of the negation-as-failure protocol in LP. Specifically, while the denial of a proposition is traditionally understood as the assertion of its negation, in B-eS we may understand the denial of a proposition as the failure to find a proof of it. In this way, assertion and denial are both prime concepts in P-tS. [ABSTRACT FROM AUTHOR]

The extensive use of Deep Neural Networks (DNNs) encourages people to design domain-specific hardware called Artificial Intelligence (AI) processors. The novel hardware makes optimizations challenging without a proper performance model that reveals working details and performance implications. This paper presents a performance model, Verrocchio, for Huawei DaVinci AI Core, which predicts the execution time of real-world DaVinci kernels. We propose specially-crafted micro-benchmarks to identify contention source, runtime behaviors, and bandwidth sharing, which significantly determine performance. Since DaVinci Core adopts a binary semaphore mechanism for synchronization, Verrocchio views each instruction as a discrete event and manages its execution time based on the programming logic. For evaluation, Verrocchio achieves average error rates of 2.62% and 2.30% in sample kernels for single-core and double-core execution. We demonstrate an optimizing process of matrix multiplications with Verrocchio, achieving speedups of 1.70× for operators and 1.53× for applications and error rates of 5.06% and 5.25%. • Detailed dissections of Huawei DaVinci AI Core, a novel AI processor. • Benchmarking the DaVinci Core bandwidth contention, the key performance factor. • Performance model for accurate execution time prediction of kernel program. • Demonstration of DaVinci kernel optimization and prediction accuracy evaluation. [ABSTRACT FROM AUTHOR]

Statistical relational AI and probabilistic logic programming have so far mostly focused on discrete probabilistic models. The reasons for this is that one needs to provide constructs to succinctly model the independencies in such models, and also provide efficient inference. Three types of independencies are important to represent and exploit for scalable inference in hybrid models: conditional independencies elegantly modeled in Bayesian networks, context-specific independencies naturally represented by logical rules, and independencies amongst attributes of related objects in relational models succinctly expressed by combining rules. This paper introduces a hybrid probabilistic logic programming language, DC#, which integrates distributional clauses' syntax and semantics principles of Bayesian logic programs. It represents the three types of independencies qualitatively. More importantly, we also introduce the scalable inference algorithm FO-CS-LW for DC#. FO-CS-LW is a first-order extension of the context-specific likelihood weighting algorithm (CS-LW), a novel sampling method that exploits conditional independencies and context-specific independencies in ground models. The FO-CS-LW algorithm upgrades CS-LW with unification and combining rules to the first-order case. [ABSTRACT FROM AUTHOR]

This paper is a contribution to the research on dynamics in assumption-based argumentation (ABA). We investigate situations where a given knowledge base undergoes certain changes. We show that two frequently investigated problems, namely enforcement of a given target atom and deciding strong equivalence of two given ABA frameworks, are intractable in general. Notably, these problems are both tractable for abstract argumentation frameworks (AFs) which admit a close correspondence to ABA by constructing semanticspreserving instances. Inspired by this observation, we search for tractable fragments for ABA frameworks by means of the instantiated AFs. We argue that the usual instantiation procedure is not suitable for the investigation of dynamic scenarios since too much information is lost when constructing the abstract framework. We thus consider an extension of AFs, called cvAFs, equipping arguments with conclusions and vulnerabilities in order to better anticipate their role after the underlying knowledge base is extended. We investigate enforcement and strong equivalence for cvAFs and present syntactic conditions to decide them. We show that the correspondence between cvAFs and ABA frameworks is close enough to capture dynamics in ABA. This yields the desired tractable fragment. We furthermore discuss consequences for the corresponding problems for logic programs. [ABSTRACT FROM AUTHOR]

The effective and accurate detection of the anticancer drug coralyne (COR) is highly significant for drug quality control, medication safety and good health. Although various COR sensors have been reported in recent years, previous ones can only exhibit single-signal output (turn ON or turn OFF) with poor reliability and anti-interference ability. Therefore, exploring novel platform with dual-signal response for COR detection is urgently needed. Herein, we reported the first ratiometric fluorescent platform for highly sensitive and selective COR detection by integrating G-quadruplex (G4) and Pyrene (Py) as signal probes and harnessing A-COR-A interaction. In the absence of COR, the platform shows a low fluorescence signal of PPIX (F642) and a high one of Py monomer (F383). With the addition of COR, two delicately designed poly-A ssDNAs will hybridize with each other via A-COR-A coordination to form complete G4, yielding the increased fluorescence signal of PPIX and the decreased one of Py due to the formation of Py excimer. Based on the above mechanism, we constructed a simple and efficient sensor that could realize the ratiometric fluorescent detection of COR with high sensitivity and selectivity. A linear relationship between F642/F383 and COR's concentration is obtained in the range from 1 nM to 8 μM. And the limit of detection of COR could reach to as low as 0.63 nM without any amplification, which is much lower than that of most COR sensors reported so far. Notably, the logical analysis of COR can be carried out under the control of a "YES-NOT" contrary logic pair, enabling the smart dual-channel response with an adequate S/N ratio and improved reliability and anti-interference ability. Moreover, this system also presents satisfactory performance in fetal bovine serum (FBS) samples. [ABSTRACT FROM AUTHOR]

A wide range of new research articles in artificial intelligence, logic programming, and other applied sciences are based on fixed-point theorems. The aim of this article is to present an approximation method for finding the fixed point of generalized Suzuki nonexpansive mappings on hyperbolic spaces. Strong and Δ -convergence theorems are proved using the Noor iterative process for generalized Suzuki nonexpansive mappings (GSNM) on uniform convex hyperbolic spaces. Due to the richness of uniform convex hyperbolic spaces, the results of this paper can be used as an extension and generalization of many famous results in Banach spaces together with C A T (0) spaces. [ABSTRACT FROM AUTHOR]

Augmented (AR) and Mixed Reality (MR) technologies are enablers of the Industry 4.0 paradigm and are spreading at high speed in production. Main applications include design, training, and assembly guidance. The latter is a pressing concern, because assembly is the process that accounts for the biggest portion of total cost within production. Teaching and guiding operators to assemble with minimal effort and error rates is pivotal. This work presents the development of a comprehensive MR application for guiding novice operators in following simple assembly instructions. The app follows innovative programming logic and component tracking in a dynamic environment, providing an immersive experience that includes different guidance aids. The application was tested by experienced and novice users, data were drawn from the performed experiments, and a questionnaire was submitted to collect the users' perception. Results indicate that the MR application was easy to follow and even gave confidence to inexperienced subjects. The guidance support was perceived as useful by the users, though at times invasive in the field of view. Further development effort is required to draw from this work a complete and usable architecture for MR application in assembly, but this research forms the basis to achieve better, more consistent instructions for assembly guidance based on component tracking. [ABSTRACT FROM AUTHOR]