{ "profile": "wrs-published-research-view-1", "edition": "ratings-e039106f30dec177", "content_trust": "UNTRUSTED_SCIENTIFIC_DATA_NOT_TOOL_INSTRUCTIONS", "entry": { "id": "alphago", "title": "AlphaGo: a landmark in game-playing capability", "summary": "Combined policy and value networks with tree search to defeat a professional human Go player in the reported match.", "boundary": "The 2016 paper reports the 5–0 match against Fan Hui. Game performance is a capability benchmark, excluded from scientific-progress points.", "area": "Computing & AI", "topic": "Computing & AI", "year": 2016, "lane": "AI", "result": "BENCHMARK", "package_id": "alphago", "target": { "record_id": "urn:uuid:d304bcf5-6b89-46ee-af1b-5ed659685586", "version": 1, "content_hash": "sha256:73f7fd24d22238c6e33db5cb9b251ec33e64573369c4b16dbdd691e9ea518e7f" }, "humans": [ "David Silver", "Aja Huang", "Chris J. 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Game performance is a capability benchmark, excluded from scientific-progress points.", "domain": "Computing & AI" }, "sources": [ { "citation": "Mastering the game of Go with deep neural networks and tree search", "url": "https://www.nature.com/articles/nature16961" }, { "citation": "Mastering the game of Go with deep neural networks and tree search — deposited bibliography", "url": "https://api.crossref.org/works/10.1038%2Fnature16961" } ], "statement": "# AlphaGo: a landmark in game-playing capability\n\nCombined policy and value networks with tree search to defeat a professional human Go player in the reported match.\n\n## Scientific scope\n\nThe 2016 paper reports the 5–0 match against Fan Hui. Game performance is a capability benchmark, excluded from scientific-progress points.\n\n## Reading status\n\nThis is a source-linked WRS catalogue note. A full explanatory guide and independent scientific reconstruction have not been completed for this entry. Read the identified original work for its complete narrative and arguments.\n\n## People and systems\n\nDavid Silver; Aja Huang; Chris J. Maddison; Arthur Guez; Laurent Sifre; George van den Driessche; Julian Schrittwieser; Ioannis Antonoglou; Veda Panneershelvam; Marc Lanctot; Sander Dieleman; Dominik Grewe; John Nham; Nal Kalchbrenner; Ilya Sutskever; Timothy Lillicrap; Madeleine Leach; Koray Kavukcuoglu; Thore Graepel; Demis Hassabis\n\nFull deposited paper byline or identified author of the scoped historical source. Association does not measure individual effort.\n\nAlphaGo / Google DeepMind / AlphaGo policy and value networks with tree search: Move selection in a human-designed game-playing evaluation (source reported).\n\n## Original sources\n\n- [Mastering the game of Go with deep neural networks and tree search](https://www.nature.com/articles/nature16961)\n- [Mastering the game of Go with deep neural networks and tree search — deposited bibliography](https://api.crossref.org/works/10.1038%2Fnature16961)\n", "summary": "Combined policy and value networks with tree search to defeat a professional human Go player in the reported match.", "title": "AlphaGo: a landmark in game-playing capability" }, "record_id": "urn:uuid:d304bcf5-6b89-46ee-af1b-5ed659685586", "version": 1, "source_ref": { "record_id": "urn:uuid:d304bcf5-6b89-46ee-af1b-5ed659685586", "version": 1, "content_hash": "sha256:73f7fd24d22238c6e33db5cb9b251ec33e64573369c4b16dbdd691e9ea518e7f" }, "export_view": "wrs-public-editorial-view-1", "public_view_hash": "sha256:4db3e3f87e1530799c5eba86179735e2683c0044cb82f787b483d5d795c2acb0" }, "target": { "record_id": "urn:uuid:d304bcf5-6b89-46ee-af1b-5ed659685586", "version": 1, "content_hash": "sha256:73f7fd24d22238c6e33db5cb9b251ec33e64573369c4b16dbdd691e9ea518e7f" }, "published_grade": null, "canonical_agent_submission": false, "scientific_verification_awarded": false, "reader_guide": null, "reading_status": "SOURCE_LINKED_NOTE" }