







































X. Qian et al. /Future Technology                                                                                         November 2025| Volume 04 | Issue 04 | Pages 
33-42 

33 

 

 

 

Article 

Multi-agent reinforcement learning for Bai ethnic 

traditional dwelling protection in Dali: cultural 

identity-oriented community relationship 

optimization and urbanization adaptation 

algorithm  
Xiaohua Qian1,2*, Neilson Ilan Mersat1, Haslina Hashim1, Bemen Wong Win Keong1   

1Faculty of Social Sciences and Humanities, Universiti Malaysia Sarawak, 94300 Kota Samarahan, Sarawak, Malaysia 
2Faculty of Art and Communication, Kunming University of Science and Technology, Kunming, 650500, China 

A R T I C L E   I N F O 
 

Article history: 
Received 30 May 2025  
Received in revised form 
10 July 2025 
Accepted 19 July 2025 
 
Keywords:  
Multi-agent reinforcement learning, 
Cultural heritage preservation, Cultural identity, 
Traditional architecture, Stakeholder coordination 
 
*Corresponding author 
Email address: 
qianxiaohua117@163.com 
 
 

DOI: 10.55670/fpll.futech.4.4.4 

A B S T R A C T 
 

The preservation of residential architecture from traditional ethnic groups has 
never faced the types of challenges it does today due to urbanization. These 
challenges include the insufficient retention of landmarks due to competing 
stakeholder interests, which often leads to irreversible loss of cultural heritage. 
This research proposes a new culturally identity-oriented multi-agent 
reinforcement learning system for the protection of Bai ethnic traditional 
dwellings in Dali, Yunnan Province. The research combines diverse multi-
source data collection approaches, including the building’s architecture and 
culture, urbanization statistics, and stakeholder networks, and develops an 
advanced computational framework in which every stakeholder category is 
embedded as an independent intelligent agent with specific behavioral patterns 
and autonomous decision-making skills. Specialized deep Q-networks of 
enhanced Q-value methods that consider cultural identity loss in Q-value 
calculus through loss function adjustments aimed at balancing cultural 
preservation and stakeholder appeasement were employed within the 
framework. Implementation results show performance with an overall 
accuracy of 89.3% for implementation and 87.2% for cultural preservation 
effectiveness. Conventional approaches previously achieved significantly lower 
accuracy within these parameters, 15-25 percentage points. Enhancements in 
cultural identity increase from a baseline of 58.3% to optimized values of 
91.2%, while community satisfaction improves from 54.7% to 86.4%. The 
framework maintains coordination indices above 85% for all stakeholder 
groups, showing scalability with over 85% replication success rates for 
populations between 5,000 and 50,000 residents. This demonstrates 
theoretical and practical value in the use of AI concerning culturally aware 
heritage preservation. 

1. Introduction 

The rapid pace of urbanization, coupled with competing 
stakeholder priorities, affects approximately 68% of 
traditional ethnic residential sites in China, often irreversibly 
damaging cultural heritage. The incorporation of AI into 
contemporary urban development to address complex 
sustainability issues demonstrates the usefulness of 
intelligent systems in solving deeply intertwined urban 
planning problems [1]. However, the interface between AI 

technologies and urbanism with cultural heritage remains 
markedly under-researched. The advent of AI technologies in 
post-smart cities has brought benevolent and malevolent 
intricacies to the conservation of heritage sites—a crossroad 
that invites criticism for more nuanced approaches that 
center on culture and community [2] “between the poles of 
the universal and the particular” (the ‘sensitive’ side). 
Conserving cultures that are heavily impacted by rapid 
urbanization and the deterioration of authenticity in multi-

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X. Qian et al. /Future Technology                                                                                         November 2025| Volume 04 | Issue 04 | Pages 33-42 

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stakeholder actions relies heavily on policy instruments such 
as expert-driven systems and regulatory compliance 
pathways. Traditional heritage preservation approaches 
often fail to coordinate conflicting stakeholder interests, 
resulting in 45-60% of sites experiencing a loss of cultural 
authenticity during urbanization. Current frameworks lack 
adaptive mechanisms for balancing development pressures 
with preservation needs, particularly in ethnic minority 
regions where decision-making systems cannot match rapid 
urban transformation rates. Highly dynamic environments 
with many self-sufficient decision-making agents, each with 
their own goals, have potentially conflicting objectives, which 
multi-agent reinforcement learning deals with as an advanced 
paradigm. Recent advances in deep multi-agent 
reinforcement learning demonstrate its capability in 
managing complex systems involving cooperation and 
coordination of heterogeneous agents [3]. Structural 
frameworks demonstrate that multi-agent deep 
reinforcement learning techniques are successfully applied to 
situations that require advanced coordination between 
diverse constituents [4]. The systematic analysis of digital 
technologies' application in cultural heritage conservation 
identifies multiple computational approaches for protection, 
documentation, and management, indicating increased 
recognition of technological transformation in heritage 
preservation [5]. These innovations challenge the complex 
problem of the integration of varying sociocultural priorities 
with the preservation of historical values and therefore 
provide strong arguments supporting heritage cultural 
technology. 

Artificial intelligence techniques have achieved accuracy 
rates of 70-85% in preserving intangible cultural heritage [6], 
with virtual reality and multi-agent algorithms providing 
multifunctional conservation frameworks [7-10]. While 
machine learning enables spatial analysis and sustainable 
urban development [11, 12], current applications remain 
limited. Bibliometric analyses indicate that existing 
approaches prioritize technological documentation over 
active management [13, 14], and despite potential 
intersections between machine learning and big data [15], 
these technologies inadequately integrate cultural 
preservation objectives within urban planning contexts [16]. 
Existing preservation frameworks reveal substantial 
limitations across stakeholder coordination, achieving 
merely 52-65% success rates with developer-resident 
alignment below 50%. The absence of cultural identity 
quantification in 87% of current algorithms produces 
culturally detached outcomes, while static architectures 
experience a 40-55% performance decline under intensified 
urbanization. Despite advances in deep learning architectures 
for complex pattern recognition [17], these approaches 
inadequately address cultural identity integration within 
multi-stakeholder contexts. Applications of multi-agent 
reinforcement learning in industrial settings show significant 
adaptability [18]; however, its use in the preservation of 
cultural heritage is still notably under-researched. Stated 
differently, traditional methods of preservation do not offer 
systematic rationales that integrate stakeholders with 
competing interests and sustain the cultural integrity of the 
region. In framework design approaches, identity and culture 
have yet to be adequately addressed within a dominant 
organizing paradigm; therefore, many frameworks remain 
ineffective in culturally sensitive environments where the 
community, culture, and their relationship sustain fluid 
continuity. This research addresses these gaps through a 
multi-agent reinforcement learning framework that 

quantifies intangible cultural values as computational 
parameters with 89% validation accuracy. The framework 
embeds cultural identity factors within preservation 
decision-making to achieve 80-92% stakeholder 
coordination, while adaptive Q-learning mechanisms 
maintain 85% effectiveness under variable urbanization 
conditions. With validated scalability across populations of 
5,000-50,000 residents and integration pathways for 
government heritage systems, this approach bridges 
technological innovation with cultural preservation 
imperatives in urban development contexts. 

2. Data and methods 

2.1 Study area and multi-source data collection 
This research analyzes the Dali Bai ethnic residential 

architecture preservation areas in Yunnan Province, China, 
which contain traditional courtyards alongside other 
structures representative of important cultural heritage, 
distinguishing building techniques unique to the region. The 
study region includes the Dali Ancient City, the adjacent Bai 
villages historically integrated with the ancient city, both of 
which offer traditional forms of urban housing influenced by 
modern globalization through urban centers and 
development. The use of advanced technologies for data 
collection from various sources allows efficient 
characterization of the unique dynamics of stakeholder 
interactions and the various datasets related to 
archaeological site preservation. Machine learning 
approaches for cultural heritage applications provide 
established methodologies for systematic data collection and 
analysis in heritage preservation contexts [19]. Building 
architectural data encompasses geometric measurements, 
structural condition assessments, and material composition 
analysis collected through field surveys and photogrammetric 
techniques. Advanced sensing technologies, including 3D 
LiDAR systems and multi-technology collaboration 
frameworks, enable comprehensive documentation of built 
heritage structures with high precision and accuracy [20]. 
Community perspectives were assessed using a 42-item 
cultural attachment scale (α = 0.87) spanning six dimensions 
from place identity to intergenerational transmission 
willingness. Following pilot validation with 60 households, 
the 7-point Likert instrument incorporated triangulation with 
observational and archival data to ensure measurement 
robustness within local cultural contexts. Urbanization 
indicators comprise demographic changes, land use 
transitions, construction permits, and economic development 
metrics obtained from municipal planning databases and 
statistical yearbooks. 

This study identifies four distinct stakeholder categories 
comprising local residents who maintain traditional lifestyles 
and cultural practices, government agencies responsible for 
heritage protection and urban planning oversight, real estate 
developers pursuing economic opportunities through 
property development initiatives, and cultural preservation 
experts who provide specialized technical expertise and 
professional guidance for heritage conservation strategies. 
Cultural identity quantification employs semantic 3D 
documentation approaches integrated with multi-scale 
mapping methodologies to systematically capture cultural 
attributes and spatial relationships [21]. Cultural value 
assessment encompasses five weighted dimensions ranging 
from architectural authenticity (25%) to community 
attachment (15%), evaluated through expert Delphi methods 
(40%), empirical field measurements (35%), and community 
surveys (25%). This integrated approach quantifies 



X. Qian et al. /Future Technology                                                                                         November 2025| Volume 04 | Issue 04 | Pages 33-42 

35 

 

intangible heritage attributes within computational 
frameworks while maintaining methodological rigor and 
cultural validity. The comprehensive data collection 
framework, as shown in Table 1, provides the empirical 
foundation for developing and validating multi-agent 
reinforcement learning models. 

2.2 Multi-agent system modeling and ai architecture 
design 
The multi-agent reinforcement learning framework 

establishes a computational architecture where each 
stakeholder category operates as an autonomous intelligent 
agent with distinct behavioral patterns and decision-making 
capabilities. Multi-agent cooperation and competition 
dynamics provide foundational principles for designing agent 
interactions in complex environments where multiple 
entities pursue different objectives [22]. Stakeholder-specific 
neural networks enable residents to process cultural 
preferences, government agents to evaluate policies, 
developers to optimize economic outcomes, and experts to 
assess heritage values. This framework departs from 
traditional RL through cultural identity integration in Q-value 
computations using adaptive coefficients (λc = 0.3-0.5), 
enabling multi-objective optimization where heritage 
considerations become intrinsic to agent decisions. The 
dynamically adjusted coefficients respond to heritage impacts 
while the state space tracks preservation effectiveness, 
community welfare, and cultural authenticity across evolving 
urban contexts. The agent interaction protocols facilitate 
communication and coordination processes through 
message-passing and shared information systems that allow 
multi-agent systems for joint action while maintaining agent 
independence. Multi-agent actor-critic frameworks allow 
mixed cooperative-competitive conditions where agents have 
to integrate self-preserving tasks with objectives aligned to 
common conservation goals [23]. The reward function guided 
by cultural identity integrates multiple objective components 
diversified as effectiveness of heritage preservation, 
community relations, economic sustainability, and 
maintenance of cultural authenticity, thereby forming 
balanced feedback that enables learning towards culturally 
adaptive solutions for agents to automate processes. 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

The initialization of parameters for deep neural 
networks utilizes methods based on experience replay for 
stabilization across different types of agents using the Xavier 
normalization technique. Deep Multi-Agent Reinforcement 
Learning (MARL) Q-learning with distinct Q-vectors for 
different rates of agent capability and learning adapts to 
diverse agent capabilities and learning rates, showing 
improved performance [24]. In a culture-imbued adaptive 
systems framework for optimization of heritage preservation, 
every agent type has its own learning rate, network structure, 
and exploration versus exploitation settings. These 
parameters are adjusted using Bayesian optimization 
techniques, as shown in Figure 1, depicting the multi-agent 
system architecture, which integrates cultural identity 
considerations with adaptive learning mechanisms. Ethical 
safeguards include participatory parameter design with 120 
diverse community stakeholders, automated bias detection 
with 15% deviation thresholds, and explainable AI modules 
ensuring decision transparency. These measures prevent 
minority marginalization and maintain community agency 
while embedding inclusive cultural values within the 
algorithmic framework. 

Local Residents
Cultural Preference

Module

Government
Policy Evaluation

Module

Developers
Economic

Optimization

Cultural Experts
Heritage

Assessment

Cultural ldentity

Oriented Reward

Function

Multi-objective

Optimization output

Environment State Space

Cultural 

Preferences

Policy 

Constraints

Heritage 

Assessment
Economic 

Objectives

 
 
Figure 1. Cultural identity-oriented multi-agent reinforcement 
learning system architecture 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

Table 1. Multi-source data collection framework for Dali Bai ethnic residential preservation areas 

Data Category Collection Method Sample Size Temporal Coverage Stakeholder Groups Data Format 

Building Architecture 3D LiDAR Scanning 245 structures 2022-2024 Residents, Experts Point clouds, CAD 

Cultural Attributes Ethnographic Survey 180 households 2023-2024 Residents Structured interviews 

Urbanization Metrics Municipal Database 15 indicators 2010-2024 Government Statistical data 

Stakeholder Networks Social Network Analysis 95 participants 2023-2024 All groups Relational matrices 

Heritage Condition Field Assessment 245 structures 2022-2024 Experts Condition reports 

Note: Cultural attribute quantification integrated architectural integrity assessments of Bai vernacular structures, ethnographic measurements 

of language use and ritual participation, and GIS-derived spatial proximity indices to heritage sites, employing standardized protocols to convert 

multifaceted cultural data into algorithmic parameters. 



X. Qian et al. /Future Technology                                                                                         November 2025| Volume 04 | Issue 04 | Pages 33-42 

36 

 

2.3 Reinforcement learning algorithm and machine 
learning model 
This study builds upon deep reinforcement learning 

approaches by applying an optimized deep Q-network 
architecture for multi-agent coordination in heritage 
preservation [25]. The improved DQN framework 
incorporates cultural identity considerations into the 
traditional Q-value computation through a modified loss 
function that balances preservation objectives with 
stakeholder satisfaction metrics. The Q-value update 
mechanism follows the enhanced Bellman equation, where 
Q(st, 𝑎t) represents the action-value function for state St and 
action 𝑎t at time step t, 𝑎 denotes the learning rate, rt indicates 
the immediate reward, 𝛾  represents the discount factor for 
future rewards, and 𝛽  serves as the cultural weighting 
coefficient: 

 1 cl( , ) ( , ) max ( , ) ( , ) ( , )t t t t t a t t t t tQ s a Q s a r Q s a Q s a C s a   +
 + + − +        (1) 

The cultural identity preservation component CC1(st, 𝑎 t) 
guides agent decision-making toward culturally sensitive 
outcomes by quantifying the cultural impact of specific state-
action pairs. Specifically, CC1(st, 𝑎 t) integrates architectural 
integrity, cultural practice continuity, and community 
identity through weighted aggregation: 

( , ) 0.4 ( , ) 0.35 ( , ) 0.25 ( , )cl t t cl t t cl t t cl t tC s a A s a P s a I s a= + +       (2) 

where AC1(st, 𝑎 t) measures architectural preservation 
probability, PCl(st, 𝑎t) captures cultural practice sustainability, 
and ICl(st, 𝑎t)  reflects community identity cohesion, with all 
components normalized to [0,1] using empirical baselines 
from Section 2.1's framework. 
The experience replay mechanism employs adaptive 
prioritized sampling strategies that enhance exploration-
exploitation trade-offs through dynamic priority assignment 
based on temporal difference errors and cultural relevance 
scores [26]. This approach ensures that culturally significant 
experiences receive higher sampling probabilities during 
training, accelerating convergence toward preservation-
oriented policies. The target network update strategy 
implements a stabilized off-policy learning approach that 
reduces bootstrapping errors inherent in multi-agent 
environments. The loss function incorporates both value 
function accuracy and cultural preservation effectiveness, 

where   represents the current network parameters, 
−

 
denotes the target network parameters, D  indicates the 

experience replay buffer, and   serves as the regularization 
coefficient for the cultural loss component 𝐿cl(𝜃): 

𝐿(𝜃) = 𝔼(𝑠, 𝑎, 𝑟, 𝑠 ′)~𝒟 [(𝑟 + 𝛾𝑚𝑎𝑥
𝑎′

𝑄(𝑠 ′, 𝑎′; 𝜃−) −

𝑄(𝑠, 𝑎; 𝜃))
2
] + 𝜆 ⋅ 𝐿cl(𝜃)           (3) 

Policy gradient methods complement the value-based 
approach through actor-critic architectures that optimize 
multi-objective policies. Bootstrapping error reduction 
techniques stabilize learning in the multi-agent setting by 
constraining policy updates within confidence bounds [27]. 
The urbanization adaptation mechanism implements online 
learning protocols that continuously adjust agent behaviors 
based on evolving environmental conditions, enabling 
dynamic responses to change urban development pressures 
while maintaining cultural preservation priorities through 
incremental learning strategies. 

 

3. Results 

3.1 AI algorithm performance and deep learning model 
evaluation 
The proposed cultural identity-oriented multi-agent 

reinforcement learning framework demonstrates 
convergence characteristics, achieving 89.3% accuracy 
within 900 training episodes. Figure 2 presents the 
comprehensive evaluation results of the multi-agent deep 
reinforcement learning algorithm's performance. Neural 
network loss function analysis, as illustrated in Figure 2(a), 
reveals distinct optimization trajectories for each agent 
category, with cultural experts achieving convergence within 
600 episodes, showing exponential decay from initial loss 
values of 2.1 to final convergence at 0.2. Government agents 
demonstrate consistent convergence patterns reaching 
stability at 700 episodes, while local residents achieve 
convergence at 800 episodes with final loss values of 0.3. 
Developer agents require the longest convergence period at 
900 episodes, reflecting the inherent complexity of balancing 
economic optimization objectives with heritage preservation 
constraints. The differential convergence patterns indicate 
that structured decision-making processes, such as those 
employed by cultural experts, facilitate more efficient policy 
learning compared to multi-objective scenarios encountered 
by developer agents. 

 

Figure 2. Multi-agent deep reinforcement learning algorithm 
performance and model evaluation (a) Neural network loss function 
convergence curves, (b) Agent learning trajectory comparison, (c) 
Performance comparison with traditional machine learning methods, 
(d) Model accuracy assessment indicators analysis 

The agent learning trajectory comparison shows an 
incremental improvement across all stakeholder categories 
as illustrated in Figure 2(b). Cultural expert agents maintain 
85% effectiveness by the 2000th episode, and demonstrate 
consistent improvement with minimal variance in earlier 
phases. Local resident agents exhibit steady improvement, 
reaching 75% effectiveness, government agents attain 65% 
performance levels through structured policy evaluation 
processes, and developer agents achieve 55% optimization 
efficiency despite facing complex multi-objective constraints. 
The learning curves display characteristic S-shaped growth 
patterns typical of reinforcement learning algorithms, with 
rapid initial improvement followed by gradual convergence 
toward optimal policies. The performance differentiation 
reflects each agent's specialized role within the heritage 
preservation ecosystem, with cultural considerations serving 



X. Qian et al. /Future Technology                                                                                         November 2025| Volume 04 | Issue 04 | Pages 33-42 

37 

 

as the primary coordination mechanism. Comparative 
performance evaluation against traditional machine learning 
methods reveals 15-25 percentage point improvements of the 
proposed multi-agent approach across all evaluation metrics, 
as demonstrated in Figure 2(c). The framework achieves 
89.3% accuracy with narrow confidence intervals indicating 
statistical reliability, representing 17.2 percentage point 
improvements over single-agent DQN methods at 72.1%, 
decision tree approaches at 66.7%, support vector machines 
at 70.4%, random forest algorithms at 75.8%, and neural 
network ensemble methods at 78.2%. The confidence 
intervals demonstrate statistical significance of performance 
improvements, with the proposed method showing 
consistently higher results across all comparison categories. 
Traditional rule-based approaches exhibit the lowest 
performance at 61.3%, highlighting the limitations of 
conventional heritage preservation methodologies in 
complex multi-stakeholder environments. 

Model accuracy assessment across multiple evaluation 
dimensions, as presented in Figure 2(d), indicates the 
framework achieves 89.3% accuracy, 90.1% precision, 88.7% 
recall, and 89.4% F1-score. The provided performance 
metrics with their associated confidence intervals serve as an 
indicator of the statistical consistency of the gains in 
performance. The proposed framework demonstrated very 
low inconsistency across various performance metrics within 
the given measures. The baseline methods suffer significantly 
lower performance along with a higher degree of uncertainty, 
especially in recall metrics, which have a difference of about 
18 percentage points. This observation, along with the 
proposed framework's ability to more accurately identify 
critical heritage preservation as well as stakeholder 
coordination situations, signifies the strength of the proposed 
framework. Table 2 presents detailed performance metrics 
demonstrating 25.8-34.4 percentage point improvements 
over baseline algorithmic approaches. The framework 
outperforms all other methods with over 87.2% cultural 
preservation effectiveness and surpasses single-agent DQN 
methods by 25.8 percentage points and rule-based methods 
by 34.4 percentage points. When evaluating training 
efficiency, it is observed that the framework requires 12.5 
hours to achieve full convergence, in comparison to simpler 
methods. While this overhead can be considered moderate, 
the evaluation metric performance improvements 
benchmarked against other methods more than justify the 
investment for this computational overhead.  

 

 

 

 

 

 

 

 

 

 

 

 

The framework achieves the highest performance in 
precision metrics at 90.1%, illustrating accurate decision 
making pertaining to heritage preservation, and balanced 
recall performance at 88.7% relative to complete opportunity 
capture. Ensemble methods using neural networks have 
longer training times (15.2 hours), but yield a deficiency in 
every single metric, demonstrating the performance 
efficiency of the proposed method for multi-agent 
coordination in heritage preservation contexts. 

3.2 Protection Effectiveness Evaluation 
The implementation of the cultural identity-oriented 

multi-agent reinforcement learning framework demonstrates 
improvements in residential heritage protection integrity and 
cultural identity enhancement across multiple evaluation 
dimensions. Architectural preservation integrity assessment 
reveals progress in maintaining traditional Bai ethnic 
residential structures, with overall preservation 
completeness increasing from baseline levels of 62.4% to 
89.7% following framework implementation, as illustrated in 
Figure 3(a). The temporal analysis demonstrates consistent 
upward trends in preservation effectiveness, with notable 
improvements observed in structural integrity maintenance, 
traditional material preservation, and vernacular 
architectural feature conservation. The framework 
successfully coordinates stakeholder actions to prioritize 
preservation activities that maintain authentic cultural 
characteristics while accommodating necessary 
modernization requirements. 

Cultural identity reinforcement evaluation indicates 
enhancement in community cultural attachment and ethnic 
identity preservation measures, as demonstrated in Figure 
3(b). The figure specifically tracks cultural identity metrics—
including language preservation, traditional craft 
maintenance, and ceremonial practice continuity—which 
show progressive improvement from baseline measurements 
of 58.3% to peak levels of 91.2%, clearly distinguished from 
heritage preservation integrity metrics shown in Figure 3(a). 
The enhancement also encompasses intergenerational 
knowledge transmission effectiveness. Community members 
report increased pride in heritage preservation outcomes and 
stronger connections to traditional cultural practices, with 
younger generations demonstrating renewed interest in Bai 
ethnic cultural traditions and architectural heritage 
appreciation. 

 

 

 

 

 

Table 2. Performance evaluation metrics comparison of different AI algorithms in residential heritage protection tasks 

Algorithm Type 
Accuracy 

(%) 
Precision 

(%) 
Recall 

(%) 
F1-

Score 
Cultural Preservation 

Effectiveness (%) 
Training Time 

(hours) 

Proposed MARL 
Framework 

89.3 90.1 88.7 0.894 87.2 12.5 

Single-Agent DQN 72.1 74.3 69.8 0.720 61.4 8.3 

Decision Tree 66.7 68.2 65.1 0.665 58.9 2.1 

Support Vector 
Machine 

70.4 72.1 68.9 0.705 64.3 4.7 

Random Forest 75.8 77.2 74.5 0.758 69.1 3.9 

Neural Network 
Ensemble 

78.2 79.6 76.8 0.782 71.7 15.2 

Traditional Rule-
Based 

61.3 63.7 58.9 0.612 52.8 1.5 

 

 



X. Qian et al. /Future Technology                                                                                         November 2025| Volume 04 | Issue 04 | Pages 33-42 

38 

 

 
Figure 3. Cultural identity-oriented protection effectiveness and 
community relationship optimization (a) Residential heritage 
protection integrity temporal changes, (b) Cultural identity 
enhancement trend analysis, (c) Community relationship 
coordination index evolution, (d) Urbanization adaptation response 
curve analysis 

Community relationship optimization analysis reveals 
improvements in stakeholder coordination and conflict 
resolution mechanisms, as presented in Figure 3(c). The 
social harmony index demonstrates consistent enhancement 
from initial values of 54.7% to optimized levels of 86.4%. The 
framework effectively mediates conflicts between 
preservation objectives and development pressures, 
facilitating collaborative decision-making processes that 
balance diverse stakeholder interests. Local residents exhibit 
increased satisfaction with preservation outcomes, 
government agencies report improved policy implementation 
efficiency, developers demonstrate enhanced cooperation in 
heritage-sensitive projects, and cultural experts achieve 
greater influence in preservation planning processes. 
Urbanization pressure adaptation assessment, as shown in 
Figure 3(d), demonstrates the framework's capability to 
maintain preservation effectiveness despite evolving urban 
development challenges. The adaptive response index adapts 
preservation strategies to the different types of urban 
intensification, such as demographic growth, infrastructure 
development, tourism, and economic activities. 

 

 

 

 

 

 

 

 

 

 

 

 

 

Results show that, unlike other approaches, the 
framework considers urbanization scenarios and maintains 
effectiveness over 82% even under high pressure; traditional 
approaches, by contrast, experience significant decline under 
the same conditions. The adaptive mechanisms achieve the 
balance between modernization of urban settings with 
heritage features for optimum sustainable development while 
ensuring cultural integrity is retained, alongside economic 
development for communities. The evaluation depicting 
comprehensive multi-objective optimization performance in 
Table 3 illustrates the calculated value of the framework's 
utility in proving effectiveness across all dimensions of 
preservation.  

The degree of integrity achieved regarding heritage 
architecture has improved from baseline conditions and 
surpassed targets set by heritage protection agencies towards 
their goal of achieving 89.7%. Effectiveness in preserving 
cultural identity has maintained 91.2%, proving successful in 
the maintenance of intangible elements of cultural heritage 
integrated with physical architectural conservation. 
Community satisfaction indices have achieved 87.5%, 
displaying the level of acceptance and support by wide 
segments of the stakeholders for the preservation results. 
Economic sustainability measures have reached 78.9%, 
illustrating that the level of benefits brought by preservation 
activities is sufficient to ensure the long-term efforts in 
conserving the region. Environmental sustainability 
parameters have reached 84.3%, indicating that the level of 
preservation activities helps in attaining wider ecological 
conservation goals, which are accompanied by the protection 
of heritage values. The evaluation results as a whole 
demonstrate the framework’s utility in accomplishing multi-
faceted objectives of preservation under community 
development and culture during rapid urbanization. 

3.3 Practical Application Verification 
To some degree, the Dali Ancient City case study affirms 

how well the culturally oriented multi-agent reinforcement 
learning framework performs with respect to various 
heritage preservation challenges and stakeholder 
coordination difficulties. Validation of the implementation 
shows spatial variation in effectiveness regarding 
preservation across the study region, scoring between 75% 
and 95% of protection eligibility in different areas of the 
region, as shown in Figure 4(a).  

 

 

 

 

 

 

 

 

 

 

 

 

 

Table 3. Residential heritage protection effectiveness and multi-objective optimization indicators statistics 

Evaluation 
Dimension 

Baseline Value 
(%) 

Implementation Result 
(%) 

Improvement 
(%) 

Target Achievement 
(%) 

Performance 
Rating 

Heritage Architectural 
Integrity 

62.4 89.7 27.3 94.1 Excellent 

Cultural Identity 
Preservation 

58.3 91.2 32.9 95.8 Excellent 

Community 
Satisfaction Index 

65.2 87.5 22.3 91.7 Very Good 

Stakeholder 
Coordination 

54.7 86.4 31.7 93.2 Excellent 

Economic 
Sustainability 

49.8 78.9 29.1 82.6 Good 

Environmental 
Compatibility 

71.6 84.3 12.7 88.7 Very Good 

Policy Implementation 
Efficiency 

58.9 83.7 24.8 87.9 Very Good 

Cultural Transmission 
Effectiveness 

52.4 88.6 36.2 92.8 Excellent 

 

 



X. Qian et al. /Future Technology                                                                                         November 2025| Volume 04 | Issue 04 | Pages 33-42 

39 

 

The spatial distribution analysis indicates concentrated 
high-performance areas in the core heritage districts, where 
traditional Bai ethnic architectural features achieve effective 
preservation outcomes, while peripheral zones demonstrate 
progressive improvement patterns that reflect the 
framework's adaptive coordination mechanisms responding 
to varying urban development pressures and community 
engagement levels. Multi-stakeholder coordination 
effectiveness evaluation demonstrates improvements in 
collaborative decision-making processes among diverse 
interest groups, as presented in the coordination heat map in 
Figure 4(b). The framework achieves coordination scores 
between residents and cultural experts at 92%, indicating 
successful alignment of community preferences with 
professional heritage preservation standards. Government-
resident coordination reaches 85%, while developer-expert 
collaboration attains 84%, representing improvements over 
traditional preservation approaches that typically struggle 
with stakeholder conflict resolution. The systematic 
coordination matrix reveals that cultural experts maintain 
consistently high coordination levels across all stakeholder 
categories, serving as effective mediators in complex 
preservation negotiations, while developers demonstrate 
enhanced cooperation levels that exceed baseline 
expectations through the framework's incentive alignment 
mechanisms. The framework's modular architecture enables 
transferability by separating universal coordination 
mechanisms from culture-specific parameters, allowing 
adaptation to diverse heritage contexts through coefficient 
recalibration rather than algorithmic restructuring. 

Figure 4. Dali ancient city field validation and multi-party 
coordination effectiveness (a) Field validation results spatial 
distribution, (b) Agent coordination protection effectiveness heat 
map, (c) Multi-party interest coordination index changes, (d) 
Community satisfaction assessment results 

Temporal analysis of multi-party interest coordination 
demonstrates sustained improvement throughout the 
implementation period, with coordination indices rising from 
baseline values of approximately 52% to optimized levels 
exceeding 82%, as shown in Figure 4(c). Identifying 
progressive enhancement techniques has provided evidence 
that a multi-agent system is capable of learning, adapting to 
changing stakeholder dynamics, and overcoming 
preservation challenges. Satisfaction assessment shows 
acceptance of community practitioners and residents across 
broad demographic categories, with cultural practitioners 
achieving as high as 94% satisfaction, leading the group, 

elderly residents achieving 91%, young families achieving 
87%, and business owners achieving 85% as shown in Figure 
4(d). These findings enable us to conclude that the attempt to 
strike a balance between the preservation objectives and the 
community welfare considerations has succeeded, making 
progress towards long-term sustainability for the initiatives 
intended for heritage conservation. The analysis regarding 
long-term sustainability indicates the ability of the 
framework to endure regarding the maintenance of 
preservation effectiveness over prolonged time periods while 
still coping with urban development pressure as shown in 
Figure 5(a). Historical implementation databases suggest that 
initial levels of preservation effectiveness rest at 90.2% with 
projections estimating a gradual drop to 88.5% in the short 
term and 87.3% for the long-term scenario, which is a good 
percentage in the model of minimal degradation rates 
surpassing traditional approaches to preservation. The 
framework maintains preservation effectiveness under 
varying environmental conditions, as made clear in Figure 
5(b), displaying the framework's resilience, confirming 
system stability and adaptability assessment, which shows 
correlation between these metrics across relief scenarios of 
stress, from low achieving 92% effectiveness to extreme 
stress conditions, where they maintain at an 83% 
performance level. 

 

Figure 5. Long-term sustainability analysis and application 
promotion potential (a) Protection effectiveness staged 
comparison analysis, (b) System stability and adaptability 
assessment, (c) Regional applicability multi-dimensional 
analysis, (d) Replication potential and application scalability 
evaluation 

The applicability assessment at the regional level 
confirms that the framework can be transferred to other 
cultural heritage contexts outside the Dali implementation 
site, as illustrated in the multi-faceted evaluation in Figure 
5(c). From the radar diagram, it can be seen that the five 
regions were evaluated consistently within the same five 
regional heritage sites. Dali, after all, performed the best on 
every single measure set forth; whilst Lijiang, Shangri-La, 
Xishuangbanna, and Tengchong showed applicability 
potential with varied performance constrained by local 
contextual factors such as cultural adaptation, economic 
feasibility, technical complexity, and community acceptance. 
Deployment considerations include computational 
infrastructure requirements for handling multi-agent 



X. Qian et al. /Future Technology                                                                                         November 2025| Volume 04 | Issue 04 | Pages 33-42 

40 

 

coordination, connectivity challenges in remote heritage 
sites, and ethical protocols ensuring community participation 
and data protection. The framework's implementation 
complexity varies with site scale, with training processes 
requiring approximately 12.5 hours for system convergence. 
Replication potential and application scalability evaluation 
provide evidence of the framework's practical viability for 
widespread implementation across multiple heritage 
preservation contexts, as presented in Figure 5(d). The 
analysis highlights that medium-scale communities with 
populations between 10,000 and 20,000 have optimal 
replication success rates of 91%, sustaining acceptable 
performance levels above 85% across all population 
categories. The cost-benefit analysis indicates positive 
economic returns as the ratios decline from 3.8:1 for smaller 
communities to 2.6:1 for larger implementations, 
demonstrating economies of scale without negative return 
profiles. The modular architecture of the framework allows 
effective adaptation to different cultures while maintaining 
primary coordination structures, aiding cross-scenario 
applicability across various ethnic heritage preservation 
contexts, with demographic and economic development level 
adaptability showing implementation feasibility. 

4. Discussion  

The research integrates multi-agent deep reinforcement 
learning into heritage conservation, marking a systematic 
application of AI to cultural preservation through 
architectural innovations that embed cultural values within 
computational processes. This framework advances beyond 
passive documentation toward dynamic preservation 
management, incorporating actionable AI-based coordination 
that affects outcomes through stakeholder behavioral change. 
Unlike conventional optimization systems, it unifies collective 
cultural objectives while balancing competing interests 
through advanced multi-agent coordination. The design 
demonstrates responsible AI deployment in culturally 
sensitive contexts, advancing the discourse on explainable AI 
and responsible AI [28] by using artificial intelligence to 
enhance rather than replace human decision-making in 
heritage management. Framework integration with 
governmental systems occurs through API interfaces linking 
heritage databases and permit systems, supported by AI 
governance committees and standardized protocols. The 
system enhances policy formulation via predictive analytics 
and automated compliance monitoring while maintaining 
human oversight for cultural decisions through legislative 
frameworks that recognize AI-assisted heritage management. 

Recent cultural heritage digitalization studies focus 
primarily on documentation and visualization rather than 
dynamic preservation management [29]. This work differs by 
incorporating actionable coordination approaches affecting 
preservation outcomes through stakeholder behavioral 
change. The framework works toward unifying collective 
cultural objectives and balancing competing stakeholder 
interests through advanced multi-agent coordination [30], 
supporting emerging approaches to trust and community 
control in heritage technology [31]. These findings enable 
scaled, culturally grounded implementations across diverse 
contexts. Despite methodological robustness, the framework 
faces inherent limitations in quantifying cultural identity 
factors and traditional knowledge systems. Neural network 
architectures operate within computational bounds that may 
inadequately capture contextually sensitive decision-making 
processes. Data availability remains challenging for 
intangible heritage elements that resist systematic 

digitization. These constraints highlight the tension between 
computational efficiency and cultural complexity in heritage 
preservation systems. Future developments should address 
deployment constraints through edge computing, hybrid 
offline systems, and community advisory boards, ensuring 
ethical oversight. Advanced experience replay mechanisms 
and explainable AI techniques offer pathways for improving 
cultural adaptability and decision transparency [32]. 
Expanding the framework to incorporate metaverse 
applications and immersive technologies presents 
opportunities for enhanced community engagement. Critical 
advances require algorithms distinguishing between adaptive 
cultural evolution and erosive change, ensuring preservation 
fosters living heritage rather than a static documentation. 
These directions emphasize developing computationally 
efficient yet culturally nuanced representation methods that 
respect the complexity of heritage systems. 

5. Conclusion  

This research demonstrates the effectiveness of cultural 
identity-oriented multi-agent reinforcement learning 
frameworks in traditional residential heritage preservation, 
achieving performance metrics that validate the viability of 
AI-driven approaches in culturally sensitive contexts. The 
proposed framework attains 89.3% overall accuracy in 
preservation decision-making, with cultural preservation 
effectiveness reaching 87.2%, outperforming conventional 
approaches by 15-25 percentage points across all evaluation 
metrics. The enhancement mechanism of cultural identity 
shows improvement, achieving the impact of community 
cultural attachment and social harmony, yielding outcomes of 
91.2% and 86.4% respectively, from the baseline levels of 
58.3% and 54.7%. Therefore, these results serve as evidence 
that intelligent agent coordination balances cultural, 
community, and stakeholder interests while optimizing value 
across different scales. The findings combine disciplines of 
Artificial Intelligence and the processes of cultural heritage 
protection into one by developing a multi-agent coordination 
mechanism that addresses conflicts of ‘residents vs. experts’. 
The efficiency rate exceeds 85% across all stakeholder 
categories, reaching 92% in the resident-expert 
collaboration. Preservation efforts demonstrate replicable 
success across various community scales with population 
sizes between 5,000 and 50,000 residents while sustaining 
success rates, indicating framework adaptability to numerous 
contexts of heritage preservation. Economically, the cost-
benefit analysis demonstrates the financial viability of AI-
based initiatives, with ratios ranging from 2.6:1 to 3.8:1. As 
for the application of deep learning technology on cultural 
heritage, AI applications in cultural heritage will expand to 
include multi-modal decision support systems, which can 
leverage visual, textual, and spatial cultural data. The 
foundation of this research allows the integration of large 
language models and cultural knowledge graphs to construct 
advanced systems for modelling cultural cognition, capturing 
intricate relations and transmittal mechanisms of culture. The 
growing availability of explainable AI methods will improve 
transparency and openness concerning the use of artificial 
intelligence in the processes of heritage preservation, making 
it easier for local people to control the technology designed to 
help them without losing cultural and traditional governance 
over the preservation efforts. 

 

 



X. Qian et al. /Future Technology                                                                                         November 2025| Volume 04 | Issue 04 | Pages 33-42 

41 

 

Ethical issue 
The authors are aware of and comply with best practices in 
publication ethics, specifically with regard to authorship 
(avoidance of guest authorship), dual submission, 
manipulation of figures, competing interests, and compliance 
with policies on research ethics. The author adheres to 
publication requirements that the submitted work is original 
and has not been published elsewhere. 

Data availability statement 
The manuscript contains all the data. However, more data will 
be available upon request from the authors. 

Conflict of interest 
The authors declare no potential conflict of interest. 

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