{"id":"W92902385","doi":"10.1609/aiide.v2i1.18750","title":"Improving Collaborative Pathfinding Using Map Abstraction","year":2006,"lang":"en","type":"article","venue":"Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":61,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Pathfinding; Computer science; Abstraction; Construct (python library); Plan (archaeology); Space (punctuation); Theoretical computer science; Artificial intelligence; Programming language; Operating system; Geography","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001665056,0.001023654,0.001218835,0.001020167,0.0009923432,0.001137436,0.002625581,0.0009408959,0.00331418],"category_scores_gemma":[0.006406467,0.0007055646,0.001041206,0.001154259,0.0008561641,0.004215216,0.004314801,0.001410779,0.0008826148],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006125237,"about_ca_system_score_gemma":0.001593877,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007851981,"about_ca_topic_score_gemma":0.009338621,"domain_scores_codex":[0.9987183,0.0002838977,0.00005329412,0.0003222048,0.0004843091,0.0001379704],"domain_scores_gemma":[0.9972724,0.0011234,0.0001528958,0.001006294,0.0003509572,0.00009407021],"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":[0.0002166867,0.0001709663,0.002373312,0.0002398277,0.0001542413,0.0001512766,0.001172059,0.4815705,0.01460543,0.03857288,0.002937604,0.4578352],"study_design_scores_gemma":[0.00004047811,0.0001138861,0.0005387446,0.00001510187,0.00004945922,0.0001080831,0.0001949496,0.9629011,0.007894337,0.02191369,0.006199756,0.00003043171],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02182786,0.0001144091,0.9750186,0.0000567358,0.00001501658,0.00003740842,0.00002827088,0.001201786,0.001699865],"genre_scores_gemma":[0.3519106,0.0002010978,0.6447001,0.00005802115,0.00002007717,0.0001184069,0.0002644746,0.000224466,0.002502681],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007851981,"threshold_uncertainty_score":0.01561254,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03406828781603419,"score_gpt":0.272134442562923,"score_spread":0.2380661547468888,"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."}}