Application of Large Language Model in Connecting Non-Player

Characters Intelligent Conversations

Peiyuncheng Ding

Shen Fu Yucai Experimental School International High School, Liao Ning, Shen Yang 110000 China

Keywords: Unit, Non-Player Character, Large Language Model, Intelligent Dialogue.

Abstract: The large language model is a kind of Advanced natural language processing model. By inserting the large

language model, non-player characters can understand the question and flow the personal character of non-

player characters from players. This kind of intelligent dialogue not only can improve the game experience

of players but also can improve the interaction and fun of the games. This paper will link the large language

model to the non-player characters to communicate with non-player characters. By using Unity, Ollama and

ChatGPT3.5 and build API communication interface and some optimization. This paper has solved some

problems of Performance optimization of large language models and let the reply be closer to the character's

personality and also improve the interactivity and the experience of players. Finally, this paper has linked the

large language model with non-player characters in the game successfully and chats with it fluently and

continually. In the future, there must be a more efficient and perfect solution to link the large language model

to the non-player characters of games to provide a better experience for players.

1 INTRODUCTION

Video games have evolved significantly at the global

level, offering players different experiences that are

each more immersive and staffing that are very

similar to a real scenario. (Aguas, Toasa, Urdaneta, et

al, 2024) And in the current field of game

development, Players have increasing expectations

for non-player characters in games, especially in

aspect of interactivity. Although, traditional dialogue

system can achieve basic interaction, it is too boring

for players. Whether a game can be loved by players

and maintain long-term interest in the game depends

on whether the behaviour of non-real players in the

game is similar to that of real players, in other words,

whether it can be truly anthropomorphic. (Meng,

2015) With the technology development especially

large language model, like ChatGPT. However, to

develop a stealth game non-player characters AI

system is difficult. (Lin, Zhang, Sun, et al, 2024)

From the current development status of computer

online games, there are many different algorithms

that can achieve artificial intelligence behavior. In

practical production, each algorithm has its own

advantages and specialties, such as the commonly

https://orcid.org/0009-0004-4189-2612

used finite state machines and behavior trees. (MAO,

2016)

This paper will using Application

Programming Interface (API) to conect Ollama

modle with the non-player characters in games. The

final target is players can chat with the non-player

characters fluently and non-player characters can

follow the order to give precise answers is also very

important. It is very difficult for this task. First,

because Ollama modle Need to rely on CPU, GPU

and RAM to operation, so, the response from large

language model of computers with low configuration

will be very slow and even have errors. So change a

large language model to make sure the fluency of

games is important. In the end this essey down the

target successfully.

In 2024 Chen qijie had offer an essay it name is

AI reinventing games and it had discribe in the AI is

transforming the gaming industry had passthe

specific cases and analysis the industry, to show the

application, advantages and challenges of AI in game

development also had discribe thefuture development

trend is predicted. (Chen, 2024) And in the same year

Degen, C. had public the essay about Managers

racing to keep ahead of growing threats: AI making

Ding, P.

Application of Large Language Model in Connecting Non-Player Characters Intelligent Conversations.

DOI: 10.5220/0013235000004558

In Proceedings of the 1st International Conference on Modern Logistics and Supply Chain Management (MLSCM 2024), pages 135-138

ISBN: 978-989-758-738-2

135

detection more difficult, making it a 'cat and mouse

game' (Degen, 2024) And in 2023 Wang Ming had

conducts research on the problem of game AI

pathfinding based on reinforcement learning. (Wang,

2023)

And in 2020 Min Guohao had public the essay

about Design and implementation of card game AI

system based on supervised learning. (Min, 2020)

This paper will basic on these essay to explore how to

conect the AI into games.

Nowadays, ChatGPT bring the application of

generative artificial intelligence in various of fields

such as journalism, design industry and game

industry, which has changed the human and the way

of communication. (Zhou, Zhang, 2023) This paper

will use Unity to link the Ollama with non-player

characters in games. Let non-player characters have

an intelligent dialogue with players. To improve the

immersion and interactivity of the game and improve

the gaming experience for the player by intelligent

dialogue.

Exploring new areas is definitely exciting, and

video games are a new thing that combines many

fields, including animation, character design, level

design, music, visual art.(Liu, 2023) In the future,

engineers can let non-player characters "come alive"

to bring some richer, more fun gaming experience. It

not only an important breakthrough on game

development, but also provides some solutions for

future game development and innovation.

2 RESEARCH METHOD

2.1 Programming Environment

Preparation

In this part three things are needed: 1. Unity app to

crate the scene and the character 2. large language

model Ollama local language model and ChatGPT3.5

3. Vision Studio to programming the C sharp

programming language.

2.2 Model Deployment

2.2.1 Deploy of Large Language Model

This paper had prepared two types of large language

model Ollama local large model and ChatGPT3.5

network large model. Ollama need to download and

install, and this requires at least 4G of memory and at

least 16G of RAM. However, ChatGPT3.5 need login

an ID of OpenAI.

2.2.2 The Construction of the Game Scene

This requires the deployment of character models

well as scenes and dialog systems. Also, here need

adds some actions for model. The built game scene is

shown in the picture below as shown in Figure 1:

Scene construction this paper had put the model in the

mid and there is a background behind it and a dialog

box.

Figure 1: Scene construction (Photo/Picture credit:

Original).

*1 The character model and background used in this paper

are from the Genshin Impact produced by MiHoYo Co., Ltd.

2.3

Communication Between Unity and

Ollama Large Model

Here this paper using C# programming language to

programme the API include sand the request from

players to large language model and give back the

answers to players by large language model. Then

attach the code to the character model and dialog

system.

2.3.1 Design the API for Ollama

Because of the official website of Ollama has already

show the code of API, so this paper can use it directly.

But to make sure it will work correctly in the local

environment, so debug the API code is also needed

and configure the port number of API, it always be

the installation address of Ollama.

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Figure 2: The Response processing of Ollama (Photo/

Picture credit: Original).

As the figure 2 show, firstly, start the Ollama and

make sure it can receive and dispose the data. Then

using the dialog system in Unity to send messages to

Ollama by API. Normally, it uses the HTTP and

POST to send the texts from players to the API

endpoint. When the request comes to the servers, it

will be parsed and pre-processed including cleaning

the text, removing invalid characters. Then send it to

large language model to generate response. And it

will send bake to the Unity. Finally, Attach the code

file to the character model.

3 TEST AND OPTIMIZATION

3.1 Test Results

After the programming code, testing the code is

needed. Typing some simple questions in the dialog

system for example: Hello, who are you? The model

responded successfully and complied with the

personality of character.

Figure 3: Test results (Photo/Picture credit: Original).

The result is showing as Figure 3: Test results.

The large language model had given the response and

is showed on the screen the responds was: Hutu gives

a sly grin, eyes twinkling with mischief* Ah, you

wanna know who I am, huh? Well, let me tell you, I'm

Hutu, the one and only, I'm a bit of a troublemaker,

always looking for ways to get into scrapes and stir

up some, drama. But hey, that's just part of being a

Liyue Qixing, right? *winks* Because the Ollama

depends on the CPU, GPU and RAM to calculate, so

it's going to be very slow, it had use for 50 seconds.

3.2 Optimize

Follow the test results some problem need to be

solved. Because of the Ollama Rely on the CPU, GPU

and RAM, but ChatGPT3.5 rely on the internight, so

it is a better choice for the problem. So, design a good

API is needed.

3.2.1 Design the API for ChatGPT3.5

As shown in figure 4, when the player enters a

question in the dialog system. These requests are

usually sent to API endpoints by using HTTP JOSN.

To ensure that only paying users can use API of

ChatGPT, API authentication is required. API of

OpenAI usually using API Key to authentication. API

Key is sent in the request to verify the identity of users.

When the request reaches the API server, the server

parses the content and preprocesses then it will send

to the large language model. Next GPT3.5 will give

the reply and turn back to the Unity to show for users.

Figure 4: The Response processing of ChatGPT 3.5 (Photo/

Picture credit: Original).

3.2.2 Optimize Test Results

When the code is modified the second experiment

began. Taping the same question as first time: Holle,

who are you. ChatGPT3.5 was also successfully

given the answer by using shorter times.

The optimized program takes only 10 seconds,

which is 40 seconds faster than Ollama. The second

test result is showing as Figure 5: Second test result.

Application of Large Language Model in Connecting Non-Player Characters Intelligent Conversations

137

Figure 5: Second test result (Photo/Picture credit: Original).

The ChatGPT3.5 had given the answer

successfully and faster than Ollama the. Response

was looks at you with a hint of mischief in her eyes*

Oh, I'm the one and only Hououin Kyoukai, but my

friends call me Diao Mu. *smirks*You can just call

me Houhou for short, though. What's your story?

This not only solves the problem of the large language

model depending on CPU, GPU and RAM, but also

reduces the internal storage of the program and

provide a better experience for the players.

4 CONCLUSIONS

This paper linking the large language model into the

non-player characters in games to achieve having

Intelligent dialogue with non-player characters and it

can also improve the Interactivity and player

experience of games. This paper successfully

completed the model deployment of the game, API

programming, programming in Unity, text display

and interaction design and optimization. During the

working process of this paper which had face a lot of

challenges including chose better large language

model, improved the efficiency of the model,

improved the efficiency of the model, and make sure

that naturalness and coherence of dialogue. This

paper not only solved all the problems by effective

solutions and optimization measures, but also ensures

the smooth experience for players when they are

talking to the non-player characters.

This paper not only had improved the immersion

and interactivity of the game successfully, but also

allows players to have an intelligent dialog with the

non-player characters. Therefore, during the working

process this paper had have accumulated many

valuable experience and technology and provided

some new ideas and methods for the development of

games in the future.

In the future, this system can be optimized and

expanded to achieve more application scenarios and

functions in the future. Overall, this paper has not

only achieved the technical goals and functions, but

also provides a successful mothed for the intelligent

dialogue system in games. This paper hopes to

provide some references and research direction for

the research in this field in the future.

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