{"id":"W2189416168","doi":"10.1609/aaai.v27i1.8672","title":"A Fast Pairwise Heuristic for Planning under Uncertainty","year":2013,"lang":"en","type":"article","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","topic":"AI-based Problem Solving and Planning","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Partially observable Markov decision process; Heuristic; Pairwise comparison; Mathematical optimization; Bottleneck; Computer science; Greedy algorithm; Markov decision process; Sequence (biology); Markov process; Markov chain; Mathematics; Artificial intelligence; Markov model; Machine learning","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":[0.0005294069,0.00025763,0.000274865,0.0001213279,0.0003508897,0.0004414003,0.001832882,0.0001053334,0.00006164113],"category_scores_gemma":[0.0004339688,0.0001911376,0.0001393681,0.0004497133,0.0001811865,0.0004413629,0.0002600266,0.0002976329,0.0001034531],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004952701,"about_ca_system_score_gemma":0.0001241316,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001119698,"about_ca_topic_score_gemma":0.000003186645,"domain_scores_codex":[0.9980568,0.00001937624,0.0005222855,0.0005271527,0.0003798374,0.0004945423],"domain_scores_gemma":[0.9980782,0.0003637823,0.0003781708,0.0003090172,0.0007461284,0.0001247382],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00006595381,0.000134365,0.00049096,0.0001313496,0.00003394035,5.045226e-7,0.003566512,0.0150897,0.01250388,0.891892,0.00276369,0.0733272],"study_design_scores_gemma":[0.00004665459,0.0002819881,0.0002047799,0.0004736413,0.00001378368,0.000004401166,0.00105229,0.6080973,0.03979956,0.3495613,0.0001472311,0.0003170148],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1940513,0.00009116478,0.7822768,0.01240996,0.0009403665,0.001732289,0.00001724578,0.000388901,0.008091957],"genre_scores_gemma":[0.9882662,0.000004084447,0.01060495,0.0004911166,0.00007944059,0.0001220887,0.000001260736,0.00001556846,0.0004152517],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7942149,"threshold_uncertainty_score":0.7794367,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08542905099668778,"score_gpt":0.2954106598448335,"score_spread":0.2099816088481457,"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."}}