{"id":"W2740968888","doi":"10.24963/ijcai.2017/726","title":"Robots in Retirement Homes: Applying Off-the-Shelf Planning and Scheduling to a Team of Assistive Robots (Extended Abstract)","year":2017,"lang":"en","type":"article","venue":"","topic":"Constraint Satisfaction and Optimization","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; Government of Canada","keywords":"Scheduling (production processes); Computer science; Constraint programming; Robotics; Robot; Automated planning and scheduling; Artificial intelligence; Motion planning; Constraint (computer-aided design); Operations research; Distributed computing; Industrial engineering; Human–computer interaction; Mathematical optimization; Engineering; Mathematics","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":[0.001055947,0.0005058508,0.0003918953,0.0002343272,0.0007695346,0.0007596529,0.0008538581,0.0008804863,0.004082682],"category_scores_gemma":[0.003142201,0.0002302862,0.0005296188,0.000430211,0.00079478,0.0005705655,0.00112804,0.0007168808,0.0003202042],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009661806,"about_ca_system_score_gemma":0.002001577,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02783296,"about_ca_topic_score_gemma":0.02784284,"domain_scores_codex":[0.9995491,0.000218662,0.00001888227,0.00007711109,0.00006080659,0.0000755711],"domain_scores_gemma":[0.9987181,0.0008354873,0.0000933182,0.0001034264,0.0001145171,0.0001351216],"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":[0.000276077,0.0002485242,0.001418177,0.0001129035,0.00003132549,0.0006046615,0.0004666247,0.9369081,0.003507459,0.009959769,0.001713231,0.04475322],"study_design_scores_gemma":[0.00005152649,0.0001384374,0.0004156643,0.00001065305,0.00001418617,0.00005241514,0.0004006381,0.9872829,0.002426676,0.006839072,0.002353879,0.00001398332],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3300968,0.000198755,0.6572798,0.0009691993,0.00009143395,0.0004517277,0.0002290531,0.0005413521,0.01014192],"genre_scores_gemma":[0.6980012,0.0001571867,0.2977917,0.0001065919,0.0000203541,0.0002566458,0.0001281604,0.00005762171,0.003480537],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02783296,"threshold_uncertainty_score":0.0553419,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03317380109541748,"score_gpt":0.3033306277825641,"score_spread":0.2701568266871466,"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."}}