Academic literature on the topic 'Inductive logic approach'

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Journal articles on the topic "Inductive logic approach"

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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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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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Dissertations / Theses on the topic "Inductive logic approach"

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Selpi. "An inductive logic programming approach to learning which uORFs regulate gene expression." Thesis, Robert Gordon University, 2008. http://hdl.handle.net/10059/224.

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Some upstream open reading frames (uORFs) regulate gene expression (i.e. they are functional) and can play key roles in keeping organisms healthy. However, how uORFs are involved in gene regulation is not het fully understood. In order to get a complete view of how uORFs are involved in gene regulation, it is expected that a large number of functional uORFs are needed. Unfortunately , lab experiments to verify that uORFs are functional are expensive. In this thesis, for the first time, the use of inductive logic programming (ILP) is explored for the task of learning which uORFs regulate gene e
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Mamer, Thierry. "A sequence-length sensitive approach to learning biological grammars using inductive logic programming." Thesis, Robert Gordon University, 2011. http://hdl.handle.net/10059/662.

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This thesis aims to investigate if the ideas behind compression principles, such as the Minimum Description Length, can help us to improve the process of learning biological grammars from protein sequences using Inductive Logic Programming (ILP). Contrary to most traditional ILP learning problems, biological sequences often have a high variation in their length. This variation in length is an important feature of biological sequences which should not be ignored by ILP systems. However we have identified that some ILP systems do not take into account the length of examples when evaluating their
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Maclaren, Heather. "A divide and conquer approach to using inductive logic programming for learning user models." Thesis, University of York, 2003. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.428450.

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Yonge, Katherine Chandler. "Criminal profile accuracy following training in inductive and deductive approaches." Master's thesis, Mississippi State : Mississippi State University, 2008. http://library.msstate.edu/etd/show.asp?etd=etd-03312008-194642.

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Chitsaz, Mahsa. "Approaches to Abductive and Inductive Reasoning in Lightweight Ontologies." Thesis, Griffith University, 2015. http://hdl.handle.net/10072/367603.

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Ontologies have been widely used in various application domains such as the Semantic Web, information systems and bio-informatics. Ontology is often modelled using description logics (DLs) to provide the mechanisms for repre- senting knowledge of a domain, defining its semantics and reasoning upon it. Lightweight DL ontologies are those which purposefully designed to have high performance on standard reasoning tasks, including satisfiability, subsump- tion and query answering, while sacrificing to certain degree their expressive power. Along with the vast use of ontologies, non-standard re
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Williaume, Géraldine. "Graded signal inputs to binary cell fate decisions : a quantitative approach based on ascidian neural induction." Electronic Thesis or Diss., Sorbonne université, 2020. https://accesdistant.sorbonne-universite.fr/login?url=https://theses-intra.sorbonne-universite.fr/2020SORUS426.pdf.

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Afin de comprendre comment les cellules interprètent un signal graduel en une réponse binaire, j’ai étudié l’induction neurale de l’ascidie. Lors de ce processus, quatre cellules ectodermiques parmi les seize vont adopter un destin neural. FGF9/16/20, exprimé par les cellules mésendodermiques, est l’inducteur neural et active l’expression du gène Otx via ERK. Les surfaces de contact (SC) quantifiées entre les cellules ectodermiques et les cellules mésendodermiques exprimant FGF montrent que chaque cellule ectodermique est exposée à FGF et que les précurseurs neuraux ont les plus grandes SC ave
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Bradley, William J. "Current Based Fault Detection and Diagnosis of Induction Motors. Adaptive Mixed-Residual Approach for Fault Detection and Diagnosis of Rotor, Stator, Bearing and Air-Gap Faults in Induction Motors Using a Fuzzy Logic Classifier with Voltage and Current Measurement only." Thesis, University of Bradford, 2013. http://hdl.handle.net/10454/7265.

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Induction motors (IM) find widespread use in modern industry and for this reason they have been subject to a significant amount of research interest in recent times. One particular aspect of this research is the fault detection and diagnosis (FDD) of induction motors for use in a condition based maintenance (CBM) strategy; by effectively tracking the condition of the motor, maintenance action need only be carried out when necessary. This type of maintenance strategy minimises maintenance costs and unplanned downtime. The benefits of an effective FDD for IM is clear and there have been numerous
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Bradley, William John. "Current based fault detection and diagnosis of induction motors : adaptive mixed-residual approach for fault detection and diagnosis of rotor, stator, bearing and air-gap faults in induction motors using a fuzzy logic classifier with voltage and current measurement only." Thesis, University of Bradford, 2013. http://hdl.handle.net/10454/7265.

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Induction motors (IM) find widespread use in modern industry and for this reason they have been subject to a significant amount of research interest in recent times. One particular aspect of this research is the fault detection and diagnosis (FDD) of induction motors for use in a condition based maintenance (CBM) strategy; by effectively tracking the condition of the motor, maintenance action need only be carried out when necessary. This type of maintenance strategy minimises maintenance costs and unplanned downtime. The benefits of an effective FDD for IM is clear and there have been numerous
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Varun, Varuneet, Bhargavi Govindarajan, and Suneet Nayak. "Speed Control of Induction Motor using Fuzzy Logic approach." Thesis, 2012. http://ethesis.nitrkl.ac.in/3278/1/Final_Thesis_2012_-_Varuneet%2C_Bhargavi%2C_Suneet.pdf.

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This thesis presents a methodology for implementation of a rule-based fuzzy logic controller applied to a closed loop Volts/Hz induction motor speed control. The Induction motor is modeled using a dq axis theory. The designed Fuzzy Logic Controller’s performance is weighed against with that of a PI controller. The pros of the Fuzzy Logic Controllers (FLCs) over the conventional controllers are: (i) they are economically advantageous to develop, (ii) a wider range of operating conditions can be covered using FLCs, and (iii) they are easier to adapt in terms of natural language. Another advantag
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Tang, Lap Poon Rupert. "Integrating top-down and bottom-up approaches in inductive logic programming: applications in natural language processing and relational data mining." Thesis, 2003. http://hdl.handle.net/2152/986.

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Books on the topic "Inductive logic approach"

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Raedt, Luc de. Interactive theory revision: An inductive logic programming approach. Academic Press, 1992.

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Kersting, Kristian. An inductive logic programming approach to statistical relational learning. IOS Press, 2006.

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Bhattacharjee, Sukhendu. Twin problems of causality and induction: A Neo-Humean approach. Firma KLM Private Limited, 2013.

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Schmid, Ute. Approaches and Applications of Inductive Programming: Third International Workshop, AAIP 2009, Edinburgh, UK, September 4, 2009. Revised Papers. Springer-Verlag Berlin Heidelberg, 2010.

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Reddy, Chandrasekhara K. Learning hierarchical decomposition rules for planning: An inductive logic programming approach. 1998.

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Ternovska, Evgenia. The situation calculus: Decidability and an approach based on the logic for non-monotone inductive definitions. 2002.

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An Inductive Logic Programming Approach to Statistical Relational Learning (Frontiers in Artificial Intelligence and Applications, Vol. 148) (Frontiers in Artificial Intelligence and Applications). IOS Press, 2006.

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Shulman, Michael. Homotopy Type Theory: A Synthetic Approach to Higher Equalities. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198748991.003.0003.

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Homotopy type theory and univalent foundations (HoTT/UF) is a new foundation of mathematics, based not on set theory but on “infinity-groupoids”, which consist of collections of objects, ways in which two objects can be equal, ways in which those ways-to-be-equal can be equal, ad infinitum. Though apparently complicated, such structures are increasingly important in mathematics. Philosophically, they are an inevitable result of the notion that whenever we form a collection of things, we must simultaneously consider when two of those things are the same. The “synthetic” nature of HoTT/UF enable
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Feeney, Aidan, and Evan Heit. Inductive Reasoning: Experimental, Developmental, and Computational Approaches. Cambridge University Press, 2007.

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Feeney, Aidan, and Evan Heit. Inductive Reasoning: Experimental, Developmental, and Computational Approaches. Cambridge University Press, 2007.

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Book chapters on the topic "Inductive logic approach"

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Kazakov, Dimitar, and Suresh Manandhar. "A hybrid approach to word segmentation." In Inductive Logic Programming. Springer Berlin Heidelberg, 1998. http://dx.doi.org/10.1007/bfb0027316.

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Tamaddoni-Nezhad, Alireza, and Stephen Muggleton. "A Genetic Algorithms Approach to ILP." In Inductive Logic Programming. Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/3-540-36468-4_19.

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Moldovan, Bogdan, Laura Antanas, and McElory Hoffmann. "Opening Doors: An Initial SRL Approach." In Inductive Logic Programming. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-38812-5_13.

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Apice, Annalisa, Michelangelo Ceci, and Donato Malerba. "Mining Model Trees: A Multi-relational Approach." In Inductive Logic Programming. Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-540-39917-9_3.

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Jorge, Alípio, and Pavel B. Brazdil. "Integrity constraints in ILP using a Monte Carlo approach." In Inductive Logic Programming. Springer Berlin Heidelberg, 1997. http://dx.doi.org/10.1007/3-540-63494-0_58.

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Horváth, Tamás, Gerhard Paass, Frank Reichartz, and Stefan Wrobel. "A Logic-Based Approach to Relation Extraction from Texts." In Inductive Logic Programming. Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-13840-9_5.

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Kaur, Navdeep, Gautam Kunapuli, Tushar Khot, Kristian Kersting, William Cohen, and Sriraam Natarajan. "Relational Restricted Boltzmann Machines: A Probabilistic Logic Learning Approach." In Inductive Logic Programming. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-78090-0_7.

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Brown, Solly, and Claude Sammut. "A Relational Approach to Tool-Use Learning in Robots." In Inductive Logic Programming. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-38812-5_1.

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Nassif, Houssam, Hassan Al-Ali, Sawsan Khuri, Walid Keirouz, and David Page. "An Inductive Logic Programming Approach to Validate Hexose Binding Biochemical Knowledge." In Inductive Logic Programming. Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-13840-9_14.

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Ratle, Alain, and Michèle Sebag. "A Novel Approach to Machine Discovery: Genetic Programming and Stochastic Grammars." In Inductive Logic Programming. Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/3-540-36468-4_14.

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Conference papers on the topic "Inductive logic approach"

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Parać, Roko, Lorenzo Nodari, Leo Ardon, Daniel Furelos-Blanco, Federico Cerutti, and Alessandra Russo. "Learning Robust Reward Machines from Noisy Labels." In 21st International Conference on Principles of Knowledge Representation and Reasoning {KR-2023}. International Joint Conferences on Artificial Intelligence Organization, 2024. http://dx.doi.org/10.24963/kr.2024/85.

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This paper presents PROB-IRM, an approach that learns robust reward machines (RMs) for reinforcement learning (RL) agents from noisy execution traces. The key aspect of RM-driven RL is the exploitation of a finite-state ma- chine that decomposes the agent’s task into different sub- tasks. PROB-IRM uses a state-of-the-art inductive logic pro- gramming framework robust to noisy examples to learn RMs from noisy traces using the Bayesian posterior degree of be- liefs, thus ensuring robustness against inconsistencies. Piv- otal for the results is the interleaving between RM learning and policy lear
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Ranasinghe, D. D. M., and A. S. Karunananda. "Qualitative Knowledge Driven Approach to Inductive Logic Programming." In 2006 International Conference on Industrial and Information Systems. IEEE, 2006. http://dx.doi.org/10.1109/iciinfs.2006.347125.

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Ranasinghe, D. D. M., and A. S. Karunananda. "Qualitative Knowledge Driven Approach to Inductive Logic Programming." In First International Conference on Industrial and Information Systems. IEEE, 2006. http://dx.doi.org/10.1109/iciis.2006.365640.

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Tarzariol, Alice, Martin Gebser, and Konstantin Schekotihin. "Lifting Symmetry Breaking Constraints with Inductive Logic Programming." In Thirtieth International Joint Conference on Artificial Intelligence {IJCAI-21}. International Joint Conferences on Artificial Intelligence Organization, 2021. http://dx.doi.org/10.24963/ijcai.2021/284.

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Efficient omission of symmetric solution candidates is essential for combinatorial problem solving. Most of the existing approaches are instance-specific and focus on the automatic computation of Symmetry Breaking Constraints (SBCs) for each given problem instance. However, the application of such approaches to large-scale instances or advanced problem encodings might be problematic. Moreover, the computed SBCs are propositional and, therefore, can neither be meaningfully interpreted nor transferred to other instances. To overcome these limitations, we introduce a new model-oriented approach f
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Lima, Rinaldo, Bernard Espinasse, Hilario Oliveira, and Fred Freitas. "Ontology Population from the Web: An Inductive Logic Programming-Based Approach." In 2014 Eleventh International Conference on Information Technology: New Generations (ITNG). IEEE, 2014. http://dx.doi.org/10.1109/itng.2014.60.

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Manson, Gaël, and Sid-Ahmed Berrani. "An Inductive Logic Programming-Based Approach for TV Stream Segment Classification." In 2008 Tenth IEEE International Symposium on Multimedia (ISM) (Formerly MSE). IEEE, 2008. http://dx.doi.org/10.1109/ism.2008.62.

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Al-Jamimi, Hamdi A., and Moataz A. Ahmed. "Knowledge acquisition in model driven development transformations: An inductive logic programming approach." In TENCON 2014 - 2014 IEEE Region 10 Conference. IEEE, 2014. http://dx.doi.org/10.1109/tencon.2014.7022470.

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D'Asaro, Fabio A., Matteo Spezialetti, Luca Raggioli, and Silvia Rossi. "Towards an Inductive Logic Programming Approach for Explaining Black-Box Preference Learning Systems." In 17th International Conference on Principles of Knowledge Representation and Reasoning {KR-2020}. International Joint Conferences on Artificial Intelligence Organization, 2020. http://dx.doi.org/10.24963/kr.2020/88.

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In this paper we advocate the use of Inductive Logic Programming as a device for explaining black-box models, e.g. Support Vector Machines (SVMs), when they are used to learn user preferences. We present a case study where we use the ILP system ILASP to explain the output of SVM classifiers trained on preference datasets. Explanations are produced in terms of weak constraints, which can be easily understood by humans. We use ILASP both as a global and a local approximator for SVMs, score its fidelity, and discuss how its output can prove useful e.g. for interactive learning tasks and for ident
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Okawara, Moeko, Junji Fukuhara, MUNEHIRO TAKIMOTO, Tsutomu Kumazawa, and Yasushi Kambayashi. "Efficient Inductive Logic Programming based on Predictive A*-like Algorithm." In 9th International Conference on Human Interaction and Emerging Technologies - Artificial Intelligence and Future Applications. AHFE International, 2023. http://dx.doi.org/10.54941/ahfe1002934.

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Various machine learning (ML) techniques have been developed widely over the last decade. Especially, deep learning (DL) contributes to ML for creating a lot of structured data such as tables from unstructured data such as images and sounds. The results have led to a lot of successes in engineering, but most of their decisions and actions are hard to be explained or verified. On the other hand, as a perfectly explainable ML approach, i.e., inductive logic programming (ILP), has been used in data mining. ILP, which is based on the first order predicate logic, is one of the symbolic approaches t
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Hocquette, Céline, Sebastijan Dumancic, and Andrew Cropper. "Learning Logic Programs by Discovering Higher-Order Abstractions." In Thirty-Third International Joint Conference on Artificial Intelligence {IJCAI-24}. International Joint Conferences on Artificial Intelligence Organization, 2024. http://dx.doi.org/10.24963/ijcai.2024/379.

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We introduce the higher-order refactoring problem, where the goal is to compress a logic program by discovering higher-order abstractions, such as map, filter, and fold. We implement our approach in Stevie, which formulates the refactoring problem as a constraint optimisation problem. Our experiments on multiple domains, including program synthesis and visual reasoning, show that refactoring can improve the learning performance of an inductive logic programming system, specifically improving predictive accuracies by 27% and reducing learning times by 47%. We also show that Stevie can discover
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