{"id":"W19153916","doi":"10.1080/00048670802607154","title":"A framework for representing and solving NP search problems","year":2005,"lang":"en","type":"article","venue":"National Conference on Artificial Intelligence","topic":"Logic, Reasoning, and Knowledge","field":"Computer Science","cited_by":91,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Constraint satisfaction problem; Answer set programming; Constraint programming; Parameterized complexity; Computer science; Constraint satisfaction; Extension (predicate logic); Satisfiability; Boolean satisfiability problem; Set (abstract data type); Theoretical computer science; Solver; Strengths and weaknesses; Backtracking; Constraint (computer-aided design); Mathematical optimization; Programming language; Mathematics; Artificial intelligence; Algorithm; Stochastic programming","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":[0.00543383,0.002122176,0.001482221,0.002894539,0.002135702,0.007674553,0.00616708,0.003560746,0.01110929],"category_scores_gemma":[0.0142594,0.001049513,0.004559846,0.003776079,0.005304051,0.009001701,0.005648603,0.004562002,0.00268751],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002570286,"about_ca_system_score_gemma":0.004506437,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01450931,"about_ca_topic_score_gemma":0.01389455,"domain_scores_codex":[0.9960067,0.001722057,0.0006417679,0.0004782115,0.0008550359,0.0002963691],"domain_scores_gemma":[0.9966565,0.001981554,0.0002210736,0.0005954347,0.0004000744,0.0001453649],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00002668928,0.00003489647,0.0002396727,0.0003480704,0.00004682766,0.0001703071,0.0006262401,0.04760859,0.0003431354,0.8910618,0.004056651,0.05543717],"study_design_scores_gemma":[0.00003096871,0.00002688102,0.00005751279,0.0001638828,0.0000270032,0.0001328647,0.0002826079,0.1321461,0.0002769028,0.8249682,0.04186016,0.00002695447],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0007786952,0.0005127194,0.9909423,0.0009927464,0.00006348867,0.0001824687,0.0001740707,0.0004901338,0.005863363],"genre_scores_gemma":[0.02238987,0.0008916196,0.9718986,0.0001981195,0.00008707107,0.0006658044,0.0004542288,0.0001090915,0.003305617],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01450931,"threshold_uncertainty_score":0.03716427,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2083022900782457,"score_gpt":0.3866366578885083,"score_spread":0.1783343678102626,"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."}}