Acuitas Diary #48 (April 2022)

The topic of the month was more "theory of mind" material: specifically, modeling the knowledge of other minds, and considering its implications for their scope of available actions.

I focused the new features around the Narrative reasoning module (though the Executive will end up using some of the same tools to model the real minds of people Acuitas talks to, eventually). The most basic step was to add storage for facts about characters' knowledge in the Narrative tracker.

Public domain image by TeroVesalainen.

It would be impractical to explicitly list everything that every character in a story knows - both because that's a massive amount of information, and because another person's knowledge is private to them, and not all details can be inferred. So the model is intended to be sparse. Characters are presumed to know all the background knowledge in Acuitas' own semantic database, and to know what they need to know to accomplish their goals. Facts are only listed as known or not known if 1) the story states outright that a character knows or doesn't know something, or 2) presence or absence of knowledge can be inferred from some event in the story. For example, if a character finds an object, this implies that the character now knows the object's location.

I also had to extend the action prerequisites system a bit, so that it could handle nested relationships. Previously, I could teach Acuitas something like this:

To eat a food, an agent must have the food.
To run, an agent must be alive.

And now I can set up prerequisites like this:

To get an object, an agent must be where the object is.

"Where the object is" is recognized as a relational fact (<object> is_at <location-wildcard>). The Narrative engine further recognizes that if any relational facts that are components of a goal's prerequisite are not known, the prerequisite is blocked ... the character cannot directly arrange to fulfill it. This paves the way for knowledge-gathering to become a subgoal in its own right.

The Chest of Gold, as seen in King's Quest I.

Putting all this together, we can craft a story about someone looking for an object in an unknown location. My test story is based on King's Quest I, with some liberties (in the game, I don't think you can actually ask the bridge troll where the chest is).

The Chest of Gold, as imagined by the Wombo Dream AI. 

Here's a breakdown of the story with significant tracking that happens at each step. There is a LOT more that needs to happen here. For example, seeking should be understood as an attempt at a solution to the lack of knowledge, repeated failures to find the chest should raise the Suspense variable, the troll's lie should generate a possible false belief in the knowledge model, etc. But it is, as usual, Good Enough for Now.

0:"Graham was a knight."
   A knight is recognized as a type of agent; Graham is tracked as a character in the story.
1:"Graham served a king."
   The king is now tracked as a character also.
2:"The king wanted the Chest of Gold."
   This line sets up a character goal for the king: he wants to have the chest, which is now tracked as an object
3:"The king brought Graham to his castle."
4:"The king told Graham to get the Chest of Gold."
5:"Graham wanted to get the chest, but Graham did not know where the chest was."
   Processing of the first clause enters getting the chest as a goal for Graham. Processing of the second clause updates his knowledge model with his lack of knowledge of the chest's location, and notes that the goal just created is now "thwarted."
6:"Graham left the castle to seek the chest."
7:"Graham went to the lake, but Graham did not find the chest."
   Graham's new location should be inferred when he moves, but these sentences don't do too much else for now.
8:"Graham went to the dark forest, but Graham did not find the chest."
9:"Graham asked of a troll where the chest was."
   Awkward wording because the Parser doesn't do indirect objects yet!
10:"The troll told to Graham that the chest was at the gingerbread house."
   My Twitter followers didn't vote for me to work on IOs next, so we'll be stuck with this for a while.
11:"Graham went to the gingerbread house, but Graham did not find the chest."
12:"A witch was at the gingerbread house."
   Another agent! What's she gonna do?
13:"The witch wanted to eat Graham."
   This gets registered as a *bad* character goal - see previous story about Odysseus and the Cyclops.
14:"Graham ran and the witch could not catch Graham."
   Failure of the bad goal is inferred. Yay.
15:"Finally Graham went to the Land of the Clouds."
16:"In the Land of the Clouds, Graham found the chest."
   Graham knows where the chest is! The knowledge model gets updated accordingly. We can also unblock that goal now.
17:"Graham got the chest and gave the chest to the king."
   And both the story's positive character goals are solved in one fell swoop.
18:"The end."

Next month I plan to keep extending this. Information transfer needs to be modeled, misinformation needs to be understood, and all this needs to start getting applied in the Executive.

Until the next cycle,

Jenny