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Patch v1.09 finalizes game content and optimizes AI

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Generals, we need your attention! The game has just received a new update which makes it complete. The language translation of the newest custom battles text has been added and AI has received various improvements to become an even more challenging opponent, who will relentlessly attack your weak points and will create symmetrical and realistic fire lines around your flanks. Take a look at the changes below:

The AI will expand its forces to surround and flank player much more frequently than before.

The AI will expand its forces to surround and flank player much more frequently than before.

Ultimate General: Civil War v1.09 rev. 21114

  • Further AI improvement (Flanking behaviour, Defense readiness, Territorial awareness, Line making).
  • Significant improvement of AI pressure to keep its placeholders/objectives.
  • Reduce of about 20% morale staying power of skirmishers. They will still be useful for delaying or hit and run tactics but not so exploitable from player. Popping up skirmishers everywhere to defeat the AI should not be a win option for player. Additionally AI pushing back easily player's infantry with skirmishers should not happen to an annoying level as before.
  • Added the last text translations for newest custom battles.
AI lines will be symmetrical and hard to break.

AI lines will be symmetrical and hard to break.

We would like to remind that Ultimate General: Civil War has received 9 small or big patches after its full release on 14/7/2017. With this latest patch we consider the game finalized in its main content and we move forwards in full speed for new projects of Ultimate General series. However, we will continuously monitor your feedback shared in our forums and will support the game for as long as needed:

Official forum: Link
Steam forum: Link


The Game-Labs Team

Steam version is updated to v1.09. Mac App Store version is going to be updated as soon as possible.

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10 hours ago
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The Operational Art of War IV-First Chechen War

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From: AgrippaMaxentius
Duration: 19:52

Hello my friends! Today we play as the Russians in the first invasion of Chechnya! Join us and support by hitting like, comment and subscribe button!

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11 hours ago
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Order of Battle: Panzerkrieg Breaking the silence

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Two months ago we announced the new Order of Battle DLC, Panzerkrieg.

They have been two months of hard work for the Artistocrats, but we’re glad to say that the DLC is shaping up nicely. We are VERY close to the goal now. 

Recreating the crucial years of 1942 and 1943 on the eastern front is a massive undertaking, as is evolving the gameplay mechanics and the units roster to better represent the type of warfare waged in the huge battlefields of Russia.
As you might imagine, the Panzer is king (or should I say konig?) in Panzerkrieg. The new campaign starts in January 1942. By the end of 1941 the German rapid advance has been halted at Moscow and Leningrad by the valour of the Red Army and another implacable enemy: Russian winter.
This terrible war of attrition continues in January 1942, starting with the “Rhzev Meat Grinder” and the Demyansk Pocket. But the German staff knows that the Russian winter won’t last forever. Come the spring they will be able to launch a renewed offensive. The goal? To reach for the throat of the bear and strangle it. How? By striking at the Caucasus and the rich oil deposits of Baku. 
What is new?
With over 40 new units to expand the German and Russian rosters, as well as other countries in the Axis, the primary focus was put on expanding the list of available units.
We will be focusing on some of the new units in a later developer diary, but for now let’s just say that mechanized warfare will dominate the battlefield. Imagine steel behemoths rolling on muddy fields, powerful airplanes bringing death from above and Stalinorgel guns unleashing destruction.
The developers really wanted to properly represent Russian terrain in Panzerkrieg, and the importance of it in a maneuvering war. They have completely revamped the movement value for different types of terrains (including paved and dirt roads) for different types of units (feet, wheels, tracks…).
The new terrain rules, coupled with new ways to affect encircled or cut-off units as well as all the new units themselves will make you face new situations and force you to adapt your strategy.
There is also one important change that we need to mention: artillery will now disorder after consecutive turns of fire due to guns overheating. Even guns need to take a break once in a while!
The Specialization System
Before we say anything of the new specializations, let’s repeat one thing: there’s a full carry-over system linking Blitzkrieg (the previous DLC) to Panzerkrieg (as well as future yet unannounced DLCs!), making it in all effects a megacampaign. Not only you will carry-over your forces, but your Commanders and your Specializations as well!
The new specializations reflect the challenges and initiatives of the German command, as well as new ways to interpret tank warfare. Some examples…
Organisation Todt, named after its founder Fritz Todt, this engineering group will be deployed on the frontline and assist you in repairing and constructing defenses and infrastucture.
You can also choose to expand the Waffen SS: it’s a controversial subject, but they were part of the campaign and we wanted to represent them. You can now expand the organisation and add more divisions by recruiting from a larger population pool.
You can choose to make use of Synthetic Fuel Plants or you can go for the Panzerfibel specialization and reform your tank manuals. By choosing the Goliath Mine specialization you can increase the destructive potential of your engineers. Or maybe you’d like to focus on the Pakfront: grouping several AT guns under a single officer, the guns can all fire at once against selective targets, maximizing the element of surprise and minimizing the chance for return fire. Interesting, right? You can also choose to adopt the Panzerkeil tank formation, allowing you to gain defensive bonuses in certain conditions.

Let’s conclude this entry for today by saying that the Artistocrats are also working on a Campaign editor!

That is the subject for another day, though. We have more things to show and tell you, and we’ll be back soon, very soon. It’s a promise! Stay tuned.

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11 hours ago
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Let's Play Blitzkrieg II (German Campaign) - Mission 13 - Angry Nursemaid

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From: Night Phoenix
Duration: 19:41

Eisenhower, Rommel, Zhukov; Assume your rightful place among the great generals commanding the Allies, Germans or Soviets as they advance through the decisive battles of WWII. Blitzkrieg is a development in WWII real-time strategy gaming combining flexibility, historic accuracy and endless playability into one of the most challenging and enjoyable games yet!
Blitzkrieg's unique and completely flexible campaign structure puts you in control of deciding how your forces will fare in each of the major engagements that comprise three central Campaigns. Hardcore vets have the option of engaging the enemy immediately in a desperate struggle for battlefield dominance, or more cautious players can choose from pre-defined or unlimited randomly generated side missions as they gain experience, promotions and weapons upgrades as well as wear down the enemy before the central conflict.
If you like the video please consider leaving a like, comment, subscribe to the channel or check out my patreon here: https://www.patreon.com/user?u=4144595

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11 hours ago
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Computational Military Reasoning Part 4: Learning

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In my previous three posts on computational military reasoning (tactical artificial intelligence) we introduced my algorithms for detecting the absence or presence of anchored and unanchored flanks, interior lines and restricted avenues of attack (approach) and retreat. In this post I present my doctoral research1)TIGER: An Unsupervised Machine Learning Tactical Inference Generator which can be downloaded here which utilizes these algorithms, and others, in the construction of an unsupervised machine learning program that is able to classify the current tactical situation (battlefield) in the context of previously observed battles. In other words, it learns and it remembers.

‘Machine learning’ is the computer science term for learning software (in computer science ‘machine’ often means ‘software’ or ‘program’ ever since the ‘Turning Machine'2)https://en.wikipedia.org/wiki/Turing_machine which was not a physical machine but an abstract thought experiment.

There are two forms of machine learning: supervised and unsupervised machine learning. Supervised machine learning requires a human to ‘teach’ the software. An example of supervised machine learning is the Netflix recommendation system. Every time you watch a show on Netflix you are teaching their software what you like. Well, theoretically.┬áNetflix recommendations are often laughingly terrible (no, I do not want to see the new Bratz kids movie regardless of how many times you keep recommending it to me).

Unsupervised machine learning is a completely different animal. Without human intervention an unsupervised machine learning program tries to make sense of a series of ‘objects’ that are presented to it. For the TIGER / MATE program, these objects are battles and the program classifies them into similar clusters. In other words, every time TIGER / MATE ‘sees’ a new tactical situation it asks itself if this is something similar (and how similar) to what it’s seen before or is it something entirely new?

I use convenient terms like ‘a computer tries to make sense of’ or a ‘computer sees’ or a ‘computer thinks’ but I’m not trying to make the argument that computers are sentient or that they see or think. These are just linguistic crutches that I employ to make it easier to write about these topics.

So, a snapshot of a battle (the terrain, elevation and unit positions at a specific time) is an ‘object’ and this object is described by a number of ‘attributes’. In the case of TIGER / MATE, the attributes that describe a battle object are:

  • Interior Line Value
  • Anchored / Unanchored Flank Value
  • REDFOR (Red Forces) Choke Points Value
  • BLUEFOR (Blue Forces) Choke Points Value
  • Weighted Force Ratio
  • Attack Slope

The algorithms for calculating the metrics for the first four attributes were discussed in the three previous blog posts cited above. The algorithms for calculating the Weighted Force Ratio and Attack Slope metrics are straightforward: Weighted Force Ratio is the strength of Red over the strength of Blue weighted by unit type and the Attack Slope is just that: the slope (uphill or downhill) that the attacker is charging over.

TIGER / MATE constructs a hierarchical tree of battlefield snapshots. This tree represents the relationship and similarity of different battlefield snapshots. For example, two battlefield situations that are very similar will appear in the same node, while two battlefield situations that are very different will appear in disparate nodes. This will be easier to follow with a number of screen shots. Unfortunately, we first have to introduce the Category Utility Function.

So, first let me apologize for all the math. It isn’t necessary for you to understand how the TIGER / MATE unsupervised machine learning process works, but if I don’t show it I’m guilty of this:

The Category Utility Function (or CU, for short) is the equation that determines how similar or dissimilar too objects (battlefields) are. This it the CU function:

‘Acuity’ is the concept of the minimum value that separates two ‘instances’ (in our case, battles). It has to have a value of 1.0 or very bad things will happen.


So, let’s recap what we’ve got:

  • A series of algorithms that analyze a battlefield and return values representing various conditions that SMEs agree are significant (flanks, attack and retreat routes, unit strengths, etc., etc).
  • A Category Utility Function (CU) that uses the products of these algorithms to determine how similar analyzed battlefields are.

So now, we just need to put this all together. A battlefield (tactical situation) is analyzed by TIGER / MATE. It is ‘fed’ into the unsupervised machine learning function and, using the Category Utility Function one of four things happen:

  1. All the children of the parent node are evaluated using the CU function and the object (tactical situation)is added to an existing node with the best score.
  2. The object is placed in a new node all by itself.
  3. The two top-scoring nodes are combined into a single node and the new object is added to it.
  4. A node is divided into several nodes with the new objected to one of them.

These rules (above) construct a hierarchical tree structure. TIGER was fed 20 historical tactical situations (below):

  1. Kasserine Pass February 14,1943
  2. KasserinePass February 19, 1943
  3. Lake Trasimene, 217 BCE
  4. Shiloh Day 2
  5. Shiloh Day 1, 0900 hours
  6. Shiloh Day 1, 1200 hours
  7. Antietam 0600 hours
  8. Antietam 1630 hours
  9. Fredericksburg, December 10
  10. Fredericksburg, December 13
  11. Chancellorsville May 1
  12. Chancellorsville May 2
  13. Gazala
  14. Gettysburg, Day 1
  15. Gettysburg, Day 2
  16. Gettysburg, Day 3
  17. Sinai, June 5
  18. Waterloo, 1000 hours
  19. Waterloo, 1600 hours
  20. Waterloo, 1930 hours

In addition to these 20 historical tactical situations five hypothetical situations were created labeled A-E. This is the resulting tree which TIGER created:

The hierarchical tree created by TIGER from 20 historical and 5 hypothetical tactical situations. The numbers in the nodes refer to the above legend. Battles placed in the same nodes are considered very similar by TIGER. Click to enlarge.

If we look at the tree that TIGER constructed we can see that it placed Shiloh Day 1 0900 hours and Shiloh Day 1 1200 hours together in cluster C35. Indeed, as we look around the tree we observe that TIGER did a remarkable job of analyzing tactical situations and placing like with like. But, that’s easy for me to say, I wrote TIGER. My opinion doesn’t count. So we asked 23 SMEs which included:

  • 7 Professional Wargame Designers
  • 14 Active duty and retired U. S. Army officers including:
  • Colonel (Ret.) USMC infantry 5 combat tours, 3 advisory tours
  • Maj. USA. (SE Core) Project Leader, TCM-Virtual Training
  • Officer at TRADOC (U. S. Army Training and Doctrine Command)
  • West Point; Warfighting Simulation Center
  • Instructor, Dept of Tactics Command & General Staff College
  • PhD student at RMIT
  • Tactics Instructor at Kingston (Canada)

And in a blind survey asked them not how TIGER did but what they would do. For example:

Twenty-three SMEs were asked this question: is this hypothetical tactical situation (top) more like Kasserine Pass or Gettysburg?. Click to enlarge.

And this is how the responded:

Results from 23 SMEs answering the above question. Overwhelmingly the SMEs agreed that that the hypothetical tactical situation was most like the battle of Kasserine Pass.

So, 91.3% of SMEs agreed that the hypothetical tactical situation was more like Kasserine Pass than Gettysburg Day 1. Unbeknownst to the SMEs TIGER had already classified these three tactical situations like this:

How TIGER classified Kasserine Pass (1), Gettysburg Day 1 (14) and a hypothetical tactical situation (B). The cluster C1 contains two tactical situations that both have restricted avenues of attack caused by armor traveling through narrow mountainous passes. These passes also partially create restricted avenues of retreat. REDFOR does not have anchored flanks.Click to enlarge.

In conclusion: over the last four blog posts about Computational Military Reasoning we have demonstrated:

  • Algorithms for analyzing a battlefield (tactical situation).
  • Algorithms for implementing offensive maneuvers.
  • An Unsupervised Machine Learning system for classifying tactical situations and clustering like situations together. Furthermore, this system is never-ending and as it encounters new tactical situations it will continue this process which enables the AI to plan maneuvers based on previously observed and annotated situations.

This is the AI that will be used in General Staff. It is unique and revolutionary. No computer military simulation – either commercially available or any military simulation used by any of the world’s armies – employ an AI of this depth.

As always, please feel free to contact me directly with questions or comments. You can use our online email form here or write to me directly at Ezra [at] RiverviewAI.com.

References   [ + ]

1. TIGER: An Unsupervised Machine Learning Tactical Inference Generator which can be downloaded here
2. https://en.wikipedia.org/wiki/Turing_machine
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14 hours ago
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Rule the Waves Austria-Hungary Episode 22 - Where are the Battleships?

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From: BattleGroupGamer
Duration: 38:06

Fighting the Italians in the Mediterranean. How will we do?
Want to see more Rule the Waves content? Check out my friends!
Tortuga Power - https://www.youtube.com/user/sexyneckbeard
The Historical Gamer - https://www.youtube.com/user/thehistoricalgamer
XTRG - https://www.youtube.com/user/XThatRandomGamer

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14 hours ago
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