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The use of in-situ cross-linked acids (ICAs) is common in stimulating oil and gas reservoirs. However, their rheological models have not been widely incorporated into simulation software due to their complex behavior and lack of valid data. Accordingly, in this study, development of new models for estimation of viscosity of ICAs as a function of pH and shear rate was intended. For this purpose, 33 experimental data points were collected from literature and methods of genetic programming, neural network (NN), and fuzzy logic (FL) were used to develop models. In summary, all three models performed equally well and better than a previously published model, with an R2 = 0.99 and an average absolute percent error of 7%. In terms of computational costs, genetic programming correlation was found to be 76 and 786% faster than NN and FL, respectively. Therefore, the model developed by genetic programming was suggested to be used in numerical solvers for estimation the viscosity of ICAs as a function of pH and shear. Sensitivity analysis on temperature, pH, and shear rate showed that pH would have the highest impact on the apparent viscosity of the considered ICAs. [ABSTRACT FROM AUTHOR]

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]

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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]

Introduction to Computer Science is traditionally the first course that all computer science and software engineering majors take. The course introduces many problem‐solving techniques which can be challenging for many freshman students. In order to mitigate some of the issues of this course, we, at the Higher Education Institute, introduced a new prerequisite course, Introduction to Programming Logic, which is a required course for all students who have not taken any previous computer science course. In the Summer Session of 2022, we included prize‐based learning in one of the sections of the course. Prize‐based learning is similar to both problem‐based learning and project‐based learning in many aspects, including the principle of student‐centred learning. However, it differs with respect to the motivation for student success. This approach utilises the students' ambition to win, to encourage students to work harder and learn more both inside as well as outside the classroom. [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 article proposes a project aimed at introducing secondary school students to artificial intelligence and logic programming using the Prolog language. In commemoration of the 50th anniversary of Prolog's development, the authors participate in the international initiative "Prolog Education and Thinking" through the "Digital Bulgaria in Prolog" activity, implemented in Bulgarian secondary schools. The article offers a concise overview of a STEM (Science, Technology, Engineering, and Mathematics) educational program and training for secondary school students in Bulgaria. STEM serves as a project-based learning approach, fostering students' understanding of multiple disciplines and utilizing diverse technologies to enhance their skills. Additionally, the article showcases examples of student initiatives spanning natural sciences, informatics, humanities, culture, and art, illustrating the interdisciplinary nature of STEM education. [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]

Logic programming is a declarative programming paradigm that finds extensive use in the field of Artificial Intelligence (AI). As a result, it has become a valuable tool used in university courses for teaching students AI techniques. Besides Prolog language, the more recent Answer Set Programming (ASP) language turns out to be a powerful tool for developing advanced applications due to the expressiveness of the language and the availability of efficient solving systems. Unfortunately, the output of ASP solvers can be difficult to interpret, since it is a set of atoms, often long and verbose. This is most true in the case of students learning the language or in the case of experts developing applications for complex real-world problems. For these reasons, the ability to produce, when possible, a graphical representation of the solver output becomes useful to ensure easier interpretation of the results. In this paper we present ASPECT, a sub-language of ASP in which the user can directly define, in an intuitive and declarative way, a graphical representation of the answer set. The ASPECT atoms can be converted into the popular LaTeX markup language to produce vector graphics. The documents produced by ASPECT are easy to embed in documents such as scientific articles, course handouts and presentations. Also, the development of user-friendly interfaces is critical for wider use of similar technologies in the industrial sector as well. Moreover, ASPECT is also extended to deal with temporal information, and provide graphical animations of answer sets that enclose the temporal dimension, such as in planning problems. Finally, we advocate the use of ASPECT to create complex and animated presentations starting from a declarative specification. [ABSTRACT FROM AUTHOR]

Weighted knowledge bases for description logics with typicality under a 'concept-wise' multi-preferential semantics provide a logical interpretation of MultiLayer Perceptrons. In this context, Answer Set Programming (ASP) has been shown to be suitable for addressing defeasible reasoning in the finitely many-valued case, providing a |$\varPi ^{p}_{2}$| upper bound on the complexity of the problem, nonetheless leaving unknown the exact complexity and only providing a proof-of-concept implementation. This paper fulfills the lack by providing a |${P^{NP[log]}}$| -completeness result and new ASP encodings that deal with both acyclic and cyclic weighted knowledge bases with large search spaces, as assessed empirically on synthetic test cases. The encodings are used to empower a reasoner for computing solutions and answering queries, possibly interacting with ASP Chef for obtaining an interactive visualization. [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]

In multi-task learning, a learner adds the learned programs into background knowledge (BK) and reuses them to learn other programs. Continually acquiring BK can lead to the problem of excessive BK, which overwhelms a learning system. Hence, it is necessary to forget irrelevant BK. However, existing forgetting approaches rarely consider the relevance between BK and learning tasks, commonly providing the same BK for different induction tasks. To address this issue, this paper proposes a relevance identification approach based on counterfactual thinking, termed counterfactual acquisition. This approach first measures each hypothesis' s contribution to the learning task using a relevance function. Then, it retains only those hypotheses whose relevance function values exceed a predefined threshold. Moreover, this approach is applied to inductive logic programming (ILP) through the introduction of a multi- task ILP learner named Countergol. Theoretical analysis demonstrates that Countergol can reduce the hypothesis space and sample complexity size. Experimental comparisons against other forgetting approaches show that Countergol outperforms similar methods. [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]

In this paper, we present a modular methodology that combines state-of-the-art methods in (stochastic) machine learning with well-established methods in inductive logic programming (ILP) and rule induction to provide efficient and scalable algorithms for the classification of vast data sets. By construction, these classifications are based on the synthesis of simple rules, thus providing direct explanations of the obtained classifications. Apart from evaluating our approach on the common large scale data sets MNIST, Fashion-MNIST and IMDB, we present novel results on explainable classifications of dental bills. The latter case study stems from an industrial collaboration with Allianz Private Krankenversicherung which is an insurance company offering diverse services in Germany. [ABSTRACT FROM AUTHOR]

In this paper, we study Horn formulas from the perspective of read-once resolution refutations (RORs). A Horn formula is a Boolean formula in conjunctive normal form (CNF), in which each clause contains at most one positive literal. Horn formulas are used in a number of domains, including program verification, logic programming, and econometrics. In particular, deduction in ProLog is based on unification. Unification is based on resolution and instantiation. Resolution is a system used to prove the infeasibility of Boolean formulas. It is important to note that resolution is both sound and complete. However, resolution is inefficient in the following sense: There exist CNF formulas with resolution refutations whose lengths are bounded below by an exponential function of the input size. At the same time, these formulas admit shorter (polynomially bounded) proofs of infeasibility in other proof systems, such as Frege Systems. Despite this inefficiency, resolution is simple and easy to implement and hence used in a wide variety of theorem provers. In this paper, we study two variants of resolution. These are read-once resolution (ROR) and read-once unit resolution (UROR). Both ROR and UROR are sound. However, they are incomplete since there exist infeasible Boolean formulas which do not have either an ROR or a UROR. In this paper, we look at the problems of determining if a Horn formula has an ROR or a UROR. We also examine the problem of finding the optimal length ROR of a Horn formula from both the computational complexity and the approximation perspectives. Finally, we analyze the copy complexity of Horn formulas with respect to URORs. [ABSTRACT FROM AUTHOR]

Gödel's Incompleteness Theorems suggest that no single formal system can capture the entirety of one's mathematical beliefs, while pointing at a hierarchy of systems of increasing logical strength that make progressively more explicit those implicit assumptions. This notion of implicit commitment motivates directly or indirectly several research programmes in logic and the foundations of mathematics; yet there hasn't been a direct logical analysis of the notion of implicit commitment itself. In a recent paper, we carried out an initial assessment of this project by studying necessary conditions for implicit commitments; from seemingly weak assumptions on implicit commitments of an arithmetical system |$S$|⁠ , it can be derived that a uniform reflection principle for |$S$| —stating that all numerical instances of theorems of |$S$| are true—must be contained in |$S$| 's implicit commitments. This study gave rise to unexplored research avenues and open questions. This paper addresses the main ones. We generalize this basic framework for implicit commitments along two dimensions: in terms of iterations of the basic implicit commitment operator, and via a study of implicit commitments of theories in arbitrary first-order languages, not only couched in an arithmetical language. [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]

Variable sharing is a fundamental property in the static analysis of logic programs, since it is instrumental for ensuring correctness and increasing precision while inferring many useful program properties. Such properties include modes, determinacy, non-failure, cost, etc. This has motivated significant work on developing abstract domains to improve the precision and performance of sharing analyses. Much of this work has centered around the family of set-sharing domains, because of the high precision they offer. However, this comes at a price: their scalability to a wide set of realistic programs remains challenging and this hinders their wider adoption. In this work, rather than defining new sharing abstract domains, we focus instead on developing techniques which can be incorporated in the analyzers to address aspects that are known to affect the efficiency of these domains, such as the number of variables, without affecting precision. These techniques are inspired in others used in the context of compiler optimizations, such as expression reassociation and variable trimming. We present several such techniques and provide an extensive experimental evaluation of over 1100 program modules taken from both production code and classical benchmarks. This includes the Spectector cache analyzer, the s(CASP) system, the libraries of the Ciao system, the LPdoc documenter, the PLAI analyzer itself, etc. The experimental results are quite encouraging: we have obtained significant speedups, and, more importantly, the number of modules that require a timeout was cut in half. As a result, many more programs can be analyzed precisely in reasonable times. [ABSTRACT FROM AUTHOR]

FOLD-RM is an explainable machine learning classification algorithm that uses training data to create a set of classification rules. In this paper, we introduce CON-FOLD which extends FOLD-RM in several ways. CON-FOLD assigns probability-based confidence scores to rules learned for a classification task. This allows users to know how confident they should be in a prediction made by the model. We present a confidence-based pruning algorithm that uses the unique structure of FOLD-RM rules to efficiently prune rules and prevent overfitting. Furthermore, CON-FOLD enables the user to provide preexisting knowledge in the form of logic program rules that are either (fixed) background knowledge or (modifiable) initial rule candidates. The paper describes our method in detail and reports on practical experiments. We demonstrate the performance of the algorithm on benchmark datasets from the UCI Machine Learning Repository. For that, we introduce a new metric, Inverse Brier Score, to evaluate the accuracy of the produced confidence scores. Finally, we apply this extension to a real-world example that requires explainability: marking of student responses to a short answer question from the Australian Physics Olympiad. [ABSTRACT FROM AUTHOR]

Answer Set Programming with Quantifiers (ASP(Q)) has been introduced to provide a natural extension of ASP modeling to problems in the polynomial hierarchy (PH). However, ASP(Q) lacks a method for encoding in an elegant and compact way problems requiring a polynomial number of calls to an oracle in $\Sigma _n^p$ (that is, problems in $\Delta _{n+1}^p$). Such problems include, in particular, optimization problems. In this paper, we propose an extension of ASP(Q), in which component programs may contain weak constraints. Weak constraints can be used both for expressing local optimization within quantified component programs and for modeling global optimization criteria. We showcase the modeling capabilities of the new formalism through various application scenarios. Further, we study its computational properties obtaining complexity results and unveiling non-obvious characteristics of ASP(Q) programs with weak constraints. [ABSTRACT FROM AUTHOR]

Many inductive logic programming (ILP) methods are incapable of learning programs from probabilistic background knowledge, for example, coming from sensory data or neural networks with probabilities. We propose Propper, which handles flawed and probabilistic background knowledge by extending ILP with a combination of neurosymbolic inference, a continuous criterion for hypothesis selection (binary cross-entropy) and a relaxation of the hypothesis constrainer (NoisyCombo). For relational patterns in noisy images, Propper can learn programs from as few as 8 examples. It outperforms binary ILP and statistical models such as a graph neural network. [ABSTRACT FROM AUTHOR]

Dung's abstract Argumentation Framework (AF) has emerged as a key formalism for argumentation in artificial intelligence. It has been extended in several directions, including the possibility to express supports, leading to the development of the Bipolar Argumentation Framework (BAF), and recursive attacks and supports, resulting in the Recursive BAF (Rec-BAF). Different interpretations of supports have been proposed, whereas for Rec-BAF (where the target of attacks and supports may also be attacks and supports) even different semantics for attacks have been defined. However, the semantics of these frameworks have either not been defined in the presence of support cycles or are often quite intricate in terms of the involved definitions. We encompass this limitation and present classical semantics for general BAF and Rec-BAF and show that the semantics for specific BAF and Rec-BAF frameworks can be defined by very simple and intuitive modifications of that defined for the case of AF. This is achieved by providing a modular definition of the sets of defeated and acceptable elements for each AF-based framework. We also characterize, in an elegant and uniform way, the semantics of general BAF and Rec-BAF in terms of logic programming and partial stable model semantics. [ABSTRACT FROM AUTHOR]

When we want to compute the probability of a query from a probabilistic answer set program, some parts of a program may not influence the probability of a query, but they impact on the size of the grounding. Identifying and removing them is crucial to speed up the computation. Algorithms for SLG resolution offer the possibility of returning the residual program which can be used for computing answer sets for normal programs that do have a total well-founded model. The residual program does not contain the parts of the program that do not influence the probability. In this paper, we propose to exploit the residual program for performing inference. Empirical results on graph datasets show that the approach leads to significantly faster inference. The paper has been accepted at the ICLP2024 conference and under consideration in Theory and Practice of Logic Programming (TPLP). [ABSTRACT FROM AUTHOR]

We propose a stable model semantics for higher-order logic programs. Our semantics is developed using Approximation Fixpoint Theory (AFT), a powerful formalism that has successfully been used to give meaning to diverse non-monotonic formalisms. The proposed semantics generalizes the classical two-valued stable model semantics of Gelfond and Lifschitz as well as the three-valued one of Przymusinski, retaining their desirable properties. Due to the use of AFT, we also get for free alternative semantics for higher-order logic programs, namely supported model , Kripke-Kleene , and well-founded. Additionally, we define a broad class of stratified higher-order logic programs and demonstrate that they have a unique two-valued higher-order stable model which coincides with the well-founded semantics of such programs. We provide a number of examples in different application domains, which demonstrate that higher-order logic programming under the stable model semantics is a powerful and versatile formalism, which can potentially form the basis of novel ASP systems. [ABSTRACT FROM AUTHOR]

The employment of Machine Learning (ML) techniques in embedded systems has seen constant growth in recent years, especially for black-box ML techniques (such as Artificial Neural Networks (ANNs)). However, despite the successful employment of ML techniques in embedded environments, their performance potential is constrained by the limited computing resources of their embedded computers. Several hardware-based approaches were developed (e.g., using FPGAs and ASICs) to address the constraints of limited computing resources. The scope of this work focuses on improving the performance for Inductive Logic Programming (ILP) on embedded environments. ILP is a powerful logic-based ML technique that uses logic programming to construct human-interpretable ML models, where those logic-based ML models are capable of describing complex and multi-relational concepts. In this work, we present a hardware-based approach that accelerates the hypothesis evaluation task for ILPs in embedded environments that use Description Logic (DL) languages as their logic-based representation. In particular, we target the \(\mathcal {ALCQ}^{\mathcal {(D)}}\) language. According to experimental results (through an FPGA implementation), our presented approach has achieved speedups up to 48.7-fold for a disjunction of 32 concepts on 100 M individuals, where the baseline performance is the sequential CPU performance of the Raspberry Pi 4. For role and concrete role restrictions, the FPGA implementation achieved speedups up to 2.4-fold (for MIN cardinality role restriction on 1M role assertions); all FPGA implemented role and concrete role restrictions have achieved similar speedups. In the worst-case scenario, the FPGA implementation achieved either a similar or slightly better performance than the baseline (for all DL operations); the worst-case scenario resulted from using small datasets such as: using conjunction and disjunction on < 100 individuals, and using role and concrete (float/string) role restrictions on < 100,000 assertions. [ABSTRACT FROM AUTHOR]

Deep 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. [ABSTRACT FROM AUTHOR]

We describe some recent computer investigations with the 'Constraint Logic Programming over Finite Domains' library in the Prolog programming environment to search for new simple Lie algebras over the field \operatorname {GF}(2) of 2 elements. Motivated by a paper of Grishkov et al., we specifically look for those with a thin decomposition , and we settle one of their conjectures. We extrapolate from our results the existence of two new infinite families of simple Lie algebras, in addition to finding seven new sporadic examples in dimension 31. We also better contextualise some previously discovered simple algebras, putting them into families which do not seem to have ever appeared in the literature, and give an updated table of those currently known. [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]

In temporal extensions of answer set programming (ASP) based on linear time, the behavior of dynamic systems is captured by sequences of states. While this representation reflects their relative order, it abstracts away the specific times associated with each state. However, timing constraints are important in many applications like, for instance, when planning and scheduling go hand in hand. We address this by developing a metric extension of linear-time temporal equilibrium logic, in which temporal operators are constrained by intervals over natural numbers. The resulting Metric Equilibrium Logic (MEL) provides the foundation of an ASP-based approach for specifying qualitative and quantitative dynamic constraints. To this end, we define a translation of metric formulas into monadic first-order formulas and give a correspondence between their models in MEL and Monadic Quantified Equilibrium Logic, respectively. Interestingly, our translation provides a blue print for implementation in terms of ASP modulo difference constraints. [ABSTRACT FROM AUTHOR]

We present the Answer Set Programming (ASP)-based visualization tool clingraph , which aims at visualizing various concepts of ASP by means of ASP itself. This idea traces back to the aspviz tool and clingraph redevelops and extends it in the context of modern ASP systems. More precisely, clingraph takes graph specifications in terms of ASP facts and hands them over to the graph visualization system graphviz. The use of ASP provides a great interface between logic programs and/or answer sets and their visualization. Also, clingraph offers a Python application programming interface (API) that extends this ease of interfacing to clingo 's API and in turn to connect and monitor various aspects of the solving process. [ABSTRACT FROM AUTHOR]

Epistemic logic programs (ELPs), extend answer set programming (ASP) with epistemic operators. The semantics of such programs is provided in terms of world views , which are sets of belief sets, that is, syntactically, sets of sets of atoms. Different semantic approaches propose different characterizations of world views. Recent work has introduced semantic properties that should be met by any semantics for ELPs, like the Epistemic Splitting Property , that, if satisfied, allows to modularly compute world views in a bottom-up fashion, analogously to "traditional" ASP. We analyze the possibility of changing the perspective, shifting from a bottom-up to a top-down approach to splitting. We propose a basic top-down approach, which we prove to be equivalent to the bottom-up one. We then propose an extended approach, where our new definition: (i) is provably applicable to many of the existing semantics; (ii) operates similarly to "traditional" ASP; (iii) provably coincides under any semantics with the bottom-up notion of splitting at least on the class of Epistemically Stratified Programs (which are, intuitively, those where the use of epistemic operators is stratified); (iv) better adheres to common ASP programming methodology. [ABSTRACT FROM AUTHOR]