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High Interaction Multi-Agent System Model for Automatic Prediction
Mohammed Ali1, Ali Obied2
1
University of ALQadisiyah, College of computer science and Information Technology, Iraq.
Email: Mohamed.ali@qu.edu.iq
2
University of ALQadisiyah, College of computer science and Information Technology, Iraq.
Email: ali.obied@qu.edu.iq
Abstract
In a cooperative multi-agent system, also known as a MAS, the behaviors of several agents are
coordinated with one another so that they may work together to achieve a common goal, such as
the completion of a task or the maximization of utility. As a consequence of this, there has been a
surge in enthusiasm for applying techniques of machine learning to the task of automating the
search and enhancement that is necessary when attempting to code answers to MAS problems.
This is because these techniques can improve the accuracy of the search results. For this reason,
we provide an interactive multi-agent model exploiting three different machine learning models
that can predict the cost of a smartphone. A smartphone dataset was collected from Kaggle, and
it was used in an investigation on the efficacy of the tactics that were recommended. The results
of the experiments yield a prediction accuracy of 95% and a decision accuracy of 100%,
demonstrating that a multi-agent system that learns may produce more accurate predictions than
approaches that are currently considered state-of-the-art.
Keywords: Multi-agent system, machine learning, Random forest, Naïve Bayes, KNN.
1. Introduction
It is conceivable to see the relatively young field of Multi-Agent Systems as the intersection of
numerous subfields within the field of artificial intelligence. MAS has grown more popular as a
result of the expansion of computers that are based on the Web and the Internet. These kinds of
computers make it easier to create an environment in which agents may cohabit with one another
and share information. The individuals involved in the scenario are not separate entities but
rather are components of a bigger system that is collectively referred to as a Multi-Agent System
(MAS).
The intelligent agent characteristics of autonomy, sociability, and adaptability, as indicated in
table 1, are an appropriate alternative for coping with the problem that has been presented to
agents. This is because these characteristics allow agents to adapt to new situations. Automated
categorization refers to the technique of automatically assigning a certain class label to the price
of a mobile device. The technique of automatic categorization is one that is founded on the

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concept of machine intelligence. Manual classification is not only labor-intensive but also fraught
with the danger of making errors since there are so many distinct elements to take into
consideration.
Table 1- Properties of agent [1]
Property Meaning
Situated It means that it does exist in an environment
Autonomous It means that it is independent, controlled externally
Reactive
It means that it can respond to the potential changes in its
environment
Proactive It persistently pursues the tasks and goals
Flexible It has multiple techniques and ways of achieving the goals.
Robust
It means that whenever it faces a problem or in case of any failure it
can recover from a failure.
Social Agents are capable of working and interacting with other agents.
Artificially intelligent software agents, in particular, benefit greatly from the learning approach to
AI since it allows them to quickly and effectively choose the most appropriate action to do in any
given circumstance. Clearly, we require rational behavior in a world of such vast size and
dizzying variety. Furthermore, in MAS work, developing a high degree of engagement is crucial,
and this can only be done via working together in a collaborative way to achieve goals while
optimizing value and reducing time consumption.
2. Intelligent agent
Because of the lack of consensus in the current literature, it is challenging to explain the concept
of the agent in a clear and technical way. The notion of a third party acting as an agent is not
new. an all-purpose concept that may be used in many contexts. However, there are numerous
exceptions. Here, we highlight the most often used definitions. To this end, "agents may be
described as computer systems capable of flexible and autonomous activities in dynamic,
unpredictable, and generally multi-agent environments." [2].
In specifically, "Agents are computational entities that can operate effectively and take
autonomous behaviors in dynamic, unpredictable, and open contexts; they may also be
programmed to solve problems on their own. Typically, agents are placed in settings where they
must communicate, and even work together with, other agents whose goals may clash with their

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own. Multi-agent systems describe this kind of setting. [3]. Furthermore, "To accomplish its aims
and fulfill its wants, an agent is a software-based computer system with characteristics such as
autonomy, introspection, social ability, responsiveness, pro-activity, mobility, rationality, etc.
[4].
In this way, "an agent is a computer software capable of autonomous action on behalf of its
owner to fulfill a set of objectives" [5]. It can figure out what to do on its own without being told.
Each agent receives data from sensors, processes that data using goal- and perception-based
planning logic, and then takes some kind of action (shown in Figure 1) that has an impact on the
environment. It must meet the criteria in Table 1 to be considered intelligent.
The construction of cutting-edge artificial intelligence systems [6] often involves the
employment of learning agents, which may be thought of as a kind of general intelligent agent.
This is the method that is preferred. Even if it starts with very basic knowledge and then modifies
itself via the process of learning, a learning agent still has the capacity to learn from those
experiences it has had in the past.
Fig 1- Agent idea
3. Multi-agent system
Changes in the practical application of robotics, complex networks, and transportation have
occurred in recent years due to MAS collaborative intelligence technology [7], [8]. Cooperative
tasks for large-scale MASs are complicated by the fact that they must rely on dispersed and
trustworthy intelligence technology in an environment with limited local relative knowledge.

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Recent decades have seen a great deal of interest across academic fields in the collaborative
awareness, job assignment, and intelligent control of MASs [9]-[11]. These innovations were
motivated by the cooperative patterns seen in nature, such as the migrating of birds in flocks and
the schooling of fish. As the bedrock of effective MAS combined missions.
Based on the structure of the control system, intelligent MAS control strategies may be classified
as either centralized [12] or distributed [13]. In a centralized system, one hub oversees all
operations, from receiving data to processing it. But if the CPU goes down, the entire rig goes
down with it. To improve the robustness of the MASs as a whole, researchers have looked at
distributed control approaches, whereby each agent makes its own behavior decision
independently based on local knowledge. Research [14–16] into the development of distributed
intelligent control that exploits imperfect local knowledge has increased in recent years.
4. Related work
Numerous studies have focused on working on multi-agent systems and incorporating them into
proposed models to carry out cooperative classification and prediction based on cooperative
multi-agent systems by making use of machine learning algorithms. This area of study has
attracted a great deal of attention in recent years.
Using concepts from distributed data mining, J. Ponni, et, al [17] presents an effective technique
for mining significant classification rules in multi-relational databases. This study has designed a
unique distributed data mining approach in order to mine (classify) essential rules in many
relations. Additionally, in order to increase the efficiency of the mining process, a combined
Support Vector Machines (SVM) algorithm has been done.
The study [18] describes an effort to use the AI strategy of Multi-Agent Systems (MAS) for
Classifying Electroencephalographic (EEG) Data. The plan was to use a low-cost EEG gadget to
create a Brain-Computer Interface (BCI). Several other ML methods were tested on the same
dataset as MAS, but none of them were able to match its performance. MAS was even able to
improve upon the best model obtained by SVM by 17%.
For the purpose of collaborative failure prediction and maintenance optimization in large fleets of
industrial assets, A.Salvador et, al [19] look at the reliability and cost implications of adopting
various multi-agent systems architectures. The results indicate that for high-value assets, a totally
distributed design is best, whereas hierarchical systems are best for minimizing communication
costs. That way, asset managers may reduce the total cost of ownership by using multi-agent
systems for predictive maintenance.
The future vehicle trajectories and the degree to which each rule is satisfied are both reasoned
about together in [20]. Through the use of joint reasoning, we can simulate interactions between
vehicles, which improves our ability to make accurate predictions. The proposed system
simulates human driving behavior by anticipating the movements of other cars and taking safety
precautions into account.

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To speed up learning systems, A. Tacchetti [21] shows how to include Relational Forward
Models (RFM) modules into agents. As the autonomous systems we create and interact with
grow more multi-agent, we will need to refine our analytic methods to better characterize the
factors that influence agents' choices. Furthermore, it is essential to create artificial agents that
can swiftly and securely learn to collaborate with one another and with people in shared settings.
The first effort to investigate the continuous learning issue in multi-agent interaction behavior
prediction problems was suggested by Hengbo Ma et al. [22]. We provide empirical evidence
that various methods in the literature are affected by catastrophic forgetting, and we demonstrate
that our method is able to maintain a low prediction error even when datasets are introduced one
after the other. In order to demonstrate the efficacy of our technique, we also do an ablation
analysis.
5. Proposed model
In this part, we provide a high-level overview of the multi-agent system paradigm we propose.
Using a variety of agent designs is a suggested method for achieving high levels of interaction
(simple, learner and model-based agent). By decomposing the overall job (classification) into
smaller, more manageable tasks and assigning them to agents, a multi-agent system is able to
achieve its defining characteristic of working in concert and in order (MAS).
A highly interactive MAS was designed and constructed in this study. This MAS is made up of
five agents, each of which has its own distinct architecture and cooperates with the others to
achieve the system's aim and earn high points.
The first agent, which is illustrated in figure 2 as the preprocessing agent and whose
responsibility it is to organize the data set and make it ready for the other agents, is shown to
have the responsibility of doing so. This agent examines the data set to evaluate whether or if
there is a need for an adjustment, such as the addition of new data, the deletion of existing data,
or a modification to the existing data. Following the conclusion of its duties, this agent, known as
the preprocessing agent, will save the modified dataset in order to make it accessible to the agent
that comes after it.
Staff members who are specifically designated as future training agents (hence referred to as
"learning agents staff") will be responsible for carrying out the actual training. Data classification
phase whereby three distinct categorization methods are performed on the training data set. The
algorithms Random Forest, Naive Bayes, and KNN were used throughout the training process.
Once the training phase of each algorithm is complete, a model TR.model (Training Model) is
built and saved for use in following prediction phases.

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Fig. 2 the proposed model
6. Agents Employments
Figure (2) depicts how agents engage with their surroundings by recognizing an input, processing
that information with a function, and then taking some kind of action in response. Each agent's
position in the system, as well as their inputs, beliefs, desirers, and intents, are described in depth
here. Following this introduction, I'll go into further depth about the roles and responsibilities of
each agent in the system.
6.1 The Role of the Preprocessing Agent
The suggested system's initial agent is in charge of the information set. This agent takes in
information from its surroundings (beliefs), processes it by erasing or altering irrelevant details,
and saving the resultant data in a database for use by the Learning agents and DM Agent. This
agent's greatest performance on his assigned assignment represents the completion of a

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component of the overall work, which was split up among the agents and is being completed in a
coordinated, seamless, and sequential manner. In (Algorithm (3.1)(3.2)(3.3) we see how the
preprocessing agent operates.
ALGORITHM 3-1: OPEN T. DATASET
Input: text file of dataset
Output: post event no.
1 open folder containing dataset.
2 select a file of training dataset.
3 saving the path of the T. dataset (post event =0)
4 prepare it for the training in the T. staff
5 send the post event number to the GUI
ALGORITHM 3.2: FEATURE EXTRACTION
Input: text file of dataset
Output: post event(1)
1 open folder containing dataset
2 select a file of prediction dataset.
3 determine the feature must be extracted.
4 choosing feature index.
5 extract the feature from the prediction dataset
6 saving modified dataset for prediction operation
7 post event (1)
ALGORITHM 3.3: GUI events
Input: post event no.

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This agent's design is that of a basic reflex type, with its operations taking place on a preexisting
basis (condition-action rule). Reflexive agents that just consider the current perception are said to
be "simple," yet such agents fail to take into account any prior perceptions. The percept history of
an agent stores all the information it has ever perceived. The agent's functioning rests on the
condition-action concept. In this sense, a rule may be thought of as a "condition-action rule" that
"maps" one state to another. An action is carried out if and only if the condition holds true. This
capacity of the agent is optimal only in a fully observable environment.
6.2 The Role of Learning Agents Staff
Learning agents (LAs) in this system are responsible for training on the data set of categorization
as a sub-task of the primary job given to the system. After each member of this team has sensed
the incoming data, they divide it into training and testing data according to your specifications.
These agents have a learning-agent structure. The most significant advantages of learning are that
it expands an agent's ability to perform in novel situations and that it allows an agent to acquire
more expertise than would have been possible with its initial level of knowledge.
A "learning agent" in the field of artificial intelligence is one that changes and grows in response
to its surroundings. It starts off with very basic knowledge, but as it learns more, it develops the
capacity to act and adapt on its own. A learning agent is comprised of the following four ideas:
Learning element: Its job is to better itself by taking in new information from its
surroundings.
Learning component hears from critic who describes the agent's progress toward a
predetermined goal.
Performance element: that decides what happens in the world.
The Problem Generator: is in charge of making suggestions on how to create interesting
and novel problems.
Output: actions (based on event no.)
1 Switch (received events)
2 Case0: get dataset path for reading.
3 Case1: get the index of feature should be extracted.
4 Case2: saving dataset to file.
5 Case3: open next agent.
End switch

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ALGORITHM 3.4: T. AGENT (RANDOM FOREST)
Input: array of features
Output: X
1 receive dataset from preprocessing agent.
2 reading dataset.
3 set a last attribute as a class.
4 convert class attribute from numeric to nominal.
5 split the dataset to training dataset & testing dataset.
6 specifying the number of instances for training.
7 creating size of instances for training (0-train size).
8 creating size of instances for testing (train size -end ).
9 set the classifier (rf) for Random Forest.
10 set the parameters for Random Forest for training.
11
12
Training.
Testing.
13 evaluation
14
15
16
17
Saving T. model in DB.
Receiving array of features from DM agent.
Predicting
Send the result of predicting to DM aent
Three different classification algorithms were used to complete the task, yielding a system with a
wide variety of classifications with differing degrees of accuracy that may be used to improve the
quality of the decision-input. maker's The predictive power of the DM agent may be considerably
enhanced by using the results from three independent agents, each of which has executed its own
algorithm.
The first learner will use the Random Forest technique to create a training model for itself. Using
this model, we can make a prediction and get a one-of-a-kind output X (Algorithm (3.4)). Naive
Bayes is used by the second learner to develop its own training model. Using this model, we can

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carry out the prediction process and derive a one-of-a-kind output Y (Algorithm (3.5)). The third
agent will classify the data using the KNN algorithm; it will produce its own training model for
use in prediction and will provide a unique Z (Algorithm (3.6)).
ALGORITHM 3.5: T. agent (KNN)
Input: array of features
Output: Z
1 receive dataset from preprocessing agent.
2 reading dataset.
3 set a last attribute as a class.
4 convert class attribute from numeric to nominal.
5 split the dataset to training dataset & testing dataset.
6 specifying the number of instances for training.
7 creating size of instances for training (0-train size).
8 creating size of instances for testing (train size -end ).
9 set the (KNN) classifier .
10 set the parameters (K) for KNN for training.
11
12
training.
testing
13 evaluation
14
15
16
17
18
Saving T. model in DB.
Saving T. model in DB.
Receiving array of features from DM agent.
Predicting
Send the result of predicting to DM aent
The DM agent will rely on the instantaneous transmission of the three results generated by the
application of the three algorithms in the X, Y, and Z learning agents in order to form his own
conclusions about the class to which each result belongs, after conducting comparisons on it. As
soon as the data is ready, we'll do this.

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ALGORITHM 3.6: T. agent (Naïve Bayes)
Input: array of features
Output: Y
1 receive dataset from preprocessing agent.
2 reading dataset.
3 set a last attribute as a class.
4 convert class attribute from numeric to nominal.
5 split the dataset to training dataset & testing dataset.
6 specifying the number of instances for training.
7 creating size of instances for training (0-train size).
8 creating size of instances for testing (train size -end ).
9 set the classifier (nf) for Naïve Bayes.
10
11
training.
testing
12 evaluation.
13
14
15
16
Saving T. model in DB.
Receiving array of features from DM agent.
Predicting
Send the result of predicting to DM aent
17 open DM agent.
6.3 The role of DM Agent
The last stage of the system is handled by this decision-making agent, which performs the
prediction process using learning agents. It simultaneously distributes test data to all participating
learning agents (LAs) and then waits for their collective response. Selecting a single result from a
set of alternatives is where this agent's intelligence shines. The correct classification of the
worksheet will be decided by selecting this option. A classifier is then chosen, after some
processing, depending on the features of the results acquired from the collection of learning
agents. As can be seen in Figure (3.5), the outcomes (X, Y, Z) are evaluated in this standby state

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by being compared to:
Three possible results (X=Y=Z) are equivalent. Choosing a result means making a
completely arbitrary selection from the available possibilities.
Two identical, one dissimilar ((X=Y) Z) There are two choices that both lead to the same
end result but differ in the details they leave out.
In this situation, not only the results themselves (X, Y, Z) but also the accuracy acquired
from applying the algorithms are taken into account, with the outcome that gives the highest
accuracy being chosen.
ALGORITHM 3.7: PREDICTING AGENT
Input: X,Y,Z
Output: class no.
1 open modified predicting dataset.
2 send the predicting dataset to the training staff
3 get results from training staff.
4 choosing correct result from the three received results.
5 let results be X,Y,Z.
6 IF X=Y=Z
7 THEN the prediction is any result (X|Y|Z)
8 ELSE IF (X=Y)≠Z
9 THEN the prediction is (X|Y)
10 ELSE IF X≠Y≠Z
11 THEN the prediction is the result with highest accuracy.
If X, Y, and Z are all equal, or if any two of them are equal, then we may use statistical mode to
assess the agent's ultimate choice. A set's mode is the value that occurs most often inside that set.
X=x
the maximum value of the probability mass function. In addition to its usefulness in other
contexts, the high suggested classification accuracy obtained in this study using the Random
Forest approach is X. Consequently, the study provided here allows us to describe the optimal

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choice that a DM agent may make as an equation (1).
Where RD represents Right Decision, X,Y,Z ∈ N
X: classification number receiver from RF
Y: classification number receiver from NB
Z: classification number receiver from KNN
Finally, equation (3.1) can be changed in case of the classification mode changed.
Fig 3 The flowchart of the Predicting Agent

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7. Experimental results
The goals of this chapter are to (1) test the suggested system, (2) discuss the findings gained by
implementing the system with different parameters, and (3) offer the design requirements for
constructing a high interactive agent for autonomous predicting intelligent system. For the goal
of testing the suggested system, a mobile price classification dataset was employed. The
suggested system displayed perfect behavior, functioning properly one hundred percent of the
time with a prediction accuracy of 95%.
Additionally, this chapter begins with a tutorial on how to use the GUI windows, and it ends with
the results of the implemented system shown in the GUI windows for all of the cases. Finally, the
results are discussed in depth. The presented prototype system implements the desired system
utilizing the JADE agent platform, a computerized distribution system.
7.1 JADE (Java Agent Development Framework)
JADE, short for Java Agent Development Environment, is a fully Java-based platform for
creating intelligent software agents. It provides graphical tools for debugging and deploying code
written in a language that meets the FIPA standards [23], making it easier to create and release
multi-agent systems. A JADE-based system's installation may be administered from a central
GUI accessible from several computers (which need not even run the same OS). You may see an
image of the JADE administration console in figure (4.1). Agents may be moved from one
machine to another during operation to make changes to the settings. JADE is a Java application
that requires the use of the Java Development Kit (JDK) or at least the JAVA 5 run time
environment [24].
7.2 The proposed Framework Component
The proposed framework relies on the following element to be fully operational:
Java programing language (JDK version 1.8)
Apache NetBeans IDE 13
JADE platform
JADE library (jade.jar)
Weka library 3.8.6 (weka.jar)
Dataset (text file)
In Figure (4), you can see the first step of software execution, which details how to run the
system in a Windows environment. When using the JADE framework, applications must be
written in the Java programming language. After we run this program, the JADE was completely
functional and could make the agents we had designed.

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7.3 Dataset
The data set that has been used in the suggested system that we have been working on is data for
categorizing mobile phones according to their respective price ranges [25]. This data set is
comprised of two files, the first of which is the training file, which has a total of 2000
occurrences. In addition to the class column, each instance is made up of a total of 20 columns.
The second file has all of the test data, which totals one thousand different occurrences, and is
utilized by the system to carry out the testing procedure for determining which pricing group
mobile phones would fall into.
Fig.4 first step for the running of the program
8. Conclusion
During the process of creating and developing the intelligent software agents known as MAS, the
following observations were made as conclusions:
O The purpose of this project is to investigate and develop several approaches to machine
learning in order to create an automated mobile pricing prediction system.
O Increasing the degree of interaction is extremely essential in the process of working with
multi-agent systems, since their work is done in an interactive and cooperative way, which leads
to the best outcomes and increases the pace of production.
O The framework that has been provided is an automated prediction system that is highly
significant as an alternative predictor for humans. This system divides the pricing of mobile

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phones into several price categories based on a wide variety of criteria, and it does so with a high
degree of precision.
O The usage of many agents with varied architectures has a favorable influence on the
outcomes of the proposed study. This is particularly true when it comes to the agents' use of
various machine learning approaches in the process of creating machine predictions.
O The findings demonstrated that the random forest algorithm is the most accurate of the
algorithms used for classification on the chosen data set, as it produced a prediction with a high
accuracy of 95%. This proved that the random forest algorithm is the most accurate of the
classification algorithms.
O The process of decision-making was quite effective, as shown by the fact that it provided
accuracy rates 100% when determining pricing categories via the choices that were made. This
high level of accuracy was made possible as a consequence of the enrichment of the system by its
staff of learning agents. Each agent had a unique set of outcomes and a unique level of accuracy,
which resulted in a system that was abundant in numerous categorization strategies.
O Because our proposed system is based on a multi-agent system, it has been able to
achieve relatively higher levels of satisfaction compared to earlier works that directly utilize
machine learning methods in prediction operations. This is due to the fact that our proposed
system is dependent on a multi-agent system.
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