Speaker 1: Okay, So let's stick into something that it's way more
Speaker 1: fascinating than just making you know, bad guys shoot straight
Speaker 1: in games, we're talking about artificial intelligence, but specifically in games,
Speaker 1: that kind of hidden intelligence that makes the world's feel alive.
Speaker 2: Rights, the stuff making characters seem like they're thinking or
Speaker 2: moving believably.
Speaker 1: Exactly, and sometimes, let's be honest, it leads to those
Speaker 1: glitches that are just hilariously memorable.
Speaker 2: Yeah, it's that invisible layer, isn't it, ye, shaping so
Speaker 2: much of how you experience the game, making it feel reactive,
Speaker 2: not just static.
Speaker 1: Totally, and to really get our heads around how it
Speaker 1: all works. We've gathered a whole stack of material. We're
Speaker 1: talking algorithms, techniques, some history. It's pretty deep stuff.
Speaker 2: You can get technical.
Speaker 1: Yeah, but our mission here for this deep dive is
Speaker 1: to sort of cut through that jargon. We want to
Speaker 1: find the really cool, important ideas and give you listening
Speaker 1: now a short tut to understanding what's going on behind.
Speaker 2: The scenes, like lifting the curtain a bit, Yeah.
Speaker 1: Uncover some surprises, maybe have a few aha moments along
Speaker 1: the way.
Speaker 2: So think of it like getting a peek under the hood,
Speaker 2: looking at the simulated brains, making everything tick, your opponents,
Speaker 2: your allies, even the whole world sometimes.
Speaker 1: Okay, so where do we start. The sources actually bring
Speaker 1: up an interesting point about the history. Back in the
Speaker 1: mid nineties, AI was actually like a selling point on
Speaker 1: the back of the box.
Speaker 2: Oh yeah, I remember that era. Beneath a Steel Sky
Speaker 2: is a good example.
Speaker 1: Right, It promised virtual theater. What was that supposed to be?
Speaker 2: Well, the idea, the ambition was for characters who weren't
Speaker 2: just puppets. They'd move around, do their own thing, make
Speaker 2: the world feel lived in. Virtual theater. Sounded grand, but
Speaker 2: the reality, the reality is the sources kind of point
Speaker 2: out was maybe a bit less revolutionary. Often it just
Speaker 2: met characters pacing back and forth on pretty fixed paths. Still,
Speaker 2: it was an early.
Speaker 1: Attempt, a bit underwhelming then maybe, But then something came
Speaker 1: along that really shifted things, didn't it, gold enneye double
Speaker 1: O seven.
Speaker 2: Yeah, that was definitely a game changer for a lot
Speaker 2: of people's perception of AI.
Speaker 1: What did it do differently?
Speaker 2: It wasn't just pre programmed patterns. I mean, yes, the
Speaker 2: guards us simple state machines, that's true, But the key
Speaker 2: thing was this basic sense simulation.
Speaker 1: They added sense simulation.
Speaker 2: Yeah, characters could essentially see their buddies. If one guard
Speaker 2: saw another get taken out, they'd react, maybe investigate, maybe
Speaker 2: raise the alarm. It wasn't super complex, but that idea
Speaker 2: of awareness of perceiving the world a change things big time,
Speaker 2: and that whole concept simulating senses, site, sound, it became
Speaker 2: absolutely central to stealth games.
Speaker 1: Later on, like Thief or Metal Here sawid.
Speaker 2: Exactly Thief, Metal Gear, splinter Cell. The core gameplay loop
Speaker 2: often revolves around exploiting or avoiding the AI senses how
Speaker 2: guards react to a noise or a shadow or seeing
Speaker 2: something suspicious.
Speaker 1: So this points to a fundamental cycle that seems to
Speaker 1: run through all the material we looked.
Speaker 2: At pretty much. Yeah, you can boil a lot of
Speaker 2: it down to perception leads to decision making, which leads
Speaker 2: to action.
Speaker 1: Per section decision action.
Speaker 2: Okay, the AI gathers info for the world, decides what
Speaker 2: to do about it, and then actually does something, move, shoots, hides, talks.
Speaker 1: Whatever, right, and to actually do those actions. Especially back then,
Speaker 1: developers were always fighting against hardware limits, weren't they.
Speaker 2: Oh, absolutely huge constraint. Early days, you had maybe one
Speaker 2: CPU doing everything. Now we've got multiple cores, powerful GPUs.
Speaker 1: PPUs are more for graphics though.
Speaker 2: Mostly Yeah, they're built for massively parallel stuff, tons of
Speaker 2: simple calculations at once, which is perfect for graphics, but
Speaker 2: it does influence AI. Sometimes. If you can break an
Speaker 2: AI task into lots of small independent bits, maybe you
Speaker 2: can leverage the GPU. But CPUs are still king for
Speaker 2: that complex, sequential thinking part of AI.
Speaker 1: So it's always a trade off performance versus complexity.
Speaker 2: Always has been, always will being gamed of.
Speaker 1: And it's not just consoles and PCs anymore. You've got phones, tablets, iOS, Android,
Speaker 1: AI has to run everywhere.
Speaker 2: Now, Yeah, the rise of mobile is huge, and that's
Speaker 2: where engines like Unreal, Unity, godot are so important. They
Speaker 2: provide this layer of this framework.
Speaker 1: So you can write the AI once.
Speaker 2: Pretty much, write at once, deploy it on PC, console, mobile,
Speaker 2: maybe with some tweaks, but the core logic can often
Speaker 2: be reused. That definitely shapes how you design the AI
Speaker 2: systems in the first place.
Speaker 1: Okay, so keeping that cycle in mind, perception, decision, action,
Speaker 1: and let's talk about the action bit, specifically movement. How
Speaker 1: do AI characters actually you know, get around because unless
Speaker 1: it's maybe an economic sim they need to move.
Speaker 2: Movement's fundamental. Yeah, an AI character has a position, maybe
Speaker 2: an orientation, physical properties. Yeah, and the movement algorithms figure
Speaker 2: out frame by frame where they need to go next
Speaker 2: to reach their goal.
Speaker 1: But you don't want them just snapping between locations. Right,
Speaker 1: has to look physical exactly.
Speaker 2: That's where kinematics comes in. It's basically applying physics Newton's laws. Really,
Speaker 2: even in a simple way. Velocity can't change instantly right inertia. Yeah,
Speaker 2: So the algorithms don't teleport the character. They consider its
Speaker 2: current speed and direction and use acceleration to make the
Speaker 2: movement smooth and believable. No instance stops or starts.
Speaker 1: And the sources talk about these things called steering behaviors
Speaker 1: like building blocks for a movement.
Speaker 2: They are think of them as simple reactive urges. Seek
Speaker 2: tells a character to move towards a target. Flea tells
Speaker 2: it to run away. Very basic, but you build on those,
Speaker 2: you combine them. So pursue isn't just seek. It tries
Speaker 2: to predict where a moving target will be and seeks
Speaker 2: that point. Collision avoidant steers away from obstacles. Often these
Speaker 2: more complex behaviors work by delegating to the simpler ones
Speaker 2: seek the target, but also flee from that wall that's
Speaker 2: getting too close.
Speaker 1: The material mentioned a potential issue with a simple wander behavior,
Speaker 1: something about rotational jerkiness. What does that look like?
Speaker 2: Imagine a character just randomly picking a new direction every
Speaker 2: single frame. They kind of twitch and spin around unnaturally.
Speaker 2: It looks terrible. Uh so simple wander needs smoothing. Maybe
Speaker 2: you pick a random point on a circle slightly ahead
Speaker 2: of the character and steer towards that. Updating it less
Speaker 2: frequently makes it look more like they're meandering, not having
Speaker 2: a seizure.
Speaker 1: Gotcha. So steering handles the how of moving smoothly, But
Speaker 1: how does the AI figure out the where? Especially in
Speaker 1: a big complex level. That sounds like pathfinding.
Speaker 2: That's exactly what pathfinding is for finding a route a
Speaker 2: sequence of steps from point A to point B that
Speaker 2: avoids opticles like walls, pits, whatever the level design are
Speaker 2: put in the way.
Speaker 1: It's the how do I get there from here? Problem?
Speaker 1: Precisely the source of mentioned classic algorithms Dykstra's algorithm. I
Speaker 1: think I remember that from a computer science class.
Speaker 2: Yeah, dykstras is fundamental graph theory, it's guaranteed to find
Speaker 2: the absolute shortest path from your start point to every
Speaker 2: other reachable point in the graph, representing.
Speaker 1: The level every other point.
Speaker 2: That sounds like a lot, it can be, and that's
Speaker 2: the catch. For games. Usually you only care about the
Speaker 2: past to one specific goal. Calculating the shortest path to
Speaker 2: everywhere else is often effort.
Speaker 1: So games tend to use a more A.
Speaker 2: Star A is generally the go to. Yeah. Yeah, it's
Speaker 2: smarter about searching, how so it uses a heuristic think
Speaker 2: of it as an educated guess or a rule of
Speaker 2: thumb to estimate the distance remaining to.
Speaker 1: The goal, like as the crow flies distance.
Speaker 2: Often yeah, or Manhattan distance. In grid based worlds, Igelegan
Speaker 2: combines the actual cost traveled so far with this estimated
Speaker 2: future cost. This lets to prioritize exploring paths that seem
Speaker 2: to be heading more directly towards the goal.
Speaker 1: Ah, so it doesn't waste time exploring paths that are
Speaker 1: clearly going the wrong way, even if they look short initially.
Speaker 2: Exactly, it focuses the search, makes it much faster than
Speaker 2: Diikstra in most typical game scenarios. Now there's a small
Speaker 2: trade off mentioned. Oh, if you're heuristic, your guess is bad. Specifically,
Speaker 2: if it overestimates the actual cost to the goal, then
Speaker 2: a might not find the absolute mathematically shortest path. It
Speaker 2: might find a path that looks shorter based on the
Speaker 2: bad gas.
Speaker 1: So developers might accept a path that's like ninety nine
Speaker 1: percent optimal if it means the calculation is way faster.
Speaker 2: That's the aha moment right there, exactly. Performance often trump's
Speaker 2: perfect optimality. In real time games, good enough, fast enough
Speaker 2: is often the.
Speaker 1: Mantra makes sense. So for A or diestra to even work,
Speaker 1: they need a map, right, but not the full three
Speaker 1: D graphics geometry.
Speaker 2: Where they need a simplified representation of the navigable space.
Speaker 2: You can't search through raw triangles.
Speaker 1: Like navigation meshes. I've heard that.
Speaker 2: Term nav meshes are super common. They basically represent all
Speaker 2: the walkable surfaces as a set of connected polygons like
Speaker 2: piles on the floor. The AI path finds across these polygons.
Speaker 1: How does that work with the algorithm? Are the polygons
Speaker 1: the nodes?
Speaker 2: Sometimes the center of the polygons, but often it's the
Speaker 2: edges between the polygons. The edges as nodes approach is mentioned.
Speaker 2: Path finding becomes finding a sequence of edges to cross
Speaker 2: to get from the starting polygon to the goal polygon.
Speaker 1: Okay, but what about huge open worlds. Those nav meshes
Speaker 1: must get enormous.
Speaker 2: They do, and that's where hierarchy pathfinding comes into play.
Speaker 2: Layers kind of you group areas together, like maybe this
Speaker 2: whole building is one high level note and that neighborhood
Speaker 2: is another. The AI first finds a path between these
Speaker 2: big regions, like planning a trip using major highways. Then
Speaker 2: once it enters a region, it does a more detailed
Speaker 2: pathfind within that smaller area, figuring out the local streets.
Speaker 1: That seems way more manutable. Okay, so characters can move
Speaker 1: realistically with steering, and they can find paths with pathfinding,
Speaker 1: But how do they decide decide where to go, whether
Speaker 1: to fight or hide or talk. That's the decision making
Speaker 1: part of the.
Speaker 2: Cycle, right, This is the core intelligence, the brain making
Speaker 2: the choices. It determines what the character intends to do.
Speaker 1: The sources mentioned decision trees as a common technique, simple
Speaker 1: if thin stuff.
Speaker 2: Pretty much, they're like flow charts, a series of questions
Speaker 2: usually yes no, leading down branches to an action is
Speaker 2: the enemy visible? Yes? Do I have AMMO? Yes?
Speaker 1: Shoot?
Speaker 2: No reload back up the tree is the endy visible?
Speaker 2: No patrol.
Speaker 1: They sound fast and easy for designers to work with.
Speaker 2: They are very fast, execute, modular, easy to build and tweak,
Speaker 2: use for tons of stuff from simple reactions to choose
Speaker 2: the animations.
Speaker 1: What about state machines those seem classic too.
Speaker 2: Finite state machines yeah, FSMs. You define distinct states. The
Speaker 2: AI can be in patrolling, investigating, attacking, fleeing, and transitions
Speaker 2: between them. If you're patrolling and you hear a noise,
Speaker 2: the transition condition you switch to the investigating state.
Speaker 1: Sounds neat, but maybe it gets messy. If you have
Speaker 1: lots of states.
Speaker 2: It can become a spaghetti diagram really quickly if you're
Speaker 2: not careful, lots of arrows criss crossing. That's why higer
Speaker 2: Arsenill state machines.
Speaker 1: Were developed, so states within states exactly.
Speaker 2: Your combat state might contain substates like taking cover, firing, reloading.
Speaker 2: It helps organize complexity, and you can have transitions that
Speaker 2: jump across levels, like a sudden loud noise pulling you
Speaker 2: out of reloading right back to a top level alert state.
Speaker 1: Now The material really highlights behavior trees as being super popular. Now,
Speaker 1: what's the deal with them?
Speaker 2: Behavior trees are very powerful and crusely very intuitive for designers.
Speaker 2: They structure tasks in a tree like way. The AI
Speaker 2: executes the tree usually depth first, trying branches until it
Speaker 2: finds an action or sequence that succeeds.
Speaker 1: Can you give an example?
Speaker 2: Sure, a root node might be engage enemy. It has
Speaker 2: children like is enemy visible? If yes, it goes down
Speaker 2: the attack branch. If no, it tries the search for
Speaker 2: enemy branch, which might have subtasks like go to last
Speaker 2: known position, then scan area. It feels logical, like how
Speaker 2: a person might approach a problem.
Speaker 1: Again, they're reusable, hugely reusable.
Speaker 2: You create nodes for common actions, move to play, animation,
Speaker 2: check condition, and assemble them into trees. You can make
Speaker 2: a complex take cover behavior and just drop it into
Speaker 2: different AI characters. Trees engines often have visual editors like
Speaker 2: Lego for.
Speaker 1: AI like al prefabs.
Speaker 2: Okay, that makes sense exactly that concept.
Speaker 1: Are there other ways? The sources mention things like utility
Speaker 1: based systems. Yeah, goal oriented behavior.
Speaker 2: Yeah, those are a different philosophy instead of pre defined
Speaker 2: logic flows. The AI has goals or motives hunger, thirst, safety, curiosity,
Speaker 2: each with a current insistence level.
Speaker 1: How much it wants it.
Speaker 2: Basically, yeah, the AI looks at all the actions it
Speaker 2: could do and evaluates how well each action would satisfy
Speaker 2: its most insistent needs. It picks the action that leads
Speaker 2: to the lowest overall discontentment.
Speaker 1: Sounds more adaptive, maybe less predictable, it can be.
Speaker 2: The big challenge is often calculating the utility of an action.
Speaker 2: How much will eating this apple reduce my hunger? How
Speaker 2: long will it take to pathfind to the apple? Often
Speaker 2: requires good estimation heuristics.
Speaker 1: Again, seems complex to set up right.
Speaker 2: It can be more work up front than say, a
Speaker 2: state machine. Rule based systems are another alternative mentioned, using
Speaker 2: if then rules like if player is visible and health
Speaker 2: is low, then find cover. But writing good comprehensive rule
Speaker 2: sets is really hard.
Speaker 1: The knowledge acquisition bottleneck they called it.
Speaker 2: Right, So while powerful in theory, they're less common in
Speaker 2: practice for moment to moment character AI compared to trees
Speaker 2: or state machines.
Speaker 1: Okay, individual decisions covered. What about coordinating groups or making
Speaker 1: bigger strategic plans?
Speaker 2: Yeah, moving beyond single characters, that's tactical and strategic. AI
Speaker 2: tactical is about local coordination soldiers using cover fire, flanking maneuvers.
Speaker 2: Strategic is the big picture, deciding which continent to invade
Speaker 2: in a global strategy game, or managing an economy in
Speaker 2: an RTS.
Speaker 1: For tactics, the sources mention tactical waypoints, not just points
Speaker 1: to walk to, but points with extra info.
Speaker 2: Exactly, a waypoint might be tagged as good cover spot
Speaker 2: versus north or ambush point, but just relying on static
Speaker 2: points placed by a designer has limits. What if the
Speaker 2: situation changes.
Speaker 1: So run time analysis.
Speaker 2: Yeah, better systems analyze the environment dynamically, calculating lines of sight,
Speaker 2: checking actual cover effectiveness based on where the enemies are
Speaker 2: right now.
Speaker 1: The frag maps idea sounded cool learning from where players die.
Speaker 2: It's a neat example of simple tactical learning. Basically, areas
Speaker 2: on the map build up a danger value. If AI
Speaker 2: units keep getting killed there, future AI might then learn
Speaker 2: to avoid those spots or be more cautious.
Speaker 1: But they need to unlearn it too, right, so they
Speaker 1: don't avoid a place forever after one unlucky death.
Speaker 2: Exactly, a decay factor or unlearning bias is important and.
Speaker 1: For strategy, Like in an RTS, how does the AI
Speaker 1: decide whether to build more units or research tech.
Speaker 2: Often with multi tiered AI, you have layers. A top
Speaker 2: level strategic layer makes broad decisions we need more resource
Speaker 2: income or prepare for an attack on their.
Speaker 1: Base, and that gets passed down right.
Speaker 2: A mid level tactical layer might translate attack their base
Speaker 2: into a simple strikeforce alpha A coordinates x y and
Speaker 2: proceed along route Z. Then the lowest level AI, the
Speaker 2: individual unit AI, handles the actual movement in shooting based
Speaker 2: on those orders.
Speaker 1: So the player is often the top strategic layer in
Speaker 1: an RTS.
Speaker 2: In many cases, yes, the player sets the goals and
Speaker 2: the AI executes the details, though some games have high
Speaker 2: level AI opponents making strategic choices.
Speaker 1: Too, and tactical pathfinding came up again. Path cost isn't
Speaker 1: just distance, right.
Speaker 2: It's about the quality of the path caxically speaking. Is
Speaker 2: it exposed to enemy fire? Does it offer good ambush opportunities?
Speaker 2: A path might be longer but much safer, so tactical
Speaker 2: pathfinding would prefer it.
Speaker 1: Okay, The big one learning Can game AI actually learn
Speaker 1: like you know? Learn learn?
Speaker 2: The sources definitely cover learning, but with a crucial caveat
Speaker 2: for games, the goal isn't just effective AI. It's AI
Speaker 2: that results in good gameplay. An AI that learns an
Speaker 2: unbeatable exploit isn't fun.
Speaker 1: That's a really important distinction. So what kinds of learning
Speaker 1: do they use?
Speaker 2: Some simple stuff like parameter modification basically tweaking numbers like
Speaker 2: if the AI keeps losing, maybe slightly increase its accuracy.
Speaker 2: Parameter uses optimization techniques like kill climbing. Then there's action prediction,
Speaker 2: trying to guess what the player will do next based
Speaker 2: on past behavior using techniques like n grams.
Speaker 1: And reinforcement learning. That's the hot top AI. Generally trial
Speaker 1: and error.
Speaker 2: Exactly the AI tries ashs, gets rewards or penalties from
Speaker 2: the game, find treasure, reward, fall in lava, penalty, and
Speaker 2: learns over time which actions lead to good outcomes in
Speaker 2: different situations or states. Q learning is a common example mentioned.
Speaker 1: What's the challenge there? For games?
Speaker 2: Defining the state? Real game worlds are incredibly complex and continuous.
Speaker 2: Trying to break down every possible situation, player, position, enemy health,
Speaker 2: ammial count, time of day into discrete states for the
Speaker 2: algorithm can be really.
Speaker 1: Hard and neural networks. Deep learning.
Speaker 2: Neural nets are powerful pattern recognizers. They learn by adjusting
Speaker 2: connections between artificial neurons. They were used historically in things
Speaker 2: like checkers programs or td GAMIN for evaluating board positions.
Speaker 2: Deep blue used complex evaluation, possibly including n ends.
Speaker 1: But are they used for deciding if a guard should
Speaker 1: open a door?
Speaker 2: Less so, according to the sources, for general character behavior logic,
Speaker 2: things like behavior trees are often more press tactical, easier
Speaker 2: to design, debug and control. Neural nets are great for
Speaker 2: specific tasks like image recognition or maybe complex evaluation, but
Speaker 2: maybe overkill or too inch of a black box for
Speaker 2: typical moment to moment decisions.
Speaker 1: Right now, So, traditional methods are still really dominant for
Speaker 1: a lot of character AI for.
Speaker 2: The actual decision logic very much so. The complexity, data
Speaker 2: needs and training time for deep learning for general flexible
Speaker 2: character behavior are still pretty significant hurdles compared to more
Speaker 2: established techniques.
Speaker 1: Okay, wow, so we've got movement, pathfinding, decisions, tactics, strategy,
Speaker 1: even learning. How does all this stuff actually run inside
Speaker 1: the game, and how does the AI even know what's
Speaker 1: happening in the world. This is the.
Speaker 2: Plumbing right exactly, The implementation details Yeah, super important. First off, scheduling,
Speaker 2: you might have hundreds of AI characters. You can't afford
Speaker 2: to run every single AI calculation for every character every
Speaker 2: single frame. Your game would grind to a halt.
Speaker 1: So you need to manage who thinks.
Speaker 2: When precisely you schedule AI updates. Maybe important enemies nearby
Speaker 2: update every frame, but background characters only update every few
Speaker 2: seconds or when something relevant happens. High archo of scheduling
Speaker 2: helps manage this complexity.
Speaker 1: And anytime algorithms what are they?
Speaker 2: Those clever algorithms design so you can interrupt them part
Speaker 2: way through their calculation and they can still give you
Speaker 2: a usable, even if not perfect result.
Speaker 1: Ah. So the AI doesn't just freeze for three seconds
Speaker 1: while it calculates the perfect path exactly.
Speaker 2: You can get a rough path, quickly, start moving, and
Speaker 2: maybe refine the path in the background if it gets
Speaker 2: more processing time later. Avoids that deer in headlights.
Speaker 1: Lack that can level of detail for AI similar to graphics.
Speaker 2: Yep, same principle. AI complexity scales with importance usually distance
Speaker 2: faraway AI runs simpler logic, closer AI runs the full simulation.
Speaker 2: Behavior Compression is mentioned as a way to smoothly transition
Speaker 2: between these levels. So the change is in jarring.
Speaker 1: Okay, how does the AI get its info? How does
Speaker 1: it perceive the world state?
Speaker 2: Good question. The simplest way is polling. The AI just
Speaker 2: asked the game can I see the player? Is there
Speaker 2: a wall in front of me? Whenever it needs to know?
Speaker 1: Sounds simple, but maybe slow if everyone's asking all the.
Speaker 2: Time, it doesn't scale well exactly. Imagine hundreds of AIS
Speaker 2: constantly querying the world state. Very inefficient.
Speaker 1: So what's better?
Speaker 2: The event passing is much more common in complex games.
Speaker 2: The game generates events sound created at location X, player
Speaker 2: spotted by camera, Y, door Z opened. There's usually a
Speaker 2: central manager, oh, like a dispatcher, Yeah, an event manager.
Speaker 2: AI components listen or subscribe to events they care about.
Speaker 2: When an event happens, the manager notifies only the relevant listeners.
Speaker 2: Much cleaner, more modular, easier to debug.
Speaker 1: That makes way more sense, and.
Speaker 2: This ties back nicely to the sophisticated sense management we
Speaker 2: talked about earlier. Simulating how information actually propagates.
Speaker 1: Not just line of sight sound, bouncing, smell.
Speaker 2: Exactly, using sense graphs or similar structures to model how
Speaker 2: a sound travels around corners gets muffled by walls. Maybe
Speaker 2: how a smell lingers or travels with airflow. That's how
Speaker 2: you get really deep stealth gameplay. The AI isn't cheating,
Speaker 2: it's reacting to information propagated through a simulated sensory system.
Speaker 1: That's how a guard hears you from another room.
Speaker 2: Maybe right, they didn't see you, but the sound event
Speaker 2: reached their ears through the sense graph. Triggering an investigate
Speaker 2: state adds huge depth.
Speaker 1: Okay, last piece, Maybe the tools developers aren't just writing
Speaker 1: C plus plus for everything right. Designers need ways to
Speaker 1: interact with this stuff.
Speaker 2: Oh, tools are absolutely critical. Level designers need to place
Speaker 2: path finding nodes, or to find clever points or paint
Speaker 2: areas the AI should avoid. AI designers need ways to
Speaker 2: build and tune behaviors.
Speaker 1: Can some of that be automated, like generating the navmesh?
Speaker 2: Some can, Yes, initial navmesh generation is often automatic, but
Speaker 2: designers almost always need tools to tweak it, fix errors,
Speaker 2: add metadata like this area is dangerous and for behavior.
Speaker 1: Logic, visual editors like for behavior trees exactly.
Speaker 2: Visual editors for state machines or behavior trees are invaluable.
Speaker 2: They led designers who might not be hardcore programmers build
Speaker 2: complex AI logic visually. Tools like Behavior Designer were mentioned,
Speaker 2: and scripting languages are huge too, Lua, Python custom languages
Speaker 2: allowing rapid iteration on specific character behaviors.
Speaker 1: Gotcha, Okay, we've gone through the whole pipeline. Basically, how
Speaker 1: does this all come together? In specific game genres?
Speaker 2: Quick examples sure shooters heavy on movement pathfinding for complex
Speaker 2: levels decision making often behavior trees or state machines for
Speaker 2: combat logic like flanking using cover and robust perception systems
Speaker 2: are key. Think Halo's AI using behavior trees or Golden
Speaker 2: eyed early sense simulation. Driving games big focus on movement
Speaker 2: AI obviously sophisticated steering behaviors for realistic car handling, collision avoidance, drafting,
Speaker 2: racing lines.
Speaker 1: Artist games must be in Nightmare.
Speaker 2: They're definitely demanding large scale pathfinding for potentially hundreds of units,
Speaker 2: group movement, streetrategic decision making AI for base building and
Speaker 2: resource management, tactical AI for unit formations and battles, lots
Speaker 2: of layers, lots of units to manage efficiently.
Speaker 1: And then some games are about the AI.
Speaker 2: Yeah. AI center genres, creature training games maybe using neural nets,
Speaker 2: or reinforcement learning or simulation games where the core is
Speaker 2: an ecosystem driven by AI agents following rules, maybe a
Speaker 2: food chain where player interaction is about influencing that system.
Speaker 1: Wow. Okay, so we've really covered a lot of ground,
Speaker 1: from basic movement back in the nineties all the.
Speaker 2: Way to complex layered decisions, tactical reasoning, learning systems, and
Speaker 2: the nitty gritty of how it's implemented.
Speaker 1: We've kind of peeled back the layers on that hidden intelligence. Yeah,
Speaker 1: that perception, decision action loop, how characters navigate, how they
Speaker 1: choose what to do.
Speaker 2: Hopefully you've got a much clearer picture now of the
Speaker 2: smarts and the engineering that goes into making game characters
Speaker 2: feel believable or challenging or sometimes just playing alive.
Speaker 1: Yeah, it's definitely not just simple scripting most of the time.
Speaker 1: It's some serious computer science and clever design work.
Speaker 2: Absolutely hopefully gives you a new appreciation next time you play.
Speaker 1: Okay, so here's something for you to chew on listening
Speaker 1: right now. Think about a game you've played recently, an
Speaker 1: enemy that felt really smart, or maybe an NPC that
Speaker 1: did something unexpected but believable.
Speaker 2: Base on what we talked about. What techniques do you
Speaker 2: think might be driving that behavior? Was it reacting to
Speaker 2: sound because of sense simulation? Did it seem to follow
Speaker 2: a logical plan maybe from a behavior tree?
Speaker 1: Could you see hints of prioritize steering when it dodged
Speaker 1: while still trying to get somewhere, or maybe hierarchical pathfinding
Speaker 1: letting it cross a huge map without getting lost.
Speaker 2: What kind of Aha moment might you be having right
Speaker 2: now about how that character actually worked. Next time you
Speaker 2: play Keep an Ear Out, Keep an Eye Out, you
Speaker 2: might start noticing the subtle signs of the AI mind
Speaker 2: at work. It's fascinating stuff once you know what to
Speaker 2: look for.