{"id":"W2982378153","doi":"10.1609/aiide.v15i1.5228","title":"Pathfinding and Abstraction with Dynamic Terrain Costs","year":2019,"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":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Pathfinding; Terrain; Abstraction; Computer science; Path (computing); Programming language; Theoretical computer science; Shortest path problem; Geography; Cartography","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001912428,0.0002836617,0.0002676903,0.0001513117,0.0001260885,0.0007508105,0.0006521093,0.00006633,0.00003112565],"category_scores_gemma":[0.0001057173,0.0001952615,0.0000700719,0.0002255781,0.0002682175,0.001814907,0.0003864361,0.0002986621,0.0000719636],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001365471,"about_ca_system_score_gemma":0.00003486156,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004279724,"about_ca_topic_score_gemma":0.00001925027,"domain_scores_codex":[0.9981964,0.00001185953,0.0004518579,0.0006070808,0.0004139337,0.000318905],"domain_scores_gemma":[0.9988703,0.0001485171,0.0003798566,0.0002218112,0.0002782319,0.0001013013],"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":[0.0004933841,0.0004398462,0.009266024,0.00007870177,0.0001043263,0.000002932345,0.006759699,0.00005971816,0.0454318,0.3388064,0.0000200906,0.5985371],"study_design_scores_gemma":[0.0001951646,0.003957225,0.007262585,0.00260216,0.00004403407,0.0001244588,0.03367323,0.182676,0.6918956,0.07587777,0.0004233636,0.001268359],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9786063,0.00001552454,0.01012189,0.001505293,0.0002662184,0.0005979468,0.000006680221,0.00006530511,0.008814881],"genre_scores_gemma":[0.9991758,0.00003794413,0.0002770898,0.0001652464,0.00001549519,0.00002572762,7.745954e-7,0.00001770118,0.0002841924],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6464638,"threshold_uncertainty_score":0.7962531,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02032147801674987,"score_gpt":0.266035848648368,"score_spread":0.2457143706316181,"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."}}