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Project 2: Creation of an NPC with Decision Making

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In this project, you will create a Non-Player Character (NPC) that can make decisions based on different AI techniques. This project will help you understand how to implement decision-making systems in game characters, making them more dynamic and responsive to the game environment.

Objectives

  • Implement a Finite State Machine (FSM) for basic decision-making.
  • Create a Decision Tree to handle more complex decisions.
  • Integrate a Behavior Tree for modular and scalable decision-making.
  • Test and debug the NPC to ensure it behaves as expected.

Prerequisites

Before starting this project, ensure you have a good understanding of:

  • Basic programming concepts.
  • Finite State Machines (FSM).
  • Decision Trees.
  • Behavior Trees.

Tools and Languages

For this project, you can use any game engine or programming language you are comfortable with. Popular choices include:

  • Unity with C#
  • Unreal Engine with C++
  • Godot with GDScript

Step-by-Step Guide

Step 1: Setting Up the Project

  1. Create a new project in your chosen game engine.
  2. Set up the game environment with basic elements like terrain, obstacles, and a player character.
  3. Add an NPC character to the scene. This will be the character you will control with AI.

Step 2: Implementing a Finite State Machine (FSM)

  1. Define the states for your NPC. Common states include Idle, Patrol, Chase, and Attack.
  2. Create a state machine class that can transition between these states based on certain conditions.

Example Code (C# in Unity)

public enum NPCState { Idle, Patrol, Chase, Attack }

public class NPCController : MonoBehaviour
{
    public NPCState currentState;
    public Transform[] patrolPoints;
    private int currentPatrolIndex;
    public Transform player;
    public float chaseDistance;
    public float attackDistance;

    void Update()
    {
        switch (currentState)
        {
            case NPCState.Idle:
                Idle();
                break;
            case NPCState.Patrol:
                Patrol();
                break;
            case NPCState.Chase:
                Chase();
                break;
            case NPCState.Attack:
                Attack();
                break;
        }
    }

    void Idle()
    {
        // Idle behavior
        if (Vector3.Distance(transform.position, player.position) < chaseDistance)
        {
            currentState = NPCState.Chase;
        }
    }

    void Patrol()
    {
        // Patrol behavior
        if (Vector3.Distance(transform.position, player.position) < chaseDistance)
        {
            currentState = NPCState.Chase;
        }
        else
        {
            // Move to next patrol point
        }
    }

    void Chase()
    {
        // Chase behavior
        if (Vector3.Distance(transform.position, player.position) < attackDistance)
        {
            currentState = NPCState.Attack;
        }
        else if (Vector3.Distance(transform.position, player.position) > chaseDistance)
        {
            currentState = NPCState.Patrol;
        }
        else
        {
            // Move towards player
        }
    }

    void Attack()
    {
        // Attack behavior
        if (Vector3.Distance(transform.position, player.position) > attackDistance)
        {
            currentState = NPCState.Chase;
        }
    }
}

Step 3: Creating a Decision Tree

  1. Define the decision nodes and actions for your NPC.
  2. Implement the decision tree logic to handle more complex decision-making scenarios.

Example Code (Pseudo-code)

class DecisionTreeNode
{
    function Decide() { }
}

class IsPlayerInRange : DecisionTreeNode
{
    function Decide()
    {
        if (distanceToPlayer < chaseDistance)
            return trueNode;
        else
            return falseNode;
    }
}

class PatrolAction : DecisionTreeNode
{
    function Decide()
    {
        // Patrol logic
    }
}

class ChaseAction : DecisionTreeNode
{
    function Decide()
    {
        // Chase logic
    }
}

// Constructing the tree
root = new IsPlayerInRange();
root.trueNode = new ChaseAction();
root.falseNode = new PatrolAction();

Step 4: Integrating a Behavior Tree

  1. Define the behavior tree structure with composite nodes (selectors, sequences) and leaf nodes (actions, conditions).
  2. Implement the behavior tree to allow for modular and scalable decision-making.

Example Code (Pseudo-code)

class BehaviorTreeNode
{
    function Execute() { }
}

class Selector : BehaviorTreeNode
{
    children = []
    function Execute()
    {
        foreach (child in children)
        {
            if (child.Execute() == SUCCESS)
                return SUCCESS;
        }
        return FAILURE;
    }
}

class Sequence : BehaviorTreeNode
{
    children = []
    function Execute()
    {
        foreach (child in children)
        {
            if (child.Execute() == FAILURE)
                return FAILURE;
        }
        return SUCCESS;
    }
}

class PatrolAction : BehaviorTreeNode
{
    function Execute()
    {
        // Patrol logic
        return SUCCESS;
    }
}

class ChaseAction : BehaviorTreeNode
{
    function Execute()
    {
        // Chase logic
        return SUCCESS;
    }
}

// Constructing the tree
root = new Selector();
patrolSequence = new Sequence();
patrolSequence.children.add(new PatrolAction());
chaseSequence = new Sequence();
chaseSequence.children.add(new ChaseAction());
root.children.add(patrolSequence);
root.children.add(chaseSequence);

Step 5: Testing and Debugging

  1. Test the NPC in different scenarios to ensure it behaves as expected.
  2. Debug any issues that arise, such as incorrect state transitions or unexpected behaviors.
  3. Optimize the decision-making logic for performance if necessary.

Practical Exercises

  1. Exercise 1: Extend the FSM

    • Add more states to the FSM, such as Flee or Search.
    • Implement the logic for these new states.

  2. Exercise 2: Enhance the Decision Tree

    • Add more decision nodes and actions to handle additional scenarios.
    • Test the decision tree with different game conditions.

  3. Exercise 3: Expand the Behavior Tree

    • Add more composite nodes and leaf nodes to create a more complex behavior tree.
    • Ensure the NPC can handle a variety of situations dynamically.

Common Mistakes and Tips

  • Common Mistake: Not handling state transitions correctly, leading to stuck states.
    • Tip: Always ensure there are clear conditions for transitioning between states.
  • Common Mistake: Overcomplicating the decision tree or behavior tree.
    • Tip: Start simple and gradually add complexity as needed.
  • Common Mistake: Ignoring performance implications.
    • Tip: Optimize your decision-making logic to run efficiently, especially for large numbers of NPCs.

Conclusion

In this project, you have learned how to create an NPC with decision-making capabilities using FSM, Decision Trees, and Behavior Trees. These techniques are fundamental for developing intelligent behaviors in game characters, making your games more engaging and dynamic. Continue experimenting with different AI techniques and scenarios to further enhance your skills.

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