Tag Archives: AI

A Human-Level Intelligence at Antietam

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“Map of the battlefield of Antietam,” by William H. Willcox. Published in Philadelphia. Lithograph of P. S. Duval and Son, 1862. From the US Library of Congress.

There are many reasons that I am intensely interested in this particular American Civil War battle fought on less than twenty square miles wedged in between the Potomac River and Antietam Creek. The battle of Antietam (September 17, 1862) exhibits a number of significant battlefield attributes which I use as base line cases to test algorithms used in creating a human-level tactical artificial intelligence 1)MATE: Machine Analysis of Tactical Environments. Specifically, Antietam definitively demonstrates 2)see http://riverviewai.com/download/SidranThesis.pdf the following attributes:

  • Choke Points
  • Anchored Flank
  • Interior lines of communication
  • Exterior lines of communication
  • Restricted Avenue of Retreat
  • Restricted Avenues of Attack

For example, in a blind survey of Subject Matter Experts (SMEs), it was overwhelmingly agreed that the RED (Confederate) left flank at the battle of Antietam exhibited the attribute of ‘anchored flank‘ 3)a flank that is attached to or protected by terrain, a body of water, or defended fortifications. and other positions, such as RED’s (Russian and Austrian) left flank at Austerlitz SMEs overwhelmingly agreed that the flanks do not exhibit the attribute of ‘anchored’ and are, therefore, unanchored. Once we have an example of an anchored flank and another example of an unanchored flank we can begin testing algorithms to detect the attribute of an anchored flank.

In my doctoral thesis (above) I demonstrated the algorithm 4)see pages 45-6 http://riverviewai.com/download/SidranThesis.pdf  for detecting the attribute of anchored and unanchored flanks. I have made a number of substantial improvements to the original algorithm since then which are now incorporated into the current MATE.

We have recently posted analyses of other battles that did not exhibit the attribute of an anchored flank (Ligny and 1st Bull Run, or Manassas). MATE correctly recognized that Ligny and Manassas do not have these attributes.

The tactical situation for Blue at Antietam is quite different than Blue’s positions at Ligny and Manassas (is it not curious how often Blue is the attacker in wargames?). The key difference, of course, is the lack of an open flank to attack. MATE will always attack an open flank if it can. Without an obvious objective, like an exposed flank, MATE will next look at opportunities to fulfill victory conditions. For Antietam, as Blue, MATE sees the situation like this:

MATE Analysis of Antietam from the Blue position. Screen shot. Click to enlarge.

Below is a list of statements, predicates and conclusions generated by MATE during the above analysis with my commentary added on the right:

MATE analysis of Antietam. Click to enlarge.

I recently added a set of algorithms that recognize the composition of battle groups and exploits any possible advantages. For example:

Screen shot showing MATE analysis of BLUE position at Ligny. NB: Battle Group #3 (Pajol’s and Exelmans’ cavalry divisions) are, “snatching the pawn,” at Balatre. Click to enlarge.

At Ligny, above, MATE has recognized that Battle Group #3 and Battle Group #4 are uniquely cavalry (and horse artillery) battle groups and are to be used differently. While Battle Group #4 is held in reserve, Battle Group #3 will snatch Balatre. Though it is valued at only 10 Victory Points, MATE realized that no enemy force could oppose it. That said, I can still hear the voice of my old chess tutor, Mr. Selz,  warning me against ‘pawn snatching’; that is grabbing a minor point that can lead to defeat because the position was not thoroughly analyzed. MATE, however, is correct in this analysis and can safely seize the objective.

While, at Antietam, Battle Group #1 (all the cavalry of the Army of the Potomac commanded by Brigadier General Alfred Pleasonton) is frozen ‘in reserve’. This is not a case where MATE can snatch a pawn. MATE looked at the situation and said, ‘nope’, there are no unattended Victory Points to snatch and there is not an open flank to exploit so, the default setting is ‘in reserve’.

This leads to the interesting conundrum: what exactly was the Union cavalry at Antietam doing? Honestly, I had never really thought of it before. Now, when I look into the question I find, Was McClellan’s Cavalry Deployment at Antietam Doctrinally Sound? This monograph argues that McClellan massing his cavalry in the center for a great coup de grâce exploitation of a breakthrough across the Middle Bridge was acceptable within the framework of Jomini’s theories as taught at West Point before the Civil War. But, then it is countered with this:

In Landscape Turned Red, Stephen Sears has this to say: Shortly before noon, McClellan had ventured to push several batteries across the Middle Bridge, supported by Pleasonton’s cavalry and a force of regulars from George Syke’s Fifth Corps. He was nervous about the move-it was taken against the advice of Porter and Sykes-and he cautioned Pleasonton not to risk the batteries unduly. As an afterthought, he asked, “Can you do any good by a cavalry charge?” Pleasonton wisely ignored the suggestion. – Sears, Stephen, Landscape Turned Red: The Battle of Antietam, New York: Ticknor and Fields, 1983. page 271. (as cited in above)

Would a great massed cavalry attack across Middle Bridge have been suicide? Or brilliant? For the first time in memory I took the 1st edition of McClellan’s Own Story off the shelf and discovered… nothing. McClellan died suddenly of heart failure just as he was writing about Antietam and his memoirs end abruptly with very little insight into his side of the story. But, using cavalry to support horse artillery – rather than the other way around – seemed a bit odd.

I do not know of any other great cavalry charge in the American Civil War than Sheridan at Five Forks (above). Is this what McClellan envisioned at Antietam? Would it have worked? Could American Civil War regiments have formed square against a massed cavalry charge?

Moving on, let’s drill down to the Course of Action (COA) for Blue Battle Group #3 (Burnside’s IX Corps) at Antietam:

MATE tactical analysis for Blue Battle Group #3 at Antietam (Burnside’s IX Corps). Screen shot. Click to enlarge.

The author walking across Burnside’s Bridge in 1966 (age 12).

The above is MATE’s output that concludes with the COA for Burnside’s IX Corps. Perhaps, the greatest mystery of the battle of Antietam is what took Burnside so long to take this bridge (now forever linked with his name)? It is true that there were numerous, futile and bloody attempts to cross it. Note that MATE, above, recognizes the bridge as a critical Choke Point. When MATE sees a Choke Point that is within the enemy’s control (see statement #8, above, “Chokepoint (bridge) is under Red’s Range of Influence ROI = 5958″ and 5,998 is very high ROI value) it brings up artillery (see statements #9, #10, #11, #12, above). All the artillery in the IX Corps is to be within 630 meters of the objective. Why 630 meters? Because at that distance it is guaranteed a 50% accuracy rate. This rate, by the way, was set in the Army Editor:

The accuracy curve for the 1st Division, IX Corps artillery as set in the Army Editor. Screen capture. Click to enlarge.

So, MATE says 5)I apologize but I find it easier to describe how the AI works using such phrases as ‘thinks’, ‘says’, and ‘decides’. It’s not worth straining over. Trust me, “My objective is a Choke Point. I’m not sending my units into a meat grinder. I’m sending artillery to a point where they are guaranteed a 50% accuracy per volley and have a clear 3D Line of Sight to the target. This is how I’m going to project as much force as I can at the objective.” War is about force projection. MATE knows this. Is this a better plan than what Burnside actually did? Yeah, it is a lot better with a far greater probability of success. I’ve stood on that plain just east of Burnside’s Bridge and thought that nine batteries of 12 lb. Napoleons aimed at the crest of that hill just beyond the bridge would provide a substantial amount of force projection and covering fire. About half an hour of force projection followed up with an infantry assault would probably take the bridge.

I once described good AI as: Don’t do anything stupid, fast. MATE is doing that. I think MATE is on the way to beat most human opponents because humans do stupid things, fast.

We’ll see. Should be an interesting journey.

References

References
↑1 MATE: Machine Analysis of Tactical Environments
↑2 see http://riverviewai.com/download/SidranThesis.pdf
↑3 a flank that is attached to or protected by terrain, a body of water, or defended fortifications.
↑4 see pages 45-6 http://riverviewai.com/download/SidranThesis.pdf
↑5 I apologize but I find it easier to describe how the AI works using such phrases as ‘thinks’, ‘says’, and ‘decides’. It’s not worth straining over. Trust me

A Human-Level Tactical Artificial Intelligence at Ligny

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Map 159 from the superb, “West Point Atlas of the Napoleonic Wars,” (Esposito & Elting, 1999, Stackpole). Scanned from the author’s collection. Click to enlarge.

The seeds of Napoleon’s defeat at Waterloo were sown two days earlier at his victory near Ligny. Napoleon needed to surround and completely remove the Prussian army as a viable force on the battlefield. Instead, they escaped to Wavre in the north and resurfaced at the worst possible time on Napoleon’s right flank two days later at Waterloo.

MATE1)Machine Analysis of Tactical Environments 2.0 is now capable of analyzing the battle of Ligny, June 16, 1815 from both the Blue (L’Armée du Nord on the offensive) and Red (Prussian on the defensive) positions. (MATE is the AI behind General Staff: Black Powder. For more information about MATE see these links).

The Ligny map was donated by Glenn Frank Drover. Jared Blando is the artist. Ed Kuhrt did the elevation, roads and mud terrain overlays. The unit positions are from the West Point Atlas of the Napoleonic Wars (above) and from David Chandler’s Waterloo: The Hundred Days. If any one has a better source for unit positions, please contact me directly.

Screen capture of the Ligny scenario in General Staff. Elevation and slope layers enhanced.

Below is MATE’s analysis from the Blue (L’Armée du Nord) perspective. MATE correctly identifies the key positions and realities of the battlefield:

  • Red is on the defensive
  • Red has an exposed flank
  • There are two key choke points on the route to Red’s exposed flank

MATE then creates an appropriate Course of Action (COA) for Blue:

  • Battle Group #1 (The French III Corps) is assigned the flanking maneuver.
  • Battle Group #0 (The Imperial Guard) is assigned the objective of St. Amand with the support of Battle Group #4 (IV Corps cavalry and reserve artillery).
  • Battle Group #2 (IV Corps) demonstrates against Ligny.
  • Battle Group #3 (The Cavalry Reserve) seizes Balatre and a crucial bridge located there.

MATE’s analysis of Blue’s position at Ligny. Screen capture. Click to enlarge.

A log of MATE’s thought processes, with my commentary, follows:

Text output of MATE’s analysis of Blue’s position at Ligny. Click to enlarge.

MATE also analyzed Ligny from the Prussian (Red) perspective:

Screen capture of MATE’s analysis of Ligny for Red (Prussian army). MATE recognizes the two choke points on the route of the enemy’s flank attack and dispatches cavalry units to cover these critical areas. Click to enlarge.

MATE, analyzing the Prussian (Red) position correctly recognizes that it is on the defensive, it has an exposed flank, there are two crucial choke points on the route that Blue will take on its flanking maneuver and dispatches two cavalry units to cover the bridges. A log of MATE’s thought processes, with my commentary, follows:

Text output of MATE’s analysis of Red’s position at Ligny. Click to enlarge.

Critique of MATE’s analysis:

As the author of MATE any critique I have of its performance should be taken with a grain of salt (also, see this video). If I was back in academia I would put together twenty or thirty Subject Matter Experts (SMEs), set up a double blind web site, get all the SME’s solutions to the problem, and compare their solutions to MATE’s. If they match to a statistical significance it proves the ‘human-level’ part. But, I’m not in academia anymore and you’ll just have to take my word for it. That said, MATE did what I expected it to do.

It first sussed out if it was on offense or defense and what it had to do to win.

Then, as Blue, MATE discovered a back door to Red’s position and ordered a classic enveloping maneuver. MATE assigned Blue Battle Group #1 the task of implementing the flanking maneuver. Blue Battle Groups #0, #4 and #2 are the fixing force. See my paper, Implementing the Five Canonical Offensive Maneuvers in a CGF Environment (free download here) for details and algorithms.  The Blue Cavalry Reserve is given the COA to seize the town of Balatre. This, in my opinion, is a pretty good tactical plan.

When MATE finds itself on defense, as it does as Red at Ligny, one of the first things it does is ask itself, “how would I attack myself?” So, of course it finds the back door right away. Then it compiles a list of available units that are not actively engaged in holding crucial parts of a defensive line, selects the optimal (fastest) units and assigns them orders to defend the crucial choke points. This was a better plan than Field Marshal Gebhard Leberecht von Blücher had. So, again, I’m going to argue that MATE is operating at a ‘human-level’.

As always, please feel free to write me with the questions or comments. MATE is going to take a look at Antietam next.

References

References
↑1 Machine Analysis of Tactical Environments

General Staff will Ship in 2022

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A few of Grant’s unsuccessful attempts to take Vicksburg. Map from West Point Atlas of American Wars. Library of Congress. Click to enlarge.

Grant took Vicksburg on his sixth attempt. He first tried a direct movement, east of the Mississippi, against the city but was forced to turn back after Confederate General Van Dorn’s raid destroyed his supply base. Sherman next attempted a direct assault and failed at Champion’s Bluff. Grant then tried to cut Vicksburg off from the Mississippi by digging a great canal across the neck of a giant oxbow. Unusually low water levels in the river that year caused another failure. Grant also tried more canal digging and river clearing at Lake Providence and the Yazoo Pass without luck.

Col. Theodore Lyman, a staff officer of the Army of the Potomac, wrote of Grant, “He habitually wears an expression as if he had determined to drive his head through a brick wall, and was about to do it. I have much confidence in him.”

Grant finally succeeded with his brilliant amphibious landing south of Vicksburg followed by a series of ‘must win’ battles that concluded with besieging the city on May 22, 1863. Confederate General Pemberton surrendered Vicksburg on July 4, 1863.

The General Staff: Black Powder Wargaming System is comprised of four separate programs: the Army Editor, Map Editor, Scenario Editor and the actual Game Engine, itself. The first three have been done for some time (it’s what I use to produce scenarios for testing the AI). They were written in C# using Microsoft’s Windows Presentation Foundation (WPF) which is often used for creating desktop business applications. This decision made sense because the Army, Map and Scenario Editors function not unlike a suite of business applications that create and share data with a common look and feel. However, while WPF was a good development decision for the Editor programs it turned out not to be a great tool for creating the Game Engine.

A flow chart of the General Staff: Black Powder Wargaming System. Files created in the Army Editor and the Map Editor are combined in the Scenario Editor to create a scenario or simulation. Click to enlarge.

What the Game Engine does is it takes data created in the three Editor programs, displays them in a visually exciting way, enables the user to make input (orders), and simulates those orders (as well as the AI’s orders) and the resulting combat to the highest degree of accuracy possible. That sounds like a lot, but it really isn’t. And the heavy lifting that the Game Engine is being asked to do is all visual: making everything look good on the screen.

Recently, I’ve been looking at Grand Tactician: Civil War which is an immersive voyage into 19th century American typography, cartography, tactics and grand strategy. I eagerly look forward to having the time to do more than gaze upon it in amazement 1)Full disclosure, the developers were kind enough to give me a review copy of Grand Tactician and, damn, it looks fantastic.. I reached out to Ilja on their development team and he convinced me that we should complete the General Staff Game Engine in Unity (which is what they used for Grand Tactician). What they accomplished visually with Grand Tactician was precisely the look and feel that I wanted for General Staff 2)I’m talking about the typography and the UI, not thousands of tiny 3D soldiers on the battlefield; which look really great, but, General Staff is not that kind of a wargame..

Consequently, we are officially announcing that we are abandoning writing the General Staff: Black Powder Game Engine in WPF and, instead, will be creating it using the Unity engine. We believe that this will produce a far superior visual experience 3)Please note: we will not being using Unity to create a 3D simulation with tiny soldiers. I like playing those kind of games, but that’s not what General Staff is..

Because Unity is designed to facilitate the creation of computer games, this move will greatly accelerate the completion of the General Staff Game Engine and will allow us to ship General Staff: Black Powder this year.

I am certainly not U. S. Grant, though I like to think I have some of that bulldog determination. Like Grant before Vicksburg, I’ve had to acknowledge that our first attempt will not succeed and we’re moving on to another plan. I am sorry for the delay in the release of General Staff and am confident that, with Unity, we will soon be reporting some good news.

I am also very excited to announce that my good friend, Damien, will be joining the team to specifically work in Unity completing the General Staff Game Engine. Damien lives in Ireland and has been a devout wargamer since playing Arnhem on the ZX Spectrum. When not enjoying a Guinness or a Jamesons, he’ll be playing tabletop or computer games, whether that means pushing cardboard counters across hex boards, drawing LOS between badly painted miniatures on the tabletop, or day-long sessions in front of a computer – where he particularly enjoys playing against a challenging, non-cheating AI. Damien is a 28 year veteran of the IT industry, having worked on everything from telecoms, healthcare and financial systems, across Mainframes, Linux, Unix and Windows, and co-inventor of two messaging system patents. He has a particular passion for language processing and domain specific languages, originating from his interest in the elegance of recursive descent compiler construction.

References

References
↑1 Full disclosure, the developers were kind enough to give me a review copy of Grand Tactician and, damn, it looks fantastic.
↑2 I’m talking about the typography and the UI, not thousands of tiny 3D soldiers on the battlefield; which look really great, but, General Staff is not that kind of a wargame.
↑3 Please note: we will not being using Unity to create a 3D simulation with tiny soldiers. I like playing those kind of games, but that’s not what General Staff is.

Why Machines May Kill Us In Our Sleep

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An amazing screen capture of the AI’s solution to a problem. It has found a 1 pixel gap between the data and the edge of the screen and is exploiting it to successfully find an ‘open flank’ of Red. Click to enlarge.

Professor Alberto M. Segre was my thesis advisor and one day he said to me, “You know when your AI is really working because it will surprise you.” Today I got to have one of those weird surprises.

The screen shot (above) is a visual representation of what the AI is up to. You won’t get to see this in the actual game. The program that’s running is called the AI Editor which is a bit of a misnomer because you don’t actually edit the AI in it; you mostly just get to observe what it’s doing. There’s a lot of stuff going on in the above image. There are multiple layers visually displaying different types of data (check out the blog – Layers: Why a Military Simulation is Like a Parfait – for more information about these). But, what interests us are the AI layers: Battle Groups, Objectives, and that thin yellow line that snakes from a group of blue units, crossing Antietam Creek at the Middle Bridge and then, amazingly, exploiting a data anomaly to reach its goal: a point far behind enemy lines.

Some background on the situation:

The map of the Antietam Battlefield (screen shot) with terrain and elevation layers displayed. Click to enlarge.

Underlying all the clutter from the first screen capture, top, is the battle of Antietam (above). The map has been rotated 90 degrees to the left so north is now pointing to the left; east is at the top of the screen.

After adding Blue (Union) and Red (Confederate) units to the map in their historical positions at 0600 September 17, 1862 the AI performed a tactical analysis from the perspective of Blue.

The AI ‘strategic’ analysis for Antietam playing Blue (Union).

The above are a list of Predicate Statements all of which the AI knows to be true. Statements preceded by the logical sign ∴ (therefore) are conclusions, or inferences, derived from the predicate statement referenced in the brackets. It is this analysis that determines if the AI will be on the offensive or defensive and what its objectives will be.

Next, the AI performs Range of Influence (ROI) calculations for the entire observable battlefield. I plan on doing a video about this later, but for now the darker the red (in the topmost screen capture) the more – and more powerful – weapons the Red army can bear on that point.  The AI next divides all the units on the map into a forest of minimum spanning trees called Battle Groups. I want to do a video about this, too. However, if you can’t wait, these subjects are covered in my paper, Implementing the Five Canonical Offensive Maneuvers in a CGF Environment (free download).

Again, referring to the top screenshot you can see the AI’s calculations to this point:

  • It has determined it (Blue) will be on the offensive.
  • It has calculated enemy ROI.
  • It has assigned objectives to the first Battle Group.

Flanking Algorithm published in, “Algorithms for Generating Attribute Values for the Classification of Tactical Situations”. Click to enlarge.

Now the AI needs to determine if the enemy has an ‘open or unanchored flank’. In Algorithms for Generating Attribute Values for the Classification of Tactical Situations I published the Algorithm for Flanking Attribute Value Function (right). It basically comes down to this: can the AI trace an unbroken path from the center of the Blue Battle Group to a specific point (called the Retreat Point) far behind enemy lines without crossing into ‘No Go Areas’ (water, swamp) or entering any area controlled by Red’s ROI (literally the red areas in the topmost screen shot).

The reason that I was using Antietam as a test case for anchored / unanchored flanks is because years ago I had analyzed the battle for my doctoral thesis and knew it to be a classic example of anchored flanks; Lee’s left flank rests on the Potomac and his right flank is anchored on the Antietam. Granted, the Confederate flanks were held by Stuart’s cavalry with a little horse artillery support but they were still, by definition, anchored flanks.

Due to an error in the data that made up the Antietam terrain map a 1 pixel (about 3.8 meter wide) strip of ‘no terrain’ was inserted at the far right hand edge of the map (see blow up of screen capture, right; it’s the thin line between the water, represented in red, and the brown edge of the map). This meant there was a ‘land bridge’ across Antietam Creek where none existed in real life. A digital parting of the Red Sea, if you will. But, by the rules of the game the AI perfectly performed its function. There was no error in the AI – again, the AI performed better than I had dared hope – the error was with the data set.

And that’s how fifty years from now I can see a cyber-detective standing over the chalk marks around a body saying, “Yeah, the machine performed perfectly, brilliantly, in fact. But, the error in the data set killed him.”

It’s already happened in real life. For cars with autopilot the data set of the world in which it operates is crucial. However, “against a bright spring sky, the car’s sensors system failed to distinguish a large white 18-wheel truck and trailer crossing the highway, Tesla said. The car attempted to drive full speed under the trailer, “with the bottom of the trailer impacting the windshield of the Model S”, Tesla said.” The driver died. The AI functioned perfectly. But, the error in the data set killed him.

So, I fixed the error in the data set (probably caused by not using the right values in InkScape when I converted the Antietam Water.bmp into paths) and imported it back into the Antietam map using the General Staff Map Editor, saved it out, and ran the AI Editor again and saw this:

The AI did not display a yellow path from the center of the Blue Battle Group to the Red Retreat Point because none existed. Instead, it just wrote the first Predicate Statement in the Tactical Analysis stack: “Red’s flanks are anchored”.

Again, the machine was performing perfectly. And its results were no longer surprising.

Addendum

I recently got to experience this again (though this time it was caused by a different data bug) when I was reviewing the AI’s decisions at the battle of Manassas:

Because the Range of Influence was not calculating the very bottom row the AI found another, perfectly legal, way to reach its goal. Screen shot from the General Staff AI Editor. Click to enlarge.

In this instance, the error in the database was caused by the Range of Influence (basically a map of what red and blue can see and hit) not calculating the very last row. Consequently, the AI was able to legally trace a path from the blue forces in the northeast to their goal at the bottom of the map.

After this bug was corrected the AI performed as expected:

The AI correctly sees going around red’s left flank as the solution to the problem. Screen shot from General Staff AI Engine. Click to enlarge

In the above screen shot the AI has demonstrated the correct solution to the tactical problem facing blue at Manassas on July 20, 1861 (the day before the actual battle). Red’s left flank is unanchored. It’s wide open. Note how the AI identifies the one choke point (Sudley Springs Ford) in the plan.

So, the AI surprised me again. I think it’s looking pretty good. When you play against it, watch your flanks.

Schwerpunkt

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I first encountered the German word Schwerpunkt in Major General F. W. von Mellenthin’s Panzer Battles many years ago. The word has a number of definitions but, for our purposes, we’ll use, “the point of maximum effort;” or the point where we should hit the enemy’s lines with all our strength. For von Mellenthin it was the point where his panzers would smash through the Allied lines in the 1940 Western Front blitzkrieg.

Many years later, when I was working on my doctoral research on tactical AI, I realized that calculating the Schwerpunkt was crucial for any offensive algorithm (it’s also a good thing to know your weak points when planning a defense). On every battlefield there is at least one Schwerpunkt but calculating that point first involves numerous algorithms to analyze the terrain, elevation, unit positions, 3D Line of Sight (3DLOS) and range of influence (ROI) of these units.

TIGER1)Tactical Inference GEneratoR was the program created from my doctoral research that performed battlefield analysis. Later, with a DARPA grant, it was expanded into MATE 2)Machine Analysis of Tactical Environments. One problem, that was discussed in my last blog post, was that after analysis MATE would come to the conclusion that some forces should not attack in specific situations (for example, Lee at Gettysburg). However, for General Staff,  we need an AI that will attack when we want it to.

My solution was to create the General Staff AI Editor (this may be rolled into the General Staff Scenario Editor for convenience) which allows the scenario designer to specify objectives for each army’s battle groups.

Screen shot of Antietam with battle groups, range of influence and objectives displayed. Blue indicates areas within Blue forces Range of Influence (ROI); meaning that their weapons can fire on these locations. The darker the color the more firepower they can concentrate at that location. The same is displayed for Red forces. Note that areas under both Red and Blue ROI cancel each other out (based on accuracy and firepower). Click to enlarge.

This output is just for debugging and won’t be displayed during the actual game but you can see how MATE made it’s decision to place the Schwerpunkt for Blue Battle Group #1. MATE starts by making a series of statements. These are  similar to predicates used in predicate logic but every statement is known to be true. MATE then constructs hypothetical syllogisms by combining these statements. In the series, below, MATE identifies the opposing force that must be dealt with to achieve its assigned objective, does strength analysis of the two opposing forces, determines if the defender has anchored or unanchored flanks, calculates the slope of the attack, etc., and then calculates the Schwerpunkt after analyzing the enemy’s flank positions, supporting forces and if the attacker has an unrestricted avenue of attack.

Screen shot of the output from the General Staff AI Editor showing the analysis for Blue Battle Group #1 at Antietam. The top section, “Statements about the tactical situation,” are the results of MATE algorithms that analyze unit positions, strengths, 3D Line of Sight, etc. The bottom section, “Tactical Analysis,” are conclusions drawn from the above statements. Click to enlarge.

The Schwerpunkt for Blue Battle Group #1 is displayed on the map as a blue square.

Screen shot from the General Staff AI Editor showing the location of Blue Battle Group #1s schwerpunkt (marked by a blue square). Click to enlarge.

I have written before 3)https://www.general-staff.com/antietam-ai/ about the American Civil War battle of Antietam and I frequently use it as a ‘baseline’ for my AI work. This is because I am very familiar with the battlefield having walked it numerous times, as well as having studied it in depth. So, I know the area that MATE has indicated where Blue Battle Group #1 (Union I and XII corps) should concentrate their attack: west of the West Woods at J. R. Jones’ Confederate Division’s exposed left flank.

The original caption, “Photograph shows four Union soldiers looking at dead Confederate soldiers on Miller Farm, looking toward the west woods on September 19, 1862.” From the US Library of Congress. Click to enlarge.

Many years ago I had an accountant who would apply what he called, ‘the Reasonable Test’, to Profit & Loss statements. He would look at the P&L statement and ask out loud, “does this look reasonable?” Is this a reasonable number for income? Is this a reasonable number of expenses? So, I ask, “is this a reasonable place for the Union I Corps to attack at Antietam?” And, I have to conclude, yes, it is. I can’t mathematically prove that this is the best place to attack; but I think it’s pretty good. In general, MATE will always attempt to outflank enemy positions and, I think, this is a very solid approach to offensive tactics. Indeed, I’m reminded of Wellington’s comments about Napoleon’s tactics at Waterloo, “Never did I see such a pounding match. Both were what the boxers call gluttons. Napoleon did not manoeuvre at all. He just moved forward in the old style, in columns, and was driven off in the old style.” 4)“Wellington: The Years of the Sword,” p, 488 Longford, Elizabeth For MATE, pounding is a last resort. This is the case at Burnside’s Bridge:

MATE analysis for Blue Battle Group #4 (Union IX Corps) at Antietam. Note that MATE recognizes the choke point (Burnside’s Bridge) and orders up an artillery barrage before the assault. Click to enlarge.

In the Tactical Analysis section (above) look at statement #8: “Blue Battle Group #4 has a severely restricted Avenue of Attack (Bridge).” MATE recognizes that there is no way to maneuver around to the objective (Snavely’s Ford is not on this battle map so there really isn’t any other option except over the bridge). Frontal Assault is MATE’s maneuver of last resort. Consequently, MATE first orders up the artillery to positions within distance (> 50% accuracy as set in the General Staff Army Editor for these units) and with a clear Line of Sight (3DLOS) of the target. MATE will order the artillery to bombard enemy forces that control the bridge for an hour before ordering an infantry assault).

Let’s look at MATE’s analysis of First Bull Run:

Screen shot from the General Staff AI Editor showing objective set for Union forces at the battle of First Bull Run. The Union Schwerpunkt is the blue square to the west of the Confederate left flank. Click to enlarge.

Here Blue Battle Group #2 (Heeintzelman’s and Hunter’s Union divisions) are assigned the objective of capturing the Henry House Hill (historically accurate). MATE set’s their Schwerpunkt to the west of the Confederate left flank. This is very close to Union commander McDowell’s original strategy. Again, MATE will attempt to outflank strong positions rather than attack them directly. MATE is not a pounder.

MATE’s analysis for the Union’s right flank: they have an unrestricted Avenue of Attack and Red’s left flank is unanchored. Click to enlarge.

MATE recognizes that the Confederate’s left flank is unanchored and is a perfect target for an envelopment maneuver and places the Schwerpunkt at the end of Red’s line appropriately.

I still have work to do on MATE’s ability to construct defensive lines and that will be the next thing to add to the AI. But, as you can see, great progress has been made. I believe that the MATE tactical AI is unique and I know that there is nothing similar even among the wargames used by US and NATO allies. As always, please feel free to contact me directly if you have any questions or comments.

References

References
↑1 Tactical Inference GEneratoR
↑2 Machine Analysis of Tactical Environments
↑3 https://www.general-staff.com/antietam-ai/
↑4 “Wellington: The Years of the Sword,” p, 488 Longford, Elizabeth