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AI Theory

I've been working on the AI for the game the last couple of days.

[Don't worry, every thing is OK...]
In my smaller games AI was pretty simple. They had a simple game mechanic, try to walk in to the player and kill them. So basically they have a single type of behavior.

[I'll eat your brains!]
That's fine for simple enemies like zombies, but what about more complex characters?
Well, I've written a couple of different behaviors, we could call them:
  1. roaming
  2. seeking
  3. fleeing
  4. fighting
  5. following
  6. waiting
[while: guarding]

The behaviors are encompassed in a finite state machine. Each state can transition in to others, depending on the behavior. So a waiting agent sees an enemy they become alerted and try to attack, they transition to the seeking state.

After testing I'm confident I've got the bugs out of each of them.
But there's a problem. How to choose which behavior to use at any one time? How to know which state to enter next?

[trolls ahoy!]

If an agent sees the enemy, obviously they will do something different depending on whether they are an archer, a wizard, a dragon or a troll or whatever. When they lose sight of the enemy, which state should they return to? Clearly no single set of 6 states can fit all those scenarios.

Right away I'm back to the previous situation of wanting a state machine to manage my state machine. But again, I don't want to go down that road. Once I start looking for the complex solution, I'll get bogged down and never finish.

One simple possibility would be to have character attributes like self.default_state or self.alerted state, but then behavior becomes split between the states and the character so adding new characters means partly writing new behavior. I want all behavior to be managed from within the state machine. I don't want behavior entangled with specific agents types, but rather modular and easy to assign even during game play.

So I'm going to use inheritance to solve the problem.

[Do I need to draw you a picture?]
Firstly, some of the above states can be merged. Seeking, Fleeing and Roaming are all the same behavior, only one finds the closest tile to the target, one finds the tile furthest away from the target, and one chooses a random unvisited tile. So I now have a single state, Navigation.
[Navigation; You are here!]
Then I go back and create new states like Roaming(Navigation).
This inherits all the behavior from navigation but uses a different rule for deciding the next square. Fleeing(Navigation) and Seeking(Navigation) do likewise. *



The next step is to create specific versions of those states for a particular AI type. For example ArcherRoaming(Navigation) with some tweaks to the __init__ function to tailor how it transitions to other states. There's also a special custom exit check, to see if there's some special reason to not be roaming around. This reduces it down to just a few lines of code, rather than re-writing a specific behavior for archers.

After that I have to plan how those states will interact and which states are needed for each AI archetype. I'm using flowcharts for that:
[Inside the mind of a dungeon guard]

A little more complex now than:
try to walk in to the player and kill them.
Of course It's not difficult to go further, if I have archers who patrol instead of guarding I can reuse most of the states from archer, but subclass them again as ArcherRoaming instead of ArcherWaiting.

Where this gets really useful is being able to have switchable behaviors. By having a custom exit check we can give some AI archetypes the ability to switch to a different archetype. If we have a party of heroes and I want to give them orders I can do so through dialog choices or through hotkeys. It just asks them to switch to a different behavior archetype.


Become an archer, cast magic, follow me!
Scout ahead, serve drinks, sacrifice yourself for the good of the party!



* At this point I also diverged in to two different kinds of navigation, single tile navigation for close in to the target avoiding obstacles and tile chunk navigation which gives smoother movement (because it uses a shorter route to the target) but is worse at avoiding smaller obstacles. Then I get Attacking(TileNavigation)and Hunting(ChunkNavigation) two different behaviors for use when at different ranges from the target. However I don't want to add to the confusion here.

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