The AI Generation Revolution in Open-World Games and Its

Technological Constraints

Zhiyuan He

Perkiomen School, Pennsylvania, U.S.A.

Keywords: Generative AI, Open-World Game Design, Human-Machine Collaboration.

Abstract: As open-world games evolve toward greater scale and complexity, traditional AI tools struggle to meet the

demands of dynamic environments and player-driven interactions. This has spurred growing interest in

generative AI as a means to enhance creativity, scalability, and responsiveness in game design. This article

analyzes the decision redundancy, state collapse, and labor cost dilemma faced by traditional technologies

such as behavior trees and finite state machines in a dynamic open world, revealing the fundamental

contradictions they cause, which forces developers to abandon creative solutions to maintain feasibility; Post

argument generative artificial intelligence has achieved breakthrough improvements in development

efficiency through neural symbolic architectures such as WHAM models, director actor collaboration, and

reconstruction of art production chains. This article also points out a series of technological bottlenecks that

artificial intelligence needs to face, such as the collapse of continuity narrative, the misalignment of physical

logic, and the distortion of cultural transfer, which exposes the enormous challenges that AI faces in the fields

of logical consistency, physical rules, and culture.

1 INTRODUCTION

The gaming industry is undergoing a shift from being

driven by mechanical rules to intelligent collaborative

symbiosis. Traditional development heavily relies on

logical frameworks such as behavior trees and finite

state machines. Although these techniques are classic

in closed systems, they cannot meet the dynamic

needs of modern open worlds. The fundamental

reason is that the static decision architecture cannot

respond to real-time changes in players' strategies,

which can lead to redundant decision paths in

complex scenes, and the combination of discrete

states can cause design space collapse with the

increase of interaction dimensions. This will force

creators and developers to abandon some of their

creativity, and labor costs have also become a huge

problem. The rise of artificial intelligence is

restructuring the creative paradigm, and the neural

symbol fusion architecture achieves personalized

generation of game space topology through the

dynamic coupling of the rule constraint layer and

behavior analysis layer, greatly shortening the

development cycle. The human-machine

collaborative narrative model also demonstrates great

potential for co-creation. With the help of AI, the

efficiency of the art production chain has increased by

nearly 80%. These technological breakthroughs

confirm the enormous potential of artificial

intelligence in the gaming industry.

However, there are still many problems with

current technology, such as the frame by frame

generation mechanism of diffusion models inducing

continuous narrative collapse, and the appearance of

characters undergoing illogical changes under

semantic fine-tuning; The Newtonian paradigm of

physics engines makes supernatural actions and

phenomena impossible, leading to homogenization of

combat styles and ultimately causing AI generated

images and models to deviate from popular

aesthetics. So the future of game development is

neither a retro manual paradigm nor a complete AI

takeover, but rather the establishment of a symbiotic

system between human creative thinking and

machine execution networks.

He, Z.

The AI Generation Revolution in Open-World Games and Its Technological Constraints.

DOI: 10.5220/0014362400004718

Paper published under CC license (CC BY-NC-ND 4.0)

In Proceedings of the 2nd International Conference on Engineering Management, Information Technology and Intelligence (EMITI 2025), pages 535-539

ISBN: 978-989-758-792-4

535

Figure 1: Behavior Tree (Picture credit: Original).

2 TRADITIONAL GAME DESIGN

2.1 Behavior Tree: Paradox of

Structured Decision Making

The behavior tree constructs a logical hierarchy

through modular nodes (Selector/Condition/Action).

For example, in StarCraft II, which people are

familiar with, it follows the decision chain of

"discovering enemies → selecting weapons →

attacking". Although this "LEGO style" architecture

enhances code reusability, it falls into a static trap as

shown in Figure 1.

Deng Yongjian’s empirical study revealed that

traditional behavior trees lead to NPC behavior

rigidity in dynamic environments due to the expansion

of conditional branches (Deng, 2014). When Elden

Ring required NPCs to adjust tactics in real-time based

on player equipment, developers were forced to write

hundreds of conditional branches - a microcosm of

how behavior trees struggle to support the complexity

of modern games(Cai, 2017; Deng, 2014)

2.2 Finite State Machine: Visualization

of Dimensional Disaster

FSM abstracts game entities into discrete states (such

as Mario's run/jump/injury) and triggers state

transitions through events. Its intuitiveness was

significant in early RPGs, but it hit a ceiling due to

exploding state combinations, particularly in fighting

games:

For example, the character "Instant Shadow" in

SNK's "King of Fighters 15" has 8 basic states, 6

superpower forms, and 4 environmental interaction

modes. The theoretical number of states is 192, which

triggers "State Collapse" during actual debugging.

Development Director Yasuyuki Oda candidly stated,

"We ultimately reduced 37% of the design proposal,

which was not a creative compromise, but a

technological massacre by FSM.". It is not difficult to

see from this that the problem faced by FSM in state

combination is catastrophic(Dong et al., 2019).

2.3 The Huge Cost of Labor

According to incomplete statistics, Rockstar Games

paid a staggering price in the development of the

remastered version of GTA5. Table 1 shows the

representative manual workloads and low reusability

in traditional game development.

Table 1: Representative Manual Workloads and Low

Reusability in Traditional Game Development.

Content t

e human in

ut Reusabilit

rate

Vehicle Physical

System

≈ 3000

working hours

10-15%

Pedestrian

ehavior database

≈ 4500

workin

hours

8-12%

Random event

scri

≈ 6000

workin

hours

≤5%

This phenomenon highlights the shortcomings of

traditional development in terms of manpower(Fang

et al., 2017), but it also forces the industry to seek new

paradigms.

3 AI ENHANCED DESIGN:

CREATIVE LIBERATION OF

HUMAN-MACHINE

SYMBIOSIS

3.1 Technological Breakthroughs in

Generative Architecture

The WHAM model achieves topology generation

through neural symbol fusion.

EMITI 2025 - International Conference on Engineering Management, Information Technology and Intelligence

536

The WHAM model adopts a three-layer

collaborative architecture to achieve intelligent

content generation. The symbol rule layer system

ensures that the level design meets the technical

specification of a safe path length not less than 70%

of the straight-line distance through geometric

constraints. The neural understanding layer uses

behavior pattern analysis methods to accurately

identify the behavioral characteristics of players in

different strategies, such as stealth, strong attack, and

exploration. Based on the above analysis, the process

generation layer can dynamically construct a highly

personalized level topology structure.

The WHAM model can greatly improve

development efficiency and save time and costs. This

model significantly compresses the level generation

cycle, while also enriching the content(Hao, 2018)

3.2 Collaborative Creation between

Humans and AI

The director actor model of Microsoft GamePlot

roughly realizes the creative collaboration between

humans and AI(Dong et al., 2019). Figure 2 shows the

collaborative creation between humans and AI.

Figure 2: Collaborative Creation between Humans and AI (Picture credit: Original).

This can greatly reduce the cost of labor, but the

obvious disadvantage is that AI models lack the

ability to express emotions, which makes the dialogue

formed by AI lack dramatic tension, resulting in a

lack of emotional expression and cultural

adaptability(He, 2018)

3.3 Application of AI in the Art

Production Chain

As is well known, the traditional process involves

conceptual design, 3D modeling, and finally texture

mapping, which requires a significant amount of time

and labor costs. But if AI is applied to the art

production chain, this process becomes text

description to prototype, generation to manual

refinement, which can reduce labor costs and improve

efficiency. Figure 3 shows the AI in the art production

chain.

Figure 3: AI in the Art Production Chain (Picture credit: Original).

In the development of "Collapse: Star Dome

Railway", miHoYo reconstructed the art production

process through its self-developed AI toolchain (such

as "Star Dome Workshop"): the designer first inputs

a text description, and then the AI engine

automatically generates three versions of the basic

model, PBR material mapping, and skeleton binding

within 2 hours, compressing 98% of the basic work

hours from concept design to 3D modeling to texture

mapping in the traditional process. Finally, the artist

performs debugging and optimization. The

application of AI has compressed the total

development cycle of a single role (Qiu, 2017).

4 TECHNOLOGICAL

BOTTLENECKS AND FUTURE

PROSPECTS OF AI

4.1 Collapse of Continuous Narrative:

Breakage of Logical Chain:

The mechanism of frame by frame generation in

diffusion models lacks cross frame memory

capability (Tu & Liu, 2017). When the input prompt

word undergoes semantic fine-tuning (such as "Red

Cloak Boy" changing to "Red Clothed Boy"), the

The AI Generation Revolution in Open-World Games and Its Technological Constraints

537

model cannot recognize it as the same object,

resulting in illogical changes in the character's

appearance.

The technology blind spot AI strictly follows the

Newtonian mechanics model, while anime action

design needs to break through the laws of real physics

(such as hovering rolling, sword air shock waves, and

supernatural phenomena). This conflict leads to the

AI designed soaring motion becoming stiff and

falling due to excessive gravity simulation.

The weapon trajectory has been corrected to a

parabolic trajectory (Xu et al., 2018), losing the

unique "sharpness" of anime.

These situations have stifled creativity and

imagination in the gaming and anime fields, leading

many Japanese animators to protest that the

homogenization of character combat styles is

inevitable when AI automatically corrects "anti-joint

backflips" to regular jumps that conform to

biomechanics.

4.2 Limitations of AI Modeling

Creativity and artistry: Game modeling is not just a

technical task, it also involves a high degree of

creativity and artistry. Modelers need to transform

ideas and concepts into unique 3D models, which

requires rich imagination and aesthetic ability.

However, currently, AI still has significant

limitations in terms of creativity and artistry, and the

generated images and figures deviate from public

aesthetics(Xiao, 2020). It cannot completely replace

human creativity and aesthetics.

Flexibility and adaptability: Game modelers need

to make flexible adjustments and adaptations

according to project requirements. They not only

need to understand the needs of customers or teams,

but also transform them into models that meet the

requirements. This flexibility and adaptability are

manifestations of human intelligence and experience,

and AI is difficult to fully simulate in this regard.

Problem solving ability: During the modeling

process, various complex problems and challenges

may be encountered. A modeler needs to have

problem-solving skills and judgment, and be able to

analyze and solve technical problems in the model.

Although AI can provide assistance in certain aspects,

human judgment and decision-making abilities are

still indispensable when facing complex problems.

5 CONCLUSIONS

This article reveals the triple dilemma that traditional

rule systems face under the demands of an open

world, namely decision path redundancy, state space

collapse, and labor costs. Generative AI has been

proven to greatly improve efficiency and enrich game

content through neural symbolic architectures such as

WHAM models, director actor narrative

collaboration, and art production chain

reconstruction.

However, currently the technology still faces

significant problems such as the collapse of

continuous narrative, physical logic disorder, and the

deviation of generated images and models from

popular aesthetics. These challenges also fully

demonstrate the huge gap between algorithms and

creativity.

As mentioned in the introduction, the future of

game development is neither a retro manual paradigm

nor a complete AI takeover, but rather the

establishment of a symbiotic system between human

creative thinking and machine execution networks:

designers should act as gatekeepers of rules, defining

and planning content and ethical boundaries; AI

improves efficiency and helps designers unleash their

creativity. This human-machine symbiotic paradigm

will drive the development of games, making them an

artistic medium and reshaping the future of

interactive storytelling.

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