{"id":"W2762426729","doi":"10.1007/978-3-319-73117-9_27","title":"Exploring Graphs with Time Constraints by Unreliable Collections of Mobile Robots","year":2017,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Optimization and Search Problems","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"ca_institutions":"Carleton University; Université du Québec en Outaouais","funders":"Agence Nationale de la Recherche","keywords":"Robot; Traverse; Computer science; Graph; Mobile robot; A priori and a posteriori; Time complexity; Node (physics); Enhanced Data Rates for GSM Evolution; Adversary; Theoretical computer science; Algorithm; Artificial intelligence","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":[0.001065328,0.001420308,0.002508277,0.001433482,0.001683186,0.002116064,0.00462349,0.001907709,0.003525664],"category_scores_gemma":[0.007948767,0.002171295,0.001753932,0.003074766,0.002102056,0.005048915,0.005221745,0.002198359,0.0005775003],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001158164,"about_ca_system_score_gemma":0.0008607846,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004727061,"about_ca_topic_score_gemma":0.007037956,"domain_scores_codex":[0.9991098,0.0002884933,0.00004434909,0.0002402731,0.0001952542,0.0001218418],"domain_scores_gemma":[0.9955555,0.002900697,0.0003768986,0.0006053359,0.0002277864,0.0003337825],"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":[0.0002927554,0.00005331513,0.0007535857,0.000225661,0.0001023468,0.0003674312,0.0003470007,0.9406003,0.002486319,0.03091149,0.002347982,0.02151186],"study_design_scores_gemma":[0.00003312902,0.00006036992,0.0002269141,0.00002328969,0.00003838495,0.00008564493,0.0001188324,0.9404616,0.0007999325,0.05673224,0.001398826,0.0000208597],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1756047,0.001305281,0.8140765,0.0006520966,0.0001313591,0.00009706824,0.0003656705,0.0007133306,0.007053974],"genre_scores_gemma":[0.7946707,0.001015444,0.1943011,0.0001729418,0.0002129588,0.0002559617,0.0007675246,0.0005363671,0.008066877],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004727061,"threshold_uncertainty_score":0.01179457,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03790816574046223,"score_gpt":0.2490121587925893,"score_spread":0.2111039930521271,"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."}}