{"id":"W3022326288","doi":"10.1609/aaai.v26i1.8195","title":"Generalizing and Executing Plans","year":2021,"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":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Plan (archaeology); Generalization; Computer science; Task (project management); Representation (politics); Key (lock); Process (computing); Order (exchange); Artificial intelligence; Management science; Operations research; Computer security; Systems engineering; Engineering; Programming language; Political science; Business; 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":[0.001477309,0.0007946062,0.0005253229,0.000594619,0.0004204752,0.001209325,0.001209968,0.0007988529,0.002242792],"category_scores_gemma":[0.006818341,0.0004524991,0.001056392,0.0007195632,0.001915902,0.003034619,0.002052477,0.001809246,0.0004494204],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000857906,"about_ca_system_score_gemma":0.001182439,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004231495,"about_ca_topic_score_gemma":0.004834746,"domain_scores_codex":[0.9988054,0.000335673,0.00009587423,0.0003386357,0.0003205836,0.000103932],"domain_scores_gemma":[0.9977489,0.000938584,0.0002052044,0.0008861868,0.0001582464,0.0000628977],"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":[0.000177224,0.00009819484,0.00172557,0.0002698645,0.00008846427,0.0003702295,0.00176419,0.448423,0.01375912,0.225038,0.002857181,0.305429],"study_design_scores_gemma":[0.000032745,0.0001486758,0.0006365099,0.00008840053,0.00006628121,0.0002067949,0.0003803831,0.7113339,0.007696954,0.2603717,0.01899934,0.00003835397],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03596536,0.0001582181,0.9582112,0.0004090822,0.00002273423,0.0001771318,0.0001508406,0.001421687,0.003483721],"genre_scores_gemma":[0.3029912,0.000544198,0.6920398,0.0001779368,0.00003222178,0.0002759461,0.0007187409,0.0002510207,0.002968933],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004231495,"threshold_uncertainty_score":0.008413732,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07715901879387925,"score_gpt":0.2800754265498518,"score_spread":0.2029164077559725,"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."}}