{"id":"W2585691643","doi":"10.1609/aimag.v37i4.2684","title":"Collaborative Language Grounding Toward Situated Human‐Robot Dialogue","year":2016,"lang":"en","type":"article","venue":"AI Magazine","topic":"Speech and dialogue systems","field":"Computer Science","cited_by":68,"is_retracted":false,"has_abstract":true,"ca_institutions":"Thomson Reuters (Canada)","funders":"Office of Naval Research; University of California, Los Angeles; National Science Foundation","keywords":"Situated; Computer science; Human–robot interaction; Common ground; Robot; Human–computer interaction; Representation (politics); Joint attention; Bridge (graph theory); Perception; Action (physics); Ground; Artificial intelligence; Engineering; Communication; Psychology","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":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.000294314,0.0001803071,0.0002417186,0.0001322064,0.0001166244,0.0001583261,0.0006277428,0.00007927175,0.00006651427],"category_scores_gemma":[0.0001265719,0.0001222459,0.00005722877,0.0006069483,0.00006135977,0.0006245628,0.0002025677,0.00007954428,0.001820555],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008675626,"about_ca_system_score_gemma":0.00007672863,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008069202,"about_ca_topic_score_gemma":0.0001323315,"domain_scores_codex":[0.9985493,0.0001181489,0.000256361,0.0004153536,0.0002667251,0.0003940683],"domain_scores_gemma":[0.9989435,0.000119743,0.0001032168,0.0005348766,0.0001521207,0.0001465595],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00003611534,0.0001382576,0.002398046,0.00003699706,0.00008489961,0.0004049628,0.005541541,0.000004831179,0.8061616,0.08934397,0.05866474,0.03718408],"study_design_scores_gemma":[0.02620235,0.00397048,0.2304507,0.001429962,0.0001593946,0.0005195259,0.001674394,0.00187476,0.4284976,0.06661059,0.2316235,0.006986634],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1328984,0.0007093646,0.8082211,0.0124379,0.003318566,0.0009437257,0.00007776736,0.001498793,0.03989441],"genre_scores_gemma":[0.9934297,0.00000809461,0.002882226,0.0006125284,0.0003448671,0.00002582848,0.00001711683,0.00001498135,0.002664685],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8605313,"threshold_uncertainty_score":0.9989566,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01437155961971454,"score_gpt":0.2704817801697774,"score_spread":0.2561102205500629,"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."}}