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Journal articles on the topic 'Inductive logic approach'

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Published: 4 June 2025

Last updated: 1 August 2025

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1

Murali, Adithya, Lucas Peña, Eion Blanchard, Christof Löding, and P. Madhusudan. "Model-guided synthesis of inductive lemmas for FOL with least fixpoints." Proceedings of the ACM on Programming Languages 6, OOPSLA2 (2022): 1873–902. http://dx.doi.org/10.1145/3563354.

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Recursively defined linked data structures embedded in a pointer-based heap and their properties are naturally expressed in pure first-order logic with least fixpoint definitions (FO+lfp) with background theories. Such logics, unlike pure first-order logic, do not admit even complete procedures. In this paper, we undertake a novel approach for synthesizing inductive hypotheses to prove validity in this logic. The idea is to utilize several kinds of finite first-order models as counterexamples that capture the non-provability and invalidity of formulas to guide the search for inductive hypothes

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Lavrač, Nada, and Peter A. Flach. "An extended transformation approach to inductive logic programming." ACM Transactions on Computational Logic 2, no. 4 (2001): 458–94. http://dx.doi.org/10.1145/383779.383781.

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Yang, Ziyi, and Ilya Sergey. "Inductive Synthesis of Inductive Heap Predicates." Proceedings of the ACM on Programming Languages 9, OOPSLA1 (2025): 169–95. https://doi.org/10.1145/3720420.

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We present an approach to automatically synthesise recursive predicates in Separation Logic (SL) from concrete data structure instances using Inductive Logic Programming (ILP) techniques. The main challenges to make such synthesis effective are (1) making it work without negative examples that are required in ILP but are difficult to construct for heap-based structures in an automated fashion, and (2) to be capable of summarising not just the shape of a heap (e.g., it is a linked list), but also the properties of the data it stores (e.g., it is a sorted linked list). We tackle these challenges

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Cropper, Andrew. "Learning Logic Programs Though Divide, Constrain, and Conquer." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 6 (2022): 6446–53. http://dx.doi.org/10.1609/aaai.v36i6.20596.

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We introduce an inductive logic programming approach that combines classical divide-and-conquer search with modern constraint-driven search. Our anytime approach can learn optimal, recursive, and large programs and supports predicate invention. Our experiments on three domains (classification, inductive general game playing, and program synthesis) show that our approach can increase predictive accuracies and reduce learning times.

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Meli, Daniele, Alberto Castellini, and Alessandro Farinelli. "Learning Logic Specifications for Policy Guidance in POMDPs: an Inductive Logic Programming Approach." Journal of Artificial Intelligence Research 79 (February 28, 2024): 725–76. http://dx.doi.org/10.1613/jair.1.15826.

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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

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Meli, Daniele, Alberto Castellini, and Alessandro Farinelli. "Learning Logic Specifications for Policy Guidance in POMDPs: an Inductive Logic Programming Approach." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 27 (2025): 28743. https://doi.org/10.1609/aaai.v39i27.35134.

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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

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Unno, Hiroshi, Tachio Terauchi, Yu Gu, and Eric Koskinen. "Modular Primal-Dual Fixpoint Logic Solving for Temporal Verification." Proceedings of the ACM on Programming Languages 7, POPL (2023): 2111–40. http://dx.doi.org/10.1145/3571265.

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We present a novel approach to deciding the validity of formulas in first-order fixpoint logic with background theories and arbitrarily nested inductive and co-inductive predicates defining least and greatest fixpoints. Our approach is constraint-based, and reduces the validity checking problem of the given first-order-fixpoint logic formula (formally, an instance in a language called µCLP) to a constraint satisfaction problem for a recently introduced predicate constraint language. Coupled with an existing sound-and-relatively-complete solver for the constraint language, this novel reduction

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Wagner, Roy. "Finite high-order games and an inductive approach towards Gowers's dichotomy." Annals of Pure and Applied Logic 111, no. 1-2 (2001): 39–60. http://dx.doi.org/10.1016/s0168-0072(01)00034-3.

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9

Karimi, Hamed, and Ali Kamandi. "A learning-based ontology alignment approach using inductive logic programming." Expert Systems with Applications 125 (July 2019): 412–24. http://dx.doi.org/10.1016/j.eswa.2019.02.014.

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Cimiano, Philipp, Sebastian Rudolph, and Helena Hartfiel. "Computing intensional answers to questions – An inductive logic programming approach." Data & Knowledge Engineering 69, no. 3 (2010): 261–78. http://dx.doi.org/10.1016/j.datak.2009.10.008.

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ZHENG, LEI, CHUNNIAN LIU, DONG JIA, and NING ZHONG. "GENERATING NUMERICAL CONSTRAINTS IN CILP." International Journal of Pattern Recognition and Artificial Intelligence 19, no. 01 (2005): 91–108. http://dx.doi.org/10.1142/s0218001405003946.

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A continuing problem with inductive logic programming (ILP) has been the poor handling of numbers. Constraint inductive logic programming (CILP) aims to solve this problem with ILP. We propose a new approach to generating numerical constraints in CILP, and describe an implementation of the CILP system (namely, BPU-CILP). In our approach, methods from pattern recognition and multivariate data analysis, such as Fisher's linear discriminant, dynamic clustering and principal component analysis, are introduced into CILP. The BPU-CILP can generate various forms of polynomial constraints of multiple

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Cropper, Andrew. "Forgetting to Learn Logic Programs." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 04 (2020): 3676–83. http://dx.doi.org/10.1609/aaai.v34i04.5776.

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Most program induction approaches require predefined, often hand-engineered, background knowledge (BK). To overcome this limitation, we explore methods to automatically acquire BK through multi-task learning. In this approach, a learner adds learned programs to its BK so that they can be reused to help learn other programs. To improve learning performance, we explore the idea of forgetting, where a learner can additionally remove programs from its BK. We consider forgetting in an inductive logic programming (ILP) setting. We show that forgetting can significantly reduce both the size of the hy

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Nunung, Nurul Qomariyah, Kazakov Dimitar, and Nurul Fajar Ahmad. "On the benefit of logic-based approach to learn pairwise comparisons." Bulletin of Electrical Engineering and Informatics 9, no. 6 (2020): 2637–49. https://doi.org/10.11591/eei.v9i6.2384.

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In recent years, many daily processes such as internet web searching, e-mail filtering, social media services, e-commerce have benefited from machine learning techniques (ML). The implementation of ML techniques has been largely focused on black box methods where the general conclusions are not easily interpretable. Hence, the elaboration with other declarative software models to identify the correctness and completeness of the models is not easy to perform. On the other hand, the emerge of some logic-based machine learning techniques with their advantage of white box approach have been proven

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Bollhagen, Andrew. "Hempel’s Raven Revisited." Journal of Philosophy 118, no. 3 (2021): 113–37. http://dx.doi.org/10.5840/jphil2021118310.

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The paper takes a novel approach to a classic problem—Hempel’s Raven Paradox. A standard approach to it supposes the solution to consist in bringing our inductive logic into “reflective equilibrium” with our intuitive judgements about which inductive inferences we should license. This approach leaves the intuitions as a kind of black box and takes it on faith that, whatever the structure of the intuitions inside that box might be, it is one for which we can construct an isomorphic formal edifice, a system of inductive logic. By popping open the box we can see whether that faith is misplaced. I

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SHAKERIN, FARHAD, ELMER SALAZAR, and GOPAL GUPTA. "A new algorithm to automate inductive learning of default theories." Theory and Practice of Logic Programming 17, no. 5-6 (2017): 1010–26. http://dx.doi.org/10.1017/s1471068417000333.

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AbstractIn inductive learning of a broad concept, an algorithm should be able to distinguish concept examples from exceptions and noisy data. An approach through recursively finding patterns in exceptions turns out to correspond to the problem of learning default theories. Default logic is what humans employ in common-sense reasoning. Therefore, learned default theories are better understood by humans. In this paper, we present new algorithms to learn default theories in the form of non-monotonic logic programs. Experiments reported in this paper show that our algorithms are a significant impr

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Cropper, Andrew, and Céline Hocquette. "Learning Logic Programs by Discovering Where Not to Search." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 5 (2023): 6289–96. http://dx.doi.org/10.1609/aaai.v37i5.25774.

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The goal of inductive logic programming (ILP) is to search for a hypothesis that generalises training examples and background knowledge (BK). To improve performance, we introduce an approach that, before searching for a hypothesis, first discovers "where not to search". We use given BK to discover constraints on hypotheses, such as that a number cannot be both even and odd. We use the constraints to bootstrap a constraint-driven ILP system. Our experiments on multiple domains (including program synthesis and inductive general game playing) show that our approach can (i) substantially reduce le

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17

Bernard, Marc, and Francois Jacquenet Lirsia. "Discovering rules to design newspapers: An inductive constraint logic programming approach." Applied Artificial Intelligence 12, no. 6 (1998): 547–67. http://dx.doi.org/10.1080/088395198117668.

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Hocquette, Céline, Andreas Niskanen, Matti Järvisalo, and Andrew Cropper. "Learning MDL Logic Programs from Noisy Data." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 9 (2024): 10553–61. http://dx.doi.org/10.1609/aaai.v38i9.28925.

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Many inductive logic programming approaches struggle to learn programs from noisy data. To overcome this limitation, we introduce an approach that learns minimal description length programs from noisy data, including recursive programs. Our experiments on several domains, including drug design, game playing, and program synthesis, show that our approach can outperform existing approaches in terms of predictive accuracies and scale to moderate amounts of noise.

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XU, MAI, MARIA PETROU, and JIANHUA LU. "LEARNING LOGIC RULES FOR THE TOWER OF KNOWLEDGE USING MARKOV LOGIC NETWORKS." International Journal of Pattern Recognition and Artificial Intelligence 25, no. 06 (2011): 889–907. http://dx.doi.org/10.1142/s0218001411008610.

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In this paper, we propose a novel logic-rule learning approach for the Tower of Knowledge (ToK) architecture, based on Markov logic networks, for scene interpretation. This approach is in the spirit of the recently proposed Markov logic networks for machine learning. Its purpose is to learn the soft-constraint logic rules for labeling the components of a scene. In our approach, FOIL (First Order Inductive Learner) is applied to learn the logic rules for MLN and then gradient ascent search is utilized to compute weights attached to each rule for softening the rules. This approach also benefits

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Dumancic, Sebastijan, Tias Guns, and Andrew Cropper. "Knowledge Refactoring for Inductive Program Synthesis." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 8 (2021): 7271–78. http://dx.doi.org/10.1609/aaai.v35i8.16893.

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Humans constantly restructure knowledge to use it more efficiently. Our goal is to give a machine learning system similar abilities so that it can learn more efficiently. We introduce the knowledge refactoring problem, where the goal is to restructure a learner's knowledge base to reduce its size and to minimise redundancy in it. We focus on inductive logic programming, where the knowledge base is a logic program. We introduce Knorf, a system which solves the refactoring problem using constraint optimisation. A key feature of Knorf is that, rather than simply removing knowledge, it also introd

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CORAPI, DOMENICO, ALESSANDRA RUSSO, MARINA DE VOS, JULIAN PADGET, and KEN SATOH. "Normative design using inductive learning." Theory and Practice of Logic Programming 11, no. 4-5 (2011): 783–99. http://dx.doi.org/10.1017/s1471068411000305.

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AbstractIn this paper we propose a use-case-driven iterative design methodology for normative frameworks, also called virtual institutions, which are used to govern open systems. Our computational model represents the normative framework as a logic program under answer set semantics (ASP). By means of an inductive logic programming approach, implemented using ASP, it is possible to synthesise new rules and revise the existing ones. The learning mechanism is guided by the designer who describes the desired properties of the framework through use cases, comprising (i) event traces that capture p

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Hidayat, Enang. "Induction and Its Relevance to the Transformation of Sharia Economic Law in Indonesia: A Study of Four Madhhabs." Ulul Albab: Jurnal Studi dan Penelitian Hukum Islam 7, no. 1 (2024): 71. http://dx.doi.org/10.30659/jua.v7i1.28692.

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This research aims to determine the existence and urgency of induction in the methodology of Islamic law of the four schools of jurisprudence and its relevance to the transformation of Sharia economic law in Indonesia. This study falls under the category of normative Islamic legal research using a conceptual and philosophical approach. Primary data is sourced from the foundational books of Islamic jurisprudence of the four schools: "al-Mahsul" by Al-Razi (Shafi'i), "Syarh Tanqih al-Fusul" and "al-Furuq" by Al-Qurafi (Maliki), "Taqrib al-Usul" by Ibn Juzay (Hanbali), and "Al-Muwafaqat" by Al-Sh

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Nguyen, Hoan, Tien-Dao Luu, Olivier Poch, and Julie D. Thompson. "Knowledge Discovery in Variant Databases Using Inductive Logic Programming." Bioinformatics and Biology Insights 7 (January 2013): BBI.S11184. http://dx.doi.org/10.4137/bbi.s11184.

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Understanding the effects of genetic variation on the phenotype of an individual is a major goal of biomedical research, especially for the development of diagnostics and effective therapeutic solutions. In this work, we describe the use of a recent knowledge discovery from database (KDD) approach using inductive logic programming (ILP) to automatically extract knowledge about human monogenic diseases. We extracted background knowledge from MSV3d, a database of all human missense variants mapped to 3D protein structure. In this study, we identified 8,117 mutations in 805 proteins with known th

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Neider, Daniel, P. Madhusudan, Shambwaditya Saha, Pranav Garg, and Daejun Park. "A Learning-Based Approach to Synthesizing Invariants for Incomplete Verification Engines." Journal of Automated Reasoning 64, no. 7 (2020): 1523–52. http://dx.doi.org/10.1007/s10817-020-09570-z.

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Abstract We propose a framework for synthesizing inductive invariants for incomplete verification engines, which soundly reduce logical problems in undecidable theories to decidable theories. Our framework is based on the counterexample guided inductive synthesis principle and allows verification engines to communicate non-provability information to guide invariant synthesis. We show precisely how the verification engine can compute such non-provability information and how to build effective learning algorithms when invariants are expressed as Boolean combinations of a fixed set of predicates.

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Lukac, Martin, and Marek Perkowski. "Inductive learning of quantum behaviors." Facta universitatis - series: Electronics and Energetics 20, no. 3 (2007): 561–86. http://dx.doi.org/10.2298/fuee0703561l.

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In this paper studied are new concepts of robotic behaviors - deterministic and quantum probabilistic. In contrast to classical circuits, the quantum circuit can realize both of these behaviors. When applied to a robot, a quantum circuit controller realizes what we call quantum robot behaviors. We use automated methods to synthesize quantum behaviors (circuits) from the examples (examples are cares of the quantum truth table). The don't knows (minterms not given as examples) are then converted not only to deterministic cares as in the classical learning, but also to output values generated wit

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Cerna, David M., and Andrew Cropper. "Generalisation through Negation and Predicate Invention." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 9 (2024): 10467–75. http://dx.doi.org/10.1609/aaai.v38i9.28915.

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The ability to generalise from a small number of examples is a fundamental challenge in machine learning. To tackle this challenge, we introduce an inductive logic programming (ILP) approach that combines negation and predicate invention. Combining these two features allows an ILP system to generalise better by learning rules with universally quantified body-only variables. We implement our idea in NOPI, which can learn normal logic programs with predicate invention, including Datalog programs with stratified negation. Our experimental results on multiple domains show that our approach can imp

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Chen, Szu-Ting. "Tinkering Epistemology with Ontology: Reformulating Popper’s Reformulation of the Problem of Induction with Eastern and Western Ontology." Journal of KATHA 19, no. 1 (2023): 14–33. https://doi.org/10.22452/khata.vol19no1.2.

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Karl Popper thought that he had successfully resolved the philosophical problem of induction by replacing inductive logic with deductive logic in every aspect of the philosophical analysis of science. In examining Popper’s approach, it may seem, however, that, instead of resolving the original problem, what he has resolved was a reformulated problem of induction. Nevertheless, by “reformulating” Popper’s reformulation by using the relevant ontology from contemporary Eastern and Western philosophers, this paper maintains that this re-reformulated meta-account can be used to argue that the first

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Cohen, Joshua M., and Philip Johnson-Freyd. "A Formalization of Core Why3 in Coq." Proceedings of the ACM on Programming Languages 8, POPL (2024): 1789–818. http://dx.doi.org/10.1145/3632902.

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Intermediate verification languages like Why3 and Boogie have made it much easier to build program verifiers, transforming the process into a logic compilation problem rather than a proof automation one. Why3 in particular implements a rich logic for program specification with polymorphism, algebraic data types, recursive functions and predicates, and inductive predicates; it translates this logic to over a dozen solvers and proof assistants. Accordingly, it serves as a backend for many tools, including Frama-C, EasyCrypt, and GNATProve for Ada SPARK. But how can we be sure that these tools ar

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Shakerin, Farhad, and Gopal Gupta. "Induction of Non-Monotonic Logic Programs to Explain Boosted Tree Models Using LIME." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 3052–59. http://dx.doi.org/10.1609/aaai.v33i01.33013052.

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We present a heuristic based algorithm to induce nonmonotonic logic programs that will explain the behavior of XGBoost trained classifiers. We use the technique based on the LIME approach to locally select the most important features contributing to the classification decision. Then, in order to explain the model’s global behavior, we propose the LIME-FOLD algorithm —a heuristic-based inductive logic programming (ILP) algorithm capable of learning nonmonotonic logic programs—that we apply to a transformed dataset produced by LIME. Our proposed approach is agnostic to the choice of the ILP algo

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Wardani, S., and I. W. Kusuma. "Comparison of Learning in Inductive and Deductive Approach to Increase Student’s Conceptual Understanding based on International Standard Curriculum." Jurnal Pendidikan IPA Indonesia 9, no. 1 (2020): 70–78. http://dx.doi.org/10.15294/jpii.v9i1.21155.

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This study aimed to determine the pattern of a more effective learning approach between the inductive and deductive approach in improving students’ conceptual understanding referring to the international standard curriculum on the X graders for the material property particulate subject matter. Through the deductive approach, students already have a picture/concept in their minds. But by using an inductive approach, students use their logic to understand concepts and summarize it. The research method applied in this study is a descriptive-comparative method with a triangulation analysis techniq

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Moitra, Abha, Ravi Palla, and Arvind Rangarajan. "Automated Capture and Execution of Manufacturability Rules Using Inductive Logic Programming." Proceedings of the AAAI Conference on Artificial Intelligence 30, no. 2 (2016): 4028–34. http://dx.doi.org/10.1609/aaai.v30i2.19080.

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Capturing domain knowledge can be a time-consuming process that typically requires the collaboration of a Subject Matter Expert and a modeling expert to encode the knowledge. In a number of domains and applications, this situation is further exacerbated by the fact that the Subject Matter Expert may find it difficult to articulate the domain knowledge as a procedure or rules, but instead may find it easier to classify instance data. To facilitate this type of knowledge elicitation from Subject Matter Experts, we have developed a system that automatically generates formal and executable rules f

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Ainyn, Qurrota, Achmad Lutfi, and Emy Hermynyawati. "Upaya Meningkatkan Keterampilan Berpikir Logis dengan Pendekatan Inkuiri." JRPK - Jurnal Riset Pendidikan Kimia 14, no. 2 (2024): 114–20. https://doi.org/10.21009/jrpk.142.05.

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This research aims to improve students' logical thinking skills in chemistry through an inquiry approach. The research method used is classroom action research by applying the Kemmis and McTaggart models. The research subjects were 35 students of XI MIPA 4 at SMAN 1 Manyar Gresik. Data was collected using a logical thinking skill assessment sheet consisting of deductive and inductive thinking components. The results showed that before the intervention, students could think logically with an average of 61.19, with a deductive component of 64.286 and an inductive component of 58.1. After applyin

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Marnis Wanji, Muhammad Firmansyah, Devira Restika Salsa Bila Gulo, Zaini Nurasa Azzahra, and Siti Mujiatun. "Penerapan Metode Deduktif dan Induktif dalam Manajemen." Jurnal Manajemen dan Pendidikan Agama Islam 3, no. 2 (2025): 239–50. https://doi.org/10.61132/jmpai.v3i2.990.

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This article discusses the application of deductive and inductive methods in management, which are relevant in facing modern business challenges. Deductive methods, which move from general principles to specific cases, are used to confirm decisions based on established theories, whereas inductive methods, which move from specific observations to generalizations, allow the formation of new theories based on empirical data. These two methods have complementary advantages and disadvantages; deductive excels in efficiency and systematic logic, but is limited to initial premises, while inductive of

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KATZOURIS, NIKOS, ALEXANDER ARTIKIS, and GEORGIOS PALIOURAS. "Online learning of event definitions." Theory and Practice of Logic Programming 16, no. 5-6 (2016): 817–33. http://dx.doi.org/10.1017/s1471068416000260.

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AbstractSystems for symbolic event recognition infer occurrences of events in time using a set of event definitions in the form of first-order rules. The Event Calculus is a temporal logic that has been used as a basis in event recognition applications, providing among others, direct connections to machine learning, via Inductive Logic Programming (ILP). We present an ILP system for online learning of Event Calculus theories. To allow for a single-pass learning strategy, we use the Hoeffding bound for evaluating clauses on a subset of the input stream. We employ a decoupling scheme of the Even

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Talib, Ahmad. "Pembelajaran Berdasarkan Masalah dengan Pendekatan Deduktif-Induktif pada Matakuliah Persamaan Differensial Biasa." Sainsmat : Jurnal Ilmiah Ilmu Pengetahuan Alam 11, no. 1 (2022): 31. http://dx.doi.org/10.35580/sainsmat111325332022.

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This research applies problem based learning with deductive-inductive approach to the course of ordinary differential equation (PDB). This study aims to apply problem-based learning steps involving the student's deductive-inductive thinking approach, applying the teaching materials of PDB involving the student's deductive-inductive thinking approach, but it is also expected that these teaching materials can maximize the deductive-inductive thinking approach, and help students improve their way of thinking, based on the material characteristics of PDB.The research method used is classroom actio

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Alker, Hayward R. "The Dialectical Logic of Thucydides' Melian Dialogue." American Political Science Review 82, no. 3 (1988): 805–20. http://dx.doi.org/10.2307/1962492.

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If the realist tradition has underappreciated the formalizable quality of Thucydides' scientific investigations, neorealist teachers and writers have generally failed to see the normative and dramatical features of Thucydides' political science, each an expression of his dialectical epistemology and ontology. Nicholas Rescher's partial formalization of dialectics as a controversy-oriented approach to knowledge cumulation and Kenneth Burkes dramaturgical approach to textual understanding are both shown to fit Thucydides' argumentation in the Melian dialogue. Thus argumentation produces new know

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Mazzei, M. "A SMART DATA APPROACH TO ANALYZE VEHICLE FLOWS." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLVIII-4/W5-2022 (October 14, 2022): 105–11. http://dx.doi.org/10.5194/isprs-archives-xlviii-4-w5-2022-105-2022.

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Abstract. In the logic of Smart Cities it is of fundamental importance to analyze the traffic situation through dedicated sensors and networks. According to this approach and through the potential of smart data is based this study. Improve prediction of traffic patterns by analyzing and counting vehicles in a virtualized scene in real time. In the past, the technique of hardware inductive coils was used that were dropped in the asphalt to exploit the principle of magnetic induction in order to verify the transit of vehicles. This technique is not able to classify vehicles or estimate their spe

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BLACKMORE, CRAIG, OLIVER RAY, and KERSTIN EDER. "A logic programming approach to predict effective compiler settings for embedded software." Theory and Practice of Logic Programming 15, no. 4-5 (2015): 481–94. http://dx.doi.org/10.1017/s1471068415000174.

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AbstractThis paper introduces a new logic-based method for optimising the selection of compiler flags on embedded architectures. In particular, we use Inductive Logic Programming (ILP) to learn logical rules that relate effective compiler flags to specific program features. Unlike earlier work, we aim to infer human-readable rules and we seek to develop a relational first-order approach which automatically discovers relevant features rather than relying on a vector of predetermined attributes. To this end we generated a data set by measuring execution times of 60 benchmarks on an embedded syst

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Norton, John D. "Replicability of Experiment." THEORIA. An International Journal for Theory, History and Foundations of Science 30, no. 2 (2015): 229. http://dx.doi.org/10.1387/theoria.12691.

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The replicability of experiment is routinely offered as the gold standard of evidence. I argue that it is not supported by a universal principle of replicability in inductive logic. A failure of replication may not impugn a credible experimental result; and a successful replication can fail to vindicate an incredible experimental result. Rather, employing a material approach to inductive inference, the evidential import of successful replication of an experiment is determined by the prevailing background facts. Commonly, these background facts do support successful replication as a good eviden

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Oaksford, Mike, and Nick Chater. "New Paradigms in the Psychology of Reasoning." Annual Review of Psychology 71, no. 1 (2020): 305–30. http://dx.doi.org/10.1146/annurev-psych-010419-051132.

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The psychology of verbal reasoning initially compared performance with classical logic. In the last 25 years, a new paradigm has arisen, which focuses on knowledge-rich reasoning for communication and persuasion and is typically modeled using Bayesian probability theory rather than logic. This paradigm provides a new perspective on argumentation, explaining the rational persuasiveness of arguments that are logical fallacies. It also helps explain how and why people stray from logic when given deductive reasoning tasks. What appear to be erroneous responses, when compared against logic, often t

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Claveau, Vincent, and Marie-Claude L'Homme. "Discovering and organizing noun-verb collocations in specialized corpora using inductive logic programming." International Journal of Corpus Linguistics 11, no. 2 (2006): 209–43. http://dx.doi.org/10.1075/ijcl.11.2.04cla.

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This article presents a method for discovering and organizing noun-verb (N-V) combinations found in a French corpus on computing. Our aim is to find N-V combinations in which verbs convey a “realization meaning” as defined in the framework of lexical functions (Mel’čuk 1996, 1998). Our approach, chiefly corpus-based, uses a machine learning technique, namely Inductive Logic Programming (ILP). The whole acquisition process is divided into three steps: (1) isolating contexts in which specific N-V pairs occur; (2) inferring linguistically-motivated rules that reflect the behaviour of realization

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Jadacki, Jacek. "Wkład Kazimierza Twardowskiego w rozwój logiki w Polsce." Studia Philosophica Wratislaviensia 13, no. 4 (2019): 7–18. http://dx.doi.org/10.19195/1895-8001.13.4.1.

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Kazimierz Twardowski’s contribution to the evolution of logicin PolandTwardowski’s legacy, which has been published so far—and the detailed analysis of it—suf fices to consider him a classic of Polish semiotics. The published results of Twardowski in the field of methodology also deserve high scores. A new light on the contribution of Twardowski to the development of logic in Poland sheds the content of his unpublished legacy, including the content of his lectures on logic and unedited logical monographs. Firstly, Twardowski conducted fruitful research on many methodological problems. The resu

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Ortega, Ana M., M. Teresa García, and M. Valle Santos. "Effectuation-causation: what happens in new product development?" Management Decision 55, no. 8 (2017): 1717–35. http://dx.doi.org/10.1108/md-03-2016-0160.

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Purpose The literature provides contradictory findings on planning contradictory findings on planning as a success factor in projects which entail high innovation. Nevertheless, new product development (NPD) research has mainly adopted the causation lens, in which planning plays an important role. By embracing the logic of effectuation, the purpose of this paper is to secure a wider perspective concerning how the various NPD options develop, taking into account the role played by uncertainty. Design/methodology/approach By adopting an inductive approach, the authors attempt to advance existing

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Chaghazardi, Zahra, Saber Fallah, and Alireza Tamaddoni-Nezhad. "Explainable and Trustworthy Traffic Sign Detection for Safe Autonomous Driving: An Inductive Logic Programming Approach." Electronic Proceedings in Theoretical Computer Science 385 (August 29, 2023): 201–12. http://dx.doi.org/10.4204/eptcs.385.21.

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Akgunduz, Ahmed. "Sa‘Êd Nūrsī’s Approach to the Principles of Reasoning Vis-à-vis Analogical Inductive Reasoningp; Pendirian Pemikiran Said Nūrsī berhubung Pemikran Analogis Induktif." Journal of Islam in Asia (E-ISSN: 2289-8077) 8, no. 1 (2011): 157. http://dx.doi.org/10.31436/jia.v8i1.190.

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Abstract Analogical inductive reasoning (al-qiyās al-tamthīlī) is to some scholars a controversial issue related to Islamic law and logic. It is argued that this kind of qiyās can only afford non-certain knowledge in Islamic law. Bedi‘uzamān Said Nūrsī (1876-1960) however, evaluated this kind of qiyās and argued that there also exists al-qiyās al-tamthīlī which affords certain knowledge. This problem may not be appreciated unless information regarding the proofs (al-’adillah wa al-Íujaj) and the ways of inference (isÏinbāt) and argumentation (istidlāl) in logic and Islamic law is discussed. Fo

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Hocquette, Céline, and Andrew Cropper. "Relational Program Synthesis with Numerical Reasoning." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 5 (2023): 6425–33. http://dx.doi.org/10.1609/aaai.v37i5.25790.

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Learning programs with numerical values is fundamental to many AI applications, including bio-informatics and drug design. However, current program synthesis approaches struggle to learn programs with numerical values. An especially difficult problem is learning continuous values from multiple examples, such as intervals. To overcome this limitation, we introduce an inductive logic programming approach which combines relational learning with numerical reasoning. Our approach, which we call NumSynth, uses satisfiability modulo theories solvers to efficiently learn programs with numerical values

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BRADY, ROSS T. "METAVALUATIONS." Bulletin of Symbolic Logic 23, no. 3 (2017): 296–323. http://dx.doi.org/10.1017/bsl.2017.29.

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AbstractThis is a general account of metavaluations and their applications, which can be seen as an alternative to standard model-theoretic methodology. They work best for what are called metacomplete logics, which include the contraction-less relevant logics, with possible additions of Conjunctive Syllogism, (A→B) & (B→C) → .A→C, and the irrelevant, A→ .B→A, these including the logic MC of meaning containment which is arguably a good entailment logic. Indeed, metavaluations focus on the formula-inductive properties of theorems of entailment form A→B, splintering into two types, M1- and M2

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Ozaki, Ana. "Learning Description Logic Ontologies: Five Approaches. Where Do They Stand?" KI - Künstliche Intelligenz 34, no. 3 (2020): 317–27. http://dx.doi.org/10.1007/s13218-020-00656-9.

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Abstract The quest for acquiring a formal representation of the knowledge of a domain of interest has attracted researchers with various backgrounds into a diverse field called ontology learning. We highlight classical machine learning and data mining approaches that have been proposed for (semi-)automating the creation of description logic (DL) ontologies. These are based on association rule mining, formal concept analysis, inductive logic programming, computational learning theory, and neural networks. We provide an overview of each approach and how it has been adapted for dealing with DL on

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Byerly, T. Ryan. "Teaching for Intellectual Virtue in Logic and Critical Thinking Classes." Teaching Philosophy 42, no. 1 (2019): 1–27. http://dx.doi.org/10.5840/teachphil201911599.

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Introductory-level undergraduate classes in Logic or Critical Thinking are a staple in the portfolio of many Philosophy programs. A standard approach to these classes is to include teaching and learning activities focused on formal deductive and inductive logic, sometimes accompanied by teaching and learning activities focused on informal fallacies or argument construction. In this article, I discuss a proposal to include an additional element within these classes—namely, teaching and learning activities focused on intellectual virtues. After clarifying the proposal, I identify three reasons i

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Cleva, José, and Isabel Pita. "Verification of CRWL Programs with Rewriting Logic." JUCS - Journal of Universal Computer Science 12, no. (11) (2006): 1594–617. https://doi.org/10.3217/jucs-012-11-1594.

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We present a novel approach to the verification of functional-logic programs. For our verification purposes, equational reasoning is not valid due to the presence of non-deterministic and partial functions. Our approach transforms functionallogic programs into Maude theories and then uses the Rewriting Logic logical framework to verify properties of the transformed programs. We propose an inductive proving method based on the length of the computation on the Rewriting Logic framework to cope with the non-deterministic and non-terminating aspects of the programs. We illustrate the application o

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