{"id":"W4387218579","doi":"10.4204/eptcs.391.14","title":"ORTAC+ : A User Friendly Domain Specific Language for Multi-Agent Mission Planning","year":2023,"lang":"en","type":"article","venue":"Electronic Proceedings in Theoretical Computer Science","topic":"AI-based Problem Solving and Planning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Safran Electronics (Canada)","funders":"","keywords":"Computer science; USable; Planner; Domain (mathematical analysis); Plan (archaeology); Modeling language; Semantics (computer science); Robot; Agile software development; Software engineering; Human–computer interaction; Field (mathematics); Drone; Natural language; Programming language; Artificial intelligence; Operations research; Engineering; World Wide Web","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.003759073,0.0002951414,0.0002862921,0.0005874526,0.0006006316,0.0005914079,0.002426673,0.0001106644,0.00001032431],"category_scores_gemma":[0.00012865,0.000259406,0.0000894927,0.002935376,0.0005139738,0.000734715,0.0007214996,0.0005228178,0.00004625688],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002787644,"about_ca_system_score_gemma":0.0002825849,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00000260248,"about_ca_topic_score_gemma":4.819601e-7,"domain_scores_codex":[0.9959072,0.000033459,0.0004128202,0.001209107,0.0006680038,0.001769419],"domain_scores_gemma":[0.9988235,0.0002852979,0.0001220827,0.0003610222,0.0001467985,0.0002612865],"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":[0.00002404676,0.00005181577,0.000582865,0.00003146237,0.00000475207,0.00002753803,0.004724996,0.0003910678,0.005055877,0.9811316,0.00112657,0.006847468],"study_design_scores_gemma":[0.001112895,0.0005940035,0.0009981906,0.000298484,0.000003984311,0.00005287906,0.0002501712,0.9091805,0.009029926,0.068096,0.009763177,0.0006197788],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1848823,0.0002155457,0.8124573,0.0007931207,0.0003416601,0.0004016745,0.000001687531,0.0005166342,0.0003900766],"genre_scores_gemma":[0.7615028,0.000008799923,0.2380317,0.0002105874,0.0001338545,0.00003819931,0.000003310211,0.00002022952,0.00005051739],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9130355,"threshold_uncertainty_score":0.9999858,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01776820336026865,"score_gpt":0.2921193739152016,"score_spread":0.274351170554933,"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."}}