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21st Century AI

A blog about achieving meaningful Artificial Intelligence

Archive for the ‘Computer Intelligence’ Category

Dinosaurs, tanks and line of sight algorithms

Sunday, July 14th, 2013

A screen capture of MATE (Machine Analysis of Tactical Environments). Note the blue armor unit (labeled '0') just left of the center of the screen. Click to enlarge.

A screen capture of MATE (Machine Analysis of Tactical Environments). Note the blue armor unit (labeled ’0′) just left of the center of the screen. Click to enlarge.

MATE screen capture showing the calculated line of sight of Armor Unit 0 (click to enlarge).

MATE screen capture showing the calculated line of sight of Armor Unit 0 (click to enlarge).

 

Adjusting the height of an object in MATE to calculate its line of sight.

Adjusting the height of an object in MATE to calculate its line of sight.

(This article is cross-posted in my “Dinosaur Island” blog).

My doctoral research involved ‘computational military reasoning’¹, a phrase that I coined that means, “computers making tactical combat decisions.” My research was supported in part by DARPA (Defense Advanced Research Projects Agency, the people that really invented the internet). I was able to demonstrate in my MATE (Machine Analysis of Tactical Environments) program that a computer could make what computer scientist John Laird, called, “Human-Level” decisions and could do so very rapidly (see here for more information about MATE). Indeed, my friend, retired Lieutenant Colonel Mike Robel, once said that a computer Course of Action (COA) program like MATE was vitally important because “it’s hard to make your best decision when someone is trying to kill you.”

When I first began working on the design of Dinosaur Island I joked with some colleagues that the AI (Artificial Intelligence) wouldn’t be too difficult as I would just use my MATE program and cross out ‘tank’ and insert ‘triceratops’. There is some truth to that, as we will see today.

One of the first AI routines that I’m adding to Dinosaur Island enables the dinosaurs to find food and water. How does a dinosaur do this? Well, there are actually three ways that a dinosaur finds food and water:

  1. The dinosaur looks around for food or water.
  2. The dinosaur smells food or water.
  3. The dinosaur remembers where it last found food or water.

Right now we’re interested in the first option: looking around (this will also come in handy for spotting predators, too). How does a computer dinosaur ‘look around’?

Luckily, I’ve already solved this problem some years ago in grad school with TIGER (the predecessor of MATE). The solution is a 3D Bresenham line algorithm (I’m not going to write out the algorithm because you can see it here). The Bresenham line algorithm was invented by Jack Bresenham in 1962 when he was working at IBM and it was originally used for controlling a pen plotter (a type of printer that would pick up colored pens with a mechanical arm and draw on rolls of paper). However, if we have a 3D landscape (and we do in Dinosaur Island), we can take Bresenham’s two dimensional algorithm and extrapolate it into three dimensional space to determine if the terrain blocks an object’s view in a particular direction. If we do this in all 360 degrees and plot what can be seen (and what is obscured) we’ll have an image like the second screen shot, above.

Now, in MATE, I had to add a little dialog box so the user could input the height of the observer (the third screen shot showing the height of a tank). But in Dinosaur Island I realized that not only the height of every dinosaur can be calculated (just like the length and weight) but that taller dinosaurs, like the giant sauropods, might have a great advantage because they’ll be able to see farther. This will help them find food and water and see predators before the predators can see them.

Next, I’ll work on the ‘smell algorithm’ which will involve wind direction and speed. Luckily, I solved that problem a long time ago with a game/simulation I did in 1989 called, “UMS II: Nations at War.”

SmallRule

1) My doctoral thesis, “TIGER: An Unsupervised Machine Learning Tactical inference Generator,” can be download here. TIGER was an earlier version of MATE.

I’ll be presenting at Defense Gametech 2012

Thursday, January 5th, 2012

Just a brief plug: I will be presenting a lecture entitled, “Artificial Intelligence in Modern Military Games,” at the Defense Gametech 2012 conference in Orlando, Florida, on March 29, 2012. Link to the conference here: Gametech 2012.

During this lecture I will be demonstrating MATE as well as going into great detail describing the underlying algorithms in MATE.

If you’re interested and in the area I would love to meet you.

 

 

MATE (Machine Analysis of Tactical Environments)

Sunday, October 30th, 2011

MATEI’ve been working on this project since about 2003 (you could reasonably argue that I actually started development in 1985 when I began work on UMS: The Universal Military Simulator) and I’m finally in a position to share some of this work with the world at large. TIGER (for Tactical Inference GenERator) was my doctoral research project and it was funded, in part, by a DARPA (Defense Advanced Research Project Agency) ‘seedling-grant’.

After I received my doctorate, DARPA funded my research on computational military reasoning. Since DARPA was already funding a project called TIGR (Tactical Ground Reporting System) my TIGER was renamed MATE.

MATE was created to quickly arrive at an optimal tactical solution for any scenario (battlefield snapshot) that is presented to the program. It also designed to facilitate quickly entering unit data (such as location, strength, morale, etc.) via a point and click graphical user interface. (GUI). There are three main sections to MATE’s decision making process:

  1. Analysis of the terrain and opposing forces (REDFOR and BLUFOR) location on the terrain. This includes the ability to recognize certain important tactical situations such as ‘anchored’ or ‘unanchored flanks’, ‘interior lines of communication’, ‘restricted avenues of attack’, ‘restricted avenues of retreat’ and the slope of attack.
  2. Ability to implement the five canonical offensive maneuvers: turning maneuver, envelopment, infiltration, penetration and frontal assault. This includes the ability to determine flanking goals, objectives and optimal route planning (including avoiding enemy line of sight and enemy fire).
  3. Unsupervised Machine Learning which allows MATE to classify the current tactical situation within the context of previously observed situations (including historically critiqued battles).

I wished to test MATE with an actual tactical situation that occurred recently in either Afghanistan or Iraq. Even though my research was supported by DARPA I did not have access to recent ‘after action’ reports. However, when I saw the HBO documentary, “The Battle for Marjah,” I realized that enough information was presented to test MATE.

The clip, below, from the HBO documentary, shows the tactical situation faced by Bravo Company, 1/6 Marines February 13, 2010:

It took only a few seconds to enter RED and BLUE unit locations to MATE (the map was downloaded from Google Earth):

The Battle for Marjah as shown on MATE (actual screen capture).

Screen capture of MATE showing the Battle for Marjah tactical situation. Click on image to see full-size.

After clicking on the ‘Calculate AI’ icon, the ‘Analyze and Classify Current Situation’ button and the, ‘Generate HTML Output and Launch Browser’ button, MATE’s analysis of the tactical situation was displayed. Total elapsed time was less than 10 seconds (on a Windows XP system, or 5 seconds on a Windows 7 system).

MATE then automatically generated HTML pages of its recommendations including graphically displaying optimal paths for an envelopment maneuver that encircled enemy positions:

MATE output for Envelopment Maneuver COA.

MATE output for Envelopment Maneuver COA. Click on image to see full-size.

MATE automatically produced HTML pages of its analysis and optimal course of action (COA) routes and instructions and launched the default browser on the computer.

To see the actual HTML output of MATE’s analysis of, “The Battle for Marjah” situation click here (opens in a new window).

For more information about MATE contact sidran [at] RiverviewAI.com.

Philosophical Cybernetics & the Kurzweil Singularity

Saturday, June 18th, 2011

Many years ago, when I was an undergrad, I had to take a religion course. This was something that I flat out refused to do and I discovered, as I so often do, a loophole in the system ripe for exploitation: certain philosophy courses counted as a ‘religion credit’ and one of these, Philosophical Cybernetics, was being offered that semester.

This class had two titles; the philosophical one and Introduction to Artificial Intelligence. Same class, same professor, same credits, but depending on how you signed up for it, it would count as the required religion class.

I haven’t thought about the phrase, “Philosophical Cybernetics” in a long time. Because the professor (who shall remain nameless for reasons soon to be obvious) used the terms ‘philosophical cybernetics’ and ‘artificial intelligence’ interchangeably I always assumed that they were tautological equivalents. It’s a good thing that I checked before writing today’s blog because, like a lot of things, this professor was wrong about this, too.

Today I learned (from http://www.pangaro.com/published/cyber-macmillan.html):

Artificial Intelligence and cybernetics: Aren’t they the same thing? Or, isn’t one about computers and the other about robots? The answer to these questions is emphatically, No.

Researchers in Artificial Intelligence (AI) use computer technology to build intelligent machines; they consider implementation (that is, working examples) as the most important result. Practitioners of cybernetics use models of organizations, feedback, goals, and conversation to understand the capacity and limits of any system (technological, biological, or social); they consider powerful descriptions as the most important result.

The professor that taught Philosophical Cybernetics had a doctorate in philosophy and he freely admitted on the first day of class that he didn’t know anything about AI and that, “we were all going to learn this together.” I actually learned quite about AI that semester; though obviously little of it was in that class.

My Spiritual Journey

My Spiritual Journey by H. H. The XIV Dalai Lama

Anyway, the whole point of titling today’s blog as, “Philosophical Cybernetics” was going to be this clever word play on the philosophy of AI. This has come about because I’ve been reading, “My Spiritual Journey” by the Dalai Lama and I’ve been thinking about what I do for a living and how it relates to an altruistic, compassionate and interconnected world. Short answer: it doesn’t.

However, it did get me thinking about the power of AI and – hold on to your hats because this is what you came here to read – how AI will eventually kick a human’s ass in every conceivable game and subject.  This event – the day when computers are ‘smarter’ than humans – is commonly referred to as the Kurzweil Singularity and the Wiki link about it is: http://en.wikipedia.org/wiki/Technological_singularity .

Let’s backtrack for a second about Ray Kurzweil. He pretty much invented OCR (Optical Character Recognition) and, as I understand it, made a ton of money selling it to IBM. Then he invented the Kurzweil digital keyboard. This was the first digital keyboard I ever encountered and I can’t tell you how wonderful it was and how, eventually, digital keyboards gave me a new lease on playing piano.

Here are some links to me playing digital keyboards (I actually play an Oberheim, not a Kurzweil, but Kurzweil created most of the technology):

Nicky (an homage to Nicky Hopkins)

Boom, boom, boom! Live with Mojo Rising

Looking Dangerous (with Jerry Brewer)

Old 65

A boogie (with Jason Stuart)

When I first heard Ray Kurzweil talk about ‘The Singularity’ I remember him saying that it was going to happen during his lifetime. Well, Ray is six years older than me and my response was, “that’s not likely unless he lives to be about 115.”

NEWSFLASH: Well, this is embarrassing, Ray Kurzweil was just on the Bill Maher Show (AKA Real Time with Bill Maher) last night and I vowed that this blog would never be topical or up to date). Anyway Kurzweil did clarify a couple of important issues:

  1. Kurzweil was going to live practically forever (I can’t remember if it was him or Maher that used the phrase ‘immortal’) and he takes 150 pills a day to achieve this goal. So, I’m thinking, “well, this explains how he expects the Singularity to happen during his lifetime; he’s going to live for thousands of years!” And then he drops this:
  2. The Singularity will occur by 2029!

I think Ray Kurzweil is a brilliant guy but I am dubious that the Singularity will occur by 2029 much less during my lifetime. I would like to live as long as Ray Kurzweil thinks he’s going to live but the actuarial tables aren’t taking bets on me after another 20 years or so.

Alan Turing, the most brilliant mind of the 21st century.

Alan Turing, in my opinion, had the most brilliant mind of the 20th century. He is one of my heroes. He also wrote the following in 1950:

“I believe that in about fifty years’ time it will be possible, to programme computers, with a storage capacity of about [10^9 bytes], to make them play the imitation game so well that an average interrogator will not have more than 70 per cent chance of making the right identification after five minutes of questioning. The original question, “Can machines think?” I believe to be too meaningless to deserve discussion. Nevertheless I believe that at the end of the century the use of words and general educated opinion will have altered so much that one will be able to speak of machines thinking without expecting to be contradicted.” (Emphasis added)

- Turing, A.M. (1950).

Computing machinery and intelligence. Mind, 59, 433-460.

Okay, so my hero, Alan Turing got the whole ‘computers will be intelligent’ within 50 years completely wrong. I think that Kurzweil’s prediction of it occurring within 18 years to be just as unlikely.

But I do think that AI will eventually be everything that Turing and Kurzweil imagined. When do I think this will happen? I dunno, let’s say another 50 years, maybe longer; either way it will be after I have shuffled off this mortal coil. I like to think that my current research will play a part in this happening.  It is my opinion that the Kurzweil Singularity, a computer passing Turing’s Test, or a computer displaying, “human level intelligence” will not occur without unsupervised machine learning. Machine learning is absolutely crucial for AI to achieve the level of results that we want. ‘Supervised’ machine learning is good for some simple parlor tricks, like suggesting songs or movies, but it doesn’t actually increase a computer’s ‘wisdom’.

The last subject that I wanted to briefly touch upon was why, ultimately, AI will kick a human’s ass in any game: it’s because AI has no compassion, doubt, hesitation and it doesn’t make mistakes. I suppose you could program AI to have compassion, doubt, hesitation, and make mistakes, but it would certainly be more trouble than it’s worth.

So, someday computer AI will be the greatest baseball manager of all time. I look forward to this day. I hope I live long enough to see that day. Because that day the Chicago Cubs will finally win the World Series.