{"id":"W2964599462","doi":"10.1609/aaai.v33i01.33019955","title":"Learning Options with Interest Functions","year":2019,"lang":"en","type":"article","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","topic":"Reinforcement Learning in Robotics","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Computer science; Task (project management); Function (biology); State space; Plan (archaeology); Space (punctuation); Architecture; State (computer science); Artificial intelligence; Theoretical computer science; Mathematics; Algorithm; Economics","routes":{"ca_aff":true,"ca_fund":false,"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":[0.002119069,0.0007908164,0.00082563,0.0004992718,0.000358348,0.001373052,0.001327755,0.0014117,0.002305774],"category_scores_gemma":[0.00884419,0.0006226989,0.0007558438,0.0003616416,0.001524165,0.003577447,0.002001609,0.002279599,0.000410669],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001005695,"about_ca_system_score_gemma":0.0008866594,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001637551,"about_ca_topic_score_gemma":0.001808086,"domain_scores_codex":[0.9991121,0.000429967,0.00004131939,0.0001695959,0.0001581692,0.00008884564],"domain_scores_gemma":[0.9974397,0.001760954,0.0002105366,0.0002227483,0.0001992591,0.0001667077],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001535919,0.00005309518,0.001381669,0.00007931623,0.00006262558,0.0001414224,0.0001722429,0.8000362,0.001708786,0.1478756,0.001289401,0.04704609],"study_design_scores_gemma":[0.00001071589,0.00001951387,0.00008662363,0.000009543528,0.000005522459,0.00001240601,0.0000112354,0.9308272,0.0004174987,0.06812515,0.0004676662,0.00000699117],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02821006,0.0001996697,0.9683282,0.0003980533,0.00002530847,0.00003227192,0.00004597587,0.000259298,0.002501219],"genre_scores_gemma":[0.8470986,0.0002416109,0.1476037,0.0001728559,0.00003959215,0.0001784589,0.0001578946,0.0001075315,0.004399795],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002305774,"threshold_uncertainty_score":0.01120681,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06783980109864947,"score_gpt":0.2736481056250446,"score_spread":0.2058083045263951,"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."}}