{"id":"W2295532854","doi":"10.1609/socs.v1i1.18160","title":"Common Misconceptions Concerning Heuristic Search","year":2010,"lang":"en","type":"article","venue":"Proceedings of the International Symposium on Combinatorial Search","topic":"AI-based Problem Solving and Planning","field":"Computer Science","cited_by":34,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Heuristics; Incremental heuristic search; Heuristic; Node (physics); Null-move heuristic; Beam search; Consistent heuristic; Bidirectional search; Simple (philosophy); Computer science; Best-first search; Search algorithm; Function (biology); Mathematical optimization; Mathematics; Theoretical computer science; Algorithm; Epistemology; Engineering","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":[0.001252469,0.0001655759,0.0001815256,0.0001100476,0.0003797823,0.0003672467,0.003141575,0.0001132311,0.00005159861],"category_scores_gemma":[0.0002208428,0.0001343751,0.0001206266,0.0003554201,0.000222577,0.0003411403,0.0007482808,0.000986249,0.00004386852],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009722846,"about_ca_system_score_gemma":0.0001168507,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002066988,"about_ca_topic_score_gemma":0.000002986018,"domain_scores_codex":[0.9977111,0.00003315333,0.000327618,0.0003900391,0.001203856,0.0003342431],"domain_scores_gemma":[0.9983813,0.0004433343,0.0001541737,0.000252791,0.0006484094,0.0001199867],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00005858083,0.0001607859,0.01786193,0.00002903734,0.00004486311,0.000001634221,0.0009768826,0.0002920633,0.1603824,0.8170647,0.001831239,0.001295898],"study_design_scores_gemma":[0.004297404,0.0009911504,0.01149416,0.0008603719,0.00004582881,0.0001161558,0.0003483828,0.1119116,0.7538172,0.07672968,0.03815055,0.001237509],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9393705,0.0000123697,0.0005303187,0.01203144,0.007134639,0.0003289472,0.00001489078,0.0001497481,0.0404272],"genre_scores_gemma":[0.997801,0.000004417301,0.001026357,0.0001406385,0.0004614909,0.00001633563,0.00000327743,0.00001583916,0.0005306176],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.740335,"threshold_uncertainty_score":0.5837881,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01937709620342645,"score_gpt":0.2739802742194933,"score_spread":0.2546031780160668,"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."}}