Armed with Science Saturday: Like Robot, Like Human

I think it goes without saying that humans are flawed.

<pause for dramatic effect>

I know.  It’s a shocker.

So when we think of the things humans are capable of doing, there’s always a margin of error, isn’t there?  There’s always some bell curve that factors in because we know that we’ve got our short comings.  We’re not perfect.

Which is weird, since we are constantly asking the machines we build to be.  Especially the ones that awe and fascinate us the most.

Yeah, I’m talking about robots.

Up until now, the concept of a perfectly constructed robot was just that; a concept.  

Cyberman Robot courtesy photo copyright BBC

Such a KIND face… (Cyberman, Copyright BBC)

Our movies and video games tend to depict robots as these inhuman goofy types, or seething, wrathful, intrinsically flawed things that either take on far too many human traits (Cylons) or not NEARLY enough (Cybermen).  In any case, our creative little minds tend to presume that robots are going to lean to the extremes.

But that’s just fun fiction.


What if I told you that there was a process being developed that allowed scientists to implant a very human like thinking process into a very non-human robot brain?  Would you panic?  Because if so, I’d stop reading now.  And maybe seek out some calming tea.  Or professional help, depending on the severity.

Because it’s really happening, folks, and it’s going to change the way we think about Artificial Intelligence in a number of ways.

It’s called the Adaptive Character of Thought-Rational architecture, or ACT-R, courtesy of the Naval Research Laboratory (NRL).  So what does it do?

According to the recently released White Paper, a cognitive architecture is a set of computational modules that, working together, strive to produce human-level intelligence.

I’m just going to let that sink in for a minute there.

“But wait,” I hear you saying, “didn’t you start off this blog talking about how humans are flawed?”

Yes.  I did.  That’s what makes this all the more exciting.  They’re not trying to create the perfect, godlike deathbots of SciFi lore and repute.  Rather, they’re creating a synthetic version of people, so to speak.

No, wait!  Don’t panic.  Let me explain.

Thinking like a person means thinking imperfectly.  We remember things strangely.  Our memories degrade over time.  We let our emotions guide us.  Humans are driven by patterns and associations and experience over facts and deductive reasoning.

As it turns out, that’s exactly what these scientists are trying to capture.

The Soar architecture uses a modest set of building blocks to achieve human intelligence, including different types of memories (procedural, semantic, episodic) and different types of learning (reinforcement, chunking, semantic learning, episodic learning).

Learning is the key point there.  Not retaining information in a database, but actually learning.

These scientists are using ACT-R and ACT-R/E (Adaptive Character of Thought-Rational/Embodied (ACT-R/E) architecture) to build better, more comprehensive models of human cognition and leverage these models to improve the robot’s ability to interact with humans.  So why is this architecture so unique?

Because it’s designed to model human mentality by placing an emphasis on the limitations of human cognition.

These robots are trying to “get us” down at our level.  Well, that’s an interesting idea.  But before you act insulted, consider this: the argument is that robots who understand people are, ultimately, better teammates and more natural computational agents.  I guess they have to be able to think like us in order to be efficient and productive for us.

Not to get too philosophical on you, but what does it really mean to think like a human?

It all comes down to how we remember things.

For example, say you meet someone for the first time at a party.  They tell you their name, and if you aren’t completely disinterested in them you will likely try to remember it.  When a person remembers something, they do so by using a series of patterns.  Your mind will try to tie the new information (the name) to defining factors (face, voice, clothing, etc).

When you see this person again, you try to use certain trigger cues.  You see the hair, or the face or smell the perfume and your brain tries to tie the new information (the name) to those things.  Priming from contextual clues could provide the boost you need in memory activation, and the earlier rehearsal of associating those things together would likely be enough for you to remember the name of this person.  Ideally.

Then again, we’re not perfect.  You might end up calling them by the wrong name a few more times before it sticks.

Anyway, the ACT-R works insomuch the same concept.  When the robot (or model, as they call it) is introduced to new information like this, it uses a similar structured pattern to remember things.  So this information is not just being dumped into a memory bank as raw data to be regurgitated on command.

Rather, it becomes a piece of information that’s associated with other things.

Octavia the robot and human escort (Naval Research Lab courtesy photo)

Octavia and a human escort (photo courtesy of the Naval Research Lab)

At a high level, ACT-R is a hybrid symbolic/subsymbolic production-based system.  That means everything is connected to everything else in order to create a memory.

How do they do this?  By using a system called Specialized Egocentrically Coordinated Spaces, or SECS.  This enables human-like, cognitively plausible spatial reasoning.

This architecture is more than just retaining information as it comes in.  As we all know, our bodies tend to function as a whole; that is, memory retention is often a result of the sum of our parts.

The ACT-R/E model is designed to act as a consumer of visual information provided by external visual systems.  Senses – like sight, sound, environment – all play a part in how we absorb and interpret information around us.  This architecture wants the robot to get that full memory-making experience as well.

One of the recent threads in cognitive science has been embodied, or grounded, cognition.

The focus has been on showing that the body has a major role in shaping the mind.  When the motor and visual modules participate fully in the spreading of contextual activation, it is possible for a robot to learn which objects are best grasped with which motor commands.

Basically, these robots have the capacity to “understand” all their working parts, and those parts can work together to form information.  So if it talks like a human and thinks like a human, that doesn’t mean it is a human.

Speaking of us living, breathing specimens…

There are some things about these robots that deviate from the standard human procedure.  Things like fatigue, emotional instability, unpredictability, sleepiness, weepiness, derpiness, they’re all intrinsically human aspects.  Aspects that robots have no real reason to contend with, though that hasn’t stopped some SciFi writers from exploring the possibility of having depressed, mopey robots.

The results of that concept I’d say are…mixed.  

Anyway, that doesn’t mean these robots cannot be taught how to approach humans by understanding what makes them so crazy *ahem* interesting.  The high-level goal behind this is to give robots a deep understanding of how people think at the process level in order to make them better teammates.

They’re doing this by equipping robots with the functionality to understand human behavior – like right vs wrong – and use that information to act accordingly.  Skeptical?  Well, so was I.  I mean, how does a robot know the difference between right and wrong when philosophers have been making a living debating that very idea for centuries?

Turns out, in this case it’s more of a holistic approach to situation and crisis.  Noticing how humans tend to make mistakes in predictable ways, for example, can set a standard, or watching how their eyes move when they retrieve memories.

By developing robots that further understand how people think – including errors – they can leverage these models as tools for robots to use as they encounter humans in the world.

For example, the scientists put a robot to work on a serious project: playing hide-and-seek.  Given the fact that the ATC-R is designed to learn and understand, the robot was able to grasp the concept of the game fairly quickly.  The model was in fact able to mimic the outward behavior of the person, perfectly matching the hiding behavior.

That sounds small, but it’s really a big, big deal.  The robot was also able to play a credible game of hide and seek against a human.  Think about that.

Don’t believe me?  See for yourself:

Just when you thought you’ve seen it all, eh?  It’s like watching the early stages of robot evolution take place.

This architecture is designed with a Theory of the Mind (ToM) concept.  That is, the ability to understand beliefs, desires, and intentions of others.  So why give the robots this empathetic concept?  ToM is used to improve the robot’s ability to interact with people.  This is pertinent because research in psychology has shown that without ToM, people can be severely impaired in their abilities to interact naturally with others.  Apparently, the same goes for robots.

Simply put, robots are a little freaky when they’re disregarding of these things.

So why all of this, you wonder?  Why give robots the ability to think like humans, consider their intentions, and learn to play well with us?  Well, why else do you train?  For the mission.  These robots are being designed to be good teammates to people.  To help them.  To perform missions.  Just like us, they are given a task – like fighting fires for example – and they need to be the best equipped to complete that task to the best of their ability.

In this case, learning how to help humans means having a better robo-understanding of them.  The best part?  This is only the beginning.  The road to good, embodied cognitive models has been and continues to be long, but the scientists at NRL say it’s going to be well-worth the effort.

I guess you know how the old saying goes…

To err is human.  To learn how to err is robot. 

Jessica L. Tozer is a blogger for DoDLive and Armed With Science.  She is an Army veteran and an avid science fiction fan, both of which contribute to her enthusiasm for technology in the military.

Special thanks to the Naval Research Laboratory for providing the information and general awesomeness factor needed for this story.


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