Resources

[hugovk]

This is an open issue where you can comment and add resources that might come in handy for NaNoGenMo.

There are already a ton of resources on the old resources threads for the 2013 edition, the 2014 edition, the 2015 edition, the 2016 edition, and the 2017 edition.

[enkiv2]

Our own @aparrish wrote Sea Duck, a system for generating tracery grammars out of rules about nouns, their properties, and the actions that can apply to them.

[enkiv2]

Emily Short wrote a survey of recent academic work in narrative generation, some of which may be worth building on.

[korbonits]

https://fiction.ict.usc.edu/creativehelp/ -- Melissa Roemmele presented about it in last year's NIPS Machine Learning for Creativity and Design workshop

[enkiv2]

Wikipedia has themed word-frequency lists: https://en.wiktionary.org/wiki/Wiktionary:Frequency_lists

On Thu, Oct 18, 2018 at 1:23 PM Alex Korbonits [email protected] wrote:

https://fiction.ict.usc.edu/creativehelp/ -- Melissa Roemmele presented about it in last year's NIPS Machine Learning for Creativity and Design workshop

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[ikarth]

The alpha version of Expressionist is available: https://github.com/james-owen-ryan/expressionist

Expressionist is a tool for authoring interactive narrative text by James Ryan, Tyler Brothers, and others at UCSC. The documentation is a bit lacking at the moment, but it has some powerful abilities to generate context-specific text. James Ryan has also been researching the history of interactive and generated narrative, and has uncovered many interesting early projects, such as Grimes' Fairy Tales: A 1960s Story Generator a grammar-based narrative generator using our old friend Vladimir Propp.

[hugovk]

I just published a new python package, being inspired by #procjam and #nanogenmo I created https://pypi.org/project/dragn/ which is my take on random.randint with more of a "Dice" interface.

https://twitter.com/lurst/status/1053561798530662401

[kleer001]

What? Game dev convention videos that I've found inspiring. Why? Video games are entertainment, a book is ostensibly entertainment, they are inevitably going to be sharing attributes especially since we're sharing tools. How? With a grain of salt, not everything is going to apply, of course.

Magic: the Gathering: Twenty Years, Twenty Lessons Learned https://www.youtube.com/watch?v=QHHg99hwQGY

Best practices for Procedural Narrative Generation: https://www.youtube.com/watch?v=k2rgzZ2WXKo

Writing Modular Characters for System Driven Games: https://www.youtube.com/watch?v=qX5-2D8SP5A

Tarn Adams - Villains in Dwarf Fortress https://www.youtube.com/watch?v=4-7TtPX5uhg

The simplest Ai trick in the Book GDC 2015 https://www.youtube.com/watch?v=iVBCBcEANBc

Less is More: Designing Awesome Ai for Games https://www.youtube.com/watch?v=1xWg54mdQos

The simplest Ai trick in the Book GDC 2013 https://www.gdcvault.com/play/1018059/The-Simplest-AI-Trick-in

PCG for everyone GDC 2017 https://www.youtube.com/watch?v=WumyfLEa6bU

The design of time GDC 2017 https://www.youtube.com/watch?v=gIrfxZ4KbOA&t=1532s

[cpressey]

If you're working with sentence trees and you just want to see how a sentence might be parsed, you can paste it into the online Link Grammar Parser form at http://www.link.cs.cmu.edu/link/submit-sentence-4.html

(S (NP It)
   (VP was
       (NP a
           (ADJP dark and stormy)
           night))
   .)

(S (NP He)
   (VP wondered
       (SBAR (WHADVP why)
             (S (NP the night)
                (VP was
                    (ADJP (ADVP so)
                          dark and stormy)))))
   .)

There are other online forms which claim to also do this, but at the time I was looking into this, the Link Grammar one was the only one I found that actually worked.

The source code (actually a more recent version of it, with improvements; written in C, LGPL'ed) can be found here: https://github.com/opencog/link-grammar

[cpressey]

Our own @aparrish wrote Sea Duck, a system for generating tracery grammars out of rules about nouns, their properties, and the actions that can apply to them.

In case you read this description and thought "oh cool, it compiles a description of a simulation to a Tracery grammar, how does that work?" (as I did, before I looked into it just now,) then you should know that that's not what it does. Rather, it runs a simulation, generating a list of events, and then renders those events by creating a Tracery grammar for each event, rendering it, and then immediately discarding it.

[hugovk]

@ojahnn has created the oulipien NaNoLiPo for November, with 30 daily prompts/constraints to inspire [code to generate] creative writing:

https://github.com/ojahnn/NaNoLiPo2018