AI Strategies for Real-Time Games

1. Introduction

Welcome to this tutorial on AI strategies for real-time games. Our goal is to explore different techniques that will make your NPCs (Non-Player Characters) smarter, more challenging, and engaging in real-time game scenarios. We will learn how to implement AI in games using pathfinding, finite state machines, and a basic understanding of machine learning.

By the end of this tutorial, you will be able to:
- Understand the basics of AI in games
- Implement simple AI strategies like pathfinding
- Understand how finite state machines work
- Get an overview of how machine learning can be used in games

Prerequisites:
- Basic understanding of game development
- Basic programming knowledge in a language like JavaScript or Python

2. Step-by-Step Guide

AI in Games

AI in games refers to the decision-making process that is programmed into non-player characters. These decisions can be as simple as moving towards the player or as complex as deciding the best strategy to win a game.

Pathfinding

Pathfinding is a basic AI strategy that involves finding the shortest path from one point to another. It can be used to make NPCs move towards or away from players, find resources, and avoid obstacles.

Finite State Machines

Finite state machines (FSMs) are models of computation used to design AI in games. A FSM in a game might have states like 'Idle', 'Attack', and 'Chase'. The NPC can only be in one state at a time, and certain conditions will trigger a transition to a different state.

Machine Learning in Games

Machine learning can be used to create more complex AI that learns and adapts over time. The most common use of machine learning in games is reinforcement learning, where the AI learns by trial and error.

3. Code Examples

Pathfinding

In this Python code snippet, we're using the A* algorithm for pathfinding.

# Grid format:
# 0 = Navigable terrain
# 1 = Unnavigable terrain 
grid = [[0, 0, 0, 0, 0],
        [0, 0, 1, 0, 0],
        [0, 0, 0, 0, 0],
        [0, 0, 0, 0, 0]]

init = [0, 0]
goal = [len(grid)-1, len(grid[0])-1]
cost = 1

# The heuristic function. 
def heuristic(a, b):
    return abs(a[0] - b[0]) + abs(a[1] - b[1])

# Search function which returns a list of points from init to the goal. 
def search(grid, init, goal, cost):
    # TODO: Implement A* algorithm

# Call the search function
path = search(grid, init, goal, cost)
print(path)

Finite State Machine

In this JavaScript example, we will create a simple FSM for a game character.

class Character {
  constructor() {
    this.state = 'idle';
  }

  idle() {
    console.log('Character is idle');
  }

  attack() {
    console.log('Character is attacking');
    this.state = 'attack';
  }

  chase() {
    console.log('Character is chasing');
    this.state = 'chase';
  }

  handleInput(input) {
    switch (input) {
      case 'SPACE':
        this.attack();
        break;
      case 'ARROW_UP':
        this.chase();
        break;
      default:
        this.idle();
        break;
    }
  }
}

let character = new Character();
character.handleInput('SPACE');  // Expected output: "Character is attacking"
character.handleInput('ARROW_UP');  // Expected output: "Character is chasing"

4. Summary

We have covered the basics of AI in games, pathfinding, finite state machines, and an overview of machine learning in games. To learn more, consider exploring each of these topics in more detail and practicing with different games and scenarios.

5. Practice Exercises

1. Implement the A* algorithm in the provided search function for the pathfinding example.
2. Add more states to the FSM example and handle the transitions between them.
3. Research and find a machine learning library you could use in a game. Write a simple example using the library.

Practice is key to mastering these concepts. Try to experiment with different strategies and see which ones work best for your games. Happy coding!