Congratulations on some good work!
It looks like "Acuitas" started out in Java and then moved to Python.
http://writerofminds.blogspot.com -- is interesting.
Anybody else here doing any Symbolic AI?

On Fri, Apr 17, 2020 at 9:05 PM WriterOfMinds <jennifer.hane....@gmail.com>
wrote:

> As promised, here's an update on my own project. This is a symbolic AI
> that I've been slowly making bigger and more capable over the course of
> several years.  I just added the beginnings of narrative processing.  Check
> out the demo video in which I tell the AI a couple of brief stories:
> https://www.youtube.com/watch?v=PYEi0XkMJXw
>
> How does Acuitas go about understanding a story?  As the story is told, he
> keeps track of all the following, stored in a temporary area that I call
> the narrative scratchboard:
>
> *Who are the characters?
> *What objects are in the story? What state are they in?
> *What problems do the characters have?
> *What goals do the characters have?
> *What events take place? (Do any of them affect problems or goals?)
>
> Acuitas doesn’t try to understand the cause chain and import of every
> single event in the story, because that would be a bit much at this stage.
> However, he does try to make sure that he knows all of the following:
>
> *If a character is in some state, what does that mean for the character?
> *If a character anticipates that something will happen, how does the
> character feel about it?
> *If a character is planning to do something, what is their motive?
>
> If he can’t figure it out by making inferences with the help of what’s in
> his semantic database, he’ll bother his conversation partner for an
> explanation, as you can see him doing in the video several times.  Story
> sentences don’t go into the permanent knowledge base (yet), but
> explanations do, meaning they become available for understanding other
> stories, or for general reasoning.  Explaining things to him still requires
> a bit of skill and an understanding of what his gaps are likely to be,
> since he can’t be specific about *why* he doesn’t understand something.  A
> character state, expectation, or plan is adequately explained when he can
> see how it relates to one of the character’s presumed goals.  Once you
> provide enough new links to let him make that connection, he’ll let you
> move on.
>
> Acuitas returns feedback throughout the story.  This is randomized for
> variety (though I forced some particular options for the demo).  After
> receiving a new story sentence, he may ...
> *say nothing, or make a “yes I’m listening” gesture.
> *comment something that he inferred from the new information.
> *tell you whether he likes or dislikes what just happened.
> *try to guess what a character might do to solve a problem.
>
> He even has a primitive way of deciding whether it’s a good story or not.
> He tracks suspense (generated by the presence of more than one possible
> outcome) and tension (how dire things are for the characters) as the story
> progresses.  A story whose suspense and tension values don’t get very large
> or don’t change much is “boring.”  He also assesses whether the story had a
> positive or negative ending (did the characters solve their problems and
> meet their goals?).  Stories with happy endings that aren’t boring may earn
> approving comments.
>
> There are many directions in which this feature needs to expand and grow
> more robust, and expect I’ll be working on them soon.  More background on
> the foundations of the project is available on the blog linked under the
> YouTube video.
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