things. you no. GenMo-y things.
National Novel Generation Month 2015: Spend the month of November writing code that generates a novel of 50k+ words.
My two main projects-with-product right now are:
- http://forum.makega.me/t/procedural-narrative-dialogue/91
- http://www.blog.radiator.debacle.us/2014/04/second-times-charm-procedural-npc.html
- http://koobazaur.com/gamedev/game-design/writing-branching-game-conversations/
- https://github.com/srfoster/Conversations - look at it more
- https://github.com/ncase/conversation - probably a dead end? not sure.
- https://github.com/hinrik/hailo - chatbot thing
- https://github.com/tdcha5/Character-Conversation - uh. student work. has a group round-robin talking. hrm.
- https://github.com/crathulis/ConversationCreator - also in Java
- http://forum.makega.me/t/procedural-narrative-dialogue/91/8
- http://notepad.smedresmania.com/2011/04/20/quality-based-narrative/
- https://groups.google.com/forum/#!topic/rec.games.roguelike.development/gj0WB-ydmNY
- http://tomasjurman.blogspot.com/2013/02/interactive-dialogue-for-html5-game.html
- http://www.escapistmagazine.com/articles/view/video-games/columns/experienced-points/10168-Procedural-Stories
- https://www.reddit.com/r/gamedev/comments/1d3zrz/the_details_of_procedural_story_generation_and/
- Meta-AQUA - uses a modified version of TaleSpin to produce input
- RoleModel: Towards a Formal Model of Dramatic Roles for Story Generation
ftp://ftp.cs.ucla.edu/tech-report/1992-reports/920057.pdf
- Computer Models of Thought and Language - pdf of the TOC of the 1973 book by Schank and Colby. Meehan was one of Schank's students, and TaleSpin uses their CBR model.
- A Tale-Spin Story - has some semi-technical info
- The Story of Meehan's Tale-Spin
- Tale Spin at "Smart Machines" (1987 Boston Computer Museum exhibit)
- Micro TaleSpin
- Micro TaleSpin @ HackerNews
- Micro TaleSpin - Peter Norvig
- Inside Computer Understanding: 5 Programs plus miniatures - 2 chapters on TaleSpin and MicroTaleSpin; google books link. The book itself can be had used for < $10
- Inside Micro Tale-Spin: Symbolic Computing with Lisp applied to story telling.
- Reading Digital Literature: Surface, Data, Interaction, and Expressive Processing - Noah Wardrip-Fruin. Plus a lot of other, non-Tale-Spin reading at that link
I'm still interested in Fairy Tales (see 2014 and the resultant slightly-modified-since-then-but-not-much Malepropp).
In particular, a portion of some tales where the hero befriends several characters/creatures [despite (his) haste or advice] which then end up helping him get through a nested problem. Eg, the giant's heart is kept in a box at the top of a tower on an island in a lake past the thorns, past a guard-dragon, etc etc. I'd like to be able to generate a n-level deep problem with associated helper characters -- each of whom would have to have an attribute matched to solving the problem (bear kills dragon, eagle flies hero over lake, etc.). This would also involve some minimal conversations.
See implementation in Heartless. Pure templating as of 2015.11.07. Not as thrilling as I had hoped. But I had hoped for some attribute matching, instead of pure randomness-from-dumb-lists.
Okay, I'm going to be playing with some Python stuff again.
Because I want to understand how to work this: https://github.com/mewo2/vocab-mashup
- dariusk/NaNoGenMo-2015#72 (comment)
- https://www.kaggle.com/c/word2vec-nlp-tutorial/details/part-1-for-beginners-bag-of-words
- https://www.kaggle.com/c/word2vec-nlp-tutorial/details/part-2-word-vectors
- http://www.nltk.org/data.html
- https://github.com/karpathy/char-rnn
- http://karpathy.github.io/2015/05/21/rnn-effectiveness/
- http://alexminnaar.com/word2vec-tutorial-part-i-the-skip-gram-model.html
- https://code.google.com/p/word2vec/
- https://radimrehurek.com/gensim/models/word2vec.html
- https://medium.com/@klintcho/doc2vec-tutorial-using-gensim-ab3ac03d3a1
- https://radimrehurek.com/gensim/models/doc2vec.html
I downloaded the DVD, and will be playing with some parsing, as well....
something I poked at last year, but didn't continue with. Very simple, and I followed the much-more-complex Proppian generator down it's endless trails. However, I had made a note that putting in some characters from fan-fiction (or whatnot) could be interesting. I'd like to pursue that again, maybe. See how the effect actually plays out with the super-miniatures.
TODO: update the orginal notes to reflect the original repo I was cloning
https://github.com/anihex/SimpleStoryCreator
- https://en.wikipedia.org/wiki/Automatic_summarization
- [http://libots.sourceforge.net/](Open Text Summarizer)
- http://textcompactor.com/
- http://thetokenizer.com/2013/04/28/build-your-own-summary-tool/
- https://github.com/topliceanu/text-summarization
- http://textsummarization.net/text-summarization-api-document
- https://github.com/pdehaan/summarizer
- https://www.npmjs.com/package/summarizer
- which uses https://www.npmjs.com/package/summarize
- although this does not actually provide a text summary
- AND also uses https://github.com/jbrooksuk/node-summary
- https://www.npmjs.com/package/node-summary
- http://www.splitbrain.org/services/ots
- http://textanalysisonline.com/
- http://textminingonline.com/getting-started-with-the-automatic-text-summarization-api-on-mashape
See conceptnet
See github-narrative