BletchleyGeek

Mord shared what he found, so others could benefit. Sharing information for newer people doesn’t matter when you found it.
Plus, if he can notice anything at all behind those massive shades, he is far more perceptive than most.

Looking so hard for VERY spelled in terrain tiles somewhere.

Do you mean I look that duke from Ghent? I hope not, I hate the monarchy. Although it is a lovely city to drink in.
But to answer your question, no I've not been on a diet, I have used this picture of Ollie's dug up head a couple of times before. But, recently some idiots told me how much they hate my content on this forum, so I thought that seeing me dead might make their day.

You just wrapped up the whole shebang for me right there (with that Nerves link), BletchleyGeek.
-Telephone reference.
-Caddyshack reference.
-Post-punk take-away.
That's 4-D chess, my man. Nicely done.

If it's an interactive war movie you're looking for, then playing WEGO with the ability to really get up close and personal with the camera, and also watch cool action on instant replay should be the motivating factor. Your statement here doesn't make sense.
Be honest. Playing in realtime mode requires you to separate yourself from the minute detail to maintain overall situational awareness.
Unless you prefer playing with little more than a platoon at a time.
Everyone I see who records gameplay in real time is forced to remain in a birds-eye view, with maybe a brief zoom into a key piece of action before being forced to zoom back out to maintain awareness. If your idea of an interactive war movie is to watch unit icons march across a field, maybe you are the one looking for an animated 3d board game.

I seem to dimly recall from posts pre-2007 that the basic engine is RT only. WEGO just pauses the RT engine every 60 seconds and adds the replay feature.
personnaly I never play RT, WEGO only for me.

A few ugly turns Ian, though from your recce it seems he has a lot of heavy assets up front. I do wonder if he has as many assets in depth as he'd like you to believe. My money is on no. 

Thanks @BFCElvis for the update, the Italy games have always had a temperamental TO&E, so I keep my fingers crossed that the tidying up doesn't turn into a death march.

Shame about that lead tank Ian... but you look to have some spares.     

That paragraph could totally be in a book by Julian Barnes Nicely written, Ben.

I was about to use words to that effect. He's a very cautious and meticulous player. We played a game of the Market Garden Devil's Hill scenario a few years back and it took a while to find stumble upon his troops. 

I'm going to hang that over my mental desk. Thank you kindly.

That paragraph could totally be in a book by Julian Barnes Nicely written, Ben.

Imagine that process- A struggling actor in another country (possibly), sent a script they know little about. A job in video games seems exciting enough. They open the script and scan it. Maybe at home in front of a mic? In a plush studio with carpet on the walls, with a poster of Rick James over the boards? Taps the mic- "Sibilance, sibilance". Quiet on the set!
"Is there any direction given?" The actor inquires. No notes, beyond "should exhibit terror", or "pinned down (wot!?!)". They struggle to find the character, then are told "it's actually a lot of different guys that need distinct voices- all you!". The actor sags in his chair. Straightens up, and begins..."Advance!". 
I really want to have an alternate take mod with the flat delivery. "Move out, or take a nap, or whatever".
Nicely done, Elvis. Stuff like this makes the game educational, as well as realistic and even informative. I'm psyched to learn some Urdu.

"Listen up!" ?

Looking forward to see and hear the Indian Army in its full digital glory... they fought probably  the hardest and toughest of campaigns in Italy and Burma.
@Warts 'n' all I am personally looking forward to an Scottish voice pack.

Hindi and Urdu are similar enough to pass for each other in game. While trying to figure out which language would have had the most speakers amongst Indian Divisions in Italy I couldn't find anything more useful than this in the official history:

'As an illustration, their Army newspapers now appeared in eleven languages, instead of two as in peace time. Even the impassive Gurkhas had their Gurkhali news sheet, which they read with avidity. '

I actually found my life improved a lot after I started to err on the side of pessimism. I'm now more often pleasantly surprised.

I can't recommend Battle (also released as Anatomy of a Battle) by Kenneth Macksey enough, for stuff like this. Macksey fought through the Normandy campaign himself, and this is a thinly fictionalised account of a generic battle in Normandy from multiple perspectives, from soup to nuts, and spends quite a lot of time on the pre-battle preparations; liaison, movement, fire planning, logistic arrangements, along with reconnaissance and planning. It's quite old now - it was released in about 1974 - but a good mil-hist library, or university library should(?) have a copy. Alternately try interloan, or second hand book places (meatspace and online).
The putative battle that Macksey describes would make for a pretty good CM scenario. I know that because I made one several years ago

"Next one to make a bad joke, get's this."

They're not, are they? Well blow me down with a fevva, I'd 'ave nevva Adam 'n' Eve'd it, if you ''adn't told me.
I do remember once being in Het Scheepvaartmuseum in Amsterdam overhearing an Englishwoman exclaim "Oh, I thought that they were our friends", when she saw a painting of The Battle of the Medway. All good fun.

Gee, user, why make films when we can just take pictures of stuff?

Just some notes on the above, as a member of the research community, I feel I need to comment on this briefly.
First of all, that stance of "anything rulebased is anything but AI" is a disingenous position that does not hold when contrasted with the state-of-the-art literature. Domain knowledge can be expressed in many ways: with if-then statements, or behaviour trees, all the way to neural network architectures engineered to "capture" very specific processes and signals. The successes that have been widely hyped up - like the Star Craft/DOTA players by Deepmind and OpenAI - are fundamentally hybrids of what you refer with scorn as "AI" and machine learning. Even the Alpha players rely on not an insignificant amount of handcrafted knowledge, from the basic features used to parametrize states to the particular selection of activation units and interconnection patterns. All those choices were made by humans seeking the best combination of parameters, architectures, initialization strategies and more. If you check the paper on AlphaGo on Nature you'll see that the section devoted to explain those details is actually longer than the main paper.
 Even more interesting is to see how former preachers of the "end-to-end learning" gospel are now turning to classics like early 1980s subsumption-like architectures to bootstrap and guide those neural networks training process. Suffices to say that all major companies working on self-driving vehicles have abandoned that gospel and are scrambling to snatch leading researchers of areas which two years ago were considered to be "not relevant any more". 
I have no idea what is Palantir trying to pitch but it sounds to me as pure bull**** tbh. This is for several reasons: tactics require to deal with partial information, considering processes that flow at different time scales, on environments which are complex and dealing with a wide variety of platforms that operate autonomously (e.g. single riflemen, AFVs, a drone and its controller). At the contrary than in games like Go, where the number of pieces is fixed and known, and the board is always the same, a contemporary, near-future or past tactical environment shares little or none of those features. The most fundamental issue - there are several, and there's plenty of fundamental issues to choose and work on - to me is that neural models are not composable.
That is, you can work out a neural network to say, steer a simulated squad of simulated robots broken into two teams just all right along a given line of advance and against a specific amount and direction of enemy fires. Here is a list of the dimensions such neural network has to generalize in order to be useful and interesting:
- Initial distance to target (assuming that the order is to Assault)
- Effective volume of fires on enemy positions as distance to them changes.
- Type of terrain the unit maneuvers.
- Obstacles obscuring LOS and LOF
- Equipment of unit
- Hypothetical equipment of the enemy
There is absolutely zero guarantee that a given NN that performs at a certain level, for any meaningful performance index, on a finite sample along these directions will generalize to any possible combination of the above. If you have an algorithm for that which you can use on any problem at hand, then congratulations, you probably solved too Hilbert's 10th problem.  This applies to everything, including Starcraft: how many possible Starcraft maps there are? Can you classify all possible tactical and strategic situations neatly into discrete homogenous categories? That's also why doing funny stuff to allegedly state of the art CV pre trained networks - like adding a 1-pixel wide black border to an image - catastrophically degrades the accuracy of object identification. Luckily, other than perhaps Russia and China I think, nobody even considers to deploy deep learning systems for target identification and acquisition. If somebody does, they're criminally insane or selling snake oil, or both.
Provably you haven't done any of the above, but you may have a quite decent closed loop control strategy that works well enough to make some nice videos to impress people, or even beats some hand coded controller that somebody put a decent amount of effort in designing exploiting knowledge about the laws of Physics or some other fundamental process.
That can be good enough, it all depends what you're comparing it against. Definitely you can't make any guarantees on suitability for any purpose other than that captured by your training set: YMMV.
The problem of composability is illustrated by the following question: can I use that neural network as a building block to coordinate the movements of a platoon? The answer, so far, has been a quite deafening no.
There is no known way to constrain back propagation to guarantee that the knowledge acquired by the neural network you are using as a building block is going to be obliterated or changed in a fundamental and undesirable fashion during training for the "composite" problem.
Composability also challenges the ability to train incrementally, as the capabilities of the unit change due to casualties or changes in equipment. There's again no guarantee that any knowledge will be preserved when re-training after changing those elements in the environment that generates the training data for the neural network.
Last, composability has to do with time: what is the minimum period of time to be considered? Is there a sensible upper bound on the number of such consecutive periods of time to consider? Taking off-the-shelf techniques used for Natural Language Processing has been shown to be pretty much like dancing about architecture, spoken and written word has a very definite temporal structure, for which we know its "laws" (because we invented grammar and rules of style!).
Another fundamental problem linked to this last observation is that whatever the neural network learns we cannot be sure that it is capturing the essential first principles that allow the behaviours which are to be mimicked. This is analogous to the fundamental issue with the classic research by T. N. Dupuy and the HERO institute - in the 1960s, one could overfit a model only by hand, in the 2020s you can use neural networks too!
Contemporary machine learning has a niche, like those "rule-based" approaches you disparaged in your post do. And I certainly appreciate the good things in deep learning, for instance, the dependability and efficiency, provided that the right conditions for the techniques involved to work properly are an invariant of the set of situations I need to deploy them.

Going back to the games briefly. Regarding Graviteam, I learnt through a weird interaction with Andrey on the Steam forums a few months ago that he's pretty ignorant on any of these topics. Which is totally all right, he's not expected to be an expert on that. So my educated guess is that what you see animating those pixel truppen in Graviteam games are not too different from the techniques used in 99% of video games and 80% (?) of robotics: good old hand-designed controllers via behaviour trees, A*/D* and PIDs/SQP/Non Linear Programming.
Last, I want to address the comment which I read is blasting BFC (and video game developers in general) because of not using deep learning technologies. I have zero idea of what is the operating budget of BFC, but say, the cost in $$$ to say develop and train an Alpha-like system for one of the countless drills possible in CMx2 would probably be somewhere north of 1 million USDs (that counts salaries, on boarding of staff and compute for like 40 days with a similar amount of computation power as the one wielded by Deepmind to ensure you can beat Bil Hardenberger like 90% of the time). Indeed, they would probably amortize salaries and onboarding over time, but the cost of computation is what it is, and changes in the game mechanics, or even bug fixes, etc. would require retraining (or training new networks for that special case).
Indeed there are opportunities for more modest applications rather than end-to-end tactical battle management, but I am skeptical than they are cost effective for the return on investment Battlefront will get. I am pretty sure they're already doing this pretty much for the sake of the arts, and unless they get patronage, I can't see why should they spend tens of thousands of dollars per month on EC2 just to replace their code for animations, drills etc by neural networks. Or maybe you could work pro bono for Battlefront developing those