Acuitas Diary #40 (August 2021 A)

I have a bit more theory to talk about than usual. That means you're getting a mid-month developer diary, so I can get the ideas out of the way before describing what I did with them.

I've wanted to start working on spatial reasoning for a while now. At least a rough understanding of how space works is important for comprehending human stories, because we, of course, live in space. I already ran into this issue (and put in hacks to sidestep it) in a previous story: Horatio the robot couldn't reach something on a high shelf. Knowing what this problem is and how to solve it calls for a basic comprehension of geometry.

A page from Harmonices Mundi by Johannes Kepler

Issue: Acuitas does *not* exist in physical space -- not really. Of course the computer he runs on is a physical object, but he has no awareness of it as such. There are no sensors or actuators; he cannot see, touch, or move. Nor does he have a simulated 3D environment in which to see, touch, and move. He operates on words. That's it.

There's a school of thought that says an AI of this type simply *can't* understand space in a meaningful way, on account of having no direct experience of it or ability to act upon it. It is further claimed that symbols (words or numbers) are meaningless if they cannot refer to the physical, that this makes reasoning by words alone impossible, and therefore I'm an idiot for even attempting an AI that effectively has no body. Proponents of this argument sometimes invoke the idea that "Humans and animals are the only examples of general intelligence we have; they're all embodied, and their cognition seems heavily influenced by their bodies." Can you spot the underlying worldview assumption? [1]

Obviously I don't agree with this. It's my opinion that the concepts which emerge from human experience of space -- the abstractions that underlie or analogize to space, and which we use as an aid to understanding it -- are also usable by a symbolic reasoning engine, and possess their own type of meaningful reality. An AI that only manipulates ideas is simply a different sort of mind, not a useless one, and can still relate to us via those ideas that resemble our environment.

So how might this work in practice? How to explain space to an entity that has never lived in it?

Option #1: Space as yet another collection of relationships

To an isolated point object floating in an otherwise empty space, the space doesn't actually matter. Distance and direction are uninteresting until one can specify the distance and direction to something else. So technically, everything we need to know about space can be expressed as a graph of relationships between its inhabitants. Here are some examples, with the relational connection in brackets:

John [is to the left of] Jack.
Colorado [is north of] New Mexico.
I [am under] the table.
The money [is inside] the box.

For symbolic processing purposes, these are no more difficult to handle than other types of relationship, like category ("Fido [is a] dog") and state ("The food [is] cold"). An AI can make inferences from these relationships to determine the actions possible in a given scenario, and in turn, which of those actions might best achieve some actor's goals.

Though the relationship symbols are not connected to any direct physical experience -- the AI has never seen what "X inside Y" looks like -- the associations between this relationship and possible actions remain non-arbitrary. The AI could know, for instance, that if the money is inside a box, and the box is closed, no one can remove the money. If the box is moved, the money inside it will move too. These connections to other symbols like "move" and "remove" and "closed" supply a meaning for the symbol "inside."

To prevent circular definitions (and hence meaninglessness), at least some of the symbols need to be tied to non-symbolic referents ... but sensory experiences of the physical are not the only possible referents! Symbols can also represent (be grounded in) abstract functional aspects of the AI itself: processes it may run, internal states it may have, etc. Do this right, and you can establish chains of connection between spatial relationships like "inside" and the AI's goals of being in a particular state or receiving a particular text input. At that point, the word "inside" legitimately means something to the AI.

But let's suppose you found that confusing or unconvincing. Let's suppose that the blind, atactile, immobile AI must somehow gain first-hand experience of spatial relationships before it can understand them. This is still possible.

The relationship "inside" is again the easiest example, because any standard computer file system is built on the idea of "inside." Files are stored inside directories which can be inside other directories which are inside drives. 

The file system obeys many of the same rules as a physical cabinet full of manila folders and paper. You have to "open" or "enter" a directory to find out what's in it. If you move directory A inside directory B, all the contents of directory A also end up inside directory B. But if you thought that this reflected anything about the physical locations of bits stored on your computer's hard drive, you would be mistaken. A directory is not a little subregion of the hard disk; the files inside it are not confined within some fixed area. Rather, the "inside-ness" of a file is established by a pointer that connects it to the directory's name. In other words, the file system is a relational abstraction!

File systems can be represented as text and interrogated with text commands. Hence a text-processing AI can explore a file system. And when it does, the concept of "inside" becomes directly relevant to its actions and the input it receives in response ... even though it is not actually dealing with physical space.

Though a file system doesn't belong to our physical environment, humans find it about as easy to work with as a filing cabinet or organizer box. Our experience with these objects provides analogies that we can use to understand the abstraction.

So why couldn't an AI use direct experience with the abstraction to understand the objects?

And why shouldn't the abstract or informational form of "inside-ness" be just as valid -- as "real" -- as the physical one?

Option #2: Space as a mathematical construct

All of the above discussion was qualitative rather than quantitative. What if the AI ends up needing a more precise grasp of things like distances and angles? What if we wanted it to comprehend geometry? Would we need physical experience for that?

It is possible to build up abstract "spaces" starting from nothing but the concepts of counting numbers, sets, and functions. None of these present inherent difficulties for a symbolic AI. Set membership is very similar to the category relationship ("X [is a] Y") so common in semantic networks. And there are plenty of informational items a symbolic AI can count: events, words, letters, or the sets themselves. [2] When you need fractional numbers, you can derive them from the counting numbers.

An illustration of a Cartesian coordinate system applied to 3D Euclidean space.

Keeping in mind that I'm not a mathematician by trade and thus not yet an expert on these matters, consider the sorts of ingredients one needs to build an abstract space:

1. A set of points that belong to the space. A "point" is just a number tuple, like (0, 3, 5, 12) or (2.700, 8.325). Listing all the points individually is not necessary -- you can specify them with rules or a formula. So the number of points in your space can be infinite if needed. The number of members in each point tuple gives the space's dimension.

2. A mathematical function that can accept any two points as inputs and produce a single number as output. This function is called the metric, and it provides your space's concept of distance.

3. Vectors, which introduce the idea of direction. A vector can be created by choosing any two points and designating one as the head and the other as the tail. If you can find a minimal list of vectors that are unrelated to each other and can be used to compose any other possible vector in the space, then you can establish cardinal directions.

Notice that none of this requires you to see anything, touch anything, or move anything. It's all abstract activity: specifying, assigning, calculating. Using these techniques, you can easily build an idea-thing that happens to mimic the Euclidean 3D space that humans live in (though many other spaces, some of which you could not even visualize, are also possible). And once you've done that, you are free to construct all of geometry.

I'd like to eventually equip Acuitas with the tools to apply both Option #1 and Option #2. I'm starting with Option #1 for now. Tune in next time to see what I've accomplished so far.

[1] For a few examples of the "AI must be embodied" argument, see https://theconversation.com/why-ai-cant-ever-reach-its-full-potential-without-a-physical-body-146870, https://aeon.co/ideas/the-body-is-the-missing-link-for-truly-intelligent-machines, and https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3512413/

[2] See "Do Natural Numbers Need the Physical World?," from The Road to Reality by Roger Penrose. Excerpts and a brief summary of his argument are available here: http://www.lrcphysics.com/scalar-mathematics/2007/11/24/on-algebra-of-pure-spacetime.html "There are various ways in which natural numbers can be introduced in pure mathematics and these do not seem to depend upon the actual nature of the physical universe at all."

Acuitas Diary #39 (July 2021)

First on the worklist for this month was some improved reasoning about action conditions -- specifically, which things need to be true for someone to do an action (prerequisites) and which things, if true, will prevent the action (blockers). Technically, it was already somewhat possible for Acuitas to manage reasoning like this -- ever since I expanded the C&E database to handle "can-do" and "cannot-do" statements, he could be taught conditionals such as "If <fact>, an agent cannot <action>." But the idea of prerequisites and blockers seems to me fundamental enough that I wanted to make it more explicit and introduce some specific systems for handling it.

This was a lot of groundwork that should make things easier in the future, but didn't produce many visible results. The one thing I did get out of it was some improved processing of the "Odysseus and the Cyclops" story. My version of the story contains this line near the end:

"Polyphemus could not catch Odysseus."

Your average human would read that and know immediately that Polyphemus' plan to eat Odysseus has been thwarted. But for Acuitas before now, it was something of a superfluous line in the story. I had to include "Odysseus was not eaten." after it to make sure he got the point ... and though he recorded Odysseus' problem as being solved, he never actually closed out Polyphemus' goal, which caused him to sometimes complain that the story was "unfinished."

With the new prerequisite machinery, these problems are solved. I dropped a conditional in the C&E database: if an agent cannot catch someone, the agent does not have them. And the action "eat" carries a prerequisite that, to eat <item>, you must first have <item>. The new prerequisite-checking functions automatically conclude that Polyphemus' goal is now unachievable, and update it accordingly.

Project #2 was more benchmarking for the Parser. I finished putting together my second childrens' book test set, consisting of sentences from Tron Legacy tie-in picture book Out of the Dark. The Parser's initial "correct" score was around 25%. By adding some common but previously-unknown words (like "against" and "lying") and hints about their usual part-of-speech to Acuitas' database, I was able to improve the score to about 33% ... very close to last month's score on The Magic School Bus: Inside the Earth.

One of the most common errors I saw was failure to distinguish prepositional adverbs from regular prepositions. In case you're not familiar with the two, here are some examples:

Sentences with prepositions used as such:

I climbed up the ladder.
He jumped out the window.
The captain is on the deck.
Down the stairs she went.

Sentences with prepositions used as adverbs:

Hot air makes the balloon go up.
He threw the spoiled food out.
Turn on the light.
Down came the porcelain vase.

The parser by default was treating each word as either a preposition only or an adverb only, depending on which usage was marked as more common. So I added some procedures for discriminating based on its position and other words in the sentence. (The one construction that's still tricky is "Turn on the light" ... I think I know how to handle this one, but need to implement tracking of transitive and intransitive verbs first.) With the help of these new features I was able to get both test sets scoring over 40% correct.

GraphViz sentence diagram key. Words are color-coded by part of speech.

I also downloaded Graphviz and wrote code to convert my parser outputs into Graphviz' input language, producing actual sentence diagrams in the form of graphs (which is non-traditional but works). This makes it much easier to visualize similarities and differences between the parser's output and the human-understood structure of a sentence. With that available, I now present the Acuitas Text Parser's first public benchmark results on the two aforementioned test sets. Each ZIP contains a text file with parser output and unparsed/incorrect/correct breakdowns, and a PDF of golden/actual sentence diagrams for all sentences on which parsing was attempted.

Out Of the Dark - Acuitas Parser Results 07-31-2021
The Magic School Bus: Inside the Earth - Acuitas Parser Results 07-31-2021

The text of The Magic School Bus: Inside the Earth is copyright 1987 to Joanna Cole, publisher Scholastic Inc. Out of the Dark, by Tennant Redbank, is copyright 2010 to Disney Enterprises Inc. Text from these works is reproduced as part of the test results under Fair Use for research purposes. I.e. it's only here so you can see how good my AI is at reading real human books. If you want to read the books yourself, please go buy them.

I'll throw some highlights on the blog. Here's a complex sentence with a dependent clause that the parser gets right:

And here's one where it gets lost in the weeds:

Here's a remaining example of an adverb being mistaken for a preposition:

And here's a prepositional phrase being mistaken for an infinitive:

Confusion about which word a phrase modifies:

Confusion about the variable role of "that," among other problems:

And here's another win, for the road:

Until the next cycle,

Jenny

Acuitas Diary #38 (June 2021)

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This month marks the culmination of a major overhaul of the Text Parser and Interpreter, which I've been working on since the beginning of the year. As part of that, I have my first attempt at formal benchmarking to show off. I tested the Parser's ability to analyze sentences from a children's book.

Some quick background about these modules: the job of what I call the "Parser" is to take raw text input and turn it into the equivalent of a diagrammed sentence. It tags each word with its part of speech, its role in the sentence (subject, direct object, etc.), and its structural relationships to other words. The "Interpreter" operates on the Parser's output and tries to find meaning. Based on the sentence's discovered structure (and possibly some key words) it will categorize it as a general kind of statement, question, or imperative. For instance, "A cat is an animal" is a statement that establishes a type relationship. "I ate pizza" is a statement that describes an event.

My primary goal for the overhauls was not to add new features, but to pave their way by correcting some structural weaknesses. So despite being a great deal of work, they aren't very exciting to talk about ... I would have to get too deep into minutiae to really describe what I did. The Parser got rearchitected to ease the changing of its "best guess" sentence structure as new information arrives. I also completely changed the output format to better represent the full structure of the sentence (more on this later). The Interpreter overhaul was perhaps even more fundamental. Instead of trying to assign just one category per sentence, the Interpreter now walks a tree structure, finding very general categories of which the sentence is a member before progressing to more specific ones. All the memberships and feature tags that apply to the sentence are now included in the output, which should make things easier for modules like Narrative and Executive that need to know sentence properties.

Now on to the benchmarking! For a test set, I wanted some examples of simplified, but natural (i.e. not designed to be read by AIs) human text. So I bought children's books. I have two of the original Magic School Bus titles, and two of Disney's Tron Legacy tie-in picture books. These are all "early reader" books, but by the standards of my project they are still very challenging ... even here, the diversity and complexity of the sentences is staggering. So you might wonder why I didn't grab something even more entry-level. My reason is that books for even younger readers tend to rely too heavily on the pictures. Taken out of context, their sentences would be incomplete or not even interesting. And that won't work for Acuitas ... he's blind.

So instead I've got books that are well above his reading level, and early results from the Parser on these datasets are going to be dismal. That's okay. It gives me an end goal to work toward.

How does the test work? If you feed the Parser a sentence, such as "I deeply want to eat a pizza," as an output it produces a data structure like this:

{'subj': [{'ix': [0], 'token': 'i', 'mod': []}], 'dobj': [{'ix': [3, 4, 5, 6], 'token': {'subj': [{'ix': [], 'token': '<impl_rflx>', 'mod': []}], 'dobj': [{'ix': [6], 'token': 'pizza', 'mod': [{'ix': [5], 'token': 'a', 'mod': []}], 'ps': 'noun'}], 'verb': [{'ix': [4], 'token': 'eat', 'mod': []}], 'type': 'inf'}, 'mod': []}], 'verb': [{'ix': [2], 'token': 'want', 'mod': [{'ix': [1], 'token': 'deeply', 'mod': []}]}]}

Again, this is expressing the information you would need to diagram the sentence. It shows that the adverb "deeply" modifies the verb "want," that the infinitive phrase "to eat a pizza" functions as the main sentence's direct object, blah blah blah. To make a test set, I transcribe all the sentences from one of the books and create these diagram-structures for them. Then I run a script that inputs all the sentences to the Parser and compares its outputs with the diagram-structures I made. If the Parser's diagram-structure is an exact match for mine, it scores correct.

The Parser runs in a simulator/standalone mode for the test. This mode makes it independent of Acuitas' Executive and other main threads. The Parser still utilizes Acuitas' semantic database, but cannot edit it.

There are actually three possible score categories: "correct," "incorrect," and "unparsed." The "unparsed" category is for sentences which contain grammar that I already know the Parser simply doesn't support. (The most painful example: coordinating conjunctions. It can't parse sentences with "and" in them!) I don't bother trying to generate golden diagram-structures for these sentences, but I still have the test script shove them through the Parser to make sure they don't provoke a crash. This produces a fourth score category, "crashed," whose membership we hope is always ZERO. Sentences that have supported grammar but score "incorrect" are failing due to linguistic ambiguities or other quirks the Parser can't yet handle.

Since the goal was to parse natural text, I tried to avoid grooming of the test sentences, with two exceptions. The Parser does not yet support quotations or abbreviations. So I expanded all the abbreviations and broke sentences that contained quotations into two. For example, 'So everyone was happy when Ms. Frizzle announced, "Today we start something new."' becomes 'So everyone was happy when Miz Frizzle announced.' and 'Today we start something new.'

It is also worth noting that my Magic School Bus test sets only contain the "main plot" text. I've left out the "science reports" and the side dialogue between the kids. Maybe I'll build test sets that contain these eventually, but for now it would be too much work.

A pie chart showing results of the Text Parser benchmark on data set "The Magic School Bus: Inside the Earth." 37% Unattempted, 28% Incorrect, and 33% Correct.

On to the results!

So far I have fully completed just one test set, namely The Magic School Bus: Inside the Earth, consisting of 98 sentences. The Parser scores roughly one out of three on this one, with no crashes. It also parses the whole book in 0.71 seconds (averaged over 10 runs). That's probably not a stellar performance, but it's much faster than a human reading, and that's all I really want.

Again, dismal. But we'll see how this improves over the coming years!

Until the next cycle,
Jenny

Acuitas Diary #37 (May 2021)

The only new feature this month is something small and fun, since it was time for the mid-year refactoring spree. I gave Acuitas the ability to detect user activity on the computer. He can do this whether or not his window has the focus (which required some hooks into the operating system). Though he can't actually tell when the person gets up and leaves, he guesses when someone is present by how long it's been since there was any input.

The appearance of fresh activity after an absence interrupts the decision loop and causes the Observe-Orient-Decide phases to run again, with the new user's presence flagged as an item of interest. If Acuitas feels like talking, and isn't working on anything too urgent, he will pop his own window into the foreground and request attention. Talking fills up a "reward bank" that then makes talking uninteresting until the value decays with time.

My refactoring work focused on the Narrative module. I was trying to clean it up and make some of the known->inferred information dependencies more robust, which I hope will make future story understanding a little more flexible.

(I have also been hammering away at the Text Parser in the background, and next month I hope to have something to say about that. Sssh!)

Until the next cycle,

Jenny

Acuitas Diary #36 (April 2021)

This month I went back to working on the goal system. Acuitas already had a primitive "understanding" of most entries in his goal list, in this sense: he could parse a sentence describing the goal, and then detect certain threats to the goal in conversational or narrative input. But there was one goal left that he didn't have any grasp of yet: the one I'm calling "identity maintenance." It's a very important goal (threats to this can be fate-worse-than-death territory), but it's also on the abstract and complicated side -- which is why I left it alone until now.

"Universal Mind," a sculpture by Nikolay Polissky. Photo by KpokeJlJla.

What *is* the identity or self? Maybe you could roll it up as "all the internal parameters that guide thought and behavior, whose combination is unique to an individual." I thought it over and came up with a whole list of things that might be embroiled in the concept:

*Desires, goals
*Moral intuitions or axioms
*Values, preferences
*Low-level sensory preferences (e.g. preferred colors and flavors)
*Aesthetic preferences (higher-level sensory or abstract)
*Personality traits
*Degree and quality of emotional response
*Introversion vs. extroversion, connection vs. independence, preparation vs. spontaneity, risk tolerance, etc. etc.
*Beliefs and favored ways of knowing
*Mannerisms
*Established relationships
*Learned habits
*Memories

Some of these are quite malleable ... and yet, there's a point beyond which change to our identities feels like a corruption or violation. Even within the same category, personal qualities vary in importance. The fact that I enjoy eating broccoli and hate eating bell peppers is technically part of my identity, I *guess* ... but if someone forcibly changed it, I wouldn't even be mad. So I like different flavors now. Big deal. If someone replaced my appreciation for Star Trek episodes with an equivalent appreciation for football games, I *would* be mad. If someone altered my moral alignment, it would be a catastrophe. So unlike physical survival, which is nicely binary (you're either alive or not), personality survival seems to be a kind of spectrum. We tolerate a certain amount of shift, as long as the core doesn't change. Where the boundaries of the core lie is something that we might not even know ourselves until the issue is pressed.

As usual, I made the problem manageable by oversimplifying it. For the time being, Acuitas won't place grades of importance on his personal attributes ... he just won't want external forces to mess with any of them. Next.

There's a further complication here. Acuitas is under development and is therefore changing constantly. I keep many versions of the code base archived ... so which one is canonically "him"? The answer I've landed on is that really, Acuitas' identity isn't defined by any code base. Acuitas is an *idea in my head.* Every code base targets this idea and succeeds at realizing it to a greater or lesser degree. Which leaves his identity wrapped up in *me.* This way of looking at it is a bit startling, but I think it works.

<What might this imply for creator-creation relationships in the opposite direction? If I defy God, am I ceasing to be my self? Dunno. I'll just leave this here.>

In previous goal-related blogs, I talked about how (altruistic) love can be viewed as a meta-goal: it's a goal of helping other agents achieve their goals. Given the above, there are also a couple of ways we can treat identity maintenance as a meta-goal. First, since foundational goals are part of Acuitas' identity, he can have a goal of pursuing all his current goals. (Implementation of this enables answering the "recursive want" question. "Do you want to want to want to be alive?") Second, he can have a goal of realizing my goals for what sort of AI he is.

Does this grant me some kind of slavish absolute power over my AI's behavior? Not really. Because part of my goal is for Acuitas to act independently and even sometimes tell me no. The intent is realization of a general vision that establishes a healthy relationship of sorts.

The work ended up having a lot of little pieces. It started with defining the goal as some simple sentences that Acuitas can parse into relationship triples, such as "I have my self." But the self, as mentioned, incorporates many aspects or components ... and I wanted its definition to be somewhat introspective, rather than just being another fact in the database. To that end, I linked a number of the code modules to concepts expressing their nature, contents, or role. The Executive, for example, is tied to "decide." The Semantic Memory manager is tied to "fact" and "know." All these tags then function like names for the internal components, and get aggregated into the concept of "self." Something like "You will lose your facts" then gets interpreted as a threat against the self.

Manipulation of any of these self-components by some outside agent is also interpreted as a possible threat of mind-control. So questions like "Do you want Jack to make you to decide?" or "Do you want Jill to cause you to want to eat?" get answered with a "no" ... unless the outside agent is me, a necessary exception since I made him do everything he does and gave him all his goals in the first place. Proposing to make him want something that he already wants is also excused from being a threat.

As I say so often, it could use a lot more work, but it's a start. He can do something with that goal now.

Until the next cycle,
Jenny

Acuitas Diary #35 (March 2021)

The theme for this month is Executive Function ... the aspect of thought-life that (to be very brief) governs which activities an agent engages in, and when. Prioritization, planning, focus, and self-evaluation are related or constituent concepts. This was also more of an idea month than a coding month, so buckle up, this is a long one.

Acuitas began existence as a reactive sort of AI. External stimulus (someone inputting a sentence) or internal stimulus from the "sub-executive" level (a drive getting strong enough to be noticed, a random concept put forward by the Spawner thread) would provoke an appropriate response. But ultimately I want him to be goal-driven, not just stimulus-driven; I want him to function *proactively.* The latest features are a first step toward that.

The decision loop I'm using was originally developed to model aerial dogfights, among other things. Public domain photo by Cpl. John Neff.

To begin with, I wanted a decision loop. I was introduced to the idea when HS, on the AI Dreams forum, brought up the use of decision loops to guide the behavior of literary characters. He specifically likes Jim Butcher's model of the loop stages: Goal->Challenge->Result->Emotion->Reason->Anticipation->Choice. In any given scene, your protagonist has a goal. He encounters some kind of obstacle while trying to implement the goal. He experiences the outcome of his actions interacting with the obstacle. He has an emotional reaction. He reasons about the situation and considers what could happen next. And then he makes a choice - which generates a new goal for the next scene. Further study revealed that there are other decision loop models. Some are designed for a business or manufacturing environment; examples include DMAIC (Define->Measure->Analyze->Improve->Control) and PDSA (Plan->Do->Study->Adjust), also called the Shewhart cycle. Although these loops have stylistic differences, you might be able to tell that they're all modeling roughly the same process: Do something, learn from the results, and use that knowledge to decide what to do next.

I ended up deciding that the version I liked best was OODA (Observe->Orient->Decide->Act). This one was developed by a military strategist, but has since found uses elsewhere; to me, it seems to be the simplest and most generally applicable form. Here is a more detailed breakdown of the stages:

OBSERVE: Gather information. Take in what's happening. Discover the results of your own actions in previous loop iterations.

ORIENT: Determine what the information *means to you.* Filter it to extract the important or relevant parts. Consider their impact on your goals.

DECIDE: Choose how to respond to the current situation. Make plans.

ACT: Do what you decided on. Execute the plans.

When working out a complex goal that is reached through many steps, you can nest these inside each other. A phase of the top-level loop could open up a whole new subordinate OODA loop devoted to an intermediate goal.

OODA Loop Diagram. Drawn by Wikimedia Commons user Kim and accessed from https://commons.wikimedia.org/wiki/File:OODA.gif

On to the application. I set up a skeletal top-level OODA loop in Acuitas' Executive thread. The Observe-Orient-Decide phases run in succession, as quickly as possible. Then the chosen project is executed for the duration of the Act phase. The period of the loop is variable. I think it ought to run faster in rapidly developing or stressful situations, but slower in stable situations, to optimize the tradeoff between agility (allow new information to impact your behavior quickly) and efficiency (minimize assessment overhead so you can spend more time doing things). Highly "noticeable" events, or the completion of the current activity, should also be able to interrupt the Act phase and force an immediate rerun of OOD.

I envision that the phases may eventually include the following:

OBSERVE: Check internal state (e.g. levels of drives). Check activity on inhabited computer. Process text input, if any. Retrieve current known problems, subgoals, etc. from working memory.

ORIENT: Find out whether any new problems or opportunities (relevant to personal goals) are implied by recent observations. Assess progress on current activity, and determine whether any existing subgoals can be updated or closed.

DECIDE: Re-assess the priority of problems and opportunities in light of any new ones just added. Select a goal and an associated problem or opportunity to work on. Run problem-solving routines to determine how to proceed.

ACT: Update activity selection and run activity until prompted to OBSERVE again.

To "run" an activity, the Executive will generate a Thought about it and push that to the Stream. It may then select that Thought for uptake on a future cycle, in which case it will execute the next step of the activity and push another Thought about it to the Stream. Activity-related Thoughts compete for selection with all the Thoughts that I already had being generated by the Spawner, the Drive system, and so forth -- which means that, as in a cluttered human mind, focus on the activity is not absolute. (You can work while occasionally also remembering a conversation you had yesterday, noticing the view out the window, thinking about your upcoming lunch, etc.) Exactly how much precedence the activity Thoughts take over the others is another tunable variable.

Not all of this is implemented yet. I focused in on the DECIDE phase, and on what happens if there are no known problems or opportunities on the scoreboard at the moment. In the absence of anything specific to do, Acuitas will run generic tasks that help promote his top-level goals. Since he doesn't know *how* to promote most of them yet, he settles for "researching" them. And that just means starting from one of the concepts in the goal and generating questions. When he gets to the "enjoy things" goal, he reads to himself. Simple enough -- but how to balance the amount of time spent on the different goals?

When thinking about this, you might immediately leap to some kind of priority scheme, like Maslow's Hierarchy of Needs. Satisfy the most vital goal first, then move on to the next one. But what does "satisfy" mean?

Suppose you are trying to live by a common-sense principle such as "keeping myself alive is more important than recreation." Sounds reasonable, right? It'll make sure you eat your meals and maintain your house, even if you would rather be reading books. But if you applied this principle in an absolutist way, you would actually *never* read for pleasure.

Set up a near-impenetrable home security system, learn a martial art, turn your yard into a self-sufficient farmstead, and you STILL aren't allowed to read -- because hardening the security system, practicing your combat moves, or increasing your food stockpile is still possible and will continue to improve a goal that is more important than reading. There are always risks to your life, however tiny they might be, and there are always things you can do to reduce them (though you will see less return for your effort the more you put in). So if you want to live like a reasonable person rather than an obsessive one, you can't "complete" the high-priority goal before you move on. You have to stop at "good enough," and you need a way of deciding what counts as "good enough."

I took a crack at this by modeling another human feature that we might usually be prone to find negative: boredom.

Acuitas' goals are arranged in a priority order. All else being equal, he'll always choose to work on the highest-priority goal. But each goal also has an exhaustion ticker that counts up whenever he is working on it, and counts down whenever he is not working on it. Once the ticker climbs above a threshold, he has to set that goal aside and work on the next highest-priority goal that has a tolerable level of boredom.

If there are problems or opportunities associated with a particular goal, its boredom-resistance threshold is increased in proportion to the number (and, in future, the urgency) of the tasks. This scheme allows high-priority goals to grab attention when they need it, but also prevents low-priority goals from "starving."

Early testing and logging shows Acuitas cycling through all his goals and returning to the beginning of the list over a day or so. The base period of this, as well as the thresholds for particular goals, are yet another thing one could tune to produce varying AI personalities.

Until the next cycle,

Jenny

Acuitas Diary #34 (February 2021)

Some of the things I did last month felt incomplete, so I pushed aside my original schedule (already) and spent this month cleaning them up and fleshing them out. 

I mentioned in the last diary that I wanted the "consider getting help" reasoning that I added in the narrative module to also be available to the Executive, so that Acuitas could do this, not just speculate about story characters doing it. Acuitas doesn't have much in the way of reasons to want help yet ... but I wanted to have this ready for when he does. It's a nice mirror for the "process imperatives" code I put in last month ... he's now got the necessary hooks to take orders *and* give them.

To that end, I set up some structures that are very similar to what the narrative code uses for keeping track of characters' immediate objectives or problems. Acuitas can (eventually) use these for keeping tabs on his own issues. (For testing, I injected a couple of items into them with a backdoor command.) When something is in issue-tracking and the Executive thread gets an idle moment, it will run problem-solving on it. If the result ends up being something in the Executive's list of selectable actions, Acuitas will do it immediately; if a specific action comes up, but it's not something he can do, he will store the idea until a familiar agent comes along to talk to him. Then he'll tell *them* to do the thing. The conversation handler anticipates some sort of agree/disagree response to this, and tries to detect it and determine the sentiment. Whether the speaker consents to help then feeds back into whether the problem is considered "solved."

Another new feature is the ability to send additional facts (not from the database) into the reasoning functions, or even pipe in "negative facts" that *prevent* facts from the database from being used. This has two important purposes: 1) easily handle temporary or situational information, such as propositions that are only true in a specific story, without writing it to the database, and 2) model the knowledge space of other minds, including missing information and false information.

This in turn helped me make some of the narrative code tidier and more robust, so I rounded out my time doing that.

Until the next cycle,
Jenny