{"id":"W2947459530","doi":"10.11575/prism/36598","title":"Multi-Criteria Multi-Participant Automated Negotiation: Belief Propagation-based Proposal Preparation and Real Time Opponent Learning","year":2019,"lang":"en","type":"dissertation","venue":"Open MIND","topic":"Business Process Modeling and Analysis","field":"Business, Management and Accounting","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; Mitacs","keywords":"Negotiation; Adversary; Computer science; Artificial intelligence; Computer security; Sociology","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","scholarly_communication","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.000610579,0.0004295603,0.0005842393,0.0003598151,0.0004366048,0.00195917,0.000351715,0.0002908863,0.0007936629],"category_scores_gemma":[0.0001799064,0.0004047166,0.0000882079,0.0004505281,0.00003150247,0.00149985,0.0001132163,0.0002339146,0.001104499],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004638765,"about_ca_system_score_gemma":0.0003334643,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001074036,"about_ca_topic_score_gemma":0.0005076955,"domain_scores_codex":[0.997727,0.00005038683,0.0006899317,0.0008282473,0.0003722605,0.0003321661],"domain_scores_gemma":[0.998044,0.00002962557,0.0009307644,0.000280372,0.0006865,0.00002871386],"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":[0.006298716,0.00699378,0.02219811,0.01318374,0.002561342,0.0001268168,0.01461949,0.3735581,0.3337182,0.000182956,0.003211091,0.2233476],"study_design_scores_gemma":[0.001295561,0.0000246021,0.002866999,0.0004878989,0.0005947227,5.362051e-7,0.0004241709,0.9921535,0.0006510291,0.000005005539,0.0009622229,0.0005337934],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9938849,0.00009334121,0.00128767,0.0003170491,0.0004205231,0.001879267,0.00001405671,0.0001382527,0.001964916],"genre_scores_gemma":[0.9653796,0.00001218376,0.01123269,0.00004506914,0.0003008833,0.000164993,0.009892139,0.0000914764,0.01288096],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6185954,"threshold_uncertainty_score":0.9998405,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04882441598230607,"score_gpt":0.3327019755269026,"score_spread":0.2838775595445965,"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."}}