{"about":{"non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","site":"https://codewithpapers.app","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page","syntology":{"site":"https://syntology.ai","developers":"https://syntology.ai/developers","mcp":{"server":"https://syntology.ai/mcp","transport":"streamable-http","server_card":"https://syntology.ai/.well-known/mcp/server-card.json","auth":{"type":"trial token, no account","trial_token":"https://syntology.ai/api/oauth/trial/token","method":"POST","docs":"https://syntology.ai/developers"}},"have":"https://syntology.ai/api/graph/have?x=<method, arXiv id or title> (free, answers coverage only)","paper_base":"https://syntology.ai/paper/","atlas_base":"https://app.syntology.ai/?focus="},"machine_readable":[{"url":"https://codewithpapers.app/llms.txt","what":"the machine catalog: every machine-readable file, counted"},{"url":"https://codewithpapers.app/index/manifest.json","what":"paper-to-code index by arXiv id, with Syntology's counts"},{"url":"https://codewithpapers.app/search/manifest.json","what":"site search index (titles, authors) and its files"},{"url":"https://codewithpapers.app/download","what":"bulk files: Syntology's layer, described there"},{"url":"https://codewithpapers.app/build_manifest.json","what":"the build record: inputs, counts, exclusions, probes"}]},"url":"/task/inductive-logic-programming/papers/2","list_of":"/task/inductive-logic-programming","task":"Inductive logic programming","archive":{"snapshot":"2025-07-28"},"key_notes":{"n_ran_checked":"legacy name, kept unchanged so existing readers do not break: it counts the samples that ran with no instrument failure (honoured, violated, and ran with no contract checked); it does not mean a contract was checked, and the pages print it as 'K with no instrument failure', not 'K checked'","n_constructed":"a sub-count of the samples that ran, never subtracted from them and never a failure: an executed sample whose run returned an instance of its own class (fixture_out_type equals the entry name): the run built an object and did not compute a result (Syntology's RAN record, counts.constructed)"},"syntology_read_at":"2026-09-28T10:30:06+00:00","order":"archive","order_definition":"repositories listed in the archive (most first), then date (newest first), then slug","page":2,"pages_in_order":2,"rows_per_page":100,"rows":[101,153],"of":153,"counts":{"archive_papers_tagged":153,"with_a_code_link":54,"where_syntology_ran_a_sample":12,"not_listed_spam_title":0,"listed":153,"listed_where_code_ran":12,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":11,"every_run_a_failure_of_syntologys_instrument":1,"listed_with_a_run_with_no_instrument_failure":11,"listed_every_run_a_failure_of_syntologys_instrument":1,"filter":{"states":["a run with no instrument failure","any run, instrument failures included"],"default":"a run with no instrument failure","note":"on the 'only where code ran' pages the default hides, in the browser, the rows where every run was a failure of Syntology's instrument; the second state shows them again. Rows are hidden, never re-ordered; these twins list every row"}},"definition":"distinct papers the archive tags; 'where Syntology ran a sample' counts papers with at least one harvested sample that ran, which is not a correctness claim"},"first_page":"/task/inductive-logic-programming","prev":"/task/inductive-logic-programming","next":null,"papers":[{"url":null,"slug":"differentiable-logic-machines","title":"Differentiable Logic Machines","date":"2021-02-23","arxiv_id":"2102.11529","repositories_listed":0,"syntology":null},{"url":null,"slug":"inductive-logic-programming-at-30","title":"Inductive logic programming at 30","date":"2021-02-21","arxiv_id":"2102.10556","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-logic-programs-by-explaining","title":"Learning logic programs by explaining their failures","date":"2021-02-18","arxiv_id":"2102.12551","repositories_listed":0,"syntology":null},{"url":null,"slug":"refinement-type-directed-search-for-meta","title":"Refinement Type Directed Search for Meta-Interpretive-Learning of Higher-Order Logic Programs","date":"2021-02-18","arxiv_id":"2102.12553","repositories_listed":0,"syntology":null},{"url":null,"slug":"hyster-a-hybrid-spatio-temporal-event","title":"HySTER: A Hybrid Spatio-Temporal Event Reasoner","date":"2021-01-17","arxiv_id":"2101.06644","repositories_listed":0,"syntology":null},{"url":null,"slug":"conflict-driven-inductive-logic-programming","title":"Conflict-driven Inductive Logic Programming","date":"2020-12-31","arxiv_id":"2101.00058","repositories_listed":0,"syntology":null},{"url":null,"slug":"nsl-hybrid-interpretable-learning-from-noisy","title":"NSL: Hybrid Interpretable Learning From Noisy Raw Data","date":"2020-12-09","arxiv_id":"2012.05023","repositories_listed":0,"syntology":null},{"url":null,"slug":"symbolic-ai-for-xai-evaluating-lfit-inductive","title":"Symbolic AI for XAI: Evaluating LFIT Inductive Programming for Fair and Explainable Automatic Recruitment","date":"2020-12-01","arxiv_id":"2012.00360","repositories_listed":0,"syntology":null},{"url":null,"slug":"resource-constrained-dialog-policy-learning","title":"Resource Constrained Dialog Policy Learning via Differentiable Inductive Logic Programming","date":"2020-11-10","arxiv_id":"2011.05457","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-explainable-linguistic-expressions","title":"Learning Explainable Linguistic Expressions with Neural Inductive Logic Programming for Sentence Classification","date":"2020-11-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"proceedings-36th-international-conference-on","title":"Proceedings 36th International Conference on Logic Programming (Technical Communications)","date":"2020-09-19","arxiv_id":"2009.09158","repositories_listed":0,"syntology":null},{"url":null,"slug":"induction-and-exploitation-of-subgoal","title":"Induction and Exploitation of Subgoal Automata for Reinforcement Learning","date":"2020-09-08","arxiv_id":"2009.03855","repositories_listed":0,"syntology":null},{"url":null,"slug":"white-box-induction-from-svm-models","title":"White-box Induction From SVM Models: Explainable AI with Logic Programming","date":"2020-08-09","arxiv_id":"2008.03301","repositories_listed":0,"syntology":null},{"url":null,"slug":"inducing-game-rules-from-varying-quality-game","title":"Inducing game rules from varying quality game play","date":"2020-08-04","arxiv_id":"2008.01664","repositories_listed":0,"syntology":null},{"url":null,"slug":"evaluating-the-apperception-engine","title":"Evaluating the Apperception Engine","date":"2020-07-09","arxiv_id":"2007.05367","repositories_listed":0,"syntology":null},{"url":null,"slug":"the-ilasp-system-for-inductive-learning-of","title":"The ILASP system for Inductive Learning of Answer Set Programs","date":"2020-05-02","arxiv_id":"2005.00904","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-large-logic-programs-by-going-beyond","title":"Learning large logic programs by going beyond entailment","date":"2020-04-21","arxiv_id":"2004.09855","repositories_listed":0,"syntology":null},{"url":null,"slug":"incorporating-relational-background-knowledge","title":"Incorporating Relational Background Knowledge into Reinforcement Learning via Differentiable Inductive Logic Programming","date":"2020-03-23","arxiv_id":"2003.10386","repositories_listed":0,"syntology":null},{"url":null,"slug":"turning-30-new-ideas-in-inductive-logic","title":"Turning 30: New Ideas in Inductive Logic Programming","date":"2020-02-25","arxiv_id":"2002.11002","repositories_listed":0,"syntology":null},{"url":null,"slug":"smt-ilp","title":"SMT + ILP","date":"2020-01-15","arxiv_id":"2001.05208","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-logic-based-relational-learning-approach-to","title":"A logic-based relational learning approach to relation extraction: The OntoILPER system","date":"2020-01-13","arxiv_id":"2001.04192","repositories_listed":0,"syntology":null},{"url":null,"slug":"maximal-closed-set-and-half-space-separations","title":"Maximal Closed Set and Half-Space Separations in Finite Closure Systems","date":"2020-01-13","arxiv_id":"2001.04417","repositories_listed":0,"syntology":null},{"url":null,"slug":"one-shot-induction-of-generalized-logical","title":"One-Shot Induction of Generalized Logical Concepts via Human Guidance","date":"2019-12-15","arxiv_id":"1912.07060","repositories_listed":0,"syntology":null},{"url":null,"slug":"from-shallow-to-deep-interactions-between","title":"From Shallow to Deep Interactions Between Knowledge Representation, Reasoning and Machine Learning (Kay R. Amel group)","date":"2019-12-13","arxiv_id":"1912.06612","repositories_listed":0,"syntology":null},{"url":null,"slug":"induction-of-subgoal-automata-for","title":"Induction of Subgoal Automata for Reinforcement Learning","date":"2019-11-29","arxiv_id":"1911.13152","repositories_listed":0,"syntology":null},{"url":null,"slug":"remi-mining-intuitive-referring-expressions","title":"REMI: Mining Intuitive Referring Expressions on Knowledge Bases","date":"2019-11-04","arxiv_id":"1911.01157","repositories_listed":0,"syntology":null},{"url":null,"slug":"enriching-visual-with-verbal-explanations-for","title":"Enriching Visual with Verbal Explanations for Relational Concepts -- Combining LIME with Aleph","date":"2019-10-04","arxiv_id":"1910.01837","repositories_listed":0,"syntology":null},{"url":null,"slug":"induction-of-non-monotonic-logic-programs-to-1","title":"Induction of Non-monotonic Logic Programs To Explain Statistical Learning Models","date":"2019-09-18","arxiv_id":"1909.09017","repositories_listed":0,"syntology":null},{"url":null,"slug":"proceedings-35th-international-conference-on","title":"Proceedings 35th International Conference on Logic Programming (Technical Communications)","date":"2019-09-17","arxiv_id":"1909.07646","repositories_listed":0,"syntology":null},{"url":null,"slug":"logical-reduction-of-metarules","title":"Logical reduction of metarules","date":"2019-07-25","arxiv_id":"1907.10952","repositories_listed":0,"syntology":null},{"url":null,"slug":"efficient-predicate-invention-using-shared","title":"Efficient predicate invention using shared \"NeMuS\"","date":"2019-06-15","arxiv_id":"1906.06455","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-algorithms-via-neural-logic-networks","title":"Learning Algorithms via Neural Logic Networks","date":"2019-04-02","arxiv_id":"1904.01554","repositories_listed":0,"syntology":null},{"url":null,"slug":"increasing-city-safety-awareness-regarding","title":"Increasing city safety awareness regarding disruptive traffic stream","date":"2019-01-30","arxiv_id":"1902.06670","repositories_listed":0,"syntology":null},{"url":null,"slug":"an-inductive-logic-programming-approach-to","title":"An Inductive Logic Programming Approach to Validate Hexose Binding Biochemical Knowledge","date":"2018-10-02","arxiv_id":"1810.04707","repositories_listed":0,"syntology":null},{"url":null,"slug":"inductive-learning-of-answer-set-programs","title":"Inductive Learning of Answer Set Programs from Noisy Examples","date":"2018-08-25","arxiv_id":"1808.08441","repositories_listed":0,"syntology":null},{"url":null,"slug":"induction-of-non-monotonic-logic-programs-to","title":"Induction of Non-Monotonic Logic Programs to Explain Boosted Tree Models Using LIME","date":"2018-08-02","arxiv_id":"1808.00629","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-probabilistic-logic-programs-in","title":"Learning Probabilistic Logic Programs in Continuous Domains","date":"2018-07-15","arxiv_id":"1807.05527","repositories_listed":0,"syntology":null},{"url":null,"slug":"logical-explanations-for-deep-relational","title":"Logical Explanations for Deep Relational Machines Using Relevance Information","date":"2018-07-02","arxiv_id":"1807.00595","repositories_listed":0,"syntology":null},{"url":null,"slug":"best-effort-inductive-logic-programming-via","title":"Best-Effort Inductive Logic Programming via Fine-grained Cost-based Hypothesis Generation","date":"2017-07-10","arxiv_id":"1707.02729","repositories_listed":0,"syntology":null},{"url":null,"slug":"improving-scalability-of-inductive-logic","title":"Improving Scalability of Inductive Logic Programming via Pruning and Best-Effort Optimisation","date":"2017-06-16","arxiv_id":"1706.05171","repositories_listed":0,"syntology":null},{"url":null,"slug":"prasp-report","title":"PrASP Report","date":"2016-12-30","arxiv_id":"1612.09591","repositories_listed":0,"syntology":null},{"url":null,"slug":"neuro-symbolic-eda-based-optimisation-using","title":"Neuro-symbolic EDA-based Optimisation using ILP-enhanced DBNs","date":"2016-12-20","arxiv_id":"1612.06528","repositories_listed":0,"syntology":null},{"url":null,"slug":"deeply-semantic-inductive-spatio-temporal","title":"Deeply Semantic Inductive Spatio-Temporal Learning","date":"2016-08-09","arxiv_id":"1608.02693","repositories_listed":0,"syntology":null},{"url":null,"slug":"iterative-learning-of-answer-set-programs","title":"Iterative Learning of Answer Set Programs from Context Dependent Examples","date":"2016-08-05","arxiv_id":"1608.01946","repositories_listed":0,"syntology":null},{"url":null,"slug":"generation-of-near-optimal-solutions-using","title":"Generation of Near-Optimal Solutions Using ILP-Guided Sampling","date":"2016-08-03","arxiv_id":"1608.01093","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-weak-constraints-in-answer-set","title":"Learning Weak Constraints in Answer Set Programming","date":"2015-07-23","arxiv_id":"1507.06566","repositories_listed":0,"syntology":null},{"url":null,"slug":"skill-a-stochastic-inductive-logic-learner","title":"SkILL - a Stochastic Inductive Logic Learner","date":"2015-06-02","arxiv_id":"1506.00893","repositories_listed":0,"syntology":null},{"url":null,"slug":"consensus-based-modelling-using-distributed","title":"Consensus-Based Modelling using Distributed Feature Construction","date":"2014-09-11","arxiv_id":"1409.3446","repositories_listed":0,"syntology":null},{"url":null,"slug":"imparo-is-complete-by-inverse-subsumption","title":"Imparo is complete by inverse subsumption","date":"2014-07-14","arxiv_id":"1407.3836","repositories_listed":0,"syntology":null},{"url":null,"slug":"e-generalization-using-grammars","title":"E-Generalization Using Grammars","date":"2014-03-28","arxiv_id":"1403.8118","repositories_listed":0,"syntology":null},{"url":null,"slug":"inductive-logic-boosting","title":"Inductive Logic Boosting","date":"2014-02-25","arxiv_id":"1402.6077","repositories_listed":0,"syntology":null},{"url":null,"slug":"post-proceedings-of-the-first-international","title":"Post-Proceedings of the First International Workshop on Learning and Nonmonotonic Reasoning","date":"2013-11-19","arxiv_id":"1311.4639","repositories_listed":0,"syntology":null},{"url":null,"slug":"klog-a-language-for-logical-and-relational","title":"kLog: A Language for Logical and Relational Learning with Kernels","date":"2012-05-17","arxiv_id":"1205.3981","repositories_listed":0,"syntology":null}],"record_sha256":"937c8ed901e6b69335f2008baf3fa7dbac0f61c5be15ddf81521e46cff744e92","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}