{"id":"W3093947831","doi":"10.1109/cog47356.2020.9231637","title":"Evolving Initial Heuristic Functions for Agent-Centered Heuristic Search","year":2020,"lang":"en","type":"article","venue":"2020 IEEE Conference on Games (CoG)","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Heuristics; Incremental heuristic search; Pathfinding; Computer science; Heuristic; Beam search; Artificial intelligence; Hyper-heuristic; Context (archaeology); Domain (mathematical analysis); Class (philosophy); Consistent heuristic; Mathematical optimization; Theoretical computer science; Machine learning; Graph; Search algorithm; Mathematics; Algorithm; Robot; Mobile robot","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":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0002608832,0.000370384,0.0004426936,0.0001239155,0.0002879743,0.0005186499,0.001357778,0.0001281259,0.0001400664],"category_scores_gemma":[0.000679327,0.0003778687,0.0001716311,0.0005266737,0.0001141314,0.0003303787,0.0002293643,0.0004883599,0.000878707],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006132902,"about_ca_system_score_gemma":0.0003972132,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001988381,"about_ca_topic_score_gemma":0.000002102733,"domain_scores_codex":[0.996945,0.000189695,0.0004847468,0.001056489,0.0005803914,0.0007437388],"domain_scores_gemma":[0.9978253,0.0004783343,0.0001525863,0.0006831264,0.0003573067,0.000503348],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001290924,0.002183167,0.002900602,0.002070504,0.001730966,0.003141707,0.02740645,0.05348403,0.02181559,0.0516913,0.4882146,0.3440701],"study_design_scores_gemma":[0.001115779,0.0009152687,0.001219416,0.0001646419,0.0000699499,0.00002556267,0.0001529238,0.9928009,0.0004382104,0.0006592986,0.001937994,0.0005000567],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.00151626,0.0001026753,0.9882207,0.004274399,0.001989001,0.0007729955,0.0001158054,0.0004927707,0.002515412],"genre_scores_gemma":[0.9584839,0.0000173556,0.03801651,0.001706166,0.0007460944,0.0001516453,0.00004955689,0.00004446178,0.000784348],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9569676,"threshold_uncertainty_score":0.9998992,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1313606239383958,"score_gpt":0.3236612080701814,"score_spread":0.1923005841317856,"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."}}