{"id":"W2904811519","doi":"10.1609/aaai.v33i01.33017610","title":"Generalized Planning via Abstraction: Arbitrary Numbers of Objects","year":2019,"lang":"en","type":"article","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","topic":"AI-based Problem Solving and Planning","field":"Computer Science","cited_by":30,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; Microsoft Research","keywords":"Nondeterministic algorithm; Abstraction; Class (philosophy); Computer science; Set (abstract data type); Task (project management); Action (physics); Observable; Theoretical computer science; Mathematical optimization; Artificial intelligence; Mathematics; Programming language","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002512041,0.001180841,0.001150972,0.0008544156,0.001576722,0.002689633,0.002788908,0.001147017,0.004394335],"category_scores_gemma":[0.007170558,0.0008352243,0.002341496,0.001541762,0.004289768,0.008593607,0.006192972,0.003064645,0.0005018708],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001646395,"about_ca_system_score_gemma":0.001973961,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004236034,"about_ca_topic_score_gemma":0.005171171,"domain_scores_codex":[0.9972358,0.0009596667,0.000218968,0.0006409194,0.0006485155,0.0002961889],"domain_scores_gemma":[0.9969976,0.001400005,0.0002633207,0.0009615107,0.0001938384,0.0001836647],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001354194,0.00006085389,0.0005214667,0.0002880161,0.00007497559,0.0003876523,0.0007314278,0.1460759,0.003819351,0.7801355,0.001528293,0.06624126],"study_design_scores_gemma":[0.00003619209,0.00004928197,0.0001479457,0.00003792573,0.00003907525,0.00009426827,0.0001099243,0.2039781,0.0021271,0.7846227,0.008728944,0.00002862465],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01486214,0.000150987,0.9798171,0.0002985258,0.00003413476,0.0001211127,0.00009233814,0.000548852,0.004074762],"genre_scores_gemma":[0.2364692,0.0002690296,0.7585842,0.0001418893,0.00003622256,0.0003824412,0.0002806532,0.0001787895,0.003657545],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004394335,"threshold_uncertainty_score":0.01470047,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05131861020903025,"score_gpt":0.2826157438696489,"score_spread":0.2312971336606187,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}