{"id":"W2131335988","doi":"10.1007/s10458-006-5961-9","title":"DIAGAL: An Agent Communication Language Based on Dialogue Games and Sustained by Social Commitments","year":2006,"lang":"en","type":"article","venue":"Autonomous Agents and Multi-Agent Systems","topic":"Multi-Agent Systems and Negotiation","field":"Computer Science","cited_by":19,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université Laval","funders":"","keywords":"Computer science; Language-game; Dialectic; Semantics (computer science); Quality (philosophy); Point (geometry); Multi-agent system; Artificial intelligence; Human–computer interaction; Linguistics; Epistemology; Programming language","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":[0.002654576,0.001202182,0.0009925409,0.0008328318,0.001204021,0.003825517,0.002781609,0.001573349,0.01102435],"category_scores_gemma":[0.005506101,0.0009055285,0.001179111,0.0005473089,0.00203769,0.004704124,0.005163428,0.003029329,0.003532478],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009473338,"about_ca_system_score_gemma":0.001682564,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001843133,"about_ca_topic_score_gemma":0.002426489,"domain_scores_codex":[0.997255,0.001372663,0.0003565635,0.0003696939,0.0004502516,0.0001959525],"domain_scores_gemma":[0.9973863,0.001273559,0.0001901637,0.0004229617,0.000414088,0.0003129483],"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":[0.0009491994,0.0002437088,0.0005638089,0.0008124541,0.00009247409,0.0004867875,0.001893692,0.01656104,0.01033986,0.8562347,0.03414371,0.07767852],"study_design_scores_gemma":[0.0004394666,0.0002489331,0.0002218771,0.000216458,0.0001234545,0.0004995554,0.0003930557,0.2590175,0.01722231,0.3485066,0.3728978,0.0002131409],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.003137147,0.0001051985,0.9745991,0.0003486884,0.0002411215,0.0003259018,0.0006963261,0.01284842,0.007698213],"genre_scores_gemma":[0.2221481,0.0003008787,0.7410551,0.001038983,0.000153683,0.001753076,0.002726055,0.003698604,0.02712552],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01102435,"threshold_uncertainty_score":0.03688014,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0208363584794265,"score_gpt":0.2700609247711642,"score_spread":0.2492245662917377,"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."}}