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21st Century AI

A blog about achieving meaningful Artificial Intelligence

Posts Tagged ‘TIGER’

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.

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.

Shameless book plug

Thursday, July 15th, 2010

TIGER: An unsupervised machine learning tactical inference generator. (Click on picture to embiggen.)

My doctoral thesis has been republished by Lambert Academic Publishing and is now available through Amazon here. It has a very classy cover of Romans doing conquering stuff.

It also contains all the algorithms and pseudo-code for creating your own kick-ass tactical inference generator which is able to analyze complex tactical situations and return an optimal solution to your problem. Every commanding general needs a copy! Order yours today!