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Another option (next to DL and conventional terrain analysis, as used in General Staff) is reinforcement learning. It has a more narrow application, but is easier to fit to a game like Combat Mission. Here is an example, as shown at the ConnectionsUS 2020 on-line conference: 'Course of Action Generation with ML and a COTS Wargame', using the Flashpoint Campaigns game. https://drive.google.com/file/d/1C9nfWwwlsTMkNN-au6j5UrTfDMr9v4KF/view?usp=drive_link

I've enjoyed reading the (freely available) Official History of the Canadian Army in the Second World War, Vol III The Victory Campaign: The Operations in Northwest Europe, 1944-45. Get it at http://www.cmp-cpm.forces.gc.ca/dhh-dhp/his/oh-ho/detail-eng.asp?BfBookLang=1&BfId=29. Also recommend Brian A. Reid's 'No holding back' covering Operation Totalize. Goes into more detail describing operations, equipment, the decision making process, and what went right and wrong (in hindsight).

I can think of a number of edge cases complicating such a 'simply move to better cover' decision (having done AI for tactical FPS games): - the 'better cover' may be occupied by hostiles - the 'better cover' may be in the line-of-fire of even more dangerous threats - traveling the path to this cover position may be get the squad killed - the path to this cover position might cross friendly lines of fire (not an issue in CMSF though) or friendly lanes of movement - the 'better cover' may be overloaded already with friendlies or designated for use by other troops on the move All these cases can be checked for, but these checks typically involve a good amount of additional code, a large set of test-cases and the most expensive computation in the game: line-of-fire checks. Don't misunderstand me: I'm looking forward to play with and against AI capable of everything you describe. Sadly, game AI doesn't automatically become easy because something looks obvious.

MCWP 3-1 Ground Combat Operations - www.usmc.mil/news/publications/Documents/MCWP%203-1%20Ground%20Combat%20Operations.pdf MCWP 3-12 Tank Employment - http://www.marines.mil/news/publications/Documents/MCWP%203-12%20Marine%20Corps%20Tank%20Employment.pdf MCWP 3-13 W/CH 1 Employment of AAV - http://www.marines.mil/news/publications/Documents/MCWP%203-13%20w_Chg%201%20Employment%20of%20Amphibious%20Assault%20Vehicles%20(AAVs).pdf Direct links from usmc.mil/publications/documents and marines.mil/news/publications.

Upgraded to Nvidia latest drivers 178.13 (Sep 25 2008). Problem gone. Can run CMSF again at 1600x1200, high settings. Time to send some Marines into battle!

Hi, with CMSF 1.10, CMSF fails to start a mission with the message "Your graphics hardware is out of memory". It didn't do this for 1.08. It continues to do so even when dropping down the resolution to 1024x768, fastest 3d models, fastest textures, aa/ms off, graphics driver set to single gpu performance. Same problem for 1.08 save games and new Marines campaign. Setup: Core2Duo E6600, 3GB ram, NVidia 8800GTS 640MB with dual-monitor 1600x1200 screens, OS WinXP32SP2, graphics drivers 177.83. Tried suggested driver settings "Threaded optimizations off, Extension limit off, Triple buffering on" without improvement. Don't experience problems playing Armed Assault or Enemy Territories: Quake Wars (which might be an OpenGL game as well). Any advice? Anything I can do to hunt down the problem?

Just curious (and off-topic): Which game by Norm Kroger did you intend to refer to? Steel Panthers is by Grigsby, as you're well aware.

Thanks for creating and posting your AAR, Mark. Truly enjoyed it. Looking forward to updates. William

Steve, Just curious: does the TacAI engage threats with area/blast-effect weapons (frags, 40mmHE, AT4, RPG, ...) in situations where it doesn't have an actual (straight-line) LOF to the threats themselves but has a LOF & LOS to the building/trench/rooftop shielding the threats? William

Another request: would it be possible to lock the mouse to the game's window? The game depends on mouse movements to the edge of the window. On a dual monitor ('twin view') setup, I frequently toss my mouse outside the game's window by accident.

iLikeThisGame, another way to tackle LOS tables is to store for each spot, for several directions (or pizza slices) from that spot the distance to which LOS extends in the best case. With this, you establish whether's a likely LOS/LOF for a pair of units u1 and u2 as follows: - los(spot(u1), dir(u2, u1)) >= dist(u1, u2) && los(spot(u2), dir(u1, u2)) >= dist(u1, u2) If this expression returns false, you're done. Otherwise, you perform a LOS check (and take into account all dynamics such as vehicles, smoke, vegetation, posture, etc.). Total: 50x50x2 table lookups (worst-case). The downside of such an approach (and the downside of LOS tables in general) is that it is expensive to repair these tables when obstacles (buildings, etc.) are removed. William ref: http://www.cgf-ai.com/slides_gdc2005.html, slide 38

Steve, I'm not a math guy but a game AI developer who picked up the game to check-out your AI and improve my understanding of modern combined-arms tactics. Thusfar, I've really enjoyed your approach to 'hands-off' tactical AI, since I hate micro-managing units. (And I love zooming in on the Javelin team and seeing them take down a bunker). FYI, in theory the cost of pathfinding increases with a factor between 2 and 4.6 for a factor 2 increase in resolution, and not more than that. There is no exponential increase in costs. This is best understood as follows (assuming some kind of A* algorithm is used): - the terrain surface (number of action spots) explored by the A* algorithm for a certain path depends primarily on the cost-function and heuristic used, and will be same regardless of the resolution. In the worst case, this is the full map and contains 4 times as many spots if the resolution is doubled. In the best case, the path is a straigth line, and contains 2 times as many spots. - the 'open list' in A* (best interpreted as the out-line of the terrain surface being explored by the algorithm) will be about twice as large, for 2 to 4 times as long a search. Manipulation of the open list is O(log(size of open list)). When 'open list' manipulation is your bottleneck, the cost increase is (worst-case) (4*N * log(2*N)) / (N * log(N)), which is between 4.4 and 4.6 for N > 100 (closer to 4.4 for larger N). For a change from 8m action spots to 1m action spots, the increase in costs can be computed along these lines (somewhere between 8 and 90). However, in those situations (basically "FPS situations"), you're better off doing two path finding calls; a first at (say) 8m resolution, and a second at 1m resolution but constrained to those 8m spots (and neighboring spots) found in the first search. That's probably in the order of 2 to 5 times as expensive as a single 8m resolution search, and with very good worst-case behavior. For path-finding, it should be sufficient to find a path for the unit's leader, and have the other members move into positions based on steering behaviors. This costs less, and won't suffer from sub-optimization where one of the team's soldiers breaks cohesion because he himself is able to find a marginally cheaper path on the other side of a building. For grid reduction, LOS probably is much bigger problem (in the games I worked on, LOS/LOF consumed much more CPU than pathfinding). In addition to the problems you mention, an increase in resolution is likely to give a decrease in robustness for picking cover and attack positions. If the attack or cover position is picked based on the 1m action spot of the (known) opposing units, a slight displacement of those units is more likely to invalidate the cover or attack position (than when using 8m action spots). Again, a hybrid approach with action spots at two levels of detail (8m action spots consisting of 64 1m spots) may give you the best-of-both worlds. Only when LOF/LOS checks between 8m action spots are inconclusive, you'd need to fall back on more detailed LOF/LOS checks. In the (FPS) games I worked on, we employed a mix of action spot sizes (large sizes when terrain geometry was trivial, and small sizes when geometry was complex), we pre-computed part of the LOS/LOF checks in a compressed table, and only performed ray casts when the table's answer was inconclusive or would not be representative (there were dynamic obstacles present or absent between the unit and it's opponents). Because of these savings, we were be able to do path finding with LOF checks per position ("tactical path-finding"). As a reference, only a few FPS games today come close to the vehicles mix and unit counts as exhibited in CMSF (think Battlefield series, and Bohemia Interactive's Armed Assault [recommended, it's the Operation Flashpoint successor]). Both these games seem to use >1m tiles or exhibit sloppy cover / attack position picking. Kind regards, thanks for the Javelins, looking forward to more of your team's games (and patches). William