{"id":"W155860189","doi":"10.1609/icaps.v22i1.13537","title":"Optimally Relaxing Partial-Order Plans with MaxSAT","year":2012,"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":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Flexibility (engineering); Computer science; Plan (archaeology); Encoding (memory); Mathematical optimization; Order (exchange); Minification; Operations research; 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":[0.001164114,0.0009359845,0.0004485148,0.0004642037,0.0003622772,0.0007996019,0.0008204146,0.0005944116,0.002563164],"category_scores_gemma":[0.003305599,0.0005526214,0.000891647,0.0007733038,0.0008201032,0.001604363,0.00137112,0.001633148,0.0003514508],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007633796,"about_ca_system_score_gemma":0.001300035,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002275476,"about_ca_topic_score_gemma":0.004856212,"domain_scores_codex":[0.999199,0.0003228869,0.00003899422,0.0001135415,0.0002272411,0.00009841787],"domain_scores_gemma":[0.9987979,0.0008310041,0.0001277433,0.0001317503,0.00007529635,0.00003627652],"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":[0.0002150543,0.0001342112,0.0005339132,0.0002640331,0.00005072729,0.0002290569,0.0002188221,0.8303056,0.007221732,0.06144125,0.002981282,0.09640435],"study_design_scores_gemma":[0.0000787094,0.0001601523,0.0002165678,0.00004894668,0.00003513907,0.00008803979,0.0001497411,0.8891972,0.009013674,0.09411996,0.006870464,0.00002148607],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.05597319,0.0002767789,0.9313916,0.0004117765,0.00004854507,0.0002572022,0.0003913644,0.001078259,0.01017137],"genre_scores_gemma":[0.3313144,0.000318402,0.6636029,0.0002029391,0.00003177358,0.0003213798,0.0007747984,0.0002707804,0.003162668],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002563164,"threshold_uncertainty_score":0.008574665,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03352342834652276,"score_gpt":0.2690857539899184,"score_spread":0.2355623256433956,"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."}}