{"id":"W2808541151","doi":"10.24963/ijcai.2018/669","title":"Planning in Factored State and Action Spaces with Learned Binarized Neural Network Transition Models","year":2018,"lang":"en","type":"article","venue":"","topic":"AI-based Problem Solving and Planning","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Leverage (statistics); Computer science; Automated planning and scheduling; Exploit; Artificial intelligence; Plan (archaeology); Discretization; Curse of dimensionality; Theoretical computer science; Constraint (computer-aided design); Mathematical optimization; Mathematics","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":[0.0006614957,0.0007862689,0.0006388564,0.0004643792,0.0002812519,0.0008307806,0.001290103,0.0007815347,0.005100737],"category_scores_gemma":[0.003727128,0.0005668006,0.0008798036,0.0007480996,0.001014483,0.002466048,0.001310286,0.002157134,0.0005059656],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00166616,"about_ca_system_score_gemma":0.001697562,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01366748,"about_ca_topic_score_gemma":0.02484122,"domain_scores_codex":[0.9993743,0.000146024,0.00004130935,0.0002220344,0.000149019,0.00006730636],"domain_scores_gemma":[0.9983139,0.00107689,0.0001350747,0.0002523529,0.0001726852,0.00004915956],"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":[0.00008368976,0.00004213394,0.0003903523,0.00008599922,0.00002286535,0.00004963119,0.00005943832,0.9397647,0.001145681,0.01271662,0.0007521447,0.04488673],"study_design_scores_gemma":[0.000009388513,0.00001330429,0.00004168315,0.000006869791,0.00000520024,0.000009121405,0.000008662492,0.9879119,0.0005531619,0.01093834,0.0004988454,0.000003522184],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02789852,0.0001645554,0.967769,0.0002110067,0.00003284375,0.00007873504,0.0003784508,0.001591019,0.001875867],"genre_scores_gemma":[0.4004209,0.0002285981,0.5950119,0.0001553701,0.00002095619,0.0002584734,0.001000639,0.0002712173,0.002631971],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01366748,"threshold_uncertainty_score":0.0271759,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05009573217793577,"score_gpt":0.2693127140951196,"score_spread":0.2192169819171838,"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."}}