Using FSM and Decision Trees

1. Introduction

In this tutorial, we will delve into the world of Finite State Machines (FSM) and Decision Trees, two important concepts in AI programming. They are frequently used in game development to control complex Non-Player Character (NPC) behavior.

By the end of this tutorial, you should be able to:

• Understand what Finite State Machines and Decision Trees are
• Implement these techniques in your programs to control NPC behavior

Prerequisites

• Basic understanding of programming concepts
• Familiarity with any programming language, preferably Python

2. Step-by-Step Guide

Finite State Machines (FSM)

An FSM is a model of computation used to design both computer programs and sequential logic circuits. It is conceived as an abstract machine that can be in one of a finite number of states.

Example:

Consider a simple light switch. It has two states, ON and OFF. The transition between these states is triggered by a user action (flipping the switch).

# State 1: ON
# State 2: OFF
# User action: Flip switch

Decision Trees

A Decision Tree is a flowchart-like tree structure where each internal node denotes a test on an attribute, each branch represents an outcome of the test, and each leaf node (terminal node) holds a class label.

Example:

A simple decision tree could be used to determine whether to go outside based on the weather.

# Test: Is it raining?
    # Branch 1: Yes -> Don't go outside
    # Branch 2: No -> Go outside

3. Code Examples

Example 1: FSM

In this Python example, we create an FSM to simulate a traffic light.

class TrafficLightFSM:
    def __init__(self):
        self.state = 'Red'

    def change(self):
        if self.state == 'Red':
            self.state = 'Green'
        elif self.state == 'Green':
            self.state = 'Yellow'
        elif self.state == 'Yellow':
            self.state = 'Red'

    def __str__(self):
        return self.state

traffic_light = TrafficLightFSM()

for i in range(10):
    print(str(traffic_light))
    traffic_light.change()

In this code, the traffic light changes from Red to Green, then to Yellow, and back to Red again. This process repeats 10 times.

Example 2: Decision Tree

In this Python example, we'll use the sklearn library to create a Decision Tree for classifying Iris species.

from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.tree import DecisionTreeClassifier

# Load Iris dataset
iris = load_iris()
X = iris.data
y = iris.target

# Split data into training and test sets
X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=1)

# Create Decision Tree classifier
clf = DecisionTreeClassifier()

# Train classifier
clf.fit(X_train, y_train)

# Predict for test set
y_pred = clf.predict(X_test)

print(y_pred)

This program trains a decision tree classifier on the Iris dataset and predicts the species for the test set.

4. Summary

We've learned about Finite State Machines and Decision Trees, two powerful tools for controlling complex behavior in programs. By implementing these concepts, you can create more advanced and realistic game NPCs.

For further learning, you could explore more complex FSMs and Decision Trees or try implementing them in different programming languages.

5. Practice Exercises

Exercise 1: Create an FSM for a DVD player that has the states: Playing, Paused, and Stopped.

Exercise 2: Create a Decision Tree for deciding what to wear based on the weather and the type of event (casual or formal).

Exercise 3: Implement an FSM for a simple game character that can be in one of three states: Idle, Walking, and Running.

Remember to practice regularly to master these concepts. Happy learning!