








































Vol. 5, No.1 Januari 2024 | 9  

 

 

 

Taking Into Account Imprecision in the Modeling Voter in a 

Multi-Agent Environment

Michel Milambu Belangany1*, Pièrre Kafunda Katalayi2, Eugène Mbuyi Mukendi3 
1,2,3 Department of Mathematics and Computer Science, Faculty of Sciences and Technology, University of Kinshasa, 

1,2,3 michel.milambu@unikin.ac.cd, pierre.kafunda@unikin.ac.cd, eugenembuyi@gmail.com 

  

Abstract  
An electoral system is a set of individuals considered as agents in a multi-agent system in which voters 

communicate with each other and with the environment. In such a system, it is often difficult to 

understand the behavior of an agent that we call a voter. This is why, in this paper, we use fuzzy set 

theory as an approach to model the behavior of an imprecise voter in an electoral environment. It will 

be just a question of presenting a model of a voter with fuzzy behavior using mathematical approaches 

in this environment considered as a multi-agent environment and to propose the algorithms as the tools 

of computer modeling. 

Keywords : voter, fuzzy voter, multi-agent system, electoral environment, modeling. 

 

I. Introduction  

An electoral system is a complex system in which voters, candidates, and agents are involved and 

all of these people communicate. Such a system is considered a multi-agent system. In such a system, it 

is often difficult to determine a voter's position or choice with respect to a candidate's vote. In Boolean 

logic, an element belongs or does not belong to a given set: this is the "all or nothing". Zadeh (1965) 

notes that most of the time, the objects encountered in the real world do not have precise criteria of 

belonging. He then tried to get out of this Boolean logic by introducing the notion of weighted 

membership. 

He defines the fuzzy set as a class of objects with a continuum of degrees of membership to this 

class. Such a set is characterized by a membership function that associates to each object a degree of 

membership between zero and one. 

In the case of a specific situation, each voter is assigned to exactly one candidate, which means that 

the degree of membership of the voter is 1 in the case of that candidate and 0 in all other situations. 

Voter membership in candidates is thus mutually exclusive. On the other hand, a fuzzy voter allows 

belonging to several candidates at the same time; moreover, each voter has degrees of belonging that 

express how much this voter belongs to the different candidates.  

 

II. Related Work 

Multi-agent structures have been developed in the context of the management of complex systems 

such as electoral, epidemiological environments, in order to locate groups of agents, an agent in a group 

to monitor its behavior. These systems have allowed the understanding of areas in which there is 

communication, cooperation and collaboration between entities.  In personality detection using context-

based emotions in cognitive agents. Similarly, multi-agent systems used for search and rescue 

applications. Researchers have difficulty converging on an unambiguous definition of notions such as 

interpretation or explanation, which are often (and wrongly) used interchangeably. Moreover, despite 

the robust metaphors that multi-agent system (MAS) could easily provide to address such a challenge, 

and agent-oriented perspective on the topic is still lacking. Thus, this paper proposes an abstract and 

P-ISSN : 2715-2448 | E-ISSN : 2715-7199 
Vol.5 No.1 Januari 2024 
Buana Information Technology and Computer Sciences (BIT and CS) 

mailto:michel.milambu@unikin.ac.cd


Vol. 5, No.1 Januari 2024 | 10  

 

formal framework for XAI-based MAS, reconciling notions and results from the literature [6]. Online 

social networks are known to lack adequate support for multi-user privacy.  

They present an agent architecture that aims to help users manage multi-user privacy conflicts. By 

considering the personal utility of content sharing and the individually preferred moral values of each 

user involved in the conflict, EXPRI identifies the best collaborative solution by applying practical 

reasoning techniques. Such techniques provide the agent with the cognitive process necessary for 

explicability [4].  

In the race to automate, distributed systems are required to perform increasingly complex reasoning 

to cope with dynamic, often non-human controlled tasks. On the one hand, systems dealing with tight 

time constraints in safety-critical applications used to focus mainly on predictability, leaving little room 

for complex planning and decision making processes. Indeed, real-time techniques are most effective in 

predetermined, constrained, and controlled scenarios [3]. Theory of mind is generally defined as the 

ability to attribute mental states (e.g., beliefs, goals) to oneself and others. Building and expanding on 

previous work by providing an account of the explanation in terms of agents' beliefs and the mechanism 

by which agents revise their beliefs given the possibility [11].  

As autonomous agents become more autonomous, ubiquitous and sophisticated, it is vital that 

humans have effective interactions with them. Therefore, these autonomous agents should be able to 

explain their behavior and decisions before humans can trust them. This paper focuses on analyzing 

human understanding of the behavior of explainable agents [1].  

With the apparent societal need to design complex autonomous systems whose decisions and 

actions are humanly intelligible, the study of explainable artificial intelligence, and with it, research on 

explainable autonomous agents, a way to facilitate such studies by implementing explainable agents and 

multi-agent systems that (i) can be deployed as static files, not requiring server-side code execution, thus 

minimizing administrative and operational overhead, and (ii) can be integrated into web and other 

compatible user interfaces [22].  

A network-oriented modeling approach for voting behavior in the 2016 US presidential election. A 

network-oriented computational model is presented for voting intentions over time, specifically for the 

race between Donald Trump and Hillary Clinton in the 2016 U.S. presidential election. Emphasis was 

placed on the role of social and mass communication media and statements made by Donald Trump or 

Hillary Clinton during their speeches. The objective was to study the influence on voting intentions and 

the final vote. A sentiment analysis was conducted to test whether the statements were high or low 

language intensity [9]. 

The use of adaptive temporal-causal networks to model and simulate the development of mutually 

interacting opinion states and connections between individuals in social networks. The focus is on 

adaptive networks combining the homophily principle with the plus becomes plus principle. The model 

was used to analyze a dataset of opinions on alcohol and tobacco use and friendship [18]. 

In recent years, social networks have been increasingly used to study political opinion formation, 

monitor election campaigns, and predict election outcomes, as they are capable of generating a huge 

amount of data, usually in textual and unstructured form. The authors aim to collect and analyze data 

from Twitter messages identifying emerging trends in topics related to a constitutional referendum that 

recently took place in Italy in order to better understand and predict its outcome [17]. 

Competitive multi-agent systems (MAS) are inherently difficult to control due to agent autonomy 

and strategic behavior, which is especially the problem when there are system-level goals to achieve or 

specific environmental states to avoid. Existing solutions for this task mainly assume specific knowledge 

about agents' preferences, utilities and strategies, neglecting the fact that actions are not always directly 

related to agents' true preferences, but may also reflect the anticipated behavior of competitors, be a 

concession to a superior adversary or simply be intended to deceive other agents. The authors propose 

a new approach to governance of competitive MAS that relies exclusively on publicly observable actions 

and transitions, and uses the knowledge gained to deliberately restrict the action spaces, thereby 

achieving the system's goals while preserving a high level of autonomy for the agents [13].  



Vol. 5, No.1 Januari 2024 | 11  

 

In A New Complex Fuzzy Inference System with Fuzzy Knowledge Graph and Extensions in 

Decision Making, the authors say that, Complex fuzzy theory has strong practical implication in many 

real-world applications. Complex Fuzzy Inference System (CFIS) is a powerful technique to overcome 

the challenges of uncertain and periodic data [10].  

In Complex Cubic Fuzzy Aggregation Operators With Applications in Group Decision Making, 

The Cubic Set and Complex Fuzzy Set are presented as two useful tools that have been successfully 

used to deal with fuzziness and uncertainties (Xiaoqiang, Yameng, Zichang, Feng & Wu, 2020). In 

Formalizing fuzzy control in possibility theory via rule extraction shows that possibility system has 

recently been recognized as a potential foundational theory for fuzzy theory. Set theory although the 

concept of possibility is derived from the membership function of fuzzy sets. As an application of fuzzy 

set theory, fuzzy control has been widely used in engineering practices, where the control of laws is 

described by fuzzy if-then rules [20].  

In real life, there will be many uncertainty problems, one of which is due to the vagueness of the 

concept of things, i.e., it is difficult to determine whether an object conforms to the concept. This 

situation largely exists in some states, phenomena, parameters and interrelationships between things. 

For such uncertain events with heavy subjective influencing factors and incomplete data, fuzzy methods 

should be used to cope with them [11]. None of the aforementioned works have used fuzzy sets in the 

electoral domain. In this paper, an agent is a voter, a candidate in an electoral environment capable of 

speaking or expressing an opinion with different agents on an occasion within the environment and 

perceiving its environment, manipulating the objects in the environment including the election kits. 

Referring to the expression of opinions, it can be said that some voters have an imprecise opinion. These 

kinds of voters are fuzzy voters who are even the subject of this article in which we model the behavior 

of a fuzzy voter based on his imprecise language. 

 

III. Methods 

1. Material 

In this article, we used Anaconda Navigator as a utility particularly jupyter to manipulate the 

libraries of the python language in particular Matplotlib which is a python library which allowed us to 

visualize the data and to draw the curves [16]. 

Numpy is a python library that contains functions related to the manipulation of our data which are 

represented in matrix form (two dimensional arrays, vectors)[16]. Etc..., the draw.io environment to 

represent the electoral system composed of individuals (voters, candidates and agents ...). The notepad 

that contains the data used and the MS Excel that allowed us to organize the data and compare the 

curves.  

 

2. Methods 

We use the theory of fuzzy subsets which will allow us to present the imprecise behavior of a voter 

in an electoral system. Let X be a reference set and let x be any element of X. A fuzzy set A of X is 

defined as the set of couples :  

𝐴 = {(𝑥, 𝜇𝐴(𝑥)), 𝑥 ∈ X}                                                             (1) 

Where : 

𝜇𝐴: 𝑋 → [0, 1]                                                                       (2) 

Thus, a fuzzy set A of X is characterized by a membership function that associates, to each element 

x of X a real in the interval [0, 1];  𝜇𝐴(𝑥) represents the degree of membership of x to A. Thus, the closer 

the value of  𝜇𝐴(𝑥) is to unity, the higher the degree of membership of x to A [2]. 

If we have: 

𝜇𝐴: 𝑋 → {0, 1 } We find the Boolean case: 

 



Vol. 5, No.1 Januari 2024 | 12  

 

Either x belongs to 𝐴(𝜇𝐴 = 1)      

Or it does not belong to    𝐴(𝜇𝐴 = 0). 

 

And the following case is very useful in the sense that an element belongs partially: 

 

Let x belong partially to 𝐴(0 < 𝜇𝐴(𝑥) < 1) 

 

It is important to specify that the fuzzy set is considered as empty if the membership degrees of all 

the elements of the universe are all equal to zero. 

 

𝐴 = ∅ ⇔ 𝜇𝐴 (𝑥) = 0, ∀𝑥 ∈ 𝑋                                                       (3) 

 

Two fuzzy sets are equal if their membership degrees are equal for all elements of the reference 

set, i.e., if both fuzzy sets have the same membership function [5]. 

Two fuzzy sets A and B, defined on the same reference set X are equal if: 

 

A = B ⇔ 𝜇𝐴(𝑥) = 𝜇𝐵(𝑥), ∀𝑥 ∈ 𝑿                                                           (4) 

 

a. Modeling 

This complex system in Figure 1 we call the electoral system which is composed of individuals 

who communicate. To model a fuzzy voter, we use the theory of fuzzy subsets as below:  

 

 
Figure 1. Complex system composed of the voters. 

 

Thus, referring to the theory of fuzzy subsets above, our model can be represented as follows: 

 

μ𝐸𝑓
(𝑒) ∶ 𝑆𝑒 → [0, 1]                                                              (5) 

Where : 

 

𝑆𝑒:Universe of discourse or Reference Set  

e ∶ voter (any element of 𝑆𝑒) 

E𝑓 ∶fuzzy subset of  𝑆𝑒 

μ𝐸𝑓
(𝑒) ∶Membership function that measures the degree to which e belongs to 𝐸𝑓 

 

We then define this membership of a voter by : 

 𝜇𝐸𝑓
(𝑒) = 1  if e belongs completely to 𝐸𝑓 

 𝜇𝐸𝑓
(𝑒) = 0  if e does not belong to 𝐸𝑓 

0 < 𝜇𝐸𝑓
(𝑒) < 1 if e belongs partially to 𝐸𝑓 

Taking the array of fuzzy values 𝑇𝑣𝑓 of size n from which each part can be removed as a vector of the 

fuzzy values of a voter below: 



Vol. 5, No.1 Januari 2024 | 13  

 

Table 1. Matrix of fuzzy membership values for voters 

𝒆𝟏 0.0000 0.01 0.04 0.02 … 0.3 

𝒆𝟐 0.1 0.006 0.03 0.05 … 0.005 

𝒆𝟑 1 1 1 1 … 1 

⋮ ⋮ ⋮ ⋮ ⋮ ⋮ ⋮ 
𝒆𝒎 0.007 0.07 0.7 0.8 … 0.9 

 

b. Proposal of the algorithms 

 Proposal algorithm 

INPUT  𝑆𝑒 = {𝑒1,𝑒2, … , 𝑒𝑛};  𝑄𝑓 = [0, 1];  𝐸𝑓 

 Begin 

1.   Repeat 

2.        For i = 1 to n do  

3.              Read : {𝑆𝑒} 

4.              The fuzzy quantifier 𝑄𝑓 Fuzzyfie the {𝑒𝑖} 

5.                    If 0 < 𝜇𝐸𝑓
(𝑒𝑖) < 1 Then 

6. 𝑒𝑖 𝑎𝑑𝑚𝑖𝑡𝑠 𝑠𝑒𝑣𝑒𝑟𝑎𝑙 𝑐𝑎𝑛𝑑𝑖𝑑𝑎𝑡𝑒𝑠 

7. 𝑒𝑖  𝑖𝑠 𝑓𝑢𝑧𝑧𝑦 

8.                    Endif 

9.         Endfor 

10.   End repeat 

11.  End. 

 

The second proposed algorithm on the fuzzy behavior of a voter is the following: 

fuzzy voter algorithm 

INPUT 𝑇𝑣𝑓 = {𝑣𝑓1,𝑣𝑓2, … , 𝑣𝑓𝑛}  , 𝑆𝑒 = {𝑒1,𝑒2, … , 𝑒𝑚}, 𝐸𝑓 

                      Begin 

1. Repeat  

2.      For i = 1 to n do  

3.        If 𝑇𝑣𝑓[𝑖] 𝑖𝑠 𝑑𝑖𝑓𝑓𝑒𝑟𝑒𝑛𝑡 𝑡𝑜 𝑇𝑣𝑓[𝑖 + 1] Then 

4.         𝑒𝑖  𝑖𝑠 𝑓𝑢𝑧𝑧𝑦  

5.         𝑓𝐸𝑓
(𝑒𝑖) ≠ 𝑓𝐸𝑓

(𝑒𝑖+1) : so it is fuzzy 

6.     Else  

7.            𝑓𝐸𝑓
(𝑒𝑖) == 𝑓𝐸𝑓

(𝑒𝑖+1) : is not fuzzy, ∀𝑒𝑖 ∈ 𝑆𝑒   

8.      Endif 

9.  EndRepeat 

10. End 

 

IV. Result and Discussion 

The imprecision zone is the part covered by the fuzzy voters, while the precision zone contains the 

voters with a precise choice. 

  



Vol. 5, No.1 Januari 2024 | 14  

 

 
Figure 2. Distribution of voters 

 

The representation of the fuzzy part of voters in the figure below : 

 

 
Figure 3. The fuzzy voters 

 

Fuzzy behavior of voters represented by two curves of which in blue the language of the voter is 

totally fuzzy and in red the language of the voter is partially fuzzy 

 

 
Figure 4. The fuzzy voter membership function 

 

𝜇𝑒𝑖
(𝐿𝑓) < 50 ∶ 𝑡ℎ𝑒𝑛 𝑡ℎ𝑒 𝑣𝑜𝑡𝑒𝑟 𝑖𝑠 𝑓𝑢𝑧𝑧𝑦 

𝜇𝑒𝑖
(𝐿𝑓) = 50 ∶ 𝑇ℎ𝑒𝑛 𝑡ℎ𝑒 𝑣𝑜𝑡𝑒𝑟 𝑑𝑜𝑒𝑠 𝑛𝑜𝑡 𝑏𝑒𝑙𝑜𝑛𝑔 

50 <   𝜇𝑒𝑖
(𝐿𝑓) ≤ 100 ∶ 𝑇ℎ𝑒𝑛 𝑡ℎ𝑒 𝑣𝑜𝑡𝑒𝑟 𝑡𝑜𝑡𝑎𝑙𝑙𝑦 𝑏𝑒𝑙𝑜𝑛𝑔𝑠  

 

 



Vol. 5, No.1 Januari 2024 | 15  

 

𝐼𝑓 𝐿𝑓 = 50 ∶  𝜇𝑒𝑖
(𝐿𝑓) = 0 

𝐼𝑓 𝐿𝑓 < 50 ≤ 𝜇𝑒𝑖
(𝐿𝑓)  ∶  

𝜇𝑒𝑖
(𝐿𝑓) − 𝐿𝑓

𝜇𝑒𝑖
(𝐿𝑓) − 50

  

𝐼𝑓 𝐿𝑓 ≥ 60 ∶  𝜇𝑒𝑖
(𝐿𝑓) = 1 

 

Where :  

 

𝑳𝒇: 𝑓𝑢𝑧𝑧𝑦 𝑙𝑎𝑛𝑔𝑎𝑔𝑢𝑒 

 

 
Figure 5. Behavior of a fuzzy voter over the course of a day 

 

Figure 5 above, presents the evolution of the membership behavior of a fuzzy voter by day, i.e. 

each day he has a position for each candidate. In fact, looking at this figure, at the beginning this voter 

does not accept any candidate, then he tries to give a position for the first candidate at 0,01 and the 

following day 0,02 for another one the third day he gives less than 0,01 to the starting candidate therefore 

each day his position changes at the thirty second day he gives a position with a total membership of 1 

and after he changes his value again therefore he does not have a total membership. 

 

 
Figure 6. Behavior of a fuzzy voter over time 

In this figure 6, the fuzzy agent or voter shows different positions every 5 days interval. As we can 

see in this figure 6 where it rises with a position of 0.2 for a candidate and after the next few days it falls 

to less than 0.2 for another candidate. So for each interval of 5 days there is a position of this voter that 

is different from the previous one that makes him a fuzzy voter. 

 



Vol. 5, No.1 Januari 2024 | 16  

 

 
Figure 7. Behavior of a fuzzy voter over the course of a month 

 

In this figure 7, the voter fumbles over an interval of months taking different positions on the 

different candidates. Here, a voter blurs in the first three months his position is below 0.2 for the 

candidates and the last two months in the interval of 5 months he is below 0.1 and in the next interval 

he belongs totally to one candidate with 1 in the interval of 10 to 15 months, he makes 0.9. So for each 

interval of months it changes positions several times. And finally of account in interval of months it 

changes values of measure of degree of confidence to the candidates. We will notice that, this voter is 

inconstant. Therefore, he is a fuzzy voter whose behavior is imprecise. 

 

 
Figure 8. Behavior of a fuzzy voter and a normal voter 

 

In this figure 8, we see the behavior of a fuzzy voter and a normal or accurate voter. Looking at this 

figure, the fuzzy voter seems to be unstable or imprecise because it changes its behavior over time so 

for each sequence of weeks it displays some behavior. On the other hand, a precise or normal voter 

remains constant by keeping a certain position during all the weeks. For this purpose, it is necessary to 

identify the electoral environment in which a voter evolves after having noted among many voters that 

not all of them are stable, i.e. have a well defined position.  

 

V. Conclusion 

This article focused on the consideration of the imprecision in the modeling of a voter in order to 

be able to follow his behavior throughout an electoral process. It was in fact a question of being able to 

use the theory of fuzzy subsets in order to present a model capable of defining or showing the evolution 

of the behavior of a voter with respect to the candidates in an electoral system. The use of imprecision 

in this modeling showed that during an electoral process a voter can be imprecise in the sense that every 

day, every week or every month he has a thought about the candidates with a certain value of his 

membership function which defines the degree of confidence of this voter with regard to the candidates. 

This approach offers significant advantages using our algorithms proposed in this article to assure 

election candidates that not everyone who follows you is necessarily your voter because as he follows 

you so he follows another candidate. So for each candidate, a fuzzy voter has a confidence measure 

value that we call the candidate membership function of a fuzzy voter that can change by day, week or 

month so that on the day of the vote the voter can go without a specific choice of candidate.  



Vol. 5, No.1 Januari 2024 | 17  

 

 

 

References 

[1] Avleen, M., Samanta, K., & Kary, F. (2020). Explainable Agents for Less Bias in Human-Agent 

Decision Making. Springer Nature Switzerland pp. 129–146 https://doi.org/10.1007/978-3-030-

51924-7_8 

[2] Didier, D., & Henri, P. Fuzzy Sets and Systems : Theory and Applications. Mathematics in 

science and engineering volume 144. CNRS, Languages and Computer Systems (LSI) 

Université Paul Sabatier Toulouse, France 

[3] Francesco, A., Paolo, G., Amro, N., Michael, I., Schumacher, & Davide, C. (2020). In-Time 

Explainability in Multi-Agent Systems: Challenges, Opportunities, and Roadmap. Springer pp. 

39–53 https://doi.org/10.1007/978-3-030-51924-7_3 

[4] Francesca, M., S，Tefan, S., Jose, M. Such, & Peter, M. (2020). Agent EXPRI : Licence to 

Explain. Springer https://doi.org/10.1007/978-3-030-51924-7_2 

[5] George, J., Klir, & Bo, Y. (2013). Fuzzy sets and fuzzy logic : Theory and Applications. Printed 

in the United States of America 10 9 8 7 6 5 4 3 2 1 ISBN 0-13-101171-5 

corpsales@prenhail.com 

[6] Giovanni, C., Michael, I., Schumacher, Andrea, O., & Davide, C. (2020). Agent-Based 

Explanations in AI: Towards an Abstract Framework. Springer Nature Switzerland 

https://doi.org/10.1007/978-3-030-51924-7_1 

[7] Janusz Kacprzyk & Baoding Liu, Studies in Fuzziness and Soft Computing, Volume 239, 

springerPolish Academy of Sciences, 2nd Edition. ISSN 1434-9922 DOI 10.1007/978-3-540-

89484-1 

[8] Lindsay, S., & Julie, A. (2020). A Situation Awareness-Based Framework for Design and 

Evaluation of Explainable AI. Springer Nature Switzerland https://doi.org/10.1007/978-3-030-

51924-7_6 

[9] Linford, G., Jan, T., & Roos, V. (2018). A Network-Oriented Modeling Approach to Voting 

Behavior During the 2016 US Presidential Election. © Springer International Publishing. 

Advances in Intelligent Systems and Computing 619, DOI 10.1007/978-3-319-61578-3_1 

[10] Luong thi hong, L., Tran manh, T., Tran thi, N., Le hoang, S., Nguyen long, G., Vo truong nhu, 

N., & Pham van, H. (2020). A New Complex Fuzzy Inference System With Fuzzy Knowledge 

Graph and Extensions in Decision Making. IEEEAccess DOI :10.1109/ACCESS.2020.3021097 

[11] Maayan, S., Toryn, Q., Klassen, A., Sheila, A., & McIlraith. (2020). Towards the Role of Theory 

of Mind in Explanation. Springer Nature Switzerland https://doi.org/10.1007/978-3-030-51924-

7_5 

[12] Manuel H., Marco, Pérez-H., Ajith Kumar, P., & Joaquín, I. (2020). A Review on Control and 

Optimisation of Multi-Agent Systems and Complex Networks for Systems Engineering. 

preprints.org doi:10.20944/preprints202001.0282.v1 

[13] Michael, P., Christian, B., & Heiner, S. (2021). Governing Black-Box Agents in Competitive 

Multi-Agent Systems. Springer Nature Switzerland pp. 19–36 https://doi.org/10.1007/978-3-

030-82254-5_2 

[14] Muhammad, G., M. Haris, M., Dilshad, A., & Nasreen, K. (2020). A Novel Applications of 

Complex Intuitionistic Fuzzy Sets in Group Theory. IEEEAccess DOI : 

10.1109/ACCESS.2020.3034626 

[15] Nafiseh, J., Ali, H., Hossein, & Sayyadi, T. (2021). Designing an Intuitionistic Fuzzy Network 

Data Envelopment Analysis Model for Efficiency Evaluation of Decision-Making Units with 

Two-Stage Structures. Hindawi Advances in Fuzzy Systems, Article ID 8860634, 

https://doi.org/10.1155/2021/8860634 

[16] Patrick, F., & Pierre, P. (2020). Cours de Python. Université de Paris, France 

https://python.sdv.univ-paris-diderot.fr/ 

https://doi.org/10.1007/978-3-030-51924-7_1


Vol. 5, No.1 Januari 2024 | 18  

 

[17] Shira, F., & Debora, S. (2018). Using Twitter Data to Monitor Political Campaigns and Predict 

Election Results. Springer International Publishing Advances in Intelligent Systems and 

Computing 619, DOI 10.1007/978-3-319-61578-3 19 

[18] Sven van den, B., Simon, H., Goos, & Jan T. (2018). Understanding Homophily and More-

Becomes-More Through Adaptive Temporal-Causal Network Models. © Springer International 

Publishing Advances in Intelligent Systems and Computing 619, DOI 10.1007/978-3-319-

61578-3_2 

[19] Tomoki, Y., Yuki, M., & Toshiharu, S. (2021). Path and Action Planning in Non-uniform 

Environments for Multi-agent Pickup and Delivery Tasks. Springer Nature Switzerland pp. 37–

54 https://doi.org/10.1007/978-3-030-82254-5_3 

[20] Wei, M. (2020). Formalization of Fuzzy Control in Possibility Theory via Rule Extraction. 

IEEEAccess Digital Object Identifier 10.1109/ACCESS.2019.2928137 

[21] Xiaoqiang, Z., Yameng, D., Zichang, H., Feng, Y., & Wu L. (2020).  Complex Cubic Fuzzy 

Aggregation Operators With Applications in Group Decision-Making. IEEEAccess 

doi :10.1109/ACCESS.2020.3044456 

[22] Yazan, M., Timotheus, K., Igor, H., Tchappi, Amro, N., Stéphane, G., & Christophe N. (2020). 

Explainable Agents as Static Web Pages: UAV Simulation Example. Springer Nature 

Switzerland pp. 149–154 https://doi.org/10.1007/978-3-030-51924-7_9 

 

 


