{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001381595,0.0005998033,0.0004520803,0.001120151,0.00109418,0.002529464,0.001622195,0.001790266,0.006804334],"category_scores_gemma":[0.004156048,0.0007312926,0.001593169,0.001000004,0.006725852,0.005281108,0.001905227,0.002769743,0.001107933],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003812182,"about_ca_system_score_gemma":0.001425511,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003702312,"about_ca_topic_score_gemma":0.002794995,"domain_scores_codex":[0.999117,0.0003470886,0.00005278797,0.0001070375,0.0003005461,0.00007562241],"domain_scores_gemma":[0.9979899,0.001279225,0.0001698318,0.0003183678,0.0001673163,0.00007538302],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.000008523622,0.00001193216,0.00007992938,0.00009666957,0.000008443235,0.00004895047,0.0001464031,0.01188248,0.000564503,0.9771642,0.001194514,0.008793512],"study_design_scores_gemma":[0.000004622356,0.000007507291,0.00004986117,0.00002917137,0.000004193699,0.00002819454,0.00003796715,0.02263086,0.0004365528,0.9684286,0.008333986,0.000008541507],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.002968924,0.002141202,0.9601682,0.003279612,0.0002107805,0.00005030448,0.0001423915,0.0004112988,0.0306274],"genre_scores_gemma":[0.5297533,0.005023104,0.4393376,0.002218553,0.0005603242,0.0005679356,0.0004906653,0.0004010066,0.02164754],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006804334,"threshold_uncertainty_score":0.02765942,"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."}}