A quick video tour of the General Staff Black Powder Wargaming System. Not sure why, but the video does not capture ‘pop-up’ windows (that show unit strengths, positions, etc.).
Last week I posted an appeal to the wargaming community and backers of The General Staff Wargaming System that I needed more maps and Orders of Battle (OOBs). The General Staff Wargaming System is designed to handle any conflict in the Black Powder era and the machine learning AI needs as much input as possible.
The five layers that make up a General Staff map.
The General Staff Army Editor makes it pretty easy to create four of the five layers of a map file (see above). The problem is the beautiful background image that the user sees on screen (the computer AI couldn’t care less about the visual map). I’ve been able to locate a lot of great maps; especially from the American Civil War and the US Library of Congress but we still need more.
Waterloo from Glenn Drover (Forbidden Games) and Jared Blando. Click to enlarge.
A couple of days ago I received an email from the famous game designer, Glenn Drover (Forbidden Games), who offered us the use of three maps that he had researched and were drawn by artist Jared Blando. Here’s a link to Forbidden Games’ site. Please check out their fantastic board games!
Ligny from Glenn Drover (Forbidden Games) and Jared Blando. Click to enlarge.
The three battlefield maps were Waterloo, Ligny and Quatre Bras.
Quatre Bras from Glenn Drover (Forbidden Games) and Jared Blando. Click to enlarge.
What is especially amazing is how well these three maps fit the style that I’ve wanted to create for General Staff.
In addition to these three great maps, which we will definitely be using for the battles of Waterloo, Ligny and Quatre Bras, I’ve received emails from a number of other wargamers who have offered to research OOBs; especially some in another language.
I am completely blown away (I know it’s a cliche, but I don’t have any other words) by the kindness and generosity shown me by the wargaming community. Thank you very much!
The AI routines for calculating battle lines and range of influence have been ported over from the original C++ code to C#:
Antietam displaying Range of Influence and Battle Lines. Click to enlarge.
Battle Lines, Commanders & Computers
What’s Wrong With This Picture?
Wargame AI Continued: Range of Influence
That’s the good news. The bad news is that I’m also installing the Machine Learning AI that was the basis of my doctoral research and it needs more battles to learn from. A lot more. Currently there are 15 armies (click here) and 5 maps (click here). Ideally I would like about 50 armies and 30 maps used to create 30+ battle scenarios.
Are you a cartographer or a researcher? If you are, and you’re interested, I could use your help if you would like to volunteer. All the maps and armies were created using the tools that you, as a backer, have already been provided: The General Staff Army Editor and The General Staff Map Editor. A little bit of PhotoShop or another paint program was used to clean up the old maps and a free program, Inkscape, was used to create the paths for roads and rivers. The most difficult task is the research. Finding Order of Battle Tables (OOBs) are pretty easy but General Staff requires knowing the actual troop strength of every unit. Sometimes, that is very hard to find. For the maps, adding elevation is usually the most difficult bit, but there are a number of built-in tools to make this easier.
If you’re interested in helping add to the data files please contact me directly: [email protected].
How a map of the battle of Antietam looks to us humans. Screen shot from the General Staff Map Editor. Click to enlarge.
How the computer sees the same map (terrain and elevation). This is actually a screen shot from the Map Editor with the ‘terrain’ and ‘elevation’ layers turned on. Click to enlarge.
Computer vision is the term that we use to describe the process by which a computer ‘sees'1)When describing various AI processes I often use words like ‘see,’ ‘understand,’ and ‘know’ but this should not be taken literally. The last thing I want to do is to get in to a philosophic discussion on computers being sentient. the world in which it operates. Many companies are spending vast sums of money developing driverless or self-driving cars. However, these AI controlled cars have had a number of accidents including four that have resulted in human fatalities.2)https://en.wikipedia.org/wiki/List_of_self-driving_car_fatalities The problem with these systems is not in the AI – anybody who has played a game with simulated traffic (LA Noir, Grand Theft Auto, etc.) knows that. Instead, the problem is with the ‘computer vision’; the system that describes the ‘world view’ in which the AI operates. In one fatality, for example, the computer vision failed to distinguish a white semi tractor trailer from the sky.3)https://www.theguardian.com/technology/2016/jun/30/tesla-autopilot-death-self-driving-car-elon-musk Consequently, the AI did not ‘know’ there was a semi directly in front of it.
In my doctoral research I created a system by which a program could ‘read’ and ‘understand’ a battlefield map4)TIGER: An Unsupervised Machine Learning Tactical Inference Generator http://www.riverviewai.com/download/SidranThesis.html. This is the system that we use in General Staff.
The two images, above, show the difference in how a human commander and a computer ‘see’ the same battlefield. In the top image the woods, the hills and the roads are all obvious to us humans.
The bottom, or ‘computer vision’ image, is a bit of a cheat because this is how the computer information is visually displayed to the human designer in the General Staff Map Editor. The bottom image is created from four map layers (any of which can be displayed or turned off):
The four layers that make up a General Staff map.
The background image layer in a General Staff map is the beautiful artwork shown in the top image. The place names and Victory Points layer are also displayed in the top image. The terrain and elevation layers are described below:
The next three images are actual visual representations of the contents of memory where these terrain values are stored (this is built in to the General Staff Map Editor as a debugging tool):
Screen shot from the Map Editor showing just terrain labeled as ‘water’. Click to enlarge
Screen shot from the General Staff Map Editor showing the terrain labeled as ‘woods’. Click to enlarge.
Screen shot from the General Staff Map Editor showing the terrain labeled ‘road’. Click to enlarge.
A heightmap for Antietam. This is a visual representation of elevation in meters (darker = lower, lighter = higher). Click to enlarge.
To computers, an image is a two-dimensional array; like a giant tic-tac-toe or chess board. Every square (or cell) in that board contains a value called the RGB (Red, Green, Blue5)Except in France where it’s RVB for Rouge, Vert, Bleu ) value. Colors are described by their RGB value (white, for example, is 255,255,255). If you find this interesting, here is a link to an interactive RGB chart. General Staff uses a similar system except instead of the RGB system each cell contains a value that represents various terrain types (road, forest, swamp, etc.) and another, identical, two-dimensional array, contains values that represent the elevation in meters. To make matters just a little bit more confusing, computer arrays are actually not two-dimensional (or three-dimensional or n-dimensional) but rather a contiguous block of memory addresses. So, the terrain and elevation arrays in General Staff which appear to be two-dimensional arrays of 1155 x 805 cells are actually just 929,775 bytes long hunks of contiguous memory. To put things in perspective, just those two arrays consume more RAM than was available for everything in the original computer systems (Apple //e, Apple IIGS, Atari ST, MS DOS, Macintosh and Amiga) that I originally wrote UMS for.
So, not surprisingly, a computer stores its map of the world in which it operates as a series of numbers 6)Yes, at the lowest level the numbers are just 1s and 0s but we’ll cover that before the midterm exams. that represent terrain and elevation. But, how does a human commander read a map? I posed this question to Ben Davis, a neuroscientist and wargamer, and he suggested looking at a couple of studies. In one article7)https://www.citylab.com/design/2014/11/how-to-make-a-better-map-according-to-science/382898/, Amy Lobben, head of the Department of Geography at the University of Oregon, said, “…some people process spatial information egocentrically, meaning they understand their environment as it relates to them from a given perspective. Others navigate more allocentrically, meaning they look at how other objects in the environment relate to each other, regardless of their perspective. These preferences are linked to different regions of the brain.” Another8)https://www.researchgate.net/publication/251187268_USING_fMRI_IN_CARTOGRAPHIC_RESEARCH reports the results of fMRI scans while, “subjects perform[ed] navigational map tasks on a computer and again while they were being scanned in a magnetic resonance imaging machine.” to identify specific, “involvement or non-involvement of the brain area.. doing the task.”
So, how computers and human commanders read and process maps is quite different. But, at the end of the day, computers are just manipulating numbers following a series of algorithms. I have written extensively about the algorithms that I have developed including:
These papers, and others, can be freely downloaded from my web site here.
As always, please feel free to contact me directly if you have any questions or comments.
References
| ↑1 | When describing various AI processes I often use words like ‘see,’ ‘understand,’ and ‘know’ but this should not be taken literally. The last thing I want to do is to get in to a philosophic discussion on computers being sentient. |
|---|---|
| ↑2 | https://en.wikipedia.org/wiki/List_of_self-driving_car_fatalities |
| ↑3 | https://www.theguardian.com/technology/2016/jun/30/tesla-autopilot-death-self-driving-car-elon-musk |
| ↑4 | TIGER: An Unsupervised Machine Learning Tactical Inference Generator http://www.riverviewai.com/download/SidranThesis.html |
| ↑5 | Except in France where it’s RVB for Rouge, Vert, Bleu |
| ↑6 | Yes, at the lowest level the numbers are just 1s and 0s but we’ll cover that before the midterm exams. |
| ↑7 | https://www.citylab.com/design/2014/11/how-to-make-a-better-map-according-to-science/382898/ |
| ↑8 | https://www.researchgate.net/publication/251187268_USING_fMRI_IN_CARTOGRAPHIC_RESEARCH |
I‘m used to learning a lot when researching a battle but nothing prepared me for the ‘what ifs’ of Little Bighorn. My doctorate is in computer science but I have been an American Civil War buff since I was about five years old. I’m very familiar with brevet Major General George Armstrong Custer’s achievements during the Appomattox campaign where he commanded a division that smashed Pickett’s right flank at Five Forks. I knew that after the war Custer returned to his previous rank in the U. S. Army of Lt. Colonel, that he fell under a cloud with U. S. Grant, was stripped of his command, and had to beg for it back from President Grant, himself, at the White House.
Brevet Major General George Armstrong Custer taken May 1865. Credit: Civil war photographs, 1861-1865, Library of Congress, Prints and Photographs Division. Click to enlarge.
And, of course, I knew of the debacle at the Little Bighorn.
After I wrote UMS, the first computer wargame construction system, users began to send me Little Bighorn scenarios that included Gatling guns. I assumed that these were science fiction ‘what if’ scenarios. such as a story I read back in the ’60s about what if Civil War units had automatic weapons from the future. But, recently, while reading Stephen Ambrose’s Crazy Horse and Custer I learned that General Alfred Terry, Custer’s superior and the commander of the expedition, had indeed offered Custer not just three Gatling Guns (manned by troops from the 20th Infantry 1)The Guns Custer Left Behind; Historynet
https://www.historynet.com/guns-custer-left-behind-burden.htm ) but four extra troops from the 2nd U. S. Cavalry. Custer turned down Terry’s offer of reinforcements and more firepower with these infamous words:
“The Seventh can handle anything it meets.” – Custer to Terry
Photo taken by F. Jay Haynes of one of the Gatling guns that were available to the 7th Cavalry. Click to enlarge.
Screen capture of the Order of Battle of the 7th US Cavalry with the addition of 3 Gatling guns and 4 companies of the 2nd US Cavalry. Click to enlarge.
As for the battle of Little Bighorn, itself, I didn’t know much more than the broad outline that Custer and his command were killed to the last man by an overwhelming number of Native American warriors (this, of course, wasn’t correct as members of Reno’s and Benteen’s columns survived). Custer, himself, was the text book image of hubris and became the butt of late night comedians and humorous pop songs. But the reality turned out to be much more complex and nuanced.
Custer had a reputation of being dashing, headstrong, and gallant; the iconic description of a cavalry commander. The traditional narrative of the disastrous battle of Little Bighorn is that Custer impulsively attacked a vastly superior enemy force; possibly propelled by a belief that Native American warriors were no match for organized cavalry armed with 45-70 trap door carbines. Indeed, Napoleon’s maxim was that, “twenty or more European soldiers armed with the best weapons could take on fifty or even a hundred natives, because of European discipline, training and fire control.” 2)“Crazy Horse and Custer” p. 425 Stephen Ambrose To make matters worse, Custer had pushed the 7th mercilessly and by the time they arrived at the battlefield both men and horses were exhausted.
Custer’s plan of attack is also widely condemned as overly optimistic. He split his command of 616 officers and enlisted men of the 7th cavalry into three battalions. If the four companies of 2nd Cavalry had come along, Custer’s force would be 30% larger.3)Ibid The main force led by himself would be the right flanking column, Reno would have the left flanking attack column and Benteen and the pack train would be in the middle. Custer also drastically underestimated the Native American force at about 1,500.
In theory, Custer’s plan of attack wasn’t that bad:
Custer might have, indeed, had a great victory that would have propelled him to the US Presidency (as he had hoped). But none of these suppositions were correct.
Screen shot of the General Staff Scenario Editor where the battle of Little Bighorn scenario is being set up. Not the Order of Battle of the 7th Cavalry (with attached units of the 2nd Cavalry and Gatling guns) on the left. Units are positioned by clicking and dragging them from the Order of Battle Table on the left onto the map. Click to enlarge.
So, the question remains: what value for Leadership would you give to Custer?
Screen shot of the General Staff Army Editor showing the slider that sets the Leadership value for a commander. What value would you give Custer? Click to enlarge
By the way, there will be three separate Little Bighorn scenarios for the General Staff Wargaming System: historically accurate Order of Battle for the 7th Cavalry, the 7th Cavalry plus four companies of the 2nd US Cavalry and 7th Cavalry plus four companies of the 2nd US Cavalry and 3 Gatling guns.
References
| ↑1 | The Guns Custer Left Behind; Historynet https://www.historynet.com/guns-custer-left-behind-burden.htm |
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| ↑2 | “Crazy Horse and Custer” p. 425 Stephen Ambrose |
| ↑3 | Ibid |