{"id":"W6981478467","doi":"","title":"Enabling efficient emergent agent conversations in collaborative agent systems","year":2000,"lang":"en","type":"article","venue":"NPARC","topic":"Smart Cities and Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Multi-agent system; Intelligent agent; Collaborative software; Key (lock); Negotiation","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00007677024,0.0001067481,0.0001315908,0.0001089623,0.00004986007,0.00002956379,0.00009769432,0.00006103276,0.0009036173],"category_scores_gemma":[0.00001211623,0.00010529,0.00002757011,0.0003609658,0.00002813266,0.00003129304,0.00001590167,0.0000994471,0.0001363302],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001583236,"about_ca_system_score_gemma":0.00001431857,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000315355,"about_ca_topic_score_gemma":0.00002131363,"domain_scores_codex":[0.9993022,0.00001484552,0.0002016664,0.0001253885,0.0001286991,0.0002272084],"domain_scores_gemma":[0.9997286,0.00002390024,0.0000142153,0.0001703114,0.00002642354,0.00003650669],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000006769641,0.00004797464,0.000458901,0.00006338489,0.00005012487,0.00002762431,0.002215088,0.9721601,0.003377883,0.004870879,0.008776046,0.007945227],"study_design_scores_gemma":[0.001080255,0.00008409531,0.002230303,0.00016915,0.00002725567,0.000006834304,0.014257,0.5971471,0.0100761,0.000436653,0.3738581,0.0006271782],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.964681,0.0007931074,0.0003973838,0.000172501,0.0006960458,0.0003206361,0.00002267362,0.000454963,0.0324617],"genre_scores_gemma":[0.9986717,0.0005662999,0.000205389,0.00001396796,0.00003909117,0.00009243609,0.000006714966,0.00001487439,0.0003895195],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.375013,"threshold_uncertainty_score":0.9893976,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01233409983723851,"score_gpt":0.2113312431395405,"score_spread":0.198997143302302,"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."}}