{"id":"W80327629","doi":"10.2316/journal.206.2009.3.206-3270","title":"INTERNAL REPRESENTATION OF THE ENVIRONMENT IN COGNITIVE ROBOTICS","year":2009,"lang":"en","type":"article","venue":"International Journal of Robotics and Automation","topic":"Reinforcement Learning in Robotics","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Representation (politics); Artificial intelligence; Cognitive robotics; Computer science; Cognition; Robotics; Psychology; Robot; Neuroscience; Political science","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007951167,0.0004598071,0.0004987823,0.0008068029,0.0005174628,0.002648281,0.0009916173,0.001369364,0.00133275],"category_scores_gemma":[0.002347907,0.0003058955,0.0005577597,0.0006905214,0.006317629,0.005160221,0.001645384,0.001546911,0.0002673755],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009134331,"about_ca_system_score_gemma":0.0006474174,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001468792,"about_ca_topic_score_gemma":0.0008139663,"domain_scores_codex":[0.9995363,0.0001642701,0.00002155748,0.000100141,0.0001295863,0.00004808751],"domain_scores_gemma":[0.9994901,0.0002332904,0.00005647425,0.0001168348,0.00005972565,0.00004352927],"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":[0.00001140935,0.000009792421,0.0002290559,0.00008452169,0.00001710941,0.00005641279,0.0005357317,0.01442977,0.0009352419,0.9657138,0.0003316114,0.01764558],"study_design_scores_gemma":[0.000006951826,0.00001402023,0.0002958386,0.00002573043,0.00000834231,0.00004516555,0.00009381646,0.02296641,0.0003353745,0.9726498,0.003541261,0.00001730295],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07070585,0.01031312,0.8562634,0.005878258,0.0002981771,0.00004061961,0.000114639,0.0003283206,0.05605764],"genre_scores_gemma":[0.8849167,0.004100608,0.1057053,0.0004632688,0.0002381663,0.0001216728,0.0001156026,0.0000513271,0.004287399],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002648281,"threshold_uncertainty_score":0.006627381,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01731728384172224,"score_gpt":0.2809598773564247,"score_spread":0.2636425935147024,"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."}}