{"id":"W4396759941","doi":"10.1016/j.engappai.2024.108512","title":"Intelligent adaptive lighting algorithm: Integrating reinforcement learning and fuzzy logic for personalized interior lighting","year":2024,"lang":"en","type":"article","venue":"Engineering Applications of Artificial Intelligence","topic":"Building Energy and Comfort Optimization","field":"Engineering","cited_by":12,"is_retracted":false,"has_abstract":false,"ca_institutions":"Toronto Metropolitan University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Reinforcement learning; Fuzzy logic; Artificial intelligence; Human–computer interaction; Algorithm","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":[0.0005222975,0.0003760997,0.0006625417,0.0003709642,0.0003524793,0.0005692027,0.0008944558,0.0007030918,0.001802511],"category_scores_gemma":[0.0009277438,0.0002069601,0.0004210056,0.0002943978,0.0003577005,0.0005335443,0.0005669557,0.0006654822,0.0003074182],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005562442,"about_ca_system_score_gemma":0.0005917678,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003820645,"about_ca_topic_score_gemma":0.004225935,"domain_scores_codex":[0.9997806,0.00004642897,0.000008234151,0.00004857443,0.0000823802,0.00003384173],"domain_scores_gemma":[0.9997669,0.00007298478,0.00003034151,0.00002007754,0.00009114289,0.00001848822],"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":[0.0001586202,0.0001772661,0.0009441527,0.00004867236,0.00003958405,0.00004432975,0.00006464486,0.7798672,0.01310151,0.005807164,0.001384196,0.1983627],"study_design_scores_gemma":[0.000009821313,0.0000198676,0.00006760239,0.000002072551,0.000004266279,0.000007749688,0.000002549393,0.9983597,0.0008082021,0.0004991029,0.0002161279,0.000002757805],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02322049,0.00008690028,0.972737,0.00007005759,0.00003219327,0.00002934592,0.00001067681,0.0003258517,0.003487417],"genre_scores_gemma":[0.7771909,0.00007371203,0.2193073,0.0000949932,0.0000277375,0.00007409856,0.00003277875,0.00005939479,0.00313906],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003820645,"threshold_uncertainty_score":0.00759685,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01772688229951392,"score_gpt":0.2588962227083175,"score_spread":0.2411693404088036,"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."}}