{"id":"W4417339329","doi":"10.1109/gcaiot68269.2025.11275553","title":"Agentic AI for Personalized Trip Planning","year":2025,"lang":"","type":"article","venue":"","topic":"Transportation and Mobility Innovations","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"King Fahd University of Petroleum and Minerals","keywords":"Personalization; Stability (learning theory); Similarity (geometry); Compromise; Automated planning and scheduling","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.0008651204,0.0005253837,0.0002748558,0.0003870711,0.0004343131,0.001120338,0.001069511,0.0006860871,0.004403693],"category_scores_gemma":[0.002479077,0.0003161456,0.0006739734,0.0003869265,0.0005951832,0.001197715,0.001078652,0.001246507,0.0008981643],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008099619,"about_ca_system_score_gemma":0.001112134,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006514751,"about_ca_topic_score_gemma":0.008907329,"domain_scores_codex":[0.9995454,0.0001790494,0.00003098101,0.00007526493,0.0001388234,0.00003043491],"domain_scores_gemma":[0.9991297,0.0004661309,0.00005478021,0.0001783953,0.0001226614,0.00004830917],"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":[0.000229639,0.0002212917,0.002871853,0.0004925774,0.0001904696,0.0003542431,0.0006827763,0.6812758,0.01929107,0.07243302,0.01116784,0.2107893],"study_design_scores_gemma":[0.00001692439,0.00005492897,0.0002317718,0.00002312557,0.00003402658,0.00006197123,0.00008592431,0.9595475,0.002615952,0.02453982,0.01277512,0.00001307009],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02274346,0.0004112088,0.9582605,0.0005395378,0.00008887868,0.0001451928,0.0002196993,0.004027622,0.01356383],"genre_scores_gemma":[0.5328405,0.0005687201,0.4593231,0.00028675,0.00004381242,0.0002104509,0.0006295132,0.0002747172,0.005822378],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.006514751,"threshold_uncertainty_score":0.01473176,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02681485652546206,"score_gpt":0.3197264130262176,"score_spread":0.2929115565007556,"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."}}