Total War creator, The Creative Assembly, has announced the development of the latest in the line of acclaimed RTS games, Shogun 2. While the Total War franchise has a 10-year history and is fairly well-known for its AI, Â this blurb from their web site has spread through the web like an overturned ink well:
Featuring a brand new AI system inspired by the scriptures that influenced Japanese warfare, the millennia old Chinese “Art of War”, the Creative Assembly brings the wisdom of Master Sun Tsu to Shogun 2: Total War. Analysing this ancient text enabled the Creative Assembly to implement easy to understand yet deep strategical gameplay.
Sun Tzu’s “The Art of War” has been a staple reference tome since he penned it (or brushed it… or whatever) in the 6th century B.C. It’s hard to find many legends that have made it for over 20 centuries. Its applications have been adapted in various ways to go beyond war to arenas such as business and politics. Suffice to say that “The Art of War” lives on as “things that just make sense”.
The problem I have here is that this seems to be more of a marketing gimmick than anything. After all, most of what Sun Tzu wrote should, in various forms, already be in game AI anyway. Â To say Sun Tzu’s ideas are unique to him and would never have been considered without his wisdom is similar to saying that no one thought that killing was a bad idea until Moses wandered down the hill with “Thou Shalt Not Kill” on a big ol’ rock. No one stood around saying, “Gee… ya think?” Likewise, Sun Tzu’s advice about “knowing your enemy” is hardly an earth-shattering revelation.
Certainly, there is plenty of game AI out there that could have benefited from a quick read of a summary of Art of War.
Certainly, there is plenty of game AI out there that could have benefited from a quick read of a summary of Art of War. Things like “staying in cover and waiting for the enemy to attack you” come to mind. Of course, in the game world, we call that “camping” (as an individual) or “turtling” (as a group). I can imagine a spirited argument as to whether a camping/turtling AI is necessarily What Our Players Want™, however. It certainly beats the old “Doom model” of “walk straight towards the enemy”.
And what about the Sun Tzu concept of letting your two enemies beat the snot out of each other before you jump in? (I believe there are translations that yielded “dog shit” rather than “snot” but the meaning is still clear.) If you are in an RTS and one enemy just sits and waits for the other one whack you around a little bit, it’s going to look broken. On the other hand, I admit to doing that in free-for-all Starcraft matches… because it is a brutal tactic!
The problem I have with their claim is that we already do use many of his concepts in game AI.
The problem I have with their claim, however, is that there are many concepts in the Art of War that we already do use in game AI. By looking at Sun Tzu’s chapter headings (or whatever he called them) we can see some of his general ideas:
For ease of reference, I pillage the following list from Wikipedia:
Laying Plans/The Calculations
Waging War/The Challenge
Attack by Stratagem/The Plan of Attack
Tactical Dispositions/Positioning
Energy/Directing
Weak Points & Strong/Illusion and Reality
Maneuvering/Engaging The Force
Variation in Tactics/The Nine Variations
The Army on the March/Moving The Force
The Attack by Fire/Fiery Attack
The Use of Spies/The Use of Intelligence
Going into more detail on each of them, we can find many analogues to existing AI practices:
Laying Plans/The Calculations explores the five fundamental factors (and seven elements) that define a successful outcome (the Way, seasons, terrain, leadership, and management). By thinking, assessing and comparing these points you can calculate a victory, deviation from them will ensure failure. Remember that war is a very grave matter of state.
It almost seems to easy to cite planning techniques here because “plans” is in the title. I’ll go a step further then and point out that the practice of collecting information and assessing the relative merits of the selection, you can determine potential outcomes or select correct paths of action. This is a common technique in AI decision-making calculations. Even the lowly min/max procedure is, in essence simply comparing various potential paths through the state space.
Waging War/The Challenge explains how to understand the economy of war and how success requires making the winning play, which in turn, requires limiting the cost of competition and conflict.
This one speaks even more to the min/max approach. The phrase “limiting the cost of competition and conflict” expresses the inherent economic calculations that min/max is based on. That is, I need to get the most bang for my buck.
Attack by Stratagem/The Plan of Attack defines the source of strength as unity, not size, and the five ingredients that you need to succeed in any war. In order of importance attack: Strategy, Alliances, Army, lastly Cities.
Any coordinating aspects to the AI forces falls under this category. For example, the hierarchical structure of units into squads and ultimately armies is part of that “unity” aspect. Very few RTS games send units into battle as soon as they are created. They also don’t go off and do their own thing. If you have 100 units going to 100 places, you aren’t going to have the strength of 100 units working as a collection. Â This has been a staple of RTS games since their inception.
Tactical Dispositions/Positioning explains the importance of defending existing positions until you can advance them and how you must recognize opportunities, not try to create them.
Even simply including cover points in a shooter game can be thought of as “defending existing positions”.
Even simply including cover points in a shooter game can be thought of as “defending existing positions”. More importantly, individual or squad tactics that do leapfrogging, cover-to-cover, movement is something that has been addressed in various ways for a number of years. Not only in FPS games do we see this (e.g. F.E.A.R.), but even in some of the work that Chris Jurney did originally in Company of Heroes. Simply telling a squad to advance to a point didn’t mean they would continue on mindless of their peril. Even while not under fire, they would do a general cover-to-cover movement. When engaged in combat, however, there was a very obvious and concerted effort to move up only when the opportunity presented itself.
This point can be worked in reverse as well. The enemies in Halo 3, as explained by Damián Isla in his various lectures on the subject, defend a point until they can no longer reasonably do so and then fall back to the next defensible point. This is a similar concept to the “advance” model above.
Suffice to say, whether it be advancing opportunistically or retreating prudently, this is something that game AI is already doing.
Energy/Directing explains the use of creativity and timing in building your momentum.
This one is a little more vague simply because of the brevity of the summary on Wikipedia. However, we are all well aware of how some games have diverged from the simple and stale “aggro” models that were the norm 10-15 years ago.
Weak Points & Strong/Illusion and Reality explains how your opportunities come from the openings in the environment caused by the relative weakness of your enemy in a given area.
Identifying the disposition of the enemy screams of influence mapping…
Identifying the disposition of the enemy screams of influence mapping—something that we have been using in RTS games for quite some time. Even some FPS and RPG titles have begun using it. Influence maps have been around for a long time and their construction and usage are well documented in books and papers. Not only do they use the disposition of forces as suggested above, but many of them have been constructed to incorporate environmental features as Mr. Tzu (Mr. Sun?) entreats us to do.
Maneuvering/Engaging The Force explains the dangers of direct conflict and how to win those confrontations when they are forced upon you.
Again, this one is a bit vague. Not sure where to go there.
Variation in Tactics/The Nine Variations focuses on the need for flexibility in your responses. It explains how to respond to shifting circumstances successfully.
This is an issue that game AI has not dealt with well in the past. If you managed to disrupt a build order for an RTS opponent, for example, it might get confused. Also AI was not always terribly adaptive to changing circumstances. To put it in simple rock-paper-scissors terms, if you kept playing rock over and over, the AI wouldn’t catch on and play paper exclusively. In fact, it might still occasionally play scissors despite the guaranteed loss to your rock.
Lately, however, game AI has been far more adaptive to situations. The use of planners, behavior trees, and robust rule-based systems, for example, has allowed for far more flexibility than the more brittle FSMs allowed for. It is much harder to paint an AI into a corner from which it doesn’t know how to extricate itself. (Often, with the FSM architecture, the AI wouldn’t even realize it was painted into a corner at all and continue on blissfully unaware.)
The Army on the March/Moving The Force describes the different situations inf them.
[editorial comment on the above bullet point: WTF?]
I’m not sure to what the above refers, but there has been a long history of movement-based algorithms. Whether it be solo pathfinding, group movement, group formations, or local steering rules, this is an area that is constantly being polished.
The Attack by Fire/Fiery Attack explains the use of weapons generally and the use of the environment as a weapon specifically. It examines the five targets for attack, the five types of environmental attack, and the appropriate responses to such attack.
For all intents and purposes, fire was the only “special attack” that they had in 600 BC. It was their BFG, I suppose. Extrapolated out, this is merely a way of describing when and how to go beyond the typical melee and missile attacks. While not perfect, actions like spell-casting decisions in an RPG are not terribly complicated to make. Also, by tagging environmental objects, we can allow the AI to reason about their uses. One excellent example is how the agents in F.E.A.R. would toss over a couch to create a cover point. That’s using the environment to your advantage through a special (not typical) action.
The Use of Spies/The Use of Intelligence focuses on the importance of developing good information sources, specifically the five types of sources and how to manage them.
The interesting point here is that, given that our AI already has the game world at its e-fingertips, we haven’t had to accurately simulate the gathering of intelligence information. That has changed in recent years as the technology has allowed us to burn more resources on the problem. We now regularly simulate the AI piercing the Fog of War through scouts, etc. It is only a matter of time and tech before we get even more detailed in this area. Additionally, we will soon be able to model the AI’s belief of what we, the player, know of its disposition. This allows for intentional misdirection and subterfuge on the part of the AI. Now that will be fun!
Claiming to use Sun Tzu’s “Art of War” makes for good “back of the box” reading…
Anyway, the point of all of this is that, while claiming to use Sun Tzu’s “Art of War” makes for good “back of the box” reading, much of what he wrote of we as game AI programmers do already. Is there merit in reading his work to garner a new appreciation of how to think? Sure. Is it the miraculous godsend that it seems to be? Not likely.
In the mean time, marketing fluff aside, I look forward to seeing how it all plays out (so to speak) in the latest Total War installment. (Looks like I might get a peek at E3 next week anyway.)
I’ve written about rubber-banding before over on Post-Play’em where I talked about my observations of how it is used in Mario Kart: Double Dash. Rubber-banding is hardly new. It is often a subtle mechanism designed to keep games interesting and competitive. It is especially prevalent in racing games.
For those that aren’t familiar, the concept is simple. If you are doing well, the competition starts doing well. If you are sucking badly, so do they. That way, you always have a race on your hands—regardless of whether you’re in first, the middle of the pack, or in back.
Sometimes, it can be more apparent than others. If competitors are teleporting to keep up, that’s a bit egregious. If you come to a dead stop in last place, and so do the other racers, that’s way too obvious. The interesting thing is that sometimes, it can be more than just about fairness and a running a good race. Sometimes it can be used because it is inherently tied to a design mechanic other than racing.
I saw this review on Thunderbolt of the new gameSplit/Secondwhere it explains this phenomenon. The game is, on the surface, a racing game. However, in a mechanism borrowed from the aforementioned Mario Kart, the gameplay heavily revolves around “power plays“. These involve triggering things like exploding cars alongside the road, crumbling buildings, and helicopters dispensing explosives. You can even trigger massive changes for your foes like changing the route entirely. Needless to say, that has the effect of annoying the piss outta your opponents. The problem comes when those opponents are not human ones, but AI.
As the Thunderbolt reviewer puts it,
Split/Secondisn’t too difficult until some of the latter stages of the career, but unfair AI is a common problem throughout. It’s testament to the game’s focus on power plays that this unfair AI often occurs, since being in the lead isn’t a particularly fun experience when you can’t trigger the game’s main selling point. As a result, you’ll often find the following pack extremely close behind, often catching up six second gaps within two. Even when you know your car is much faster and you’re driving the race of your life, the AI finds a way to pass you with relative ease, performing impossibly good drifts and respawning from wrecks in the blink of an eye. Dropping from first place to fifth is such a common occurrence it would actually be quite comical if it weren’t for the frustration involved. That’s not to say Split/Second is a hard game – it’s usually pretty easy to wreck opponents with a decent power play, and you’ll normally be given ample opportunities to pass them – but the rubber band AI does cause some unwieldy races where the AI will pull ahead rather than keeping at a more realistic, surmountable distance.
As you can see above (emphasis mine), the rubber-banding is about more than keeping the pack close behind you if you are doing well. In order for the power plays to be relevant, you can’t be in the lead. You need to be behind them to use them. This is analogous to the “blue shell” in Mario Kart that would streak from wherever you were all the way up to the front of the pack and tumble the first-place kart. You simply can’t use the blue shell if you are in first place. In fact, the game won’t give you one if you are in first.
In Split/Second, the whole point of the game is blowing crap up and screwing with the other drivers. In fact, most of the fun of that is actually seeing it happen. While, in Mario Kart, you can use red or green shells, bananas, and fake blocks to mess with people behind you (and this is a perfectly normal tactic), the result of that isn’t the visually stunning and exciting experience that the power play in Split/Second is. Therefore, the designers of Split/Second had to make sure that you were able to use the power plays and see them in action.
In Split/Second the entire point of the rubber-banding is to make sure you aren’t in first—at least not for very long.
The difference between these two approaches is subtle. The rubber-banding in Mario Kart makes sure that, if you are in first, you can’t make a mistake without having people pass you. In Split/Second the entire point of the rubber-banding is to make sure you aren’t in first—at least not for very long.
You have to wonder how this mechanism would translate over to a shooter game, however. If rubber-banding in a shooter is to make sure you are challenged to a good fight… but one that in which you eventually triumph, a change to one where the AI is supposed to ensure that you don’t triumph would be a bit awkward. In fact, that would be negating the game’s purpose of having you see the rest of the content down the road (so to speak). AI in shooters is really cut from the same template of the rubber-banding in Mario Kart then. “Do well, and then lose.” It was just interesting to see a different take on this mechanism that generates a different outcome for the perfectly viable reason of making the game better.
Just saw another observation in a game review that really isn’t all that specific to the one game they were reviewing. In this case, it was a CNet review of the game, Alpha Protocol, by Obsidian Entertainment. A few of the observations about the AI were corroborated by other reviews elsewhere, but this one is the most detailed. (Most of the meat about the AI is on page 2 of the review.) The author doesn’t dance around the issue, either:
The AI is pretty dreadful. Security agents and mercenaries run about the levels in haphazard ways, may start climbing ladders as you fill them with lead, will kneel on top of exploding barrels, or might stare directly at you but fail to react unless you take a shot or give them a good punch. There’s a weird sense of randomness to your enemies’ behavior that diminishes the impact firefights may have had.
This is a common series of complaints about modern game AI. 10-20 years ago, this is mostly what we expected. Actually, to correct myself, what we usually got was enemies that turned to face us and then moved toward us in a line until we mowed them down. In the days of Doom and Quake, that was OK. Today? Not so much. Running around randomly provides a sense of activity and motion but immediately begins to trigger our sense of wrongness about the situation. This is especially noticeable as the dichotomy between excellent graphics and poor AI spreads. In any enemy that we can remotely anthropomorphize, this effect is even worse because we have an image in our head of what a human-like character should be doing in any given situation.
Where I would like to diverge from the author of the review is in the word “random”. We need to be careful with that word. Both in my book, Behavioral Mathematics for Game AIand in a number of the conference lectures I have given on the subject, I speak about the benefits of using randomness to provide variation in behavior selection. Games of all types are beginning to use these techniques as well.
For example, Richard Evans (Maxis) has spoken numerous times on the decision model for the Sims 3. The Sims go about a process of rating all the available behaviors and scoring them as to how well they match what the Sim wants or needs at the time. Then, they select from the top choices using what we call a “weighted random”. All of the actions are “in play” proportionate to their score, but the better the selection, the more likely it will be chosen. Is there randomness there? Yes, to provide variation. Does it look random? Not really. The reason is because each of the potential selections is actually fairly reasonable at the time — the result of the scoring system. To us as observers, we don’t view this as “random” — just “interesting”.
On the other hand, it seems that the behavior selection in Alpha Protocol looks random to the observer because either:
The behavior selection is, indeed, random, or
The behavior scoring algorithm is so poor that it doesn’t properly give the advantage to reasonable-looking actions.
Often times, behaviors will be chosen without consulting the current world state.
Either way, something is amiss. There is a 3rd option here as well. Often times, behaviors will be chosen without consulting the current world state. The “climbing ladders while you’re filling them with lead” bit might be an example of that but the observation where the enemy is seemingly unaware of you is a much better example. The bottom line is that the hardest part of designing AI is providing the adequate knowledge representation of the world state so that we can reason on it and make contextually appropriate decisions.
From a gameplay standpoint, the behaviors above can lead to some serious disappointment. To go back to the article:
Yet Alpha Protocol is no more a proper stealth game than it is a shooter. As with the shooting, the inconsistent AI provides a major hindrance… [snip]… Sneaking up on an enemy and taking him down with a minimum of fuss is mildly rewarding, as it tends to be in most games. But the actions you take leading up to that point involve activating certain skills and scurrying around in your silly crouched position–not outsmarting sharp AI or using the environment in clever ways.
With a generally random-acting AI, we aren’t outwitting anything.
We have had enough stealth games under our belt as an industry that we have primed the consumer with expectations of what to expect. Games ranging from Thief to Splinter Cell have shown us that the best part of stealth games is not just surprising an enemy… but outwitting him when he is actively trying to prevent you from surprising him. With a generally random-acting AI, we aren’t outwitting anything. The “surprise” aspect of it comes from merely staying out of his view. For all intents and purposes, that gameplay mechanic goes back to arcade shooters from the early 80’s. Haven’t we grown out of that yet?
Unfortunately, in a throw-back to my column earlier this week, the review goes on to compliment some of the other production values (though not nearly in as glowing of terms as the review of Lost Planet 2:
Alpha Protocol is not ugly, however; it’s just behind the times and artistically uninspired. Nevertheless, the safe houses Mike operates from between missions have some nice views, and some of the outdoor missions throw in some welcome flashes of color. Similarly, the sound design gets the job done, though without much style. The voice acting is at least solid, and the generic action-movie soundtrack ramps up at the right moments but otherwise stays out of the way.
Congratulations, I suppose. The problem is, our consumer public wants more than pretty pictures and nice sound. They are getting used to all of that and are now complaining about things being dumb. Again, this goes well beyond Alpha Protocol. This review can be copy-and-pasted to other games in as much of a templated fashion as trashy romance novels. There are way too many games that are missing the boat on this. Granted, as I mentioned in the Lost Planet 2 column, good AI developers are increasingly hard to find. That may be the case with Obsidian. I don’t know. (What I do know is that it’s time for me to give a ring to their HR guy.)
This is something that I have been seeing for a long time now. I’m sure we all have… and for good reason: A lot of times it is true. Despite what I said in my 7 minutes at the AI Devs Rant at the 2010 GDC about how reviewers like to bitch about bad AI, unfortunately too often it is justified. The juxtaposition of an otherwise good game in other areas with poorly executed AI development is a bit more tragic, however. That doesn’t point to a case of a smaller budget game. It’s an example of a well-funded project with either a priority or a talent problem.
Take this review of “Lost Planet 2″ in the Detroit News by Mike Neimoyer. Let me first give you the commentary about the rest of the aspects of the game:
The storyline is thinly tied together and barely cohesive.
OK… admittedly he gripes about the storyline. A lot of times that simply because you are doing a sequel of an established IP. Moving on… (emphasis mine):
The graphics are beautiful, and the environments are varied. Lush jungle or swamp areas appear suddenly in the midst of the glacial ice fields, with waterfalls and towering trees, and then there’s parched desertscapes or weather-battered coastal regions.
Not only is the landscape new and varied, but the Akrid, the natural inhabitants of the planet E.D.N. III, are back with new shapes, new designs and a whole new set of (usually grumpy) attitudes. The scale of the largest of the creatures, the “Cat-G type” is impressive to say the least. Like, God of War III-scale impressive.
The voice acting, for the most part, is well-done and up to the task. The music itself, however, is excellent. Swelling orchestral pieces accentuate the action sequences, and give the game an epic feel that would have been missing if they had used some “generic rock-style track #2″ soundtrack. Well done, Capcom.
Ok… so we have this love fest on the environment, the character modeling, the voice acting, and the soundtrack (he even mentions later that he would love to have a CD of the soundtrack). So what can possibly be wrong? Here’s a montage (again, emphasis mine):
The game is designed from the ground up to take advantage of four-player cooperative play. And heaven help you if you don’t have friends to play the game with. As 1UP.com states, “Brain-dead, unhelpful, and unresponsive, the computer-controlled team members are a liability rather than a resource.”
You truly, truly need a human companion or three to completely appreciate what Lost Planet 2 has to offer. For example, during one big boss battle, there are four separate tasks that need to be completed simultaneously. With four humans working together, this bit of teamwork wouldn’t be too difficult. Unfortunately, if you’re playing solo with AI teammates, you’re pretty much left to a snarled tangle of frustration and trial-and-error.
The level design is adequate, but I think that too much emphasis was put on the multiplayer portion, and not enough consideration for the solo player who will be reduced to using the criminally stupid AI companions.
Damn… so we have a Left 4 Dead-style game that is based around the idea that you have to cooperate with your teammates in order to not only survive but to actually complete mandatory parts of the campaign… and yet they don’t provide you with the companions that can do so.
Early on we were all letting our enemies die quickly because we lacked the capability to make them smarter.
In the era of single-player, shooting gallery-style games, having sub-par AI wasn’t too bad. After all, our fallback mantra was “the enemy won’t live long enough to show off his AI anyway.” I knew that was just a bad crutch when we were all saying it. The truth is, early on we were all letting our enemies die quickly because we lacked the capability to make them smarter! We were actually relieved that our characters were dying quickly. That managed to fit well with our other AI mantra: “Don’t let the AI do anything stupid.” Unfortunately, the chance of the AI doing something stupid rose exponentially with the amount of time that it was visible (and alive).
We are spending all this money on graphics, animation, voice actors, musicians… and leaving our AI to fester like an open sore.
Now that we are expecting AI teammates or squadmates or companions to come along for the ride, we have a much harder challenge. (Back in 2008, I wrote about this in my (at the time) regular column on AIGameDev in an article titled “The Art of AI Sidekicks: Making Sure Robin Doesn’t Suck“.) The problem is, we are spending all this money on graphics, animation, voice actors, musicians… and leaving our AI to fester like an open sore. Certainly, it takes more money and time to develop really good AI than it does to do a soundtrack, (I can speak to this, by the way… I was a professional musician a long time ago and am perfectly comfortable with anything from writing, arranging, and recording multi-track electronic grooves to penning entire sweeping orchestral scores. But that was in a previous life.) but it seems like a little effort might be called for. After all, the necessity of multi-player was built into the game design from the start… the necessity of a lush soundtrack was not.
To the defense of game companies, however, I’m very aware the good AI people are exceedingly and increasingly hard to find. The focus of the industry has changed in the past few years so that companies are trying to do better. However, that often means a lot more AI-dedicated manpower than they have. With many companies trying to find AI people all over the place, the demand has really out-stripped the supply. Some companies have had ads up for AI programmers for 6 to 9 months! They just aren’t out there.
So it isn’t always that the company doesn’t care or won’t spend the money on it. It’s often just the fact that AI is a very difficult problem that calls for a very deep skill set. Unfortunately, most of the game programs that exist really don’t even address game AI beyond “this is a state machine”. Academic AI programs are good for “real world AI” but don’t apply to the challenges that the game industry needs. Unfortunately, many academic AI institutions and their students don’t know this until they are rebuffed for suggesting very academia-steeped techniques that will fall flat in practice. (And no, a neural network would not have saved the AI in Lost Planet 2.)
So… in the mean time, here’s the suggestion: If your people don’t have the chops to make the required minimum AI, don’t design a game mechanic that needs that AI.
It’s amazing what pops up in my daily Google Alerts for “game AI” (although I’m getting tired of Allen Iverson news). This one caught my eye, though. On a blog called “Win My Ex Back” (no thank you, by the way), there was a post titled Online Dating Sim Game. There wasn’t a lot of detail about a specific game other than to mention online that “dating simulation games are among the new genres of online gaming that depicts romance.” To give an idea of what the author (Andy Jill) has in mind, I quote his summary:
It’s a simulation game where the main character that you’ll play (commonly fictional characters) has to achieve specific goals. The most typical one is to date numerous and different women and to have high level of relationship and among them within specified time limit. Generally, the game character must have enough funds by either securing jobs or other income-generating activities such as business.
In the same manner, attributes of the character is important in the game. Such attributes can be improved by doing different task and accomplishing it within the time limit. Most of these tasks take time to be accomplished and games of this type have real-time to them.
The author goes on to describe what apparently was the first online dating Sim, “the Dokyusei or Classmates” from 1992. Again to quote:
In this classic dating sim game, you will be controlling a male avatar that is surrounded by various female game characters. The game play will involve conversations with a variety of artificial intelligence (AI)-controlled girls, in which you will attempt to increase your “internal love meter” by means of right choices of dialogues. The game usually last for a limited game time like a month or a year.
Once the game is finished, your character may lose the game if it failed to win the hearts of any girl. However, you may “finish” one or more girls, usually by having sex with them or by attaining eternal love.
18 years later and we are still relegated to dialog trees.
To me, this sounds like the Sims titles… or for that matter, how some people try to play Mass Effect 2. Anyway, the point is not the gameplay mechanism (which is somewhat standard). Even the personality and mood meter facets are not all that uncommon. Again, think Sims 3. What I find almost joltingly alarming is that this game from 1992 was based on a dialog tree. Sure, that’s fine. We’ve had dialog trees for a while. The problem is… that’s how we would still have to do things! 18 years later and we are still relegated to dialog trees.
The only reason that the Sims doesn’t have dialog trees is that there isn’t real “dialog”. At least not the intelligible sort. Sure, there are behavior selection trees for choosing what to do next, but there is a subtle difference. When you select an action in the Sims, the game designer hasn’t necessarily hand-crafted what the response (or potential responses) will be. In a dialog tree, you are always in a specific place in that tree until you exit it. In the Sims, all that happens when you select an action is that you vaguely change the internal state of the character you are interacting with. That character’s actual response is calculated in a ridiculously expansive set of state values, formulas, and then a stochastic factor tossed in for good measure. You aren’t really ever sure exactly what you are going to get… although you may have a good idea what it might be.
On the user input level, it is still reminiscent of Zork or the early Ultima games.
I have to assume that this dating sim — and all others like it — would rely on a representation of actual dialog, however. And that brings us back to the dialog tree. Natural language processing isn’t going to cut it. Emily Short does a good job of it in her interactive fiction but the root of it all is still keyword-based input. As amazing as her work is, on the user input level, it is still reminiscent of Zork or the early Ultima games. Translated into a dating sim, the user’s free-form input could very well be reduced to “ask job”, “tell age”, and “compliment boobs”. In effect, it wouldn’t be all that different than the chat room shorthand of “a/s/l” for “age/sex/location”. Even if the game then gave elaborate (yet pre-scripted) answers to your questions, it will still be annoying to have it reply “I don’t understand what you mean” when you don’t guess the right keywords to use. Additionally, using that sort of shorthand isn’t going to ever feel really “romantic” is it?
I’m halfway through Noah Wardrip-Fruin’s book, Expressive Processing, where he talks extensively about the history and state of interactive dialog and drama. Even with all the work that has gone into this field for over 40 years, I’m sorry to say that we are no where near being able to replicate something other than a stilted parody of a romantic courtship.
That being said, it doesn’t matter how deep the AI is behind the scenes. Until we can solve the input/output problem our AI is trapped inside itself. Animation has gotten a lot better of late—especially facial and speech animation. I know there are adult-themed games out there and I assume that they are taking full advantage of photo-realism (not to mention realistic physics). However, all that nifty facial animation and subtle gesturing would still have to be tied to canned, pre-written dialog. And that is our bottleneck, isn’t it?
I don’t know how to solve it, really. Noah’s book is my first foray into even thinking about this interactive dialog and fiction stuff. (On the other hand, I would be tickled to do the personality, mood, and emotion modeling. That is in my wheelhouse!) That said, I don’t know how to solve it. I just find it sad that we are still stuck in this world where we can’t really interact with our game characters on a meaningful, natural-feeling level. I do know, however, that when we find it, that will be one of the final cusps we need to cross over in games. At that point, there’s not a lot stopping us.
I was looking at a review of the new football game, Backbreaker, by the blogger, Pastpadre, and I found an interesting combination of observations. First, for those that don’t know, Backbreaker is a football game that is developed by NaturalMotion. They are known first and foremost for their Euphoria physics engine that creates contextually realistic human body motion. Seeing as one of the biggest complaints about sports games—and football ones in particular—is that the human body physics begins to look canned and repetitive, you would think NaturalMotion had a bit of a head start in that area. The problem is, that isn’t all people gripe about with football games.
While I commend NaturalMotion for attempting to move things forward in this area, there are plenty of things that need to be addressed, if not solved, if the genre is to advance further. Physics isn’t necessarily on the top of the list. But hey, that’s what they do.
…coordinating 11 people being interfered with by 11 other people is a tall order.
This is particularly close to my heart because I’m an AI designer and a huge football fan. I am especially fond of football because of the deep intricacy of the team-based strategy that has to happen on every play. Of course, this is exactly the issue that is the hardest to address from an AI standpoint. Sports games—again, football ones in particular—are ridiculously hard to craft good AI for. For an industry that struggles to put together FPS squad tactics for 2 or 3 people, coordinating 11 people supposedly working together while being interfered with by 11 other people who are also working together is a tall order. The Madden franchise has been doing a passable job of this for some time. Sure, there are golden paths that bubble to the surface all the time, but those seem to be fewer and farther between.
Anyway, in this review, the author points out some interesting frustrations. He addresses it briefly in the first paragraph but I believe it summarizes things well (emphasis mine):
Reaction has been mixed with most gamers enjoying the Euphoria physics, polarization on the single camera angle, and the troubling CPU AI leading to the most concern.
(Brief aside: Who uses “CPU AI”? Not only is that redundant, it says the same thing twice.)
I will skip over his impressions of how Euphoria works. If you want to know all that happy-happy stuff, you can watch a Euphoria sales reel. I will address the AI-specific stuff. He goes on to comment about some of the specifics of how the AI falls flat on its face (emphasis mine).
The offensive output by the CPU has been pitiful. I’ve yet to give up more than a couple first downs on a single drive and still haven’t been scored on. The biggest reason is that the CPU turns the ball over a lot. In four minutes of gameplay it’s been close to an average of three picks thrown by the CPU. In the final demo video I posted I had three picks in three drives off the best offensive team in the game. That was with me being out of the play in all instances and the CPU just making bad passes.
This is summed up by the clincher which is my point here:
No matter how great the physics are I would not be able to play a football game if the CPU throws 10+ picks each time out.
No matter how great the physics are I would not be able to play a football game…
This certainly seems to be an example of tunnel-vision on a pet feature while ignoring (or being incapable of addressing) the rest of the game that people actually want to experience. Is this a Euphoria sales demo or a football game? This is something that is more prevalent in the game industry that we care to admit. It isn’t just Euphoria—or even physics—as the bad guy either. Swap in “game design”, “story”, “cool weapons”, “sexy chick outfits”, “huge environments” or whatever. AI is often the expression of your world. If your AI is broken, it severs the emotional connection to the game.
He continues:
The CPU goes with a jump pass way too often, whether it be springing forward or backwards, many times resulting in an interception. These aren’t instances where jumping to make a pass even would make some sense as the CPU would have been better off with their feet set.
Again, this apparently is broken decision logic. For those that don’t know football, in the pros a “jump pass” is a rare event only used in certain situations. Commentators will typically hammer on a QB for not throwing with his feet set. In fact, theoretically, you could do a football game without even including an animation for a jump pass and no one would really notice all that much. Therefore, for the author to notice that this happening too often is rather telling.
More:
The CPU defensive back AI has been terrible in instances where they aren’t running in stride. When they continue to run in stride they seem to play the ball pretty well. If they stop (like on a comeback route or a pass lobbed up for grabs) they’ll start to go the wrong way, make a terrible attempt at the ball, or just stand there. Several times I’ve completed passes with multiple defenders in the area who played the ball horribly wrong. They’ve just stood there and watched the ball go over their heads or watched the receiver make an easy catch.
Again, I’m guessing this is either lazyness on the part of the development team, not knowing about football, or an inability to solve the problem. I hope it is the latter. The 2nd one is not acceptable if you are actually making a football game. The first… well…
A few more. Apparently it is not just the player AI that is troubling:
Penalties have been really iffy to say the least. I’ve seen roughing the passer called in multiple situations when it shouldn’t have been. I have seen a pass interference [im]properly called in two instances, called once when there was clearly no interference, and in several other situations seen receivers taken completely out before the ball arrives and no penalty called. There also seems to be an issue with roughing the kicker (primarily on punts) where your CPU controlled players commit the penalty way too often and out of the user’s control. I haven’t seen this one much but it has been widely reported.
So this has to do with the logic for detecting penalty situations. These should be, in effect, simple rule-based systems. For example,
If you are botching up your static rule-based systems, then doing the contextual player-reaction AI is going to be a bitch.
Naturally, bad AI tends to lead to exploits:
Exploits have already been found with QB sneaks and the blocking of punts and field goals. These things could really damage the online play experience. The QB sneak problem, combined with the ability to no-huddle because of the lack of fatigue and not having to worry about injuries, could ruin online play. If blocked punts and kicks are prevalent online everyone will end up going for it on 4th downs.
If there is an obvious dominant strategy, you have now taken Sid Meier’s “interesting choices” and condensed them down into “choose this to win”.
This is the natural result… and is always a game-killer. If there is an obvious dominant strategy, you have now taken Sid Meier’s “interesting choices” and condensed them down into “choose this to win”. Many games with bad AI could still thrive in the online world. However, in a game where you only control 9% of your team and are entirely dependent on the other 91% for success, you can still do all the right things and still get rolled. That is not fun.
My point with all of this really has very little to do with the game itself and really less to do with the Euphoria engine. In fact, a quick browse through YouTube shows that there are some people who think the AI is just fine (although watching the videos and descriptions shows that people don’t really know what AI is or what good AI might look like). That being said, your mileage may vary. My point was that of the juxtaposition of the two points the author was making:Â you need more than pretty physics to make a compelling game.
This is really only a modified version of the graphics vs. AI debates.
This is really only a modified version of the graphics vs. AI debates. Originally, studios made pretty games with bad AI (and even bad physics). Now we seem to have moved on to making better physics… and with products like Euphoria, even better physics that take the load off of AI programmers trying to figure out what human reactions should be. None of that solves stupid AI play, though. And until we do that, we are going to be seeing otherwise decent games get shelved.
Fritz Heckel, a PhD student in the Games + Learning Group at UNC Charlotte, posted a video (below) on the research he has been doing under the supervision of G. Michael Youngblood. He has been working on using subsumption architectures to create coordination among multiple game agents.
When the video first started, I was a bit confused in that he was simply explaining a FSM. However, when the first character shared a state with the second one, I was a little more interested. Still, this isn’t necessarily the highlight of the video. As more characters were added, they split the goal of looking for a single item amongst them in that they parsed the search space.
This behavior certainly could be used in games… for example, with guards searching for the player. However, this is simply solved using other architectures. Even something as simple as influence mapping could handle this. In fact, Damián Isla’s occupancy maps could be tweaked accordingly to allow for multiple agents in a very life-like way. I don’t know what Fritz is using under the hood, but I have to wonder if it isn’t more complicated.
Obviously, his searching example was only just a simple one. He wasn’t setting out to design something that allowed people to share a searching goal, per se. He was creating an architecture for cooperation. This, too, has been done in a variety of ways. Notably, Jeff Orkin’s GOAP architecture from F.E.A.R. did a lot of squad coordination that was very robust. Many sports simulations do cooperation — but that tends to be more playbook-driven. Fritz seems to be doing it on the fly without any sort of pre-conceived plan or even pre-known methods by the eventual participants.
From a game standpoint, it seems that this is an unnecessary complication.
In a way, it seems that the goal itself is somewhat viral from one agent to the next. That is, one agent in effect explains what it is that he needs the others to do and then parses it out accordingly. From a game standpoint, it seems that this is an unnecessary complication. Since most of the game agents would be built on the same codebase, they would already have the knowledge of how to do a task. At this point, it would simply be a matter of having one agent tell the other “I need this done,” so that the appropriate behavior gets switched on. And now we’re back to Orkin’s cooperative GOAP system.
On the whole, a subsumption architecture is an odd choice. Alex Champandard of AIGameDev pointed out via Twitter:
@fwph Who uses subsumption for games these days though? Did anyone use it in the past for that matter?
That’s an interesting point. I have to wonder if, as is the case at times with academic research, it is not a case of picking a tool first and then seeing if you can invent a problem to solve with it. To me, a subsumption architecture seems like it is simply the layered approach of a HFSM married with the modularity of a planner. In fact, there has been a lot of buzz in recent years about hierarchical planning anyway. What are the differences… or the similarities, for that matter?
Regardless, it is an interesting, if short, demo. If this is what he submitted to present at AIIDE this fall, I will be interested in seeing more of it.
Raph Koster, best known as the lead designer of Ultima Online and the creative director for Star Wars Galaxies, wrote an interesting post on his blog at the end of April. In the post, “Dynamic POIs“, He discusses how, in Star Wars Galaxies, they constructed a method for the computer to generate automatic content in the MMO world.
The term POI stands for point of interest and dates back to his UO days. At the time, those POIs were hand-placed by the design team. In a world the size of SWG, this was simply not feasible. He comments how even a room full of junior designers churning these encounters out was simply not enough. The proposed solution was to algorithmically generate these locations and fill them with life.
One of the complicating factors in this was that they weren’t simply placing the D&D staple of a wandering monster. They were generating a camp or facility full of these agents complete with scenery… and plot. Here’s a quote from the article that explains it better.
Don’t roll up just a bandit; roll up a little bandit campsite, with a tent, a campfire, three bandits, one of whom hates one of the others, a young bandit who isn’t actually a bad guy but has been sucked into the life because he has a young pregnant wife at home… In fact, maybe have an assortment of bandits — twenty possible ones maybe. Then pick three for your camp. That way you always get a flavorful but slightly different experience.
So what we are looking at is a random type of encounter with a random population, in a random location. So far that’s pretty groovy. If you want a description of what it was like, SWG producer, Haden Blackman writes about his own encounter with a dynamic POI.
One advantage that they had with SWG that didn’t exist with UO is that the SWG map was procedurally generated in the first place. That made generating a random encounter simply an extension that was over and above the map generation. They could create and destroy these places by simply making sure there was no one around.
…it was just as hard to create the dynamic content as it was to create static content.
In order to provide some variety, these encounters could also be designed modularly. The actors might be different or they may send you on different missions than a similarly constructed encounter elsewhere. The n-complexity of available encounters rises fairly quickly that way, of course. Â Unfortunately, Raph says they weren’t quite able to make this whole process completely data-driven. The result is that it was just as hard to create the dynamic content as it was to create static content. In particular, the scripting of the encounters was tricky. The result of all of this is that dynamic POIs ended up coming back out of the game.
He hasn’t written off the entire idea yet, however. Neither have I. In fact, I spoke about it a little at GDC Austin in 2009. (Get my slides here.) Raph was not able to attend that lecture (for which I believe Sheri Graner Ray gave him a tongue-lashing). I don’t pretend to solve the problem of dynamic content to the extent that Raph writes about. The point I addressed was that there are ways for AI-controlled NPCs to dynamically deal with a shifting population and environment that can lead to more expressive encounters game-wide.
Anyway, with the advent of dynamic pacing in Left 4 Dead via the much-referenced AI Director, there is a lot more attention being paid to how we can break out of entirely hand-placed, hand-paced, and often linear content. Additionally, while the public’s appetite for “sandbox” worlds is increasing — thanks in no small part to games like GTA4, the industry can’t sustain GTA4-like budgets of $100 million for very long. If the procedural content issue was solved (or at least furthered along somewhat) then we can satisfy both the demands of our players and the restrictions of budget.
One place to look might be to the work that is being done by the likes of interactive fiction writer and programmer Emily Short and the work that is continually being done by Michael Mateas and the Expressive Intelligence Studio company and UCSC. Someplace in there is a hybrid of dynamic, data-driven storytelling that we can eventually work with to create truly open-ended content.
Well, after the migration to the “New Blogger”, I discovered that they won’t support my php includes so that the blog can fit into the rest of my site. Therefore, I am going to ditch Blogger entirely.
After having worked as an author for a couple of other blogs that used Wordpress, I figure that it was a decent solution. Therefore, I am going to begin installing Wordpress and see if I can get all my existing content over to it. Things may look ugly for a bit but hopefully I can get it taken care of.
Oh… and a big thank you to Google/Blogger for screwing up dozens (if not scores) of hours of my work. Good work, folks.
