Twistago AI, part 3: Hard
This is the third and final part of a series in which I explain how the artificial intelligence works in my latest game, Twistago. In case you missed the first or second part, you can catch up on them here and here.
Feeds
Browse by tag
- Game Development (48)
- Programming (38)
- Cart Frenzy (28)
- Game Design (27)
- Algorithms (22)
- Around the World (19)
- Archimedes (18)
- Business (14)
- Graphics (10)
- Experiments (9)
- Planning (9)
- Rocket Mail (9)
- Website (9)
- Artificial Intelligence (8)
- Dragon Attack (7)
- Bigcanvas (6)
- Twistago (6)
- Alakajam (5)
- Ludum Dare (5)
- Patchy (4)
- Turtle Paint (4)
- LibGDX (3)
- Audio (2)
- Godot (2)
- Blokjes (1)
Posts tagged “artificial intelligence”
Twistago AI, part 2: Normal
This is the second part of a three-part series in which I explain how the artificial intelligence works in my latest game, Twistago. In case you missed the first part, you can catch up on it here.
Twistago AI, part 1: Easy
This is the first part of a three-part series in which I explain how the artificial intelligence works in my latest game, Twistago. The AI has three different levels: easy, normal and hard. This is also the order in which I developed them, each level building upon the lessons and code of the previous, so it’s only natural that I do this writeup in that order as well, starting with the Easy level.
How to win at the game Pickomino
Pickomino (known as Regenwormen in Dutch, Heckmeck in German) is a dice game in which players try to get as many worms as possible. It is largely a game of chance, but there are some tactics involved, which always leaves me wondering: did I make the optimal choice? Only one way to find out: write an AI player that knows how to play optimally.
The manager
The manager is the third and final part of the game’s AI. He is responsible for the high-level strategic decisions.
The core of the manager is very simple. He searches the surroundings for potential objectives, divides the benefit of each objective by the cost, then instructs the Navigator to head for the best objective.
The navigator
The Navigator is the part of the AI that is responsible for pathfinding. Actually, his algorithm is fairly straightforward. Given an objective by the Manager, the Navigator determines the shortest path through a series of waypoints that are defined in the level file, then hands each waypoint in turn to the Driver.
The driver, the navigator and the manager
The new AI is making good progress; I’d say it’s about 90% finished. (The other 90% remains to be done.) After writing the code, it cleanly fell apart into three largely independent modules. I like to name my classes after corresponding real-world things, so these are called the Driver, the Navigator and the Manager.
Driver code
There are a handful of racing games that let you race purely against your own best time, but the majority of them let you race against others. It adds an element of competition that you don’t get when racing alone. The solitary racer is someone who spends hours trying to figure out the optimal way to tackle that sharp corner, just to shave a tenth of a second off his best time. Not the kind of audience I’m targeting with a somewhat (albeit not completely) casual mobile racing game. Long story short, I need opponents.