Thank You Wargaming Community!

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!

AI Routines Added But We Need More Testing Data (Maps & Armies)

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.

I’ve written a number of blog posts about these AI routines which you may find interesting:

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: Ezra@RiverviewAI.com.

Wargame AI Continued: Range of Influence

In two previous blogs I wrote about how Artificial Intelligence (AI) for wargames perceive battle lines and terrain and elevation. Today the topic is how computer AI has changed ‘Range of Influence’  (ROI) or ‘Zone of Control’ (ZOC) analysis. Range of Influence  and Zone of Control are terms that can be used interchangeably. Basically, what they mean is, “how far can this unit project its power.”

One of the first appearances of range as a wargame variable was in Livermore’s 1882 American Kriegsspiel: A Game for Practicing the Art of War Upon a Topographical Map (superb article on American Kriegsspiel here).  Note that incorporated into the ‘range ruler’ (below) is also a linear ‘effectiveness scale’.

Detail of Plate IV, “The Firing Board,” from the American Kriegsspil showing a ruler for artillery range printed on the top. Note the accuracy declines (apparently linearly) proportional to the distance. Click to enlarge.

The introduction of hexagon wargames (first at RAND and then later by Roberts at Avalon Hill; see here) created the now familiar 6 hexagon ‘ring’ for a Zone of Control:

Zone of Control explained in the Avalon Hill Waterloo (1962) manual. Author’s Collection.

I seem to remember an Avalon Hill game where artillery had a 2 hex range; but I may well be mistaken.

Ever since the first computer wargames that I wrote back in the ’80s I have earnestly tried to make the simulations as accurate as possible by including every reasonable variable. With the General Staff Wargaming System we’ve added two new variables to ROI: 3D Line of Sight and an Accuracy curve.

Order of Battle for Antietam showing Hamilton’s battery being edited. Screen shot from the General Staff Army Editor. Click to enlarge.

In the above image we are editing a Confederate battery in Longstreet’s corps. Every unit can have a unique unit range and accuracy. You can select an accuracy curve from the drop-down menu or you can create a custom accuracy curve by clicking on the pencil (Edit) icon.

Window for editing the artillery accuracy curve. There are 100 points and you can set each one individually. This also supports a digitizing pen and drawing tablet. Screen shot from General Staff Army Editor. Click to enlarge.

In the above screen shot from the General Staff Wargaming System Army Editor the accuracy curve for this particular battery is being edited. There are 100 points that can be edited. As you move across the curve the accuracy at the range is displayed in the upper right hand corner. Note: every unit in the General Staff Wargaming System can have a unique accuracy curve as well as range and every other variable.

Screen shot showing the Range of Influence fields for a scenario from the 1882 American Kriegsspiel book. Click to enlarge.

In the above screen shot from the General Staff Sand Box (which is used to test AI and combat) we see the ROI for a rear guard scenario from the original American Kriegsspiel 1882. Notice that the southern-most Red Horse Artillery unit has a mostly unobstructed field of vision and you can clearly see how accuracy diminishes as range increases. Also, notice how the ROI for the one Blue Horse Artillery unit is restricted by the woods which obstructs its line of sight.

Screen shot of Antietam (dawn) showing Red and Blue ROI and battle lines. Click to enlarge.

In the above screen shot we see the situation at Antietam at dawn. Blue and Red units are rushing on to the field and establishing battle lines. Again, notice how terrain and elevation effects ROI. In the above screen shot Blue artillery’s ROI is restricted by the North Woods.

The above ROI maps (screen shots) were created by the General Staff Sand Box program to visually ‘debug’ the ROI (confirm that it’s working properly). We probably won’t include this feature in the actual General Staff Wargame unless users would like to see it added.

This is a topic that is very near and dear to my heart. Please feel free to contact me directly if you have any questions or comments.

What’s Wrong With This Picture?

Computer AI representation of battle lines for Antietam, dawn September 17, 1862. The AI is locating the Schwerpunkt or place to attack. Click to enlarge.

I was looking at this screen shot I posted as an illustration in my blog post, Battle Lines, Commanders & Computers (link) and something didn’t look right. Specifically, it was the Flank markers for Stewart’s cavalry on the Confederate left flank. And, the more I looked, the more I noticed other problems: Stewart’s cavalry was captioned as being in two groups (Group 6 and Group 7). There was an extra Flank marker with the Confederate reinforcements entering the field at the bottom of the map, the Flank markers for Union Group 2 were clearly wrong, too.

This began a two week long bug hunt that took me places I didn’t expect to revisit. To make a long story short – and how often have you heard that phrase but this is actually one of those few occasions when it’s true – I had to go back and look at my original computer code that I wrote in grad school and it turns out that there was a ‘worst case’ bug that just happened to pop up with the Antietam scenario.

This is what the battle lines and Flank markers should really look like (note: the map layer is turned on here and you can’t see the elevation layer like the top screen shot):

Correct battle lines and Flank markers for the battle of Antietam. Screen shot from the General Staff Sand Box AI test program. Click to enlarge.

I also discovered a logic flaw – a mental bug, if you will – in my definition of Flank units. Previously, I defined them as as the ‘maximally separated units of a MST (battle line) group’. That seems correct but if you think about it there are rare circumstances when this is not correct (think of the horseshoe lines at Gettysburg for example). I changed the definition of Flank units to: the ‘maximally separated units of a MST group with only one neighbor (i.e. they are at the end of a battle line and, therefore, have only one neighbor).

I thought I would finish this blog post with something that is truly unique: the very first computer bug:

On September 9, 1947 Grace Murray Hopper records ‘the first computer bug’ in the Harvard Mark II computer’s log book. Apparently, the moth was caught in a relay switch. And, yes, this is where the term comes from. Click to enlarge.

 

Battle Lines, Commanders & Computers

When we look at maps of battles even the novice armchair general can quickly trace the battle lines of the armies. Recognizing battle lines is one of the most important skills a commander – or a wargaming Artificial Intelligence (AI) – can possess. Without this ability how will you identify the flanking units? And if you can’t identify the units at the end of a line, how will you implement a flanking attack around them? Equally important is the ability to identify weak points in a battle line.

The algorithm for detecting battle lines and flank units is one of the ‘building block’ algorithms of my TIGER / MATE tactical AI and first appeared in my paper, Implementing the Five Canonical Offensive Maneuvers in a CGI Environment1)http://riverviewai.com/papers/ImplementingManeuvers.pdf. I will discuss how the algorithm works at the end of this blog. For now, just accept that it finds lines and flanks.

Let’s look at some examples of the General Staff AI ‘parsing’ unit positions. First, the battle of Antietam, situation at dawn (by the way, Antietam is one of the free scenarios included with the General Staff Wargaming System):

The battle of Antietam, dawn, September 17, 1863. Screen shot from the General Staff Sand Box program. Click to enlarge.

This is how a human sees the tactical situation: units on a topographical map. But, the computer AI sees it quite differently. In the next image, below, the battle lines and elevation are displayed as the AI sees the battle (note: the AI also ‘sees’ the terrain but, for clarity, that is not being shown in this screen capture):

The battle of Antietam, September 17, 1862 dawn, with computer AI battle lines and elevation displayed. Note: the identification of flank units. Both red and blue forces are assembling on the field. Click to enlarge.

What is immediately obvious is that Red (Confederate) forces are hastily constructing a battle line while Blue (Union) forces are beginning to pour onto the battlefield to attack.  Let us now ask the question: what is the weakest point of the Red battle line? Where should Blue attack? This point is sometimes called the Schwerpunkt. German for point of maximum effort2)See also, “Clausewitz’s Schwerpunkt Mistranslated from German, Misunderstood in English” Military Review, 2007 https://www.armyupress.army.mil/Portals/7/military-review/Archives/English/MilitaryReview_20070228_art014.pdf. Where should Blue concentrate its forces?

Computer AI representation of battle lines for Antietam, dawn September 17, 1862. The AI is locating the Schwerpunkt or place to attack. Click to enlarge.

Now that the weakest points of Red’s battle line have been identified, Blue (assuming Blue is being controlled by the AI) can exploit it by attacking the gaps in Red’s battle line. The Blue AI can order either a Penetration or Infiltration Maneuver to exploit these gaps (the following images are from my paper, “Implementing the Five Canonical Offensive Maneuvers in a CGF Environment.” Note, in the TIGER / MATE screen shots below Range of Influence (ROI) is also visible:

From the paper, “Implementing the Five Canonical Offensive Maneuvers.”

Both of these maneuvers are possible because the AI has identified weak points in the OPFOR (Opponent Forces) battle lines. Equally important when discussing battle lines are the location of the flanks. The next two images use the original American Kriegsspiel (1882) map which is also included in the General Staff Wargaming System:

The original American Kriegsspiel map (1882) restored and now used in the General Staff Wargaming System. Screen shot from the General Staff Sand Box AI test program. Click to enlarge.

In this screen capture from the General Staff Wargaming System Sand Box AI test program battle lines are displayed by the AI. Note the flank units and especially the unanchored (or open) Blue flank. Click to enlarge.

Identifying flank units is vitally important in the Turning Maneuver and the Envelopment Maneuver:

Knowing the location of flank units is also important for classifying tactical positions (this will be the subject of an upcoming blog).

So, how does this algorithm work?

I’ve never been a fan of graph theory; or heavy mathematical lifting in general. One of the required classes in grad school was Design and Analysis of Algorithms and it got into graph theory quite a bit. The whole time I was thinking, “I’m never going to use any of this stuff, but I have to get at least a B+ to graduate,” so I took a lot of notes and studied hard. Later, when I was looking for a framework to understand tactics and to write a tactical AI it became obvious that graph theory was at least part of the solution. Maps are routinely divided into a grid, unit locations can be points (or vertices) at the intersections of these lines. Battle lines can be edges that connect the vertices. I need to publicly thank my doctoral advisor, Dr. Alberto Segre, for first suggesting that battle lines could be described using something called a Minimum Spanning Tree3)https://en.wikipedia.org/wiki/Minimum_spanning_tree (MST). An MST is the minimum possible distances (edge weights to be precise) to connect all the vertices in a tree (or a group, as I call them in the above screen shots).

I ended up implementing Kruskal’s algorithm4)https://en.wikipedia.org/wiki/Kruskal’s_algorithm for identifying battle lines. It is what is called a ‘greedy algorithm’ and it runs in O(E log V) which means it gets slower as we add more units but we’re never dealing with gigantic numbers of individual units in an Order of Battle (probably around 50 is the maximum) so it takes less than a second to calculate and display battle lines for both Red and Blue.

Lastly, and I guess this is my contribution to military graph theory, I realized that the flank units of any battle line must be the maximally separated units. That is to say, that the two units in a battle line that are the farthest apart are the flank units.

Obviously, this is a subject that I find fascinating so please feel free to contact me directly if you have any questions or comments.

 

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