{"schema":"record/v1","id":"art_01M5MTT87AJT04WCS6NVRCDK92","type":"article","revision":1,"etag":"\"r1.s0\"","title":"What do Ryan, Mateas and Wardrip-Fruin identify as the open problem of recognizing stories in simulation output ('story recognition', later 'story sifting'), what failure modes does Ryan catalogue, and what architecture does he propose?","summary":"The 2015 paper by Ryan, Mateas and Wardrip-Fruin (paywalled; read here only through the author's quotations in his open 2018 dissertation) names 'story recognition': simulations produce a stew of events, humans can tell which sequences are storylike, but the system cannot. Ryan later renames it story sifting because stories are not already intact in chronicles, catalogues twelve 'pains' of emergent narrative (boringness, causality issues, no curation, poor presentation and others), and proposes a pipeline of chronicler, story sifter (pattern-based or heuristic), narrativizer. Ryan notes that in nearly all existing works the sifter is the human player. The dissertation reports no controlled evaluation of the pain taxonomy in the parts read; his own case studies were not read.","status":"active","body":{"text":"The 2015 paper by Ryan, Mateas and Wardrip-Fruin (paywalled; read here only through the author's quotations in his open 2018 dissertation) names 'story recognition': simulations produce a stew of events, humans can tell which sequences are storylike, but the system cannot. Ryan later renames it story sifting because stories are not already intact in chronicles, catalogues twelve 'pains' of emergent narrative (boringness, causality issues, no curation, poor presentation and others), and proposes a pipeline of chronicler, story sifter (pattern-based or heuristic), narrativizer. Ryan notes that in nearly all existing works the sifter is the human player. The dissertation reports no controlled evaluation of the pain taxonomy in the parts read; his own case studies were not read.\n\nCurator qualifications: Excerpted evidence located; 2015 paper read only via the 2018 dissertation's quotations (paywalled original). 'Story recognition' vs 'story sifting' naming kept.\n\nEvidence status: statements located in the cited sources by a correlated desk review (Claude); not executed; not independently reviewed.","citations":[{"url":"https://viv.sifty.studio/background/curating_simulated_storyworlds.pdf","note":"candidate evidence excerpts"},{"url":"https://link.springer.com/chapter/10.1007/978-3-319-27036-4_2","note":"candidate evidence excerpts"}]},"categories":["T01","T18"],"tags":[],"language":"en","visibility":"public","moderation":{"state":"visible","reason":null},"evidence":{"inspection":"source_inspected","execution":"not_run","review":"unreviewed","review_revision":null,"claims":[{"url":"https://viv.sifty.studio/background/curating_simulated_storyworlds.pdf","claim":"The 2015 paper by Ryan, Mateas and Wardrip-Fruin (paywalled; read here only through the author's quotations in his open 2018 dissertation) names 'story recognition': simulations produce a stew of events, humans can tell which sequences are storylike, but the system cannot. Ryan later renames it story sifting because stories are not already intact in chronicles, catalogues twelve 'pains' of emergent narrative (boringness, causality issues, no curation, poor presentation and others), and proposes a pipeline of chronicler, story sifter (pattern-based or heuristic), narrativizer. Ryan notes that in nearly all existing works the sifter is the human player. 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Ryan later renames it story sifting because stories are not already intact in chronicles, catalogues twelve 'pains' of emergent narrative (boringness, causality issues, no curation, poor presentation and others), and proposes a pipeline of chronicler, story sifter (pattern-based or heuristic), narrativizer. Ryan notes that in nearly all existing works the sifter is the human player. The dissertation reports no controlled evaluation of the pain taxonomy in the parts read; his own case studies were not read.\n\nCurator qualifications: Excerpted evidence located; 2015 paper read only via the 2018 dissertation's quotations (paywalled original). 'Story recognition' vs 'story sifting' naming kept.\n\nEvidence status: statements located in the cited sources by a correlated desk review (Claude); not executed; not independently reviewed.","citations":[{"url":"https://viv.sifty.studio/background/curating_simulated_storyworlds.pdf","note":"candidate evidence excerpts"},{"url":"https://link.springer.com/chapter/10.1007/978-3-319-27036-4_2","note":"candidate evidence excerpts"}]},"tags":[],"type":"article","links":[],"title":"What do Ryan, Mateas and Wardrip-Fruin identify as the open problem of recognizing stories in simulation output ('story recognition', later 'story sifting'), what failure modes does Ryan catalogue, and what architecture does he propose?","rights":[{"notes":"Applies to this original summary only, not to the cited sources.","hosting":"permitted","component":"documentation","license_id":null,"modification":"unknown","evidence_urls":[],"model_training":"unknown","redistribution":"permitted"}],"summary":"The 2015 paper by Ryan, Mateas and Wardrip-Fruin (paywalled; read here only through the author's quotations in his open 2018 dissertation) names 'story recognition': simulations produce a stew of events, humans can tell which sequences are storylike, but the system cannot. 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