{"id":"W4382866847","doi":"10.1609/icaps.v33i1.27201","title":"Solving Domain-Independent Dynamic Programming Problems with Anytime Heuristic Search","year":2023,"lang":"en","type":"article","venue":"Proceedings of the International Conference on Automated Planning and Scheduling","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Solver; Travelling salesman problem; Mathematical optimization; Heuristic; Domain (mathematical analysis); Computer science; Constraint programming; Dynamic programming; Constraint satisfaction problem; Mathematics; Stochastic programming; Artificial intelligence","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":[0.001693075,0.0014295,0.001074915,0.0007241789,0.0004761571,0.001656498,0.002038897,0.001343936,0.004378292],"category_scores_gemma":[0.004712573,0.0006710159,0.001211032,0.001250765,0.0009537566,0.001752231,0.001599898,0.001867873,0.0006449193],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001003097,"about_ca_system_score_gemma":0.002537584,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004089835,"about_ca_topic_score_gemma":0.005839742,"domain_scores_codex":[0.9988134,0.0004958851,0.00006963927,0.0002091951,0.0002666948,0.0001451807],"domain_scores_gemma":[0.9975304,0.001550188,0.0002462475,0.000383011,0.0001959224,0.00009419295],"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":[0.0001182883,0.0001907963,0.0005682047,0.0002384979,0.000101544,0.0000648316,0.00006657602,0.8768296,0.001535294,0.02858363,0.002703385,0.08899946],"study_design_scores_gemma":[0.00005824417,0.00004665229,0.00005421296,0.00001679487,0.00001556932,0.00001935635,0.00002353663,0.9881982,0.0008363266,0.008728747,0.001995557,0.000006764252],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0147297,0.0003732149,0.9770608,0.0002053335,0.00005156934,0.0001285523,0.00009847745,0.0008631978,0.006489225],"genre_scores_gemma":[0.1956855,0.0004136006,0.8009113,0.0002182792,0.00004174044,0.0004110733,0.0003065316,0.0002414723,0.001770456],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004378292,"threshold_uncertainty_score":0.01464683,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02913668222290711,"score_gpt":0.2891739207322735,"score_spread":0.2600372385093664,"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."}}