{"id":"W2020401068","doi":"10.1080/08839510290030507","title":"Interfacing Indigolog and OAA: A Toolkit for Advanced Multiagent Applications","year":2002,"lang":"en","type":"article","venue":"Applied Artificial Intelligence","topic":"Multi-Agent Systems and Negotiation","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"","keywords":"Computer science; Interface (matter); Prolog; Programmer; Initialization; Interfacing; Programming language; Software engineering; Multi-agent system; Software agent; Human–computer interaction; Artificial intelligence; Operating system","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":[0.0002152366,0.0001639021,0.0001787446,0.0001020444,0.0002801759,0.0001550952,0.0004145867,0.00007618568,0.00003063841],"category_scores_gemma":[0.00003119385,0.0001613758,0.00004738131,0.0002573097,0.00005554818,0.0001741248,0.0001778371,0.000109049,0.0002020049],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003974312,"about_ca_system_score_gemma":0.000009542559,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000144515,"about_ca_topic_score_gemma":0.00002672722,"domain_scores_codex":[0.9985681,0.00001781876,0.0004294854,0.0005393684,0.0001479351,0.0002973143],"domain_scores_gemma":[0.9991097,0.0001826217,0.0001532515,0.0003934307,0.00006536848,0.00009563847],"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":[0.000007076374,0.00007789647,0.00001717998,0.00002755084,0.00001016394,4.763659e-7,0.001719935,0.0004444703,0.01856058,0.3154585,0.00006028265,0.6636159],"study_design_scores_gemma":[0.0002343564,0.000168786,0.0001661545,0.00005032649,0.00002332181,0.0000128246,0.0008819721,0.620914,0.2883769,0.07458112,0.01379434,0.0007958544],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0202466,0.0001858977,0.9765954,0.0003271959,0.000200369,0.001269524,0.000005292463,0.0001527205,0.001016992],"genre_scores_gemma":[0.9610785,0.00003449668,0.03777302,0.0001629707,0.0001030338,0.0007378843,0.000003406828,0.00001154745,0.00009514839],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9408319,"threshold_uncertainty_score":0.6580713,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07012490851349296,"score_gpt":0.2801245199437644,"score_spread":0.2099996114302715,"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."}}