After running through the demo of Terra Nova (http://www.lglass.com), I can't comment on the quality of the squad support AI, because, for the demo, there's only you in the squad.
As for the Pirates, they seem limited in certain ways: in particular, they will remain in place for quite some time after their last contact with an enemy unit-- *any* enemy unit, including one of your sensor drones-- rather than returning to regular patrol routes, or standing guard positions.
This makes it possible to send one's drone out to attract the attention of the Pirates away from their bases, which may then be destroyed in peace.
Any comments on the Pirates' AI From those of you who've purchased the game?
Actually, this begs the question of how game AI handles feints, in general.
Is it feasible for a unit (or group of units) to decide (without cheating) that a particular maneuver is a feint, and either ignore it, or go the other way?
In TN, it seems that cooperative maneuvering on the part of the squad would hold the day on any you-attacking-them scenario, especially in cases where at least one of you can move faster than them; in the case of the demo, your drone can move faster than they can, and can fly.
I can't speak to them-attacking-you scenarios, since, presumably, their targets are fairly well-defined and nonmoving.
In article <317FED2B.2...@sylvia.com>, Jerome Scheuring <jsch...@sylvia.com> wrote: -After running through the demo of Terra Nova -(http://www.lglass.com), I can't comment on the -quality of the squad support AI, because, for the -demo, there's only you in the squad. [...] - -Actually, this begs the question of how game AI -handles feints, in general. - -Is it feasible for a unit (or group of units) to -decide (without cheating) that a particular -maneuver is a feint, and either ignore it, or go -the other way? - -In TN, it seems that cooperative maneuvering on the -part of the squad would hold the day on any -you-attacking-them scenario, especially in cases -where at least one of you can move faster than -them; in the case of the demo, your drone can move -faster than they can, and can fly. - -I can't speak to them-attacking-you scenarios, -since, presumably, their targets are fairly -well-defined and nonmoving.
I've often thought that many simpler wargames (C&C for example) would be much improved if the AI just had a concept of "commensurate force". That is, when attacking, aim for a 2:1 force advantage in that area. C&C didn't seem to do this; no matter how good (or how bad) your defenses were, it would always send the same size group of units. Or maybe management took it out :-(
If the pirates responded to a single unit with just enough force to take it out reliably, then you couldn't conduct a feint this way as easily.
Richard Wesson (wes...@church.cse.ogi.edu) opined thusly: : I've often thought that many simpler wargames (C&C for example) : would be much improved if the AI just had a concept of : "commensurate force". That is, when attacking, aim for a 2:1 : force advantage in that area. C&C didn't seem to do this; no : matter how good (or how bad) your defenses were, it would always : send the same size group of units. Or maybe management took it : out :-(
Allegedly the "prequel" C&C game will do exactly this, tailor its response to the forces it sees and the size of the threat. There was a brief article about it in the recent Strategy Plus, or you can read about it over on my Game AI page (address below).
Steven
+========================================================================== ===+ | _ | | Steven Woodcock _____C .._. | | Senior Software Engineer, Gameware ____/ \___/ | | Lockheed Martin Information Real3D <____/\_---\_\ "Ferretman" | | Phone: 719-597-5413 | | E-mail: woodc...@escmail.orl.mmc.com (Work), swood...@cris.com (Home) | | Web: http://www.cris.com/~swoodcoc/wyrdhaven.html (Top level page) | | http://www.cris.com/~swoodcoc/ai.html (Game AI page) | | http://www.cris.com/~swoodcoc/software.html (AI Software page) | | Disclaimer: My opinions in NO way reflect the opinions of | | the Lockheed Martin Information Real3D | +========================================================================== ===+
>Richard Wesson (wes...@church.cse.ogi.edu) opined thusly: >: I've often thought that many simpler wargames (C&C for example) >: would be much improved if the AI just had a concept of >: "commensurate force". That is, when attacking, aim for a 2:1 >: force advantage in that area. C&C didn't seem to do this; no >: matter how good (or how bad) your defenses were, it would always >: send the same size group of units. Or maybe management took it >: out :-(
> Allegedly the "prequel" C&C game will do exactly this, tailor its >response to the forces it sees and the size of the threat. There was >a brief article about it in the recent Strategy Plus, or you can >read about it over on my Game AI page (address below).
This is one of the problems I'm facing in Enemy Nations, such that, without cheating, how does an AI player determine the proper force mixture for an assault, invasion or whatever type of attack on the human player or another AI player?
In C&C, it appears that once the terrain is revealled, then there is no "fog of war", and so the AI player seems to have reason to know the force composition of the human player (Joe B. please correct me if I'm wrong about this). Such a situation would make it easier to calculate an acceptable mixture of unit types and quantities to make for a good attack force.
However, if "fog of war" is maintained, then only those human player units sighted by, and known by, the AI player, can be used to help determine the force composition of an attack force.
My question is, outside of the obvious use of random selections, what basis have others used to arrive at how many units and of what type are to use to gather for an attack? (Game specifics withstanding)
Regards,
Eric Dybsand Glacier Edge Technology Glendale, Colorado, USA
: In C&C, it appears that once the terrain is revealled, then there : is no "fog of war", and so the AI player seems to have reason to : know the force composition of the human player (Joe B. please : correct me if I'm wrong about this). Such a situation would make : it easier to calculate an acceptable mixture of unit types and : quantities to make for a good attack force.
I'm waiting to hear from Joe on this too, but that does appear to be the way C&C works.
: However, if "fog of war" is maintained, then only those human player : units sighted by, and known by, the AI player, can be used to help : determine the force composition of an attack force.
: My question is, outside of the obvious use of random selections, what : basis have others used to arrive at how many units and of what type are : to use to gather for an attack? (Game specifics withstanding)
Some ideas of varying quality:
1.) You could use a database of pre-built attack forces of varying size. When the AI determines it wants to send out an attack force, it randomly picks one of these configurations (perhaps based on whatever knowledge it has of the defenses), gathers and/or builds the units, and sends them out. The problem with this approach is you're not dynamically designing the forces, just selecting menu options, and you have to have some AI to handle cases where you just *can't* build a given unit type for the selected force.
2.) You could simply sum up the known defensive/offensive capabilities of the area/units you plan to attack and gather enough attack strength to meet some criteria (2:1, 3:1, whatever). The problem with *that* is that not all attack points are created equally; a flamethrower in C&C, for example, is worth more than 3 machine gunners.
3.) You could analyze the force you're going up against (again, based on your best info), find the best "anti" units to use against each one, and send two of each. This is cheap but probably pretty effective.
That's it off the top of my head.
Steven
+========================================================================== ===+ | _ | | Steven Woodcock _____C .._. | | Senior Software Engineer, Gameware ____/ \___/ | | Lockheed Martin Information Real3D <____/\_---\_\ "Ferretman" | | Phone: 719-597-5413 | | E-mail: woodc...@escmail.orl.mmc.com (Work), swood...@cris.com (Home) | | Web: http://www.cris.com/~swoodcoc/wyrdhaven.html (Top level page) | | http://www.cris.com/~swoodcoc/ai.html (Game AI page) | | http://www.cris.com/~swoodcoc/software.html (AI Software page) | | Disclaimer: My opinions in NO way reflect the opinions of | | the Lockheed Martin Information Real3D | +========================================================================== ===+
Swood...@cris.com (Steven Woodcock) wrote: > Some ideas of varying quality:
> 1.) You could use a database of pre-built attack forces of > varying size. When the AI determines it wants to send > out an attack force, it randomly picks one of these > configurations (perhaps based on whatever knowledge it > has of the defenses), gathers and/or builds the units, > and sends them out. The problem with this approach is > you're not dynamically designing the forces, just > selecting menu options, and you have to have some > AI to handle cases where you just *can't* build a given > unit type for the selected force.
> 2.) You could simply sum up the known defensive/offensive > capabilities of the area/units you plan to attack and > gather enough attack strength to meet some criteria > (2:1, 3:1, whatever). The problem with *that* is that > not all attack points are created equally; a flamethrower > in C&C, for example, is worth more than 3 machine gunners.
> 3.) You could analyze the force you're going up against (again, > based on your best info), find the best "anti" units to use > against each one, and send two of each. This is cheap but > probably pretty effective. > That's it off the top of my head. >Steven
These are all good ideas. Red Alert uses all of these strategies (to one degree or another). The first strategy gives the scenario designers more control (they pick the particular groups rather than letting the computer do so). The second and third strategies are more suitable for end-game and multi player (with AI players) battles. However, there are other factors to consider.
Distance to enemy force: The greater the distance the more options the computer has. That is, it can (with more safety) delay building combatants in order to build infrastructure.
Mobility of enemy force: The computer must also know how the player is able to travel. With the threat of airborne assault, the defense posture and force composition is greatly affected. The computer must make a 'best guess' with regard to this threat by examining the player's assets and past behavior.
Pattern of enemy attacks: The computer remembers the patterns of attack the player uses. Human players have a fortunate (for the computer) tendency to use the same strategy over and over. A defense tailored to meet a particular attack strategy can be very effective.
Expert System 'AI': The computer has the advantage of leveraging all the most successful skills and strategies discovered during testing. These give the computer a 'bag of tricks' to use when the situation warrants.
Memory of past success and failure: The computer must also remember the success and failure rates of the various strategies at its disposal. Some players are better suited to defend against particular strategies than others. It is also presumed that all players will become better at the game, and so the computer must be able to compensate for successful defense strategies.
Megahertz: Finally, the computer is just plain faster at analyzing numbers than a human is. This is the basic theory behind chess programs -- they aren't necessary smarter than the grand masters, they just look more moves ahead. The computer can assess threats with much better speed and accuracy than the human player. Combine this with its lightning fast response time (and the above strategies), and the computer becomes a formidable opponent.
In <3186ca9c.341716...@news.accessnv.com> joeb...@anv.net (Joe Bostic) writes:
>Swood...@cris.com (Steven Woodcock) wrote: >> Some ideas of varying quality:
[Steve's interesting ideas snipped and saved off to disk]
>These are all good ideas. Red Alert uses all of these strategies (to >one degree or another). The first strategy gives the scenario >designers more control (they pick the particular groups rather than >letting the computer do so). The second and third strategies are more >suitable for end-game and multi player (with AI players) battles. >However, there are other factors to consider.
[some of Joe's factors snipped and saved too]
>Pattern of enemy attacks: > The computer remembers the patterns of attack the player uses. >Human players have a fortunate (for the computer) tendency to use the >same strategy over and over. A defense tailored to meet a particular >attack strategy can be very effective.
This is another favorite area of interest of mine, and that being what data values actually provide a representation of the "pattern of attack the player uses"?
Over the years, I've tried several combinations which IMO, worked to some degree, to a mediocre level at best, in accurately protraying the actual "pattern" the player demonstrates.
In my attempts at attack pattern tracking, I've saved items such as:
Timing of attack - early in the game vs. later in the game Direction of attack - compass directions only Style of attack - single front vs. multiple fronts Quantity of units - how many were used Quality of units - what experience level was for units Unit mix - what kind of units used by opfor and AI player Attack Rating - number of AI units destroyed + objectives captured
My point is that the "patterns" I've generated and observed, were incredibly subtle, if they existed at all. And those attacks with the same high rating, were often completely different.
So, while I agree that such a goal (the computer remembers the patterns) is desirable, I'm curious as to what you and others think are the data elements that are critical for demonstrating the "actual pattern of attack" as employed by a human player.
>Memory of past success and failure: > The computer must also remember the success and failure rates of >the various strategies at its disposal. Some players are better suited >to defend against particular strategies than others. It is also >presumed that all players will become better at the game, and so the >computer must be able to compensate for successful defense strategies.
Once again, I agree with the goal, but I'm curious if the ratings applied to the various strategies, are able to actually evaluate whether the given strategy was succussful or a failure.
If one simply rates a strategy based on whether it "won" the game or scenario, then that rating would be the same (in my mind) as that applied to another, completely different strategy that also was used to "win" the game. There needs to be some way to provide a differenitation of the ratings, and that (in my mind) would need an assortment of data history by which a given strategy could be rated. It is the applicability of the selected data, that leaves me wondering what makes for the most accurate rating of a strategy?
Regards,
Eric Dybsand Glacier Edge Technology Glendale, Colorado, USA
>In my attempts at attack pattern tracking, I've saved items such as:
>Timing of attack - early in the game vs. later in the game >Direction of attack - compass directions only >Style of attack - single front vs. multiple fronts >Quantity of units - how many were used >Quality of units - what experience level was for units >Unit mix - what kind of units used by opfor and AI player >Attack Rating - number of AI units destroyed + objectives captured
>My point is that the "patterns" I've generated and observed, were >incredibly subtle, if they existed at all. And those attacks with >the same high rating, were often completely different.
>So, while I agree that such a goal (the computer remembers the >patterns) is desirable, I'm curious as to what you and others think >are the data elements that are critical for demonstrating the "actual >pattern of attack" as employed by a human player.
I think that depends very much on the game. One of my preferred attack patterns is (in games that allow this sort of thing) keep the enemy at with long range weapons, load effective short range weapons onto fast trucks, move in and start the killoff (very effective against long range weapons in my exp). Now this is extremely easy to spot (for humans), but none of your criteria matches it and I frankly can't think of a method for an AI to spot it, that is, simple criteria that do not require this pattern to be known beforehand. You criteria (as I understand them) would not spot it since it is always a small group of units on a larger front and will probably disappear in the 'noise'. I've heard that Dungeon Keeper employs some sort of 'behavioural matching' which simulates player strategies in that way, I can't remember the reference. There seem to be ways though.
[snip]
>Once again, I agree with the goal, but I'm curious if the ratings >applied to the various strategies, are able to actually evaluate >whether the given strategy was succussful or a failure.
Simple suggestion: Ratio of Units lost/enemy units destroyed, maybe weighted by the power of the units. This is fairly easy to do. I am not sure whether this is always appropriate (or at all).
To: Joeb...@anv.net Subject: Re: Attacking in Strength
Jo> Distance to enemy force: Jo> The greater the distance the more options the computer has. That
I'd use a gravity-type formula ... concern is directly proportional to the inverse squared distance * enemy strength / player strength. Why inverse distance squared? In theory, the player is concerned just as much, so you get to multiply the distances... or something.
Jo> The computer must also know how the player is able to travel. With
Perhaps consider the attack range too... if you 'remember' position of unit x at t = 0, t = a and t = 2a (a is a number you make up) and average those vectors you can determine vaguely how long the unit will take to get to you. However, like Len Maxwell's 'destination predictor' (from another thread in comp.ai.games) this has major accuracy problems especially with complex terrain.
So... you could measure the distances between the enemy and player's closest unit for t and t = t-a and average that in with the current velocity vector estimation .. if the distance is coming towards you, try to extrapolate how long it will take to get to you (distance / speed).. or even better (distance - attack range / speed).. it doesn't have to be on top of you to attack you.
Concern should increase geometrically/exponentionally as t lessens (fear? well, it just make sense ... as it gets closer, there's a bigger concern it'll kill you).
Jo> Pattern of enemy attacks: Jo> Human players have a fortunate (for the computer) tendency to use the Jo> same strategy over and over. A defense tailored to meet a particular
Player analysis would do it, non?
Jo> Expert System 'AI': Jo> The computer has the advantage of leveraging all the most Jo> successful skills and strategies discovered during testing. These give Jo> the computer a 'bag of tricks' to use when the situation warrants.
This is a very good idea.
Jo> Memory of past success and failure: Jo> The computer must also remember the success and failure rates of Jo> the various strategies at its disposal. Some players are better suited
How about some reinforcement learning?
Have some values for each technique you can alter to alter the 'style' you use. For instance, if last time you used this technique you attack from the left and you select the technique again, you can approach from the right. Just a thought.
Or every time you use a strategy, lower it's chance of being selected w.r.o the other techniques. This tries to limit predictability.
Jo> Finally, the computer is just plain faster at analyzing numbers [...] Jo> more moves ahead. The computer can assess threats with much better Jo> speed and accuracy than the human player. Combine this with its
Well, that's not necessarily true. What you're trying to say is that computers are better tacticians. Strategy is where they get blown to bits.
''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''' ' Sunir Shah (ss...@intranet.ca) ' ' http://intranet.ca/~sshahftp://ftp.intranet.ca/usr/synapsis ' ' Fidonet: 1:241/11 BBS: The Open Fire BBS +1 (613) 584-1606 ' ' ' ' By the WEB: Vanity: http://intranet.ca/~sshah/ ' ' The Programmers' Booklist booklist.html ' ' ~`-,._.,-'~ Synapsis Entertainment synapsis.html ' ' _.,-`~'-,._ WASTE (Warfare AI Contest) waste/waste.html ' ' ' ' comp.ai.games FAQ: ftp://ftp.intranet.ca/usr/synapsis/cagfaq?.txt ' ' The Game Development Echo: Areafix GAMEDEV from Zone 1 (Fido) ' ''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''' ___ Blue Wave/QWK v2.12
To: Ed...@ix.netcom.com Subject: Re: Attacking in Strength (bloody long)
I'm very annoyed. I was in the middle of my relatively long post discussing player analysis and my sister (who came back yesterday from uni) blew the bloody fuse because she didn't think that eight light bulbs were enough ... noooooooo, she wanted a halogen too. (the fact that she left her stereo on for three months before I caught it shows how much she understands the concept of electricity).
AHHHHHHHHHHHHh.....
And the CD was on a good song to boot.
Ofc, she couldn't get off her butt and fix the fuse.. Noo.. that requires complex motor control.
Oh well, that should teach me... when chaos theory (read: my sister) reigns supreme, save frequently.
I hate everyone.
[I'm going to be very curt now. Suffer :)]
I'm going to outline what I'd do for a game like Warcraft...
Ed> This is another favorite area of interest of mine, and that being Ed> what data values actually provide a representation of the "pattern Ed> of attack the player uses"?
Player analysis, IOW, which isn't necessarily limited to conflict, but what the hell.
Ed> In my attempts at attack pattern tracking, I've saved items such as: Ed> Timing of attack - early in the game vs. later in the game Ed> Direction of attack - compass directions only Ed> Style of attack - single front vs. multiple fronts Ed> Quantity of units - how many were used Ed> Quality of units - what experience level was for units Ed> Unit mix - what kind of units used by opfor and AI player Ed> Attack Rating - number of AI units destroyed + objectives captured
You aren't measuring that relative to the computer's state. You have one point in space (7-D space with the above criteria, but space nonetheless), which is relative to everything. With two points (computer's state), it becomes more obvious because you have a point and a vector.
[Hands up who can tell I've been learning about vectors for the last four months? :)]
I mean, it's all well and good to know what the player does, but you need to know *why*.
Ed> patterns) is desirable, I'm curious as to what you and others think Ed> are the data elements that are critical for demonstrating the "actual Ed> pattern of attack" as employed by a human player.
Well, you'd need to measure the player relative to the computer, but not only that (and y'all missed my page long thinking process getting to this... aww..) but you need to keep in mind other landmarks and concerns in the scenario.
Here's a cheesy map (and now I have to redraw it):
t...............ttt.............tt . meadow (normal terrain) .===.==.t.tt......t.ttt....tt...tt t tree .===.==.t......MM...t..t.....t.... M Mine ....A.....t.t..MM.ttt.....t....... =, * Building .==.==.==.tttt..t.......tt....MM.. .==.==.==....t.....t..ttt.....MM.. ..................t.tttt.....ttt.t ...t..t....t.................ttt.. tt..tt.t..tt..ttttt........t.....t ..t.t...t..t...ttt..t..t..t.**.**. .t..t...tt..t.tt....tttt.t..**.**. .t...tt..t......t...ttt......B.... ....tt...ttt.t........t...***.***. ....t.....ttt...t...t...t.***.***. .tt...tt...ttt..ttttt..tt.....***. .................................t
With that map, I can see several strategies for player (A):
- Direct attack .. head for B - Flank - go around the outsides - Ambush - sprinkle units around and wait for B [This is impossible for the player as humans can only concentrate on so many things at once. so..]
- Ambush II - Find somewhere the computer is likely to go through or does go through often and stick troops there - Fan - send troops out in as many paths as possible - Attack the mine first - etc.
None of which you are even predicting, Eric, I don't think.
And then there are tactics:
- Long-range units in back guarded by short-range in front (WC classic) - Run like cowards - micro-flanking .. vs. global flanking, you just flank a little bit within the local conflict - Attack from the rear - Chasing - Run in, attack, run out - etc.
I can't think of every possible strategy, you can't .. nobody can. Everyone is different. So, what we need is a solution that is independant of knowing various strategies and tactics.
Ok, once again, your criteria, Eric, are limited to the player's attacking units' state. You can add on the victim units' state, such as the following stats:
- Strength - recent actions - distance to player - direction of motion - etc.
But still, that means nothing because the conflict isn't an isolated event. It's all related to things like the motion of the bases, mines (other important landmarks), other conflicts, movement of units (both sides) and other stuff (help me out on this one, folks ... I'm not SuperGeneralGuy)
I was just thinking, the player is clueless at the beginning. He generally experiments a bit. I suggest you do the same with the computer. Place units around the map, esp. around important landmarks.
Anyway, to determine preference to landmarks, I suggest what you do is for each landmark in the list (which you'll premake for the map, I s'pose.. which is fair, considering the player can do this, esp. with save games), you compare its distance to the conflict.. for the landmark that is closest to the conflict, add one to its conflict counter.
BTW, because we're just using relative distances, you can leave the distances squared... saves time instead of having to do a square root.
Compare each *type* of landmark's (sum the various landmarks that are of each type) count and assign guards based on that information.
Now, it's assumed there is some sort of goal. If it's something as straightforward as destroying the computer's base, you can do something like for each conflict, average the vectors from the it to the base. Ofc, for flanking manouvres it'll look the same as direct attacks... or, you can use a quantinization algorithm on the vectors to find out the more popular directions.
Now, I wouldn't leave it at that because it isn't general enough to use later on in the game ... You need to make that more abstract. Measure the degrees away from the direct vector from base A to base B for each cluster and use that later on.
You made a good point, Eric, about the time of attack... All I can say is for the graph of attacks vs. time, quantinize it so you know when the highest number of attacks occur so you can defend yourself. You can also extrapolate that the player isn't going to be ready during the low ebbs right after high peaks (she's expended her resources).
If the goal was something different, such as eradicating all units, you'll need other criteria.. hmm... there are only a few strategies for this, mostly the same as for destroying the base.
That's just strategy (barely). Now you have tactics.
Direction of attack: for each attack, take the direction and then take the difference between that direction and the direction to the player base. Average those differences over time, which should give you the expectation of attack direction.
Strength: Average the ratios of attack strength to victim strength AND the magnitude of attack strength AND the magnitude of victim strength. If the average ratio is not close to the ratio between avg. attack and avg. victim strength, you can assume that the player doesn't take into consideration how much strength the computer has. In that case, you should group units together with at least twice as much strength as the player's attack magnitude. Elsewise, 'pulse' groups of units so there are backups ready. Of course, the player will compensate for this, and the algorithm will compensate again and again. Recalculate this at the end of each scenario.
Unit mix: Another great idea. You can couple this with the player units that you got to first. That way, you can prepare to kill those quickly to get to the ones in back (such as long-range units). On the other hand, you can also prepare to take out the ones in back while you take out the ones in front. The latter would simply mean ALWAYS sending out short-range and long-range units together. So much for caring about unit mix
Style of attack: We can break this down into organization and not organization :).
The organization is really accounted for in the unit mix, so we'll skip that.
As for not-organization, all I can think of is running in, attacking, running out vs fighting vs running away vs running away after awhile.
Hmm.. measure the number of units on each side that are destroyed in each conflict as a percentage. Measure how many times you see the same unit in conflict compared to the strength ratios and its health (strong, high-stamina, healthy units can last longer than weak units). That way you can figure out bravery and berserkiness of the player.
I just realized it'll be pretty hard to understand what groups of units are... I suggest you use a fuzzy, recursive fill algorithm. Pick an opposing unit (and a computer unit if you don't know your group configurations) and recursively search out from there in all directions. Keep going until you're something like three tiles away without slamming into a unit of your own creed. [just made that up now.. <G>]
And anything else you figure out in play testing!
[Rating strategies' success/failure] Ed> Once again, I agree with the goal, but I'm curious if the ratings Ed> applied to the various strategies, are able to actually evaluate Ed> whether the given strategy was succussful or a failure. [... Both strats win game = same rating?] Ed> was used to "win" the game. There needs to be some way to provide Ed> a differenitation of the ratings, and that (in my mind) would need
What you need is something like client evaluation, and if WASTE had a worthwhile client evaluation article yet, I'd point you to it. As it is, we threw out the old one because the correct strategy was for both sides to retreat as fast as possible. :-)
In <4mk0go$...@news.bellglobal.com> ss...@intranet.ca writes:
>Subject: Re: Attacking in Strength (bloody long)
[sister stuff snipped]
>I'm going to outline what I'd do for a game like Warcraft...
> Ed> This is another favorite area of interest of mine, and that being > Ed> what data values actually provide a representation of the "pattern > Ed> of attack the player uses"?
>Player analysis, IOW, which isn't necessarily limited to >conflict, but what the hell.
First, Sunir, thank you for your _ideas_! However, I'm really not so interested in theory or "what I'd do" comments (although those type of comments do make for interesting reading), but instead, I'd really like to read about _practical implementations_ of what someone has actually done in a game, to provide for determining the pattern of an attack by another player.
> Ed> In my attempts at attack pattern tracking, I've saved items such as: > Ed> Timing of attack - early in the game vs. later in the game > Ed> Direction of attack - compass directions only > Ed> Style of attack - single front vs. multiple fronts > Ed> Quantity of units - how many were used > Ed> Quality of units - what experience level was for units > Ed> Unit mix - what kind of units used by opfor and AI player > Ed> Attack Rating - number of AI units destroyed + objectives captured
>You aren't measuring that relative to the computer's state.
Perhaps I really am. All of the measurements taken in this approach (of which I clearly posted that I felt was mediocre at best, and that I felt this approach was not successful at establishing patterns that were of much use) were relative to the state of the player initiating the attack (regardless of whether that player was human or computer controlled) as applied to the player which was the target of the attack.
Perhaps, the brevity of my post did not provided sufficient detail for readers to determine that.
Also, the above list is not meant to be all inclusive (hence the use of the qualifying phrase of "such as" while introducing the list). This list of data elements is meant only to be representative of the many data elements that I have actually implemented and tested for usage in attack pattern determination, in a completed game development project.
Since it does not work as well as I would like, I'm very interested in learning what others have *tried* and what their opinions are of the success (or failure) of different types of data and techniques.
>None of which you are even predicting, Eric, I don't think.
That is one of the goals of determining the pattern, I would suspect.
[remaining ideas snipped only for brevity]
Regards,
Eric Dybsand Glacier Edge Technology Glendale, Colorado, USA
To: Ed...@ix.netcom.com Subject: Re: Attacking in Strength
Ed> First, Sunir, thank you for your _ideas_! However, I'm really not so
What did you expect? I'm not about to write a block of code for *your* project. I'm not getting paid. :-)
Ed> like to read about _practical implementations_ of what someone has Ed> actually done in a game, to provide for determining the pattern of Ed> an attack by another player.
Most people have non-disclaimer clauses and *can't* talk about those things. But one guy did talk about his radar technique.
[Addendum: I just read that your reply to him via Lynx]
> Ed> In my attempts at attack pattern tracking, I've saved items such as: [...] >You aren't measuring that relative to the computer's state.
Ed> Perhaps I really am. All of the measurements taken in this approach Ed> (of which I clearly posted that I felt was mediocre at best, and that Ed> I felt this approach was not successful at establishing patterns that Ed> were of much use) were relative to the state of the player initiating Ed> the attack (regardless of whether that player was human or computer Ed> controlled) as applied to the player which was the target of the Ed> attack.
The problem is that the player has motivations for attacking. It's not berserk. Hence, you have to be able to construct a cause-effect chain or the expert system (because that's what it is) is completely useless.
Ed> Also, the above list is not meant to be all inclusive (hence the
No, of course not, as you point out. However, you didn't mention a single computer-player stat so I figured you might be just forgetting that. Besides, it helps to hammer out the idea completely so we can apply to solution to more than one application. I didn't mean to make it look like you never took into account the computer opponent, but ya never know.
Ed> Since it does not work as well as I would like, I'm very interested in Ed> learning what others have *tried* and what their opinions are of the Ed> success (or failure) of different types of data and techniques.
As I said, there are conflict of interest problems with that. Besides, who cares? It's obvious that this isn't a fully-hashed out problem in AI... a little conversation didn't hurt anyone.
''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''' ' Sunir Shah (ss...@intranet.ca) ' ' http://intranet.ca/~sshahftp://ftp.intranet.ca/usr/synapsis ' ' Fidonet: 1:241/11 BBS: The Open Fire BBS +1 (613) 584-1606 ' ' ' ' By the WEB: Vanity: http://intranet.ca/~sshah/ ' ' The Programmers' Booklist booklist.html ' ' ~`-,._.,-'~ Synapsis Entertainment synapsis.html ' ' _.,-`~'-,._ WASTE (Warfare AI Contest) waste/waste.html ' ' ' ' comp.ai.games FAQ: ftp://ftp.intranet.ca/usr/synapsis/cagfaq?.txt ' ' The Game Development Echo: Areafix GAMEDEV from Zone 1 (Fido) ' '''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''
In <4ms1cj$...@news.bellglobal.com> ss...@intranet.ca writes:
>To: Ed...@ix.netcom.com >Subject: Re: Attacking in Strength
> Ed> First, Sunir, thank you for your _ideas_! However, I'm really not so
>What did you expect? I'm not about to write a block of code for *your* >project. I'm not getting paid. :-)
> Ed> like to read about _practical implementations_ of what someone has > Ed> actually done in a game, to provide for determining the pattern of > Ed> an attack by another player.
>Most people have non-disclaimer clauses and *can't* talk about those things. >But one guy did talk about his radar technique.
>[Addendum: I just read that your reply to him via Lynx]
> > Ed> In my attempts at attack pattern tracking, I've saved items such as: >[...] > >You aren't measuring that relative to the computer's state.
> Ed> Perhaps I really am. All of the measurements taken in this approach > Ed> (of which I clearly posted that I felt was mediocre at best, and that > Ed> I felt this approach was not successful at establishing patterns that > Ed> were of much use) were relative to the state of the player initiating > Ed> the attack (regardless of whether that player was human or computer > Ed> controlled) as applied to the player which was the target of the > Ed> attack.
>The problem is that the player has motivations for attacking. It's not >berserk. Hence, you have to be able to construct a cause-effect chain or the >expert system (because that's what it is) is completely useless.
> Ed> Also, the above list is not meant to be all inclusive (hence the
>No, of course not, as you point out. However, you didn't mention a single >computer-player stat so I figured you might be just forgetting that. Besides, >it helps to hammer out the idea completely so we can apply to solution to more >than one application. I didn't mean to make it look like you never took into >account the computer opponent, but ya never know.
> Ed> Since it does not work as well as I would like, I'm very interested in > Ed> learning what others have *tried* and what their opinions are of the > Ed> success (or failure) of different types of data and techniques.
>As I said, there are conflict of interest problems with that. Besides, who >cares? It's obvious that this isn't a fully-hashed out problem in AI... a >little conversation didn't hurt anyone.
>''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''' >' Sunir Shah (ss...@intranet.ca) ' >' http://intranet.ca/~sshahftp://ftp.intranet.ca/usr/synapsis ' >' Fidonet: 1:241/11 BBS: The Open Fire BBS +1 (613) 584-1606 ' >' ' >' By the WEB: Vanity: http://intranet.ca/~sshah/ ' >' The Programmers' Booklist booklist.html ' >' ~`-,._.,-'~ Synapsis Entertainment synapsis.html ' >' _.,-`~'-,._ WASTE (Warfare AI Contest) waste/waste.html ' >' ' >' comp.ai.games FAQ: ftp://ftp.intranet.ca/usr/synapsis/cagfaq?.txt ' >' The Game Development Echo: Areafix GAMEDEV from Zone 1 (Fido) ' >'''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''
>___ Blue Wave/QWK v2.12
>>> Slipstream Jet - The QWK solution for Usenets #UNREGISTERED
Sunir,
I'm not going to be drawn into a pointless argument or a flame-war.
If I post a request for implementation results on AI considerations, and you respond with theories, ideas and untested opinions, and then you take offense because I pointed out my interest, on that request, was only in tested alternatives, then so be it.
Please, I've never suggested for you (or any one else to) not to discuss theories and ideas in this newsgroup, nor have I personally attacked you in any manner. Also, I'm still reading comp.ai.games, including your posts and every one else's that catch my eye, and in the meantime, I have a game I've got to get ready to ship.
Regards,
Eric Dybsand Glacier Edge Technology Glendale, Colorado, USA
ss...@intranet.ca wrote: >To: Joeb...@anv.net >Subject: Re: Attacking in Strength
>Or every time you use a strategy, lower it's chance of being >selected w.r.o the other techniques. This tries to limit >predictability.
That would work, but I feel it lacks precision .. Well, if you tune it right ?
> Jo> Finally, the computer is just plain faster at analyzing numbers >[...] > Jo> more moves ahead. The computer can assess threats with much better > Jo> speed and accuracy than the human player. Combine this with its
>Well, that's not necessarily true. What you're trying to say is >that computers are better tacticians. Strategy is where they get >blown to bits.
I don't think thats the point. In most war/strategy games I've played, the computer player wasn't able to realize a large one(two) move profit, because it stuck to RULES.
What I miss is some sort of lookahead for the computers actions. I also state here( contest it! ) that most of the people complaining about weak AI don't have the patience to wait 10 min. for the computer to move.
>' ~`-,._.,-'~ Synapsis Entertainment synapsis.html ' >' _.,-`~'-,._ WASTE (Warfare AI Contest) waste/waste.html '
To: Drmweav...@aol.com Subject: Re: Attacking in Strength
Dr> anybody got the original post in this thread... somehow my aol reader Dr> didn't pick it up....
Read da FAQ, man. Section 1.4.
Ok.. then again, maybe you don't want to read through all the archived messages. :)
''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''' ' Sunir Shah (ss...@intranet.ca) ' ' http://intranet.ca/~sshahftp://ftp.intranet.ca/usr/synapsis ' ' Fidonet: 1:241/11 BBS: The Open Fire BBS +1 (613) 584-1606 ' ' ' ' By the WEB: Vanity: http://intranet.ca/~sshah/ ' ' The Programmers' Booklist booklist.html ' ' ~`-,._.,-'~ Synapsis Entertainment synapsis.html ' ' _.,-`~'-,._ WASTE (Warfare AI Contest) waste/waste.html ' ' ' ' comp.ai.games FAQ: ftp://ftp.intranet.ca/usr/synapsis/cagfaq?.txt ' ' The Game Development Echo: Areafix GAMEDEV from Zone 1 (Fido) ' '''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''
>Or every time you use a strategy, lower it's chance of being >selected w.r.o the other techniques. This tries to limit >predictability. Sc> That would work, but I feel it lacks precision ..
But it's fast and simple... Ofc, there are better ways.
Sc> Well, if you tune it right ?
I doubt it.. it's independant of most information required to make a decent decision. All my method really does is prevent predictability a little.
>that computers are better tacticians. Strategy is where they get >blown to bits.
Sc> I don't think thats the point. In most war/strategy games I've played, Sc> the computer player wasn't able to realize a large one(two) move Sc> profit, because it stuck to RULES.
True.
Have you read the deep blue pages? I wonder if they're still up. Interesting reading.
Sc> What I miss is some sort of lookahead for the computers actions.
The only problem is the static evaluation function. That only allows one hard-coded strategy. For example, Deep Blue had great tactics but it had a predictable strategy. If you made a dynamic eval function, perhaps the computer would be better?
Sc> I also state here( contest it! ) that most of the people complaining Sc> about weak AI don't have the patience to wait 10 min. for the computer Sc> to move.
:-) Games are like that.
>' ~`-,._.,-'~ Synapsis Entertainment synapsis.html ' >' _.,-`~'-,._ WASTE (Warfare AI Contest) waste/waste.html '
Sc> Is that a synapse ?
It's whatever you want it to be (to me, it's just a filler... it's supposed to look like an 'X' <G>).
Real synapses look totally different.
+======+----} S {--------+======+ |Neuron|AXON} y {DENDRITE|Neuron| +======+----} n {--------+======+
Neurotransmitters get sent from the axon to the dendrite.. they stimulate an electrochemical reaction that effectively acts as a boolean TRUE. The dendrite also destroys the xmitter with an enzyme.
There's other important stuff involved here but this is an AI ng, not a neurology ng. :)
''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''' ' Sunir Shah (ss...@intranet.ca) ' ' http://intranet.ca/~sshahftp://ftp.intranet.ca/usr/synapsis ' ' Fidonet: 1:241/11 BBS: The Open Fire BBS +1 (613) 584-1606 ' ' ' ' By the WEB: Vanity: http://intranet.ca/~sshah/ ' ' The Programmers' Booklist booklist.html ' ' ~`-,._.,-'~ Synapsis Entertainment synapsis.html ' ' _.,-`~'-,._ WASTE (Warfare AI Contest) waste/waste.html ' ' ' ' comp.ai.games FAQ: ftp://ftp.intranet.ca/usr/synapsis/cagfaq?.txt ' ' The Game Development Echo: Areafix GAMEDEV from Zone 1 (Fido) ' ''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''' ___ Blue Wave/QWK v2.12
To: Ed...@ix.netcom.com Subject: Re: Attacking in Strength
Ed> I'm not going to be drawn into a pointless argument or a flame-war.
Did you send this after I e-mailed you or before? I'd imagine before as my news takes a while to get here.
Anyway ... uh, yeah. <insert rehash of everything I said in my last e-mail>
Ed> the meantime, I have a game I've got to get ready to ship.
G'luck.
''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''' ' Sunir Shah (ss...@intranet.ca) ' ' http://intranet.ca/~sshahftp://ftp.intranet.ca/usr/synapsis ' ' Fidonet: 1:241/11 BBS: The Open Fire BBS +1 (613) 584-1606 ' ' ' ' By the WEB: Vanity: http://intranet.ca/~sshah/ ' ' The Programmers' Booklist booklist.html ' ' ~`-,._.,-'~ Synapsis Entertainment synapsis.html ' ' _.,-`~'-,._ WASTE (Warfare AI Contest) waste/waste.html ' ' ' ' comp.ai.games FAQ: ftp://ftp.intranet.ca/usr/synapsis/cagfaq?.txt ' ' The Game Development Echo: Areafix GAMEDEV from Zone 1 (Fido) ' ''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''' ___ Blue Wave/QWK v2.12
To: 76743.1...@compuserve.com Subject: Re: Attacking in Strength
">Or every time you use a strategy, lower it's chance of being >selected w.r.o the other techniques. This tries to limit >predictability. 76> That would work, but I feel it lacks precision .. 76> Well, if you tune it right ?"
You know what... I just realized that when I replied to Peter's msg, I was dissing my own idea. Oh well.
76> But what if you used some machine learning 76> system that was fast enough to update its 76> strategy after each game without being a
It'd be a tad slower, but who cares if it's postgame?
Anyway... I think all you'd need is a decent evaluation function to adjust the weights. Suddenly I'm getting a flashback ... this sounds a lot like the nervous network I proposed. It's not very reactionary, though as there is no input. So a learning nervous network in seizure?
''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''' ' Sunir Shah (ss...@intranet.ca) ' ' http://intranet.ca/~sshahftp://ftp.intranet.ca/usr/synapsis ' ' Fidonet: 1:241/11 BBS: The Open Fire BBS +1 (613) 584-1606 ' ' ' ' By the WEB: Vanity: http://intranet.ca/~sshah/ ' ' The Programmers' Booklist booklist.html ' ' ~`-,._.,-'~ Synapsis Entertainment synapsis.html ' ' _.,-`~'-,._ WASTE (Warfare AI Contest) waste/waste.html ' ' ' ' comp.ai.games FAQ: ftp://ftp.intranet.ca/usr/synapsis/cagfaq?.txt ' ' The Game Development Echo: Areafix GAMEDEV from Zone 1 (Fido) ' ''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''' ___ Blue Wave/QWK v2.12
: I don't think thats the point. In most war/strategy games I've played, the : computer player wasn't able to realize a large one(two) move profit,
> because it stuck to RULES.
I agree with this completely Peter. I feel we've got all the tools we need to build an AI that can accurately and concisely order units around. What we lack is the methodology for giving it *purpose* for those moves and to actual *plan* the whole thing.
Eric's EN does some goal-setting, as do a couple of other games I can think of to one degree or another. I know that Eric's AI in Enemy Nations will also allow a plan to be disrupted or dropped due to changing circumstances, which is a step in the right direction.
: What I miss is some sort of lookahead for the computers actions. : I also state here( contest it! ) that most of the people complaining about : weak AI don't have the patience to wait 10 min. for the computer to move.
I contest it. Back in the days of my Amiga 1000, I used to lvoe the way the computer took time to "think" about its next move in the Perfect General. Of course, that only lasted about a week; then I realized it wasn't thinking at all (at least based on it tactics). ;)
I'll gladly wait 5 minutes in a strategic game if the AI makes it worth my while. I don't begrudge the time to a human opponent; I won't begrudge it to the AI if it plays well.
A tangentially related story: When I was working SDI ("Star Wars") a couple of years ago, I built an end-to-end analytical sim named SWARM. (Officially that stood for the Strategic Warfare Model, but in reality it stood for Steven Woodcock's Armageddon Research Model. ;) I installed a neural-network based missile attack characterization routine into the sim that allowed it to recognize various types of missile attacks and coordinate its defenses accordingly. Added nearly 20 seconds to every "minute" of simulated type, and hence nearly increased the length of the run by a third, but my defensive measures were improved by 20-odd percent too. *That* caused some eyebrows to raise in some high places, and was well worth the effort.
Steven
+========================================================================== ===+ | _ | | Steven Woodcock _____C .._. | | Senior Software Engineer, Gameware ____/ \___/ | | Lockheed Martin Information Real3D <____/\_---\_\ "Ferretman" | | Phone: 719-597-5413 | | E-mail: woodc...@escmail.orl.mmc.com (Work), swood...@cris.com (Home) | | Web: http://www.cris.com/~swoodcoc/wyrdhaven.html (Top level page) | | http://www.cris.com/~swoodcoc/ai.html (Game AI page) | | http://www.cris.com/~swoodcoc/software.html (AI Software page) | | Disclaimer: My opinions in NO way reflect the opinions of | | the Lockheed Martin Information Real3D | +========================================================================== ===+
you wouldn't be able to talk inany detail about any of that would you???
purely academically interested as a former military intelligence "weennie" who was consistently opposed when I attempted to introduce/use computer modeling at lower levels of the Navy intelligence community.
DrmWeaver2
Disowned by some An aggravation to others Independent souls aren't worried If someone else (anyone else) believes them Or understands their arguments The TRUTH must be told anyway
