{"id":"W2164846267","doi":"10.5555/1838206.1838479","title":"An agent communication protocol for resolving conflicts","year":2010,"lang":"en","type":"article","venue":"Adaptive Agents and Multi-Agents Systems","topic":"Multi-Agent Systems and Negotiation","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Protocol (science); Argumentation theory; Computer science; Persuasion; Set (abstract data type); Communications protocol; Theoretical computer science; Artificial intelligence; Epistemology; Programming language; Computer network; Social 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.009684579,0.001409752,0.001044748,0.001988946,0.003600145,0.004502625,0.004066666,0.00462936,0.009409647],"category_scores_gemma":[0.02146778,0.0009079566,0.001297583,0.001473069,0.002817543,0.008228004,0.005162735,0.004236867,0.003045887],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001387626,"about_ca_system_score_gemma":0.003811786,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001251646,"about_ca_topic_score_gemma":0.0009058011,"domain_scores_codex":[0.9885018,0.005225823,0.001270261,0.001015892,0.003549282,0.0004369179],"domain_scores_gemma":[0.9881799,0.00588369,0.0006385969,0.002303672,0.002398654,0.000595514],"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":[0.0001489253,0.0001348265,0.0002532814,0.0003132805,0.00006661539,0.0004426407,0.00139795,0.01480382,0.007235479,0.8901859,0.01028261,0.07473464],"study_design_scores_gemma":[0.0003272888,0.0002663983,0.0001872859,0.0003323383,0.0001664353,0.001177965,0.0004685711,0.2781434,0.02587084,0.3711243,0.3216698,0.0002652658],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.001765052,0.0001207737,0.9865652,0.0006397195,0.0002072153,0.0005006111,0.00007900917,0.0007643257,0.009358019],"genre_scores_gemma":[0.06890847,0.0003028883,0.9152452,0.0003467108,0.0001248036,0.002132011,0.0003822478,0.0003080312,0.01224967],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.009684579,"threshold_uncertainty_score":0.05121756,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1142804543862047,"score_gpt":0.3702068403268843,"score_spread":0.2559263859406796,"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."}}