{"id":"W1797158199","doi":"10.1609/icaps.v19i1.13364","title":"Improving Planning Performance Using Low-Conflict Relaxed Plans","year":2009,"lang":"en","type":"article","venue":"Proceedings of the International Conference on Automated Planning and Scheduling","topic":"AI-based Problem Solving and Planning","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; Australian Government","keywords":"Satisficing; Computer science; Heuristic; Plan (archaeology); Executable; Exploit; Mathematical optimization; Heuristics; Domain (mathematical analysis); State (computer science); Relaxation (psychology); Theoretical computer science; Algorithm; Artificial intelligence; Mathematics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002199562,0.001125892,0.0007575917,0.0009463536,0.0005649197,0.001049144,0.001765862,0.000753158,0.003277916],"category_scores_gemma":[0.008250393,0.0006385798,0.0007785432,0.0008227034,0.0007335718,0.001971742,0.001367477,0.001494075,0.0006788798],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000868883,"about_ca_system_score_gemma":0.001884703,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005602723,"about_ca_topic_score_gemma":0.008511561,"domain_scores_codex":[0.9984537,0.0006228842,0.00009357338,0.0002376912,0.0004054066,0.0001867144],"domain_scores_gemma":[0.9959236,0.002774665,0.0002572704,0.0006417413,0.0002804943,0.0001221276],"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":[0.0008553691,0.0002869258,0.002007779,0.0003189357,0.00009925479,0.0002225379,0.0002934614,0.7165137,0.01393358,0.01028096,0.005414045,0.2497734],"study_design_scores_gemma":[0.0001075274,0.0001730804,0.0003605684,0.00002167939,0.00003689058,0.00006146081,0.00005865113,0.9829096,0.00836414,0.006041991,0.001841336,0.00002311384],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1538554,0.000898607,0.8221924,0.0004799124,0.00008097449,0.0002884449,0.0003957634,0.01058644,0.0112221],"genre_scores_gemma":[0.4807134,0.0002762479,0.5159059,0.0001251572,0.00002083673,0.0001354222,0.0008177463,0.0003661194,0.001639038],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.005602723,"threshold_uncertainty_score":0.0116325,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04285857452177985,"score_gpt":0.2841194638274261,"score_spread":0.2412608893056462,"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."}}