Build Finite State Machine AI

This part of the Game Development path moves from knowing that Finite State Machine AI exists to being able to use it deliberately. By the end, you should be able to explain the mechanism, build or configure a small example, verify the result, and diagnose the most common ways it fails.

In this lesson

• Place Finite State Machine AI in the context of the Game AI and Procedural Systems module rather than treating it as an isolated feature.
• Build a mental model for what happens before, during, and after the operation.
• Work through a reproducible example connected to the scenario: build a small game loop with player control, collisions, state, audio and production concerns.
• Inspect the result and distinguish evidence from assumption.
• Recognize failure modes, misleading shortcuts, and production constraints.
• Leave with a verification checklist and a practical exercise rather than a memorized snippet.

Performance and unnecessary work

For a game developer, Finite State Machine AI becomes useful when it changes a decision you can verify. At the intermediate stage, the goal is not to cover every advanced option. It is to establish the correct mental model and the verification habit that later pages can extend. Where the platform has version-specific behavior, prefer the current official documentation and check the version shown by your own tools before assuming an older screenshot or blog post is authoritative. For Finite State Machine AI, apply this check in the context of the Game AI and Procedural Systems workflow before carrying the assumption into later Game Development work. In Game Development lesson 36 — Build Finite State Machine AI, use that observation as the checkpoint for this exact Game AI and Procedural Systems topic rather than generalizing it beyond the evidence.

The practical question behind build finite state machine ai is not simply whether the feature exists, but what behavior it gives you control over. One useful review technique is to remove or alter a single element and predict what should happen. If the prediction is wrong, the gap is conceptual rather than syntactic. The exercises use that technique because it gives stronger evidence of understanding than simply retyping a finished example. Keep this point tied to Finite State Machine AI. The same general engineering habit appears elsewhere, but the evidence and failure signals in this Game AI and Procedural Systems lesson are specific to this mechanism. In Game Development lesson 36 — Build Finite State Machine AI, use that observation as the checkpoint for this exact Game AI and Procedural Systems topic rather than generalizing it beyond the evidence.

Test the interaction

Before adding more syntax, make the state of the system observable. That habit matters especially when working with Finite State Machine AI. At the intermediate stage, the goal is not to cover every advanced option. It is to establish the correct mental model and the verification habit that later pages can extend. Where the platform has version-specific behavior, prefer the current official documentation and check the version shown by your own tools before assuming an older screenshot or blog post is authoritative. For Finite State Machine AI, apply this check in the context of the Game AI and Procedural Systems workflow before carrying the assumption into later Game Development work.

There are usually several ways to accomplish the same visible result. The important skill is knowing which guarantees differ when you choose one form of Finite State Machine AI over another. One useful review technique is to remove or alter a single element and predict what should happen. If the prediction is wrong, the gap is conceptual rather than syntactic. The exercises use that technique because it gives stronger evidence of understanding than simply retyping a finished example. The specific test here is about Finite State Machine AI: change one relevant input, configuration value or boundary and make sure the result still matches the contract described above.

Questions to answer about Finite State Machine AI

1. What is the smallest input or state that makes Finite State Machine AI observable?
2. What does success look like, and how can you prove it without relying on a vague UI message?
3. Which configuration, permissions, types, versions or environment details can change the result?
4. Which failure is most likely for a beginner, and what evidence distinguishes it from a different failure?
5. What should remain true after the example is repeated, automated or moved to another environment?

Visual debugging

In the Game AI and Procedural Systems part of this learning path, Finite State Machine AI is deliberately introduced now because later lessons depend on the boundary it establishes. At the intermediate stage, the goal is not to cover every advanced option. It is to establish the correct mental model and the verification habit that later pages can extend. Where the platform has version-specific behavior, prefer the current official documentation and check the version shown by your own tools before assuming an older screenshot or blog post is authoritative. In this lesson's Finite State Machine AI example, record the evidence you observed rather than treating the rule as a slogan; that note becomes useful when the next Game AI and Procedural Systems exercise changes the conditions.

A production system rarely fails at the exact line shown in a beginner example, so this section connects Finite State Machine AI to the surrounding runtime and operational context. One useful review technique is to remove or alter a single element and predict what should happen. If the prediction is wrong, the gap is conceptual rather than syntactic. The exercises use that technique because it gives stronger evidence of understanding than simply retyping a finished example. The specific test here is about Finite State Machine AI: change one relevant input, configuration value or boundary and make sure the result still matches the contract described above. In Game Development lesson 36 — Build Finite State Machine AI, use that observation as the checkpoint for this exact Game AI and Procedural Systems topic rather than generalizing it beyond the evidence.

Production UX checklist

For a game developer, Finite State Machine AI becomes useful when it changes a decision you can verify. At the intermediate stage, the goal is not to cover every advanced option. It is to establish the correct mental model and the verification habit that later pages can extend. Where the platform has version-specific behavior, prefer the current official documentation and check the version shown by your own tools before assuming an older screenshot or blog post is authoritative. In this lesson's Finite State Machine AI example, record the evidence you observed rather than treating the rule as a slogan; that note becomes useful when the next Game AI and Procedural Systems exercise changes the conditions. In Game Development lesson 36 — Build Finite State Machine AI, use that observation as the checkpoint for this exact Game AI and Procedural Systems topic rather than generalizing it beyond the evidence.

The practical question behind build finite state machine ai is not simply whether the feature exists, but what behavior it gives you control over. One useful review technique is to remove or alter a single element and predict what should happen. If the prediction is wrong, the gap is conceptual rather than syntactic. The exercises use that technique because it gives stronger evidence of understanding than simply retyping a finished example. For Finite State Machine AI, apply this check in the context of the Game AI and Procedural Systems workflow before carrying the assumption into later Game Development work.

Evidence table

What you inspect	What it tells you	What it does not prove
Source/configuration for Finite State Machine AI	What you asked the platform/runtime to do	That the request actually succeeded
Build/validation output	Whether static checks accepted the artifact	That production data and permissions behave correctly
Runtime/result output	What happened for this input	That every edge case is safe
Logs/diagnostics	Where the system spent time or failed	The root cause without interpretation
Repeat test	Whether behavior is reproducible	That the design is optimal

Start from the user task

Before adding more syntax, make the state of the system observable. That habit matters especially when working with Finite State Machine AI. At the intermediate stage, the goal is not to cover every advanced option. It is to establish the correct mental model and the verification habit that later pages can extend. Where the platform has version-specific behavior, prefer the current official documentation and check the version shown by your own tools before assuming an older screenshot or blog post is authoritative. Keep this point tied to Finite State Machine AI. The same general engineering habit appears elsewhere, but the evidence and failure signals in this Game AI and Procedural Systems lesson are specific to this mechanism.

There are usually several ways to accomplish the same visible result. The important skill is knowing which guarantees differ when you choose one form of Finite State Machine AI over another. One useful review technique is to remove or alter a single element and predict what should happen. If the prediction is wrong, the gap is conceptual rather than syntactic. The exercises use that technique because it gives stronger evidence of understanding than simply retyping a finished example. Keep this point tied to Finite State Machine AI. The same general engineering habit appears elsewhere, but the evidence and failure signals in this Game AI and Procedural Systems lesson are specific to this mechanism. In Game Development lesson 36 — Build Finite State Machine AI, use that observation as the checkpoint for this exact Game AI and Procedural Systems topic rather than generalizing it beyond the evidence.

Structure before styling

In the Game AI and Procedural Systems part of this learning path, Finite State Machine AI is deliberately introduced now because later lessons depend on the boundary it establishes. At the intermediate stage, the goal is not to cover every advanced option. It is to establish the correct mental model and the verification habit that later pages can extend. Where the platform has version-specific behavior, prefer the current official documentation and check the version shown by your own tools before assuming an older screenshot or blog post is authoritative. The specific test here is about Finite State Machine AI: change one relevant input, configuration value or boundary and make sure the result still matches the contract described above. In Game Development lesson 36 — Build Finite State Machine AI, use that observation as the checkpoint for this exact Game AI and Procedural Systems topic rather than generalizing it beyond the evidence.

A production system rarely fails at the exact line shown in a beginner example, so this section connects Finite State Machine AI to the surrounding runtime and operational context. One useful review technique is to remove or alter a single element and predict what should happen. If the prediction is wrong, the gap is conceptual rather than syntactic. The exercises use that technique because it gives stronger evidence of understanding than simply retyping a finished example. In this lesson's Finite State Machine AI example, record the evidence you observed rather than treating the rule as a slogan; that note becomes useful when the next Game AI and Procedural Systems exercise changes the conditions. In Game Development lesson 36 — Build Finite State Machine AI, use that observation as the checkpoint for this exact Game AI and Procedural Systems topic rather than generalizing it beyond the evidence.

Worked example: Finite State Machine AI

The following csharp example is written specifically for this lesson. Read the requirement first, then predict the important result before running or reproducing it.

using UnityEngine;

public class PlayerMover : MonoBehaviour
{
    [SerializeField] float speed = 5f;

    void Update()
    {
        float horizontal = Input.GetAxisRaw("Horizontal");
        float vertical = Input.GetAxisRaw("Vertical");
        Vector3 direction = new(horizontal, 0f, vertical);
        transform.position += direction.normalized * speed * Time.deltaTime;
    }
}
``` In this lesson's **Finite State Machine AI** example, record the evidence you observed rather than treating the rule as a slogan; that note becomes useful when the next Game AI and Procedural Systems exercise changes the conditions.

**Expected observation**

The GameObject moves using normalized input at a frame-rate-independent speed.

### Read the example deliberately

- **Line/construct 1:** `using UnityEngine;` — identify what state or contract this introduces, then trace where that state is consumed.
- **Line/construct 2:** `public class PlayerMover : MonoBehaviour` — identify what state or contract this introduces, then trace where that state is consumed.
- **Line/construct 3:** `{` — identify what state or contract this introduces, then trace where that state is consumed.
- **Line/construct 4:** `[SerializeField] float speed = 5f;` — identify what state or contract this introduces, then trace where that state is consumed.
- **Line/construct 5:** `void Update()` — identify what state or contract this introduces, then trace where that state is consumed.
- **Line/construct 6:** `{` — identify what state or contract this introduces, then trace where that state is consumed.
- **Line/construct 7:** `float horizontal = Input.GetAxisRaw("Horizontal");` — identify what state or contract this introduces, then trace where that state is consumed.
- **Line/construct 8:** `float vertical = Input.GetAxisRaw("Vertical");` — identify what state or contract this introduces, then trace where that state is consumed.
- **Line/construct 9:** `Vector3 direction = new(horizontal, 0f, vertical);` — identify what state or contract this introduces, then trace where that state is consumed.
- **Line/construct 10:** `transform.position += direction.normalized * speed * Time.deltaTime;` — identify what state or contract this introduces, then trace where that state is consumed.

Do not stop at “it ran.” Change one meaningful value related to Finite State Machine AI, predict the new result, run/reproduce the example again, and explain why the output changed. That mutation test is a stronger check of understanding than copying the original result.

## State and interaction model

This section needs a different question from the earlier explanation: what would make **Finite State Machine AI** fail specifically while working through **State and interaction model**? Choose one realistic boundary, reproduce it deliberately, and inspect the first useful diagnostic or intermediate value. The aim in Build Finite State Machine AI is to recognize the mechanism under changed conditions, not to repeat the same successful path with different wording.

For the **State and interaction model** part of Build Finite State Machine AI, use a separate verification pass rather than repeating the earlier explanation. Focus on **Finite State Machine AI** under one changed condition and write down the before/after evidence. This is verification pass 2 for Game Development lesson 36: the useful outcome is a concrete observation—output, state, diagnostic, generated artifact, query result or test result—that another learner can reproduce in the Game AI and Procedural Systems workflow.

## Build the smallest visible UI

Before adding more syntax, make the state of the system observable. That habit matters especially when working with Finite State Machine AI. At the intermediate stage, the goal is not to cover every advanced option. It is to establish the correct mental model and the verification habit that later pages can extend. Where the platform has version-specific behavior, prefer the current official documentation and check the version shown by your own tools before assuming an older screenshot or blog post is authoritative. In this lesson's **Finite State Machine AI** example, record the evidence you observed rather than treating the rule as a slogan; that note becomes useful when the next Game AI and Procedural Systems exercise changes the conditions.

There are usually several ways to accomplish the same visible result. The important skill is knowing which guarantees differ when you choose one form of Finite State Machine AI over another. One useful review technique is to remove or alter a single element and predict what should happen. If the prediction is wrong, the gap is conceptual rather than syntactic. The exercises use that technique because it gives stronger evidence of understanding than simply retyping a finished example. For **Finite State Machine AI**, apply this check in the context of the **Game AI and Procedural Systems** workflow before carrying the assumption into later Game Development work.

### Failure-mode matrix

| Symptom | Likely category | First evidence to collect |
|---|---|---|
| The Finite State Machine AI behavior never occurs | configuration / control flow | verify the relevant code/configuration is actually reached |
| Build or validation fails | syntax / type / unsupported option | read the first meaningful diagnostic, not the last cascade message |
| Works locally but not elsewhere | environment / version / permission | compare runtime versions, identity, configuration and data |
| Result is valid but wrong | assumption / data shape / business rule | inspect intermediate values and boundary conditions |
| Intermittent behavior | concurrency / timing / external dependency | add timestamps, correlation IDs or deterministic reproduction |

## Wire data into the interface

For this part of **Build Finite State Machine AI**, move beyond the earlier mental model and ask how the behavior survives repetition. Run or reproduce the step twice, change the ordering or boundary case where safe, and verify that the same invariant still holds. A reliable Game AI and Procedural Systems workflow is one that produces evidence you can compare, not one that succeeds only when the exact tutorial sequence is copied.

Now apply **Finite State Machine AI** to the current **Wire data into the interface** concern. Start from the smallest state that demonstrates the behavior, vary one input or configuration choice, and explain the result in terms of the Game Development runtime or platform. If two outcomes look similar in the UI, use logs, return values, generated artifacts, query results, tests or another concrete signal to distinguish them.

## Handle input and validation

For the **Handle input and validation** part of Build Finite State Machine AI, use a separate verification pass rather than repeating the earlier explanation. Focus on **Finite State Machine AI** under one changed condition and write down the before/after evidence. This is verification pass 2 for Game Development lesson 36: the useful outcome is a concrete observation—output, state, diagnostic, generated artifact, query result or test result—that another learner can reproduce in the Game AI and Procedural Systems workflow.

The practical question behind build finite state machine ai is not simply whether the feature exists, but what behavior it gives you control over. One useful review technique is to remove or alter a single element and predict what should happen. If the prediction is wrong, the gap is conceptual rather than syntactic. The exercises use that technique because it gives stronger evidence of understanding than simply retyping a finished example. The specific test here is about **Finite State Machine AI**: change one relevant input, configuration value or boundary and make sure the result still matches the contract described above.

## Accessibility and keyboard behavior

Before adding more syntax, make the state of the system observable. That habit matters especially when working with Finite State Machine AI. At the intermediate stage, the goal is not to cover every advanced option. It is to establish the correct mental model and the verification habit that later pages can extend. Where the platform has version-specific behavior, prefer the current official documentation and check the version shown by your own tools before assuming an older screenshot or blog post is authoritative. The specific test here is about **Finite State Machine AI**: change one relevant input, configuration value or boundary and make sure the result still matches the contract described above.

Now apply **Finite State Machine AI** to the current **Accessibility and keyboard behavior** concern. Start from the smallest state that demonstrates the behavior, vary one input or configuration choice, and explain the result in terms of the Game Development runtime or platform. If two outcomes look similar in the UI, use logs, return values, generated artifacts, query results, tests or another concrete signal to distinguish them.

## Responsive behavior

In the Game AI and Procedural Systems part of this learning path, Finite State Machine AI is deliberately introduced now because later lessons depend on the boundary it establishes. At the intermediate stage, the goal is not to cover every advanced option. It is to establish the correct mental model and the verification habit that later pages can extend. Where the platform has version-specific behavior, prefer the current official documentation and check the version shown by your own tools before assuming an older screenshot or blog post is authoritative. For **Finite State Machine AI**, apply this check in the context of the **Game AI and Procedural Systems** workflow before carrying the assumption into later Game Development work.

In **Responsive behavior**, look at **Finite State Machine AI** through the constraint that matters in this part of the lesson: make the relevant state visible before you change it, then compare the observed result with the contract you expected. In Game Development, this prevents a local-looking edit from hiding an environment, data, permission, lifecycle or runtime assumption. Record the evidence from this step because the next decision in the Game AI and Procedural Systems module should be based on what you measured rather than on a repeated rule of thumb.

## Loading, empty and error states

In **Loading, empty and error states**, look at **Finite State Machine AI** through the constraint that matters in this part of the lesson: make the relevant state visible before you change it, then compare the observed result with the contract you expected. In Game Development, this prevents a local-looking edit from hiding an environment, data, permission, lifecycle or runtime assumption. Record the evidence from this step because the next decision in the Game AI and Procedural Systems module should be based on what you measured rather than on a repeated rule of thumb.

The practical question behind build finite state machine ai is not simply whether the feature exists, but what behavior it gives you control over. One useful review technique is to remove or alter a single element and predict what should happen. If the prediction is wrong, the gap is conceptual rather than syntactic. The exercises use that technique because it gives stronger evidence of understanding than simply retyping a finished example. In this lesson's **Finite State Machine AI** example, record the evidence you observed rather than treating the rule as a slogan; that note becomes useful when the next Game AI and Procedural Systems exercise changes the conditions.

## A production-oriented walkthrough for Finite State Machine AI

### 1. Establish the Finite State Machine AI behavior

Establish this step in the context of build a small game loop with player control, collisions, state, audio and production concerns. Keep the change small enough that you can state the expected result before executing it. Capture the relevant input, configuration or code, then record the observable result. If the result differs from the prediction, do not add more changes yet; narrow the mismatch using diagnostics appropriate to Unity/C# as the primary path with later engine comparisons. For **Finite State Machine AI**, apply this check in the context of the **Game AI and Procedural Systems** workflow before carrying the assumption into later Game Development work.

### 2. Inspect the Finite State Machine AI behavior

Inspect this step in the context of build a small game loop with player control, collisions, state, audio and production concerns. Keep the change small enough that you can state the expected result before executing it. Capture the relevant input, configuration or code, then record the observable result. If the result differs from the prediction, do not add more changes yet; narrow the mismatch using diagnostics appropriate to Unity/C# as the primary path with later engine comparisons. Keep this point tied to **Finite State Machine AI**. The same general engineering habit appears elsewhere, but the evidence and failure signals in this Game AI and Procedural Systems lesson are specific to this mechanism.

### 3. Implement the Finite State Machine AI behavior

Implement this step in the context of build a small game loop with player control, collisions, state, audio and production concerns. Keep the change small enough that you can state the expected result before executing it. Capture the relevant input, configuration or code, then record the observable result. If the result differs from the prediction, do not add more changes yet; narrow the mismatch using diagnostics appropriate to Unity/C# as the primary path with later engine comparisons. For **Finite State Machine AI**, apply this check in the context of the **Game AI and Procedural Systems** workflow before carrying the assumption into later Game Development work.

A useful variation is to introduce one boundary case that is plausible for Finite State Machine AI: an empty value, a missing permission, an unexpected type, a repeated operation, an unavailable dependency, or a larger-than-normal input. The exact case depends on the technology, but the reasoning is the same—state the invariant you expect to remain true, then verify it explicitly. In this lesson's **Finite State Machine AI** example, record the evidence you observed rather than treating the rule as a slogan; that note becomes useful when the next Game AI and Procedural Systems exercise changes the conditions. In **Game Development lesson 36 — Build Finite State Machine AI**, use that observation as the checkpoint for this exact Game AI and Procedural Systems topic rather than generalizing it beyond the evidence.

### 4. Exercise the Finite State Machine AI behavior

Exercise this step in the context of build a small game loop with player control, collisions, state, audio and production concerns. Keep the change small enough that you can state the expected result before executing it. Capture the relevant input, configuration or code, then record the observable result. If the result differs from the prediction, do not add more changes yet; narrow the mismatch using diagnostics appropriate to Unity/C# as the primary path with later engine comparisons. In this lesson's **Finite State Machine AI** example, record the evidence you observed rather than treating the rule as a slogan; that note becomes useful when the next Game AI and Procedural Systems exercise changes the conditions.

### 5. Challenge the Finite State Machine AI behavior

Challenge this step in the context of build a small game loop with player control, collisions, state, audio and production concerns. Keep the change small enough that you can state the expected result before executing it. Capture the relevant input, configuration or code, then record the observable result. If the result differs from the prediction, do not add more changes yet; narrow the mismatch using diagnostics appropriate to Unity/C# as the primary path with later engine comparisons. The specific test here is about **Finite State Machine AI**: change one relevant input, configuration value or boundary and make sure the result still matches the contract described above.

A useful variation is to introduce one boundary case that is plausible for Finite State Machine AI: an empty value, a missing permission, an unexpected type, a repeated operation, an unavailable dependency, or a larger-than-normal input. The exact case depends on the technology, but the reasoning is the same—state the invariant you expect to remain true, then verify it explicitly. Keep this point tied to **Finite State Machine AI**. The same general engineering habit appears elsewhere, but the evidence and failure signals in this Game AI and Procedural Systems lesson are specific to this mechanism.

### 6. Verify the Finite State Machine AI behavior

Verify this step in the context of build a small game loop with player control, collisions, state, audio and production concerns. Keep the change small enough that you can state the expected result before executing it. Capture the relevant input, configuration or code, then record the observable result. If the result differs from the prediction, do not add more changes yet; narrow the mismatch using diagnostics appropriate to Unity/C# as the primary path with later engine comparisons. Keep this point tied to **Finite State Machine AI**. The same general engineering habit appears elsewhere, but the evidence and failure signals in this Game AI and Procedural Systems lesson are specific to this mechanism.

### 7. Harden the Finite State Machine AI behavior

Harden this step in the context of build a small game loop with player control, collisions, state, audio and production concerns. Keep the change small enough that you can state the expected result before executing it. Capture the relevant input, configuration or code, then record the observable result. If the result differs from the prediction, do not add more changes yet; narrow the mismatch using diagnostics appropriate to Unity/C# as the primary path with later engine comparisons. Keep this point tied to **Finite State Machine AI**. The same general engineering habit appears elsewhere, but the evidence and failure signals in this Game AI and Procedural Systems lesson are specific to this mechanism.

In **A production-oriented walkthrough for Finite State Machine AI**, look at **Finite State Machine AI** through the constraint that matters in this part of the lesson: make the relevant state visible before you change it, then compare the observed result with the contract you expected. In Game Development, this prevents a local-looking edit from hiding an environment, data, permission, lifecycle or runtime assumption. Record the evidence from this step because the next decision in the Game AI and Procedural Systems module should be based on what you measured rather than on a repeated rule of thumb.

### 8. Document the Finite State Machine AI behavior

Document this step in the context of build a small game loop with player control, collisions, state, audio and production concerns. Keep the change small enough that you can state the expected result before executing it. Capture the relevant input, configuration or code, then record the observable result. If the result differs from the prediction, do not add more changes yet; narrow the mismatch using diagnostics appropriate to Unity/C# as the primary path with later engine comparisons. Keep this point tied to **Finite State Machine AI**. The same general engineering habit appears elsewhere, but the evidence and failure signals in this Game AI and Procedural Systems lesson are specific to this mechanism.

## Where Finite State Machine AI implementations commonly go wrong

### Treating Finite State Machine AI as syntax instead of behavior
If you can reproduce the syntax but cannot predict the state after it runs, the lesson is not finished. Rewrite the example in your own words and name the input, operation and observable result.

### Copying a configuration from a different version
Game Development tooling evolves. Compare the documentation version, runtime/tool version and project settings before assuming that a screenshot or command from another environment applies unchanged.

### Verifying only the happy path
A successful first run proves one path. Add at least one negative or boundary case relevant to Finite State Machine AI. The failure should be intentional and the diagnostic should make sense.

### Hiding the important state behind too much abstraction
Abstraction is useful after the behavior is understood. During the first implementation of Finite State Machine AI, keep the decisive state and control flow visible enough to debug.

## Recovering from common Finite State Machine AI failures

Use this order when Finite State Machine AI does not behave as expected:

1. Reproduce the smallest failing case.
2. Confirm the actual version/toolchain/environment.
3. Capture the first meaningful diagnostic or unexpected value.
4. Verify identity, permissions and configuration if the operation crosses a service boundary.
5. Inspect intermediate state rather than only the final UI.
6. Change one variable and rerun.
7. Compare the corrected behavior with a negative case.
8. Record the final cause so the same failure is faster to diagnose next time.

## Practice: change the constraint

Extend the worked scenario so that **Finite State Machine AI** must handle one additional real constraint. Choose one: a second data shape, a failed dependency, an invalid input, a permission difference, a repeat operation, or a larger workload. Before implementing the change, write down the behavior you expect and the evidence that will prove it.

Your result is complete when another learner can reproduce the change from your notes, observe the expected behavior, and intentionally trigger at least one documented failure without damaging their environment. Keep this point tied to **Finite State Machine AI**. The same general engineering habit appears elsewhere, but the evidence and failure signals in this Game AI and Procedural Systems lesson are specific to this mechanism.

## Evidence that you understand Finite State Machine AI

- Can you define **Finite State Machine AI** without using the exact wording of an API/reference page?
- Can you identify the boundary where Finite State Machine AI begins and where another concept takes over?
- Can you predict the result of the worked example before running it?
- Can you explain one failure from evidence rather than guessing?
- Can you name one production constraint that the beginner example intentionally simplifies?
- Can you repeat the example from a clean state?

## Keep these Finite State Machine AI principles

- **Finite State Machine AI** is useful because it controls observable behavior, not because it adds another piece of syntax to memorize.
- Verification belongs in the workflow: build/check, run/reproduce, inspect, challenge, and repeat.
- The Game AI and Procedural Systems module uses this lesson as a foundation for the next decisions in the Game Development learning path.
- Official documentation is the source of truth for version-specific contracts; tutorials should teach you how to read and apply those contracts.

## Source material for version-specific details

The following primary documentation was used as a factual reference map for this lesson. ScrutnLearn's explanation is original synthesis rather than copied documentation prose.

- [Unreal Engine documentation](https://dev.epicgames.com/documentation/unreal-engine)
- [Godot documentation](https://docs.godotengine.org/en/stable/)
- [Microsoft C# documentation](https://learn.microsoft.com/en-us/dotnet/csharp/)
- [Unity Manual](https://docs.unity3d.com/Manual/index.html)
- [Unity Scripting API](https://docs.unity3d.com/ScriptReference/)