{"id":"W2113474338","doi":"10.1109/cig.2008.5035619","title":"An evaluation of models for predicting opponent positions in first-person shooter video games","year":2008,"lang":"en","type":"article","venue":"","topic":"Artificial Intelligence in Games","field":"Computer Science","cited_by":43,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Hidden Markov model; Artificial intelligence; Cheating; Adversary; Video game; Machine learning; Fictitious play; Human intelligence; Human–computer interaction; Game theory; Computer security; Multimedia; Psychology","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007168571,0.00008315318,0.0001155101,0.0001432455,0.0001086378,0.00002935501,0.0003845052,0.00004930283,0.00002663519],"category_scores_gemma":[0.00008966059,0.00007856741,0.00005117691,0.0001955655,0.00004690902,0.001024358,0.00003561605,0.00005105878,0.00000403464],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008078619,"about_ca_system_score_gemma":0.00008920395,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003204236,"about_ca_topic_score_gemma":0.000329713,"domain_scores_codex":[0.9987816,0.00007142904,0.0002813597,0.0002828263,0.0003997103,0.0001830981],"domain_scores_gemma":[0.9990886,0.0001459985,0.00007285178,0.0003446174,0.0002980174,0.00004993459],"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":[0.00002529606,0.0004696304,0.006696409,0.00002088959,0.00001906329,0.000002704515,0.02851398,0.8742468,0.004658345,0.03242629,0.0005840188,0.05233658],"study_design_scores_gemma":[0.00009817308,0.0001151887,0.001789904,0.00002626133,0.000006661934,0.000004914318,0.0004110486,0.965554,0.0205464,0.01135411,0.000009183462,0.00008414039],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3842012,0.00003351915,0.6139613,0.0005987493,0.00008088773,0.0003324458,0.000002482391,0.00004769215,0.0007417067],"genre_scores_gemma":[0.9457332,0.000006664306,0.05399669,0.00008466501,0.00003393053,0.0001035729,0.000003484418,0.000005897576,0.00003185783],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.561532,"threshold_uncertainty_score":0.3203886,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1616356568821887,"score_gpt":0.3436679192584451,"score_spread":0.1820322623762564,"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."}}