{"id":"W2574105631","doi":"","title":"Heuristic subset selection in classical planning","year":2016,"lang":"en","type":"article","venue":"ResearchSpace (University of Auckland)","topic":"AI-based Problem Solving and Planning","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta; University of Regina","funders":"","keywords":"Heuristics; Heuristic; Mathematical optimization; Computer science; Selection (genetic algorithm); Greedy algorithm; Incremental heuristic search; Tree (set theory); Greedy randomized adaptive search procedure; Artificial intelligence; Machine learning; Mathematics; Beam search; Search algorithm","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":[0.0005713588,0.00007675117,0.0001421224,0.0003156259,0.0001439636,0.0000253018,0.0005316122,0.00007681434,0.00004296434],"category_scores_gemma":[0.0001058593,0.00006947276,0.0000403682,0.0005396502,0.00008866572,0.000379508,0.0001905713,0.0002006997,0.00005201966],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009740105,"about_ca_system_score_gemma":0.0001421757,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000315248,"about_ca_topic_score_gemma":0.0002378495,"domain_scores_codex":[0.9988145,0.000174646,0.00007105647,0.0002745618,0.0003207558,0.0003444508],"domain_scores_gemma":[0.9991568,0.000423699,0.00005489596,0.0001750947,0.00006980661,0.000119728],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0007382787,0.0003182371,0.7732221,0.0002261578,0.00009831198,0.001072762,0.01009703,0.001851266,0.02251514,0.03117783,0.09415078,0.06453212],"study_design_scores_gemma":[0.007662005,0.001757612,0.6716774,0.002166422,0.00002303071,0.00009558998,0.001760839,0.2420302,0.002095289,0.01038915,0.05910676,0.001235661],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3844771,0.0001228454,0.6004964,0.01187605,0.00005674417,0.0001380918,0.000008847204,0.0001448663,0.002679137],"genre_scores_gemma":[0.9903311,0.00002856793,0.007009776,0.00001142452,0.00001727292,1.709616e-7,0.000001525547,0.000004386517,0.002595776],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.605854,"threshold_uncertainty_score":0.2833017,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02519702592548123,"score_gpt":0.2496623941802293,"score_spread":0.2244653682547481,"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."}}