{"id":"W4283802141","doi":"10.1609/aaai.v36i9.21219","title":"Planning to Avoid Side Effects","year":2022,"lang":"en","type":"article","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","topic":"Logic, Reasoning, and Knowledge","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University; Vector Institute; University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institute for Advanced Research; Microsoft Research","keywords":"Computer science; Side effect (computer science); Context (archaeology); Agency (philosophy); Risk analysis (engineering); Lead (geology); Relation (database); Management science; Business; Engineering; Epistemology; Data mining","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":[0.003164557,0.001237549,0.0006455268,0.0006154203,0.0008300106,0.001343069,0.0009688838,0.0008041866,0.005162064],"category_scores_gemma":[0.01626999,0.0006913313,0.00104244,0.0004708486,0.002884815,0.002214669,0.002555195,0.002043391,0.0005674209],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009083192,"about_ca_system_score_gemma":0.002674038,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001821434,"about_ca_topic_score_gemma":0.002910675,"domain_scores_codex":[0.9970085,0.001251507,0.0001474593,0.0002948654,0.0009016892,0.0003959142],"domain_scores_gemma":[0.9907032,0.006293687,0.0006354791,0.00151186,0.0006524115,0.0002033314],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0005221831,0.0002332037,0.00367934,0.0006664721,0.0002060183,0.0006230898,0.001103758,0.5305628,0.01351719,0.2972099,0.003646125,0.14803],"study_design_scores_gemma":[0.0000859877,0.0002666505,0.0008050854,0.0001336626,0.0001402624,0.0002542387,0.000273397,0.454225,0.01470033,0.5206226,0.008452415,0.00004041417],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0937328,0.0003783873,0.8830438,0.00125272,0.00009688594,0.000251478,0.0001059506,0.001214867,0.0199231],"genre_scores_gemma":[0.7211326,0.0003364981,0.2727742,0.0003560472,0.00004131195,0.0002833093,0.0001945762,0.0004936061,0.004387735],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005162064,"threshold_uncertainty_score":0.01726884,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0592639208155509,"score_gpt":0.2916186804392807,"score_spread":0.2323547596237298,"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."}}