NPC Quest Giver Tutorial

This tutorial will cover creating an NPC Quest Giver using Unity, C#, and Mixamo. We will have an NPC that wanders along a path and can be interrupted by the player approach.

NPC Quest Giver

This tutorial will build off the core functionality from the Patrolling Enemy tutorial.

Modify the Spotted Functionality

1. When the NPC spots the player, she should stand still instead of following the player
   
2. Instead of an attack animation, we will import a friendly “talking” animation from Mixamo
   
3. Import the new animation, and add it to the Animator Controller for the character
   
4. Remove the Attack animation and Trigger, setup the Talking animation as before, with a Trigger called “Talk” to transition to the Talking animation
   
5. Change the Animation trigger in the script
   
6. We also want to stop the character from returning to her patrol, so we will add a bool at the top called “talking”
   
7. And check for that bool  in our distance check in Update
   
8. We’ll also add OnTriggerExit to set the character back to her patrol
   
9. It would also help if she looks at us while she’s talking
   
10. Lastly, add popup text for the NPC’s dialogue using GameObject > UI > Text
    
11. Decide what your Quest Prompt should say, and remember to bind the text object’s Rect Transform to the bottom of the screen
    
12. Disable the text object by default, we will toggle it on from the script
    
13. Add using UnityEngine.UI to the top of the script and make a public GameObject for the quest text
    
14. Set the QuestText true and false in the appropriate places
    
15. Drag the QuestText to the empty GameObject slot on your character
    
16. Be sure to test!

Quest Items and Activating the Quest

1. Create a new C# script for the Quest Item (QuestItem.cs)
2. Find a model to use as the Quest Item and give it a BoxCollider marked as IsTrigger, be sure the collider goes around the entire object
   
3. Attach the QuestItem script to the object
4. In the QuestItem script, add the OnTriggerEnter function
   
5. Create a public bool to keep track if the player has collected the item
   
6. If the colliding object is the player, set that tracking bool to true
   
7. Back in the character’s script, we need a bool to keep track if the quest item was found
   
8. And we need a public QuestItem to link to the quest item object in the scene
   
9. In the character’s Update function, check to see if the quest item was collected
   
10. If it was collected, we want to destroy it
    
11. And we only want to run this check until it has been found, otherwise this check will throw an error after the object is destroyed
    
12. Be sure to link the Quest Item to the public slot on the character’s script
    
13. At this point if you test, the Quest Item should disappear when you collide with it
14. We should also add a public bool in the character’s script to activate the quest after the player talks to her
    
15. Activate the quest when the player talks to her
    
16. In the QuestItem script, we need a public reference to the character’s script (in this case I left it called Enemy.cs from the previous tutorial, even though it is now used as an NPC)
    
17. Before collecting the item, check if the questGiver’s quest is active
    
18. Remember to link the character to the QuestGiver slot in the scene
    
19. Now the cat can only be collected after the quest has been activated by talking to the granny

Completing the Quest

1. We need to add a new section to the talking portion of the character’s script to change the text if the Quest Item was found
   
2. Now to reward the player for completing the quest. Create a new Text object to show the reward, in this case I’m going to give the player XP
   
3. We want this text to be large, bright, and eye-catching
   
4. Let’s create an Animation to make the text popup more exciting
   
5. Attache the new animation to the Text Object
   
6. Open the Animation window
   
7. Make sure you have the text selected, then Add Property for the Text.Color and Anchored Position
   
8. Give it an Alpha of 0 for the start and end keyframe, and Alpha of 1 for a keyframe in the middle, so it fades in and out
   
9. Set the Y value higher in the last keyframe so the test floats upward
   
10. Now we need to trigger the animation. In the Animator window, create an Empty State
    
11. Set that as the Layer Default State
    
12. Make a transition to and from the Popup animation
    
13. Make a new Trigger in the Parameters tab and use that as the Condition to transition to the Popup animation
    
14. Remember to uncheck the box for Has Exit Time
    
15. Back in the character’s script, add a new public Animator to reference the popup text in the scene
    
16. In the quest completion part of the script, trigger the animation
    
17. Remember to drag the text into the public slot you created
    
18. Set the starting Alpha for the text to 0 so it is not visible from the beginning
    

Summary

You should now have a quest giver that requests a specific item, the ability to go and collect that item, and a reward when you return to the quest giver. This can easily be expanded for a quest giver to ask for a certain number of things like “collect 50 gems” or “kill 10 enemies”

Game AI: Non-Human Behavior Part 5

This is part 5 of a series on Game AI for Non-Human Behavior. Here's what you might have missed!

• Part 1: Defining "Game AI" and "Non-Human Behavior"
• Part 2: Making Decisions, Predators and Prey
• Part 3: Weird Inspirations from Nature
• Part 4: Modes of Hunting

Part 5 will be a deep dive into sensory input, and resulting behaviors.

Source: Atari

In 2015 there was an article published in nature that used Atari 2600 games to explore Reinforcement Learning in AI. Reinforcement Learning is the process of allowing AI to explore different options and learn behavior through a reward system, as opposed to Supervised Learning where AI performing sub-optimal behavior is explicitly corrected. The article, "Human-level control through deep reinforcement learning," examines sensory input used to understand the surrounding environment. In their research, the sensory input used correlates to visual input (or sight), allowing the AI to look at the pixels that make up the current game state.

Source: The Hunt, Netflix

In contrast, these blind catfish living in underwater caves have adapted to survive completely without sight. When one sense fails, we rely on honing our other senses to adapt to a world without the missing sense.

Source: https://askabiologist.asu.edu/echolocation

As children, we learn the five senses as sight, sound, smell, taste, and touch. These are the senses we as humans understand, because these are the senses we experience. However, there are other senses present in nature that are outside our area of experience. Bats, for example, use sonar in place of sight to determine the positions of things around them.

A "sense" is defined as a system of a group of cell types that responds to specific physical phenomena, which correspond to a particular region of the brain that interpret those signals. Some plants have specialized cells which detect gravity, allowing the plants to grow upright with their roots growing down into the earth.

Source: http://www.defenders.org/sharks/basic-facts

Even with senses we understand, such as smell, non-human creatures have far superior uses of them in many cases. Sharks can determine the direction a smell is coming from based on which nostril received the scent first.

Source: nature.com/scientificamericanmind/journal/v19/n4/images/
scientificamericanmind0808-22-I1.jpg

Though the focus of reinforcement learning is on changing behavior based on rewards, it relies on information about the environment being interpreted through senses. We can see something similar happen in the classic rat maze example - the hungry rat is placed in a maze that has a piece of cheese at the end and allowed to explore the maze until it finds the cheese. The rat is then placed back at the start of the maze, and this continues until the rat manages to navigate the maze perfectly. The cheese in this example is obviously the reward, but the rat needs a way to understand its environment in order to get the reward.

Source: http://www.smithsonianmag.com/smart-news/
were-terrible-distinguishing-real-and-fake-schools-fish-180953162/

Beyond just understanding the environment, the senses also allow organism to understand the creatures around them, resulting in group-based behavior like flocking. To put flocking in simple terms, we can use an algorithm that uses cohesion, alignment, and separation to simulate behavior well enough for gameplay purposes.

Source: http://harry.me/blog/2011/02/17/neat-algorithms-flocking/

Each agent in the AI flock will calculate it's desired vector of movement based on cohesion (the need to stay within in the group), alignment (the need to face the same direction as the group), and separation (the need to avoid hitting other members of the group). The calculation for cohesion is to look at the position of your neighbors within a specific range, find the center of mass, and move towards that center of mass. For alignment, the agent looks at the direction each of its neighbors is facing and aligns itself to the average. For separation, the agent will check to see if it is too close to any of its neighbors, and adjust accordingly. The three of these combined into the agent's velocity vector will result in a simple flocking behavior.

Source: https://www.gizmodo.com.au/2014/03/
what-happens-when-you-throw-four-sharks-into-a-giant-school-of-fish/

However, the flock's simple behavior may be disrupted by the presence of predators, causing the agents to need to assess their environment beyond just the position and orientation of their neighbors.

Source: https://www.wired.com/2011/12/
the-true-hive-mind-how-honeybee-colonies-think/

Bees are another wonderful example of teamwork behavior. Pheromones are special scented chemicals that allow some organisms to communicate information with each other via scent. Bees use pheromones the share information through the hive, resulting in a hive the essentially thinks together as if it were a brain with each bee acting as a neuron. I can imagine a similar system that could be used to have a group of friendly characters so coordinated that they act as a hive of bees with the player as their queen.

If you have enjoyed this series on AI for non-human behavior, please follow and subscribe!