{"id":"W2169871173","doi":"10.1109/iros.1991.174519","title":"Qualitative physics for robot task planning. I. Grammatical reasoning and commonsense augmentations","year":2002,"lang":"en","type":"article","venue":"","topic":"AI-based Problem Solving and Planning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"Natural Sciences and Engineering Research Council of Canada; Australian Government","keywords":"Commonsense reasoning; Task (project management); Computer science; Robot; Commonsense knowledge; Artificial intelligence; Motion (physics); Grammar; Domain (mathematical analysis); Qualitative reasoning; Task analysis; Natural language processing; Human–computer interaction; Domain knowledge; Engineering; Mathematics; Linguistics","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003645699,0.0001228229,0.0001483317,0.00003953577,0.0003441629,0.0001783557,0.0001854058,0.00004188924,0.000007591823],"category_scores_gemma":[0.0001195128,0.0001122564,0.00003948854,0.0001622702,0.00005732344,0.0002708852,0.00006352304,0.0001102088,0.00001661424],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001656338,"about_ca_system_score_gemma":0.00001287352,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002857332,"about_ca_topic_score_gemma":0.000002465846,"domain_scores_codex":[0.9990336,0.0001215032,0.0001902283,0.0002633267,0.0001358665,0.0002555074],"domain_scores_gemma":[0.9981714,0.001387903,0.00007201491,0.0002124344,0.0000539466,0.0001022286],"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":[0.00002361561,0.0001962835,0.001661855,0.0001244709,0.0001230751,0.00002536697,0.2059812,0.00714777,0.0004586302,0.6966802,0.04059954,0.04697795],"study_design_scores_gemma":[0.0005441708,0.000167314,0.0001401704,0.0000823661,0.00001574346,0.00001717528,0.002762638,0.975463,0.0002368262,0.01958567,0.0007344013,0.0002505722],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.00531261,0.0001764737,0.9905254,0.001877753,0.00006579382,0.0001742157,0.00000893888,0.0001801715,0.001678703],"genre_scores_gemma":[0.4904779,0.000002442746,0.5085303,0.0003792545,0.00003669044,0.00002964936,0.0000107876,0.000008160579,0.0005248275],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9683152,"threshold_uncertainty_score":0.4577684,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1048286510351694,"score_gpt":0.3589959155802623,"score_spread":0.2541672645450929,"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."}}