{"id":"https://openalex.org/W2995436254","doi":"https://doi.org/10.48550/arxiv.1912.07060","title":"One-Shot Induction of Generalized Logical Concepts via Human Guidance","display_name":"One-Shot Induction of Generalized Logical Concepts via Human Guidance","publication_year":2019,"publication_date":"2019-12-15","ids":{"openalex":"https://openalex.org/W2995436254","doi":"https://doi.org/10.48550/arxiv.1912.07060","mag":"2995436254"},"language":"en","primary_location":{"id":"pmh:oai:arXiv.org:1912.07060","doi":null,"is_oa":true,"landing_page_url":"http://arxiv.org/abs/1912.07060","pdf_url":"https://arxiv.org/pdf/1912.07060","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"volume":null,"type":"preprint","is_traditional":true,"indexed_in":["arxiv","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/1912.07060","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5026895931","display_name":"Mayukh Das","orcid":"https://orcid.org/0000-0002-4854-642X","observed_orcids":["https://orcid.org/0000-0002-4854-642X","https://orcid.org/0000-0002-1254-7543"]},"institutions":[{"id":"https://openalex.org/I162577319","display_name":"The University of Texas at Dallas","ror":"https://ror.org/049emcs32","country_code":"US","type":"education","lineage":["https://openalex.org/I162577319"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Das, Mayukh","raw_affiliation_strings":["University of Texas , Dallas","University of Texas at Dallas"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Texas , Dallas","institution_ids":["https://openalex.org/I162577319"]},{"raw_affiliation_string":"University of Texas at Dallas","institution_ids":["https://openalex.org/I162577319"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5078428949","display_name":"Nandini Ramanan","orcid":"https://orcid.org/0000-0002-7364-7097","observed_orcids":["https://orcid.org/0000-0002-7364-7097"]},"institutions":[{"id":"https://openalex.org/I162577319","display_name":"The University of Texas at Dallas","ror":"https://ror.org/049emcs32","country_code":"US","type":"education","lineage":["https://openalex.org/I162577319"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Ramanan, Nandini","raw_affiliation_strings":["University of Texas , Dallas","University of Texas at Dallas"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Texas , Dallas","institution_ids":["https://openalex.org/I162577319"]},{"raw_affiliation_string":"University of Texas at Dallas","institution_ids":["https://openalex.org/I162577319"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5055445718","display_name":"Janardhan Rao Doppa","orcid":"https://orcid.org/0000-0002-3848-5301","observed_orcids":["https://orcid.org/0000-0002-3848-5301"]},"institutions":[{"id":"https://openalex.org/I72951846","display_name":"Washington State University","ror":"https://ror.org/05dk0ce17","country_code":"US","type":"education","lineage":["https://openalex.org/I72951846"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Doppa, Janardhan Rao","raw_affiliation_strings":["washington state university"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"washington state university","institution_ids":["https://openalex.org/I72951846"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5064323671","display_name":"Sriraam Natarajan","orcid":"https://orcid.org/0000-0001-9889-6260","observed_orcids":["https://orcid.org/0000-0001-9889-6260","https://orcid.org/0009-0002-2849-2126"]},"institutions":[{"id":"https://openalex.org/I162577319","display_name":"The University of Texas at Dallas","ror":"https://ror.org/049emcs32","country_code":"US","type":"education","lineage":["https://openalex.org/I162577319"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Natarajan, Sriraam","raw_affiliation_strings":["University of Texas , Dallas","University of Texas at Dallas"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Texas , Dallas","institution_ids":["https://openalex.org/I162577319"]},{"raw_affiliation_string":"University of Texas at Dallas","institution_ids":["https://openalex.org/I162577319"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":true,"cited_by_count":1,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11010","display_name":"Logic, Reasoning, and Knowledge","score":0.4287000000476837,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T11010","display_name":"Logic, Reasoning, and Knowledge","score":0.4287000000476837,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T12072","display_name":"Machine Learning and Algorithms","score":0.4011000096797943,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10906","display_name":"AI-based Problem Solving and Planning","score":0.019999999552965164,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/inductive-logic-programming","display_name":"inductive logic programming","score":0.8389999866485596},{"id":"https://openalex.org/keywords/human-guidance","display_name":"human guidance","score":0.7459999918937683},{"id":"https://openalex.org/keywords/advice","display_name":"advice","score":0.44200000166893005},{"id":"https://openalex.org/keywords/pac-bounds","display_name":"PAC bounds","score":0.5799999833106995},{"id":"https://openalex.org/keywords/sample-efficiency","display_name":"sample efficiency","score":0.9169999957084656},{"id":"https://openalex.org/keywords/generalization","display_name":"generalization","score":0.6399999856948853}],"concepts":[{"id":"https://openalex.org/C153083717","wikidata":"https://www.wikidata.org/wiki/Q6535263","display_name":"Leverage (statistics)","level":2,"score":0.7970004081726074},{"id":"https://openalex.org/C2779382394","wikidata":"https://www.wikidata.org/wiki/Q1464197","display_name":"Inductive logic programming","level":2,"score":0.7688323259353638},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.726273775100708},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6834151148796082},{"id":"https://openalex.org/C2780009758","wikidata":"https://www.wikidata.org/wiki/Q6804172","display_name":"Measure (data warehouse)","level":2,"score":0.5613784790039062},{"id":"https://openalex.org/C21563000","wikidata":"https://www.wikidata.org/wiki/Q484511","display_name":"Inductive reasoning","level":2,"score":0.4713563323020935},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.45604732632637024},{"id":"https://openalex.org/C43971567","wikidata":"https://www.wikidata.org/wiki/Q3142865","display_name":"Logical reasoning","level":2,"score":0.4413122832775116},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.42286744713783264},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3404954671859741},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.24829399585723877},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.11309611797332764},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:oai:arXiv.org:1912.07060","doi":null,"is_oa":true,"landing_page_url":"http://arxiv.org/abs/1912.07060","pdf_url":"https://arxiv.org/pdf/1912.07060","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},{"id":"doi:10.48550/arxiv.1912.07060","doi":"https://doi.org/10.48550/arxiv.1912.07060","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.1912.07060","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:1912.07060","doi":null,"is_oa":true,"landing_page_url":"http://arxiv.org/abs/1912.07060","pdf_url":"https://arxiv.org/pdf/1912.07060","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2995436254.pdf","grobid_xml":"https://content.openalex.org/works/W2995436254.grobid-xml"},"referenced_works_count":31,"referenced_works":["https://openalex.org/W26421743","https://openalex.org/W36434594","https://openalex.org/W108329901","https://openalex.org/W1565236324","https://openalex.org/W1581006916","https://openalex.org/W1782524086","https://openalex.org/W1954974098","https://openalex.org/W1968185127","https://openalex.org/W1987902506","https://openalex.org/W2019363670","https://openalex.org/W2033377040","https://openalex.org/W2087588854","https://openalex.org/W2098443349","https://openalex.org/W2106519074","https://openalex.org/W2129894900","https://openalex.org/W2131175218","https://openalex.org/W2147357263","https://openalex.org/W2149298154","https://openalex.org/W2170282759","https://openalex.org/W2210300683","https://openalex.org/W2293330754","https://openalex.org/W2342203353","https://openalex.org/W2401823607","https://openalex.org/W2432717477","https://openalex.org/W2484153108","https://openalex.org/W2798603908","https://openalex.org/W2798789011","https://openalex.org/W2810258276","https://openalex.org/W2950801649","https://openalex.org/W2963845150","https://openalex.org/W3012178857"],"related_works":["https://openalex.org/W3103906305","https://openalex.org/W1504619461","https://openalex.org/W1971215190","https://openalex.org/W7095340463","https://openalex.org/W2287153131","https://openalex.org/W6999340261","https://openalex.org/W4241928169","https://openalex.org/W2807660311","https://openalex.org/W6947970258","https://openalex.org/W1509924892"],"abstract_inverted_index":{"We":[0,70],"consider":[1],"the":[2,44,60,66,73,98,103],"problem":[3],"of":[4,9,46,62,68,102],"learning":[5,94],"generalized":[6],"first-order":[7,108],"representations":[8,41],"concepts":[10],"from":[11],"a":[12,35,85,107],"single":[13],"example.":[14],"To":[15],"address":[16],"this":[17],"challenging":[18],"problem,":[19],"we":[20,33,54],"augment":[21],"an":[22],"inductive":[23],"logic":[24],"programming":[25],"learner":[26,110],"with":[27],"two":[28],"novel":[29],"algorithmic":[30],"contributions.":[31],"First,":[32],"define":[34],"distance":[36,75],"measure":[37,76],"between":[38],"candidate":[39],"concept":[40,50,93,109],"that":[42,72,82],"improves":[43],"efficiency":[45,101],"search":[47],"for":[48],"target":[49],"and":[51,80,100],"generalization.":[52],"Second,":[53],"leverage":[55],"richer":[56],"human":[57],"inputs":[58],"in":[59],"form":[61],"advice":[63],"to":[64,83],"improve":[65],"sample-efficiency":[67],"learning.":[69],"prove":[71],"proposed":[74,104],"is":[77],"semantically":[78],"valid":[79],"use":[81],"derive":[84],"PAC":[86],"bound.":[87],"Our":[88],"experimental":[89],"analysis":[90],"on":[91],"diverse":[92],"tasks":[95],"demonstrates":[96],"both":[97],"effectiveness":[99],"approach":[105],"over":[106],"using":[111],"only":[112],"examples.":[113]},"counts_by_year":[{"year":2025,"cited_by_count":1}],"updated_date":"2026-11-07T11:25:07.637180","created_date":"2019-12-26T00:00:00"}
