{"id":"W101213734","doi":"10.1007/978-3-642-21219-2_7","title":"Using Artificial Intelligence Techniques for Strategy Generation in the Commons Game","year":2011,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Artificial Intelligence in Games","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":false,"ca_institutions":"Carleton University","funders":"","keywords":"Computer science; Resource (disambiguation); Artificial intelligence; Commons; Hill climbing; Particle swarm optimization; Operations research; Climbing; Machine learning; Engineering","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":[0.001123031,0.0006372535,0.0005865814,0.0006501352,0.0005454659,0.001116557,0.00150431,0.0008884625,0.004662513],"category_scores_gemma":[0.005231583,0.0003591728,0.0009096473,0.0006105533,0.001147216,0.001982703,0.001340327,0.001692567,0.0004414807],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007248741,"about_ca_system_score_gemma":0.0006498146,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001511172,"about_ca_topic_score_gemma":0.002134057,"domain_scores_codex":[0.9993199,0.000326404,0.00004005042,0.00009228518,0.0001683014,0.00005299997],"domain_scores_gemma":[0.9978244,0.001840064,0.00006263441,0.0001357868,0.00009398683,0.00004305149],"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":[0.0001851765,0.0002666033,0.0007799709,0.0002928239,0.0001096664,0.0001930061,0.0007008905,0.2532544,0.007273745,0.4299011,0.003998127,0.3030446],"study_design_scores_gemma":[0.00003617687,0.00004625534,0.0001115264,0.00002223866,0.00002128169,0.00006214953,0.00005039982,0.8131433,0.001873846,0.1825156,0.002104775,0.00001245842],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01755498,0.0001468304,0.9702327,0.0002122668,0.00004481125,0.0001050603,0.00002413212,0.0002703116,0.01140881],"genre_scores_gemma":[0.4295785,0.0002091524,0.5627962,0.0001163103,0.00004880889,0.0002632017,0.0001140417,0.0001331355,0.006740709],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.004662513,"threshold_uncertainty_score":0.01559764,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1742777273187105,"score_gpt":0.3448640809961592,"score_spread":0.1705863536774487,"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."}}