{"id":"W2394540668","doi":"10.1609/aaai.v25i1.8032","title":"Provoking Opponents to Facilitate the Recognition of their Intentions","year":2011,"lang":"en","type":"article","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","topic":"Reinforcement Learning in Robotics","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"Defence Research and Development Canada; Université de Sherbrooke","funders":"Natural Sciences and Engineering Research Council of Canada; Fonds Québécois de la Recherche sur la Nature et les Technologies","keywords":"Adversary; Plan (archaeology); Contrast (vision); Observer (physics); Computer science; State (computer science); Psychology; Cognitive psychology; Artificial intelligence; Social psychology; Human–computer interaction; Computer security; Algorithm","routes":{"ca_aff":true,"ca_fund":true,"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":[0.001013895,0.0006366367,0.0002453947,0.0001556036,0.0002666777,0.0008037201,0.0005618173,0.0007561846,0.007488738],"category_scores_gemma":[0.005787362,0.0003116861,0.0003564734,0.00005546121,0.0006873725,0.0008927088,0.001286829,0.001362626,0.001130632],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002255877,"about_ca_system_score_gemma":0.0004618849,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004701203,"about_ca_topic_score_gemma":0.0007708931,"domain_scores_codex":[0.9993979,0.0001964186,0.00002495496,0.0001424927,0.0001351807,0.0001030312],"domain_scores_gemma":[0.9977103,0.001293671,0.0002975949,0.0002883478,0.0001689124,0.0002412082],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001974069,0.001288361,0.01174486,0.0004915057,0.0001079324,0.001495863,0.005842873,0.01328017,0.7842348,0.04001806,0.003570684,0.1359507],"study_design_scores_gemma":[0.00108084,0.005537154,0.03456863,0.0002306345,0.0004552532,0.002075657,0.002914728,0.3377605,0.4868894,0.06975764,0.05842243,0.0003070917],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6990337,0.0002468663,0.2437941,0.001675006,0.0004271674,0.0003877393,0.00007977148,0.002552422,0.0518033],"genre_scores_gemma":[0.962234,0.0000534939,0.03122299,0.0003463845,0.0000339917,0.00009190915,0.00006297378,0.00008584066,0.005868444],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.007488738,"threshold_uncertainty_score":0.02505231,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3161080705411999,"score_gpt":0.2937563095155837,"score_spread":0.02235176102561615,"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."}}