{"id":"W1982435807","doi":"10.1142/s0218488511007416","title":"MULTIAGENT EXPEDITION WITH GRAPHICAL MODELS","year":2011,"lang":"en","type":"article","venue":"International Journal of Uncertainty Fuzziness and Knowledge-Based Systems","topic":"Reinforcement Learning in Robotics","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph","funders":"","keywords":"Scalability; Computer science; Graphical model; Set (abstract data type); Class (philosophy); Observable; Markov decision process; Multi-agent system; Artificial intelligence; Partially observable Markov decision process; Markov chain; Mathematical optimization; Markov process; Machine learning; Markov model; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008398872,0.0008865052,0.0009231054,0.0006086999,0.000513799,0.001261573,0.001411673,0.001309444,0.002671626],"category_scores_gemma":[0.003586788,0.000548793,0.001106602,0.000779635,0.001587961,0.001892586,0.001640766,0.001615834,0.0003216888],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001400702,"about_ca_system_score_gemma":0.001061103,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006228363,"about_ca_topic_score_gemma":0.006379589,"domain_scores_codex":[0.9991663,0.0003936049,0.00003197549,0.0001579645,0.0001550214,0.00009534491],"domain_scores_gemma":[0.9978503,0.001444166,0.0002755901,0.0001796649,0.0001200726,0.0001302994],"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":[0.00002546447,0.00002096982,0.0002475688,0.00002953907,0.00001676338,0.00006491017,0.00004274177,0.9455101,0.0003668817,0.04904002,0.0002839481,0.004351221],"study_design_scores_gemma":[0.00001046495,0.0000129623,0.00004674854,0.000004184212,0.000004643858,0.00001182244,0.000008219775,0.9633332,0.000142251,0.03589683,0.0005244628,0.000004202825],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02010176,0.0002056313,0.9741343,0.0004484312,0.00002333569,0.00005303877,0.0001177151,0.000292025,0.004623762],"genre_scores_gemma":[0.8238895,0.0004170669,0.1707346,0.0001285843,0.00003114381,0.0002190699,0.000239705,0.00007201307,0.004268334],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006228363,"threshold_uncertainty_score":0.01238424,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04116553772219097,"score_gpt":0.2548521161200378,"score_spread":0.2136865783978469,"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."}}