{"id":"W4387394758","doi":"10.1609/aiide.v19i1.27530","title":"Navigation in Adversarial Environments Guided by PRA* and a Local RL Planner","year":2023,"lang":"en","type":"article","venue":"Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment","topic":"Artificial Intelligence in Games","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institute for Advanced Research","keywords":"Computer science; Adversarial system; Reinforcement learning; Markov decision process; Planner; Artificial intelligence; Pathfinding; Baseline (sea); Delegation; Process (computing); Robotics; Human–computer interaction; Machine learning; Robot; Theoretical computer science; Markov process; Programming language","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":[0.0002793555,0.0002653664,0.0002581012,0.0001789881,0.0001105754,0.0004167174,0.000701583,0.000086785,0.00001521931],"category_scores_gemma":[0.0002160944,0.000210236,0.00006929976,0.0003829837,0.000405927,0.001393976,0.0006311223,0.0002641239,0.00007403528],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001203093,"about_ca_system_score_gemma":0.00002689217,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006217998,"about_ca_topic_score_gemma":0.00000569905,"domain_scores_codex":[0.997937,0.00002042511,0.0006119799,0.000605342,0.0004577168,0.0003675059],"domain_scores_gemma":[0.9991848,0.0001507627,0.0002810308,0.0001767459,0.0001021938,0.0001044611],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.000625386,0.000901888,0.005467203,0.00008383909,0.0001092738,0.00001156978,0.01750743,0.0003839012,0.05598501,0.2604935,0.001717021,0.656714],"study_design_scores_gemma":[0.0001634044,0.0009271358,0.00102405,0.0008443571,0.0000159154,0.00001962177,0.01281405,0.2250279,0.6515564,0.1053576,0.001616584,0.000632947],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9687873,0.00002278904,0.02333703,0.003286855,0.0004069211,0.0007170596,0.00003224051,0.00006713441,0.003342743],"genre_scores_gemma":[0.9992667,0.00007034532,0.00007690513,0.0001606782,0.00002502722,0.00005067081,0.000006669774,0.00001228701,0.0003306817],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6560811,"threshold_uncertainty_score":0.8573177,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0393058403157471,"score_gpt":0.2883883967284859,"score_spread":0.2490825564127388,"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."}}