# Smart System for the Retrieval of Digital Educational Content
## 3 Model of the AIREH Architecture
### Description of the Model  
MAS methodologies provide researchers with robust and reliable architectures in which they can deploy their solutions. In recent years, researchers have proposed new procedures and methodologies for the design of open MAS. Open MAS permit heterogeneous agents to interact with different  
Figure 1: Distribution of tasks in the architecture for intelligent retrieval of educational content from heterogeneous environments (AIREH) architecture. architectures and even different languages [59]. However, because of the heterogeneity of agents, Open MAS must incorporate methodologies and tools that will help control the behavior of agents, ensuring that they act according to the architecture's social rules. Researchers have focused on developing methodologies for the organization of MAS, which seek to define the organizational structure of the system and its environment, as well as give a general description of the objectives of different organizations. Organization Theory [60] sets out the basic concepts, relationships and intrinsic characteristics of each type of organization. In order to define an organization, regardless of type (human or software agents), it is necessary to consider factors such as structure, functionality, standardization, dynamism, and environment [61]. The proposal is built on an architecture whose communication structures connect all the players, the LOs stored in the different LORs and the end-users. Thanks to the devised structure, the players remain communicated throughout the process of searching and retrieving content from LORs. Moreover, the architecture provides federated search mechanisms, which enable simultaneous access to multiple, geographically dispersed repositories. Once the architecture retrieves LOs from different LORs, the LOs are filtered and processed by a CBR system that customizes the results for the user. GORMAS (Guidelines for Organization-based Multiagent Systems) [62; 63] was used as the design methodology and THOMAS (Technical Methods and Tools for Open Multi-agent Systems) [64] as the agent-platform for the final development. To define the rules and the structure of the architecture, an organizational model has been established through the use of THOMAS. It is an open and dynamic system that adapts to users and to the environment. To establish a model of user interaction, the needs and expectations of potential system users have been analyzed. It has been possible to deduce from this analysis the roles of users who participate in the system and how they are going to exchange information. The agents involved in the LO federated search are represented in Figure 2, as well as their relations, roles, and communication. The actions between agents are numbered to show the order in which they are run during LO retrieval, whereas the rest of the agents (whose actions are not numbered) are responsible for processing the information and delivering it. Note that this figure is a more detailed representation of the tasks illustrated in Figure 1.  
Figure 2: Flow diagram of the AIREH architecture. The next step to understanding Figure 2, is to describe the role played by each agent and its relationship with other agents. Therefore, agents in Figure 2 have the following roles:  
* User agent. Defines the user or client in the system. This agent is responsible for launching the federated search process by sending a search request to the Query Manager agent. This agent evaluates the results of the query, in terms of the relevance of the content of the LOs and the order in which they are given. Also, the user agent has access to statistical information about both LORs and LOs. * Query Manager agent (QMA). This agent is in charge of supervising the entire federated search process. As mentioned previously, a federated search is a simultaneous search for LOs in multiple repositories. The agent receives a natural language query from the user agent and is responsible for completing the query by using propositional logic.