{"id":"W2585975957","doi":"10.4018/978-1-5225-0454-2.ch009","title":"A Universal Architecture for Migrating Cognitive Agents","year":2016,"lang":"en","type":"book-chapter","venue":"Advances in computational intelligence and robotics book series","topic":"AI-based Problem Solving and Planning","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"","keywords":"Cognitive architecture; Architecture; Computer science; Human–computer interaction; Cognition; Set (abstract data type); Perception; Consistency (knowledge bases); State (computer science); Cognitive science; Artificial intelligence; Psychology; Programming language; Neuroscience","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":[0.0002989142,0.0004580292,0.000271611,0.0003711207,0.0009315468,0.001457566,0.001284254,0.001000615,0.003669297],"category_scores_gemma":[0.0006794432,0.0003305549,0.0006473384,0.0003404321,0.001455569,0.001702685,0.001967254,0.00114391,0.0008930346],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008304648,"about_ca_system_score_gemma":0.0009036689,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002315597,"about_ca_topic_score_gemma":0.002330933,"domain_scores_codex":[0.9998133,0.00002869491,0.00001651332,0.00005766202,0.00004625653,0.00003755361],"domain_scores_gemma":[0.999845,0.00002572306,0.00001320063,0.00005074027,0.00003833673,0.0000269103],"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":[0.00004459324,0.00002813418,0.0002911621,0.0002166043,0.00002917218,0.0002363635,0.0009053245,0.03508542,0.01855483,0.820082,0.00444008,0.1200862],"study_design_scores_gemma":[0.00003739879,0.0001525487,0.0006350026,0.0001774126,0.00008322152,0.0008162963,0.0005571864,0.274942,0.0189509,0.4340799,0.2694991,0.0000690946],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.030437,0.001826916,0.9064789,0.0007738572,0.000313865,0.0001408293,0.00005997169,0.002484942,0.05748369],"genre_scores_gemma":[0.3730401,0.001898983,0.5952145,0.0002873718,0.00007251302,0.0003130262,0.0001865117,0.0002792542,0.02870772],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003669297,"threshold_uncertainty_score":0.01227498,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02510914789043819,"score_gpt":0.2809907285732756,"score_spread":0.2558815806828375,"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."}}