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Computer Science
Artificial Intelligence

Multi-Agent Systems: A Review Study

Profile image of Sinan NajiSinan Naji

2020, Ibn AL- Haitham Journal For Pure and Applied Sciences

https://doi.org/10.30526/33.3.2483
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27 pages

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Abstract

Multi-agent systems are subfield of Artificial Intelligence that has experienced rapid growth because of its flexibility and intelligence in order to solve distributed problems. Multi-agent systems (MAS) have got interest from various researchers in different disciplines for solving sophisticated problems by dividing them into smaller tasks. These tasks can be assigned to agents as autonomous entities with their private database, which act on their environment, perceive, process, retain and recall by using multiple inputs. MAS can be defined as a network of individual agents that share knowledge and communicate with each other in order to solve a problem that is beyond the scope of a single agent. It is imperative to understand the characteristics of the individual agent or computing entity to distinguish a simple distributed system and multi-agent system. To solve each task, the agent can take the appropriate actions, decisions, and can cooperate with other agents to fulfill its go...

Figures (16)
Besides sensory inputs, actions and goals, the agent can have knowledge (which is about a of other entities and have control over their activities. Giving the goals to agent implies that the The term “agent” has been widely used in many technologies and fields such as artificia intelligence, distributed database systems, cloud computing, operating systems, compute networks, etc. Since the agents can be found in many forms and in many application field which are varied significantly, researchers do not agree on a uniformed definition o "Agent"[1, 4]. Most definitions agreed upon defining agent as followings: autonomy entitie which are able to perceive input from its environment through sensors and parameters, ac upon that environment through effectors in order to fulfill the desired goals. The agent can b viewed as a software component that has flexibility and autonomy and gives an interoperabl interface to an arbitrary system. The agent system can be constituted from a single agen working in its environment and can interact with its users, or can consist of multipl cooperative agents which can model sophisticated systems and present the possibility of agent that have shared their goals. By acting in their environments, agents can interact and cooperat with each other indirectly, or they can use communication language (ACL) to act directly[1, 5) Autonomy in agents means that the computational agents can work without the interventio: af ather entities and have cantrol] over their activities Givino the anale ta agent imniies that th,
Besides sensory inputs, actions and goals, the agent can have knowledge (which is about a of other entities and have control over their activities. Giving the goals to agent implies that the The term “agent” has been widely used in many technologies and fields such as artificia intelligence, distributed database systems, cloud computing, operating systems, compute networks, etc. Since the agents can be found in many forms and in many application field which are varied significantly, researchers do not agree on a uniformed definition o "Agent"[1, 4]. Most definitions agreed upon defining agent as followings: autonomy entitie which are able to perceive input from its environment through sensors and parameters, ac upon that environment through effectors in order to fulfill the desired goals. The agent can b viewed as a software component that has flexibility and autonomy and gives an interoperabl interface to an arbitrary system. The agent system can be constituted from a single agen working in its environment and can interact with its users, or can consist of multipl cooperative agents which can model sophisticated systems and present the possibility of agent that have shared their goals. By acting in their environments, agents can interact and cooperat with each other indirectly, or they can use communication language (ACL) to act directly[1, 5) Autonomy in agents means that the computational agents can work without the interventio: af ather entities and have cantrol] over their activities Givino the anale ta agent imniies that th,
An agent is capable of dealing with faults and exhibits robustness in behavior against incomplete and inconsistent data[4]. Figure 3. Depicts the sample blocks of an agent. 3. ENVIRONMENT
An agent is capable of dealing with faults and exhibits robustness in behavior against incomplete and inconsistent data[4]. Figure 3. Depicts the sample blocks of an agent. 3. ENVIRONMENT
7.1.Flat Organization
7.1.Flat Organization
Figure 7. The Hierarchical Organization[2]. This organization is one of the basics and the oldest organizational structures. In this organization, the agents are organized in tree-like architecture. The agents at various layers of the tree have distinct autonomies. The data from lower layers can follow up higher to the agents in the higher levels[1]. The controlling signals may follow from higher levels to lower levels. Figure 7. depicts the hierarchical organization[2, 3]. 7.2.Hierarchical Organization
Figure 7. The Hierarchical Organization[2]. This organization is one of the basics and the oldest organizational structures. In this organization, the agents are organized in tree-like architecture. The agents at various layers of the tree have distinct autonomies. The data from lower layers can follow up higher to the agents in the higher levels[1]. The controlling signals may follow from higher levels to lower levels. Figure 7. depicts the hierarchical organization[2, 3]. 7.2.Hierarchical Organization
Figure 8. the stracture of super holon and subrholons [2]. Agents numbered 1 and 4 are the head agents of the two holons and are connected with
Figure 8. the stracture of super holon and subrholons [2]. Agents numbered 1 and 4 are the head agents of the two holons and are connected with
Figure 9. The coalition [9].
Figure 9. The coalition [9].
Figure 10.The system team structure with a limited view of the environment [3]. Teams | and 3 can communicate with each other and observe, but they cannot see teams 2, 4. The agents’ actions and their activities are arbitrary within teams. In contrast to a coalition where agents are arranged together to achieve their jobs are arbitrary and differ within teams[3]. The greater team size is not advantageous under all circumstances. As a result, a trade-off must be done among the amount of information, number of agents and their learning abilities. Bigger teams provide an enhanced clarity of the environment and a great amount of associated information. However, encompassing the expertise of agents into a singular paradigm team is influenced. Agents’ teams with smaller sizes provide vast learning abilities but lead to near- optimal performance because of the partial view of agents’ environment. A compromise may be required in selecting the best team size which may maximize the cost much greater than coalition structure. Figure 10. Depicts a team depended on structure with a limited view.
Figure 10.The system team structure with a limited view of the environment [3]. Teams | and 3 can communicate with each other and observe, but they cannot see teams 2, 4. The agents’ actions and their activities are arbitrary within teams. In contrast to a coalition where agents are arranged together to achieve their jobs are arbitrary and differ within teams[3]. The greater team size is not advantageous under all circumstances. As a result, a trade-off must be done among the amount of information, number of agents and their learning abilities. Bigger teams provide an enhanced clarity of the environment and a great amount of associated information. However, encompassing the expertise of agents into a singular paradigm team is influenced. Agents’ teams with smaller sizes provide vast learning abilities but lead to near- optimal performance because of the partial view of agents’ environment. A compromise may be required in selecting the best team size which may maximize the cost much greater than coalition structure. Figure 10. Depicts a team depended on structure with a limited view.
Figure 11. Congregation Organization [3]. 8. Agent Architecture
Figure 11. Congregation Organization [3]. 8. Agent Architecture
Figure 14.The BDI Architecture [16]. In this architecture, the beliefs expressed the assumptions and concepts of the agent world (The environment satiation in which it suited). Desires are the objectives that the agent wishes to perform. The agent has several desires. The agent can use both the beliefs and desires to select actions. To map and achieve the desires, this is what is meant by intention [16]. Figure 14. Shows the BDI architecture.
Figure 14.The BDI Architecture [16]. In this architecture, the beliefs expressed the assumptions and concepts of the agent world (The environment satiation in which it suited). Desires are the objectives that the agent wishes to perform. The agent has several desires. The agent can use both the beliefs and desires to select actions. To map and achieve the desires, this is what is meant by intention [16]. Figure 14. Shows the BDI architecture.
Figure 13. Reactive Architecture [12].
Figure 13. Reactive Architecture [12].
Figure 15. Layered Architecture.
Figure 15. Layered Architecture.
Figure 17. The Blackboard Architecture. In this approach, agents may cooperatively share a repository that contains data with other agents. The repository offers effective storage and retrieving of data for agents. Figure 17. illustrates the blackboard communication[2, 3]. Every agent can save its data and information on the blackboard which can be read by neighbor agents. The blackboard utilizes a controlled knowledge to manage agents accessing them. Every agent may reach some data in the control knowledge[2, 5]. 11. Agent Communication Language ACL
Figure 17. The Blackboard Architecture. In this approach, agents may cooperatively share a repository that contains data with other agents. The repository offers effective storage and retrieving of data for agents. Figure 17. illustrates the blackboard communication[2, 3]. Every agent can save its data and information on the blackboard which can be read by neighbor agents. The blackboard utilizes a controlled knowledge to manage agents accessing them. Every agent may reach some data in the control knowledge[2, 5]. 11. Agent Communication Language ACL
Figure 18. The three Layers of KQML Message [18].
Figure 18. The three Layers of KQML Message [18].
Table 1. Explains the contributions, architectures, application and limitations of each one of the above- mentioned MAS applications. — proposed system alert uses warnings or alarms to the managers in order to respond appropriately. The MAS allows forming and constructing the environment for network management to be autonomous, adaptive and transmitting conditions of the environment where the agents run with minimum intervention of the user. In accordance with the policies restrictions and thresholds, the running of the proposed system gives expected results. The impact of MAS execution in order to monitor and manage the network can be detected if the position policy is crushed. The agents’ detection and behavior of using message passing permit to perform a scan to the network; later, the manager agent produces an alert that is saved and transmitted to the interface agent. Finally, the interface agent is responsible for presenting it to end-users. The proposed technique raises the performance of supervising the network and decreases the policies. It permits presenting learning methods that execute the paradigm in an autonomous network managing mechanism. The use of MAS ontology in SNMP can be considered as information representation dealing with problems in the dispersed network managing systems. MAS platform is an ideal solution for dispersed network management. This technique can be contradicted with the conventional management methods according to the responding time and speed by using the performed paradigm. MAS is suggested as a solution to the network management problem. The proposed technique expands the MAS structure to be integrated with the SNMP agent.
Table 1. Explains the contributions, architectures, application and limitations of each one of the above- mentioned MAS applications. — proposed system alert uses warnings or alarms to the managers in order to respond appropriately. The MAS allows forming and constructing the environment for network management to be autonomous, adaptive and transmitting conditions of the environment where the agents run with minimum intervention of the user. In accordance with the policies restrictions and thresholds, the running of the proposed system gives expected results. The impact of MAS execution in order to monitor and manage the network can be detected if the position policy is crushed. The agents’ detection and behavior of using message passing permit to perform a scan to the network; later, the manager agent produces an alert that is saved and transmitted to the interface agent. Finally, the interface agent is responsible for presenting it to end-users. The proposed technique raises the performance of supervising the network and decreases the policies. It permits presenting learning methods that execute the paradigm in an autonomous network managing mechanism. The use of MAS ontology in SNMP can be considered as information representation dealing with problems in the dispersed network managing systems. MAS platform is an ideal solution for dispersed network management. This technique can be contradicted with the conventional management methods according to the responding time and speed by using the performed paradigm. MAS is suggested as a solution to the network management problem. The proposed technique expands the MAS structure to be integrated with the SNMP agent.
19. Conclusion In this survey, we presented a comprehensive study concerning different issues of Multi- agent systems which helps new researchers and practitioners to acquire the knowledge for basic concepts and as insightful resources in MAS. As we have shown throughout the study, MAS is a unique software architecture, distinguishable from other architectures, which provides solutions to a specific family of computational problems. Firstly, we offered definitions and features of agents and MAS. We explained their environments, organizations, and architectures. We have demonstrated communication strategies and ACLs that are used by the agents to connect with other agents. Eventually, we have discussed, compared and summarized the role of MAS in some of the most important applications such as web advertisements, cloud computing, e-commerce, image segmentation and computer networks, and how can these applications deal with MAS subject and their findings with their contributions and limitations in each application.
19. Conclusion In this survey, we presented a comprehensive study concerning different issues of Multi- agent systems which helps new researchers and practitioners to acquire the knowledge for basic concepts and as insightful resources in MAS. As we have shown throughout the study, MAS is a unique software architecture, distinguishable from other architectures, which provides solutions to a specific family of computational problems. Firstly, we offered definitions and features of agents and MAS. We explained their environments, organizations, and architectures. We have demonstrated communication strategies and ACLs that are used by the agents to connect with other agents. Eventually, we have discussed, compared and summarized the role of MAS in some of the most important applications such as web advertisements, cloud computing, e-commerce, image segmentation and computer networks, and how can these applications deal with MAS subject and their findings with their contributions and limitations in each application.

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