{"id":"W2016884651","doi":"10.1016/j.jnca.2006.04.009","title":"A protocol for multi-agent negotiation in a group-choice decision making process","year":2006,"lang":"en","type":"article","venue":"Journal of Network and Computer Applications","topic":"Multi-Agent Systems and Negotiation","field":"Computer Science","cited_by":42,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Negotiation; Computer science; Protocol (science); Component (thermodynamics); Variety (cybernetics); Game theory; Multi-agent system; Process (computing); Exploit; Artificial intelligence; Management science; Computer security; Mathematical economics; Mathematics","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":[],"consensus_categories":[],"category_scores_codex":[0.0004647658,0.0001202852,0.0001984002,0.0001411347,0.0001543981,0.0001670896,0.0003353113,0.00006040756,0.000001128948],"category_scores_gemma":[0.000006031808,0.0001019444,0.00007109342,0.000385887,0.00001091889,0.0004059286,0.00005795914,0.0001070393,0.0000014656],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005376464,"about_ca_system_score_gemma":0.00003702498,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000008606273,"about_ca_topic_score_gemma":0.00005949473,"domain_scores_codex":[0.9986563,0.00004693907,0.0006723866,0.0002280274,0.0002047209,0.0001916075],"domain_scores_gemma":[0.9987948,0.0002038575,0.0005827567,0.0001771005,0.0001915226,0.00004996018],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001660653,0.001877152,0.07563546,0.0005457818,0.0000608012,0.000009240141,0.001072743,0.1958301,0.0001765066,0.05689735,0.01092298,0.6568058],"study_design_scores_gemma":[0.001918977,0.0001157472,0.1088644,0.0002967293,0.000006980812,0.00003633694,0.000005575729,0.8625859,0.000006730703,0.006610023,0.01941478,0.0001378508],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001994036,0.00004783034,0.9733599,0.00008692465,0.0001071126,0.02436474,6.271968e-7,0.00002190227,0.0000169633],"genre_scores_gemma":[0.2769464,0.000003623062,0.6712027,0.0001723665,0.001757797,0.04988603,0.000002723359,0.00001527081,0.00001308902],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.6667558,"threshold_uncertainty_score":0.4157174,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03959679504321842,"score_gpt":0.3484922111750855,"score_spread":0.3088954161318671,"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."}}