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Andrés David Ballén Duarte 1. Helbert Eduardo What do i write in my bumble profile Cuchango 1. In this document the proposal of how many types of agent recommendation system based on multi agent is made allowing the analysis of user behavior when visiting historical and cultural memories, giving recommendations based on qualifications and duration times for the observation of art how many types of agent.
It is also possible to see the system architecture, the zgent used for the development of the multi-agent system, as well as the communication between agents to carry out a route, how many types of agent the functionality for recommending new routes to a user. The multi-agent system uses a neural network that allows to analyze the behavior of a user in a route; using the feedback given for the neural what is a phenomenon in qualitative research the data is checked, allowing determine the user preferences.
A set of historical and cultural memory data set is used to generate recommendations; in addition, a user storage API is employed. For the system visualization, this prototype is connected with an augmented reality application that allows users access to visit art pieces and use predefined preferences. En el presente documento se realiza la propuesta de un sistema de recomendación basado en multi-agentes que permite analizar el comportamiento de un usuario cuando este visualiza memorias históricas y culturales, ofreciendo recorridos recomendados mediante las calificaciones y tiempos de duración al observar las obras de arte.
También se muestra hos arquitectura del sistema, el servidor utilizado para el desarrollo del sistema multiagente, así como la comunicación entre agentes necesarias para llevar a cabo un recorrido recomendado y la funcionalidad requerida para sugerir los nuevos recorridos a un usuario. El sistema multiagente utiliza una red neuronal que permite analizar el comportamiento de un usuario en un recorrido; mediante la retroalimentación por la red neuronal se verifican los datos estableciendo los gustos del usuario.
Para generar las recomendaciones se usó un dataset de información de memorias históricas y culturales, como también un api para el almacenamiento de usuarios. Para la visualización del how many types of agent del sistema, este prototipo se conectó con un aplicativo de realidad aumentada que permite a los usuarios acceder a las obras de arte y utilizar preferencias predefinidas.
Considering applications focused on museum collections, [ 2 ] describes the importance of recommender systems because of what is meant by diagonal relationship class 11 large amount of information displayed to the user when visiting a museum. Meanwhile, multiagent systems can be considered as tgpes union of various intelligent entities coordinating actions and interacting in a specific environment trying to fulfill defined objectives to find an answer [ 3 ].
Regarding the multiagent system development, various papers focusing on the analysis of large amounts of information can be examined; one of those has to do with an integrating system of digital libraries [ 4 ] in which a multiagent system searches documents from a virtual library allowing the user a fast and mwny inquire about books. Using a personality test, [ 5 ] presents a related work proposing a multiagent system are love hearts vegan friendly allows the analysis of user preferences while using the social network Twitter.
Such works display multiagent use in different fields of science to accelerate the flow of information when establishing an answer is required. In additional works as in [ how many types of agent ]a multiagent system is described solving a problem a cooperative control where an unknown non-linear dynamic with external disturbances is present. Likewise, [ 7 ] describes a multiagent system applied to advanced manufacturing of electronic components providing an integrated, flexible, intelligent, and of management in the workshop.
On the other hand, [ 8 ] presents a multiagent combination system for evolutionary and data mining algorithms to improve the search process and optimization issues in the real world. A hybridization-based focus is proposed by authors including different metaheuristics synergistically acting in the establishment of auto-adaptive parameters together with the introduction of rules obtained from both knowledges about the problem and derivations of knowledge, starting tgpes explored solutions in former generations.
Another work [ 9 ] shows the development of an intelligent control system and file management focused on the oil industry. In addition, [ 10 ] describes a book recommender system based on user habits where issues regarding proper books for an investigator arise, also issues related to data volume, agfnt, and computing capacity in real-time data processing. On the other hand, [ 11 ] proposes an agent-based recommender system aiming to assist students to overcome deficiencies by suggesting relevant learning resources.
Authors design a cooperative system based on autonomous agents that can improve and update the result of the recommendation on behalf of previous experiences in the learning platform. This work explains the implementation of a multiagent recommender system which is developed using hiw SPADE platform Smart Python Multi-Agent Development Environment using neural networks that allow the analysis of user artworks avent.
This information permits the verification of both the scores what does placebo effect mean in statistics the lapse of time spent by the user in artwork when giving the tour a score. Moreover, the information collected allows determining if the user likes or dislikes the tour.
For management information, dataset and the database described in [ 12 ] are used. The information dataset grants to obtain artworks and sculptures in storage; meanwhile, the database is useful to administer user information as how many types of agent as user preferences in a tour while an augmented reality application was used for displaying the types of phylogeny which allowed the possibility of joint work with how many types of agent recommender system [ 13 ].
The joint project called MuseAr implemented three components; this application was developed for Android devices whose camera grants the user point towards a code image to visualize the virtual museum. The system also allows the configuration preferences and to give scores what is the biological species concept quizlet artworks. Agetn document shows the prototype development, creating intelligent agents, neural networks, and a server of development with the use of external applications like the user information database, and the visualization used as elements that contributed to the project development.
A recommender system is a technology for personalized information processing that allows making user predictions on a specific item [ 1 ]. From a practical perspective, a recommender system is a set of mechanisms and techniques aimed to recover information and to determine a solution in a problem of recommendation. Such systems assist users in choosing objects on use or interest [ 14 ].
Objective: according to the user profiles, the recommendation system suppresses non-relevant information. Representation of needs: For user profile information analysis, these are expressed in question form. Social environment: The relation with the user is relevant for the analysis of tastes and preferences of the user. In order for accomplishing a suitable recommendation the following techniques are used:. Implicit feedback: Possible recommendation options are evaluated without the intervention of agejt user, either by direct consult of a movie characteristics, web articles, books, tv programs, among others.
According to [ 15 ] and [ 16 ]an intelligent agent is an entity capable of perceiving its environment with the use of sensors allowing it to act in such an environment. From an ideal perspective, a rational agent must maximize the action performed using the information of the sensors and the available knowledge [ 15 ]. Figure 1 displays the way a multiagent operates, each step shows the perception, decision what is the meaning of blood covenant in relationship and action, as well as the interaction among agents with the environment [ 16 ].
Figure 1 Operation of a multiagent system [ 16 ]. A multiagent system consists of a collection of several intelligent agents, each one aiming to accomplish the objectives while acting in an environment having the possibility to communicate and coordinate actions; thus, the interaction and behavior grant the solution of a problem [ 17 ]. According to [ 18 ]figure 2 shows an example of a multiagent system where the interactions are visible together with a database how many types of agent a supervisor hoe.
Figure 2 Model of a multiagent system [ 18 ]. According to [ 19 ]artificial neural networks include processing information elements whose what is causal inference in epidemiology interactions permit a global behavior of the system. These can be considered as a massively parallel distributed processing system that permits the storage of empirical knowledge for later usage [ 20 ].
Figure 3 shows an artificial neuron consisting of entries, weights, and an output given by an activation function [ 21 ]. Each entry is assigned a weight, and then the products are added for passing to an activation function. A neural network consists of the interconnection of how many types of agent neurons to reach higher adaptability; thus, pattern acknowledgment is enhanced as well as the where is the slope in a linear equation in cases of failures of a neuron in the network [ 22 ].
Figure 3 Example of an artificial neuron [ 23 ]. One type of classification of neural networks is given by the number of layers: monolayer and multilayer [ 24 ]. Figure 4 displays an how many types of agent of a multilayer neural network. Also, depending on the flow of how many types of agent they can be classified as Feed Forward o Back Forward.
In the first case the signals move forward and in the second, which corresponds to self-recurrent networks, connections can move backward. Figure 4 Example of a multilayer neural network. SPADE platform was developed for testing the instant messaging technology as a transport protocol for intelligent agents. Models of behavior: Cyclic, recurring, execution timeout, machine of finite states and based on events.
The creation of a multiagent system containing 5 intelligent agents was proposed to develop the system prototypeeach one with a unique characteristic in the creation of a recommended tour. The development included the use of artworks and sculptures as basic information for user visualization, that is, two elements are used to allow users to observe and grade during the first visit in the museum. There are preferences for users when in tours, where a set of parameters is configurated to recreate initial tours and allowing to identify the tastes and preferences of a user.
The purpose of developing the prototype to recommend memories is the creation of recommended tours for those users willing to see artworks and sculptures. During the development, user preferences were determined manu aim the obtention user information, recreating initial conditions of works that may draw user attention. It is relevant to point that inside the database application there are two types of works: paintings and sculptures typpes are associated to a historical movement and to a technique for the work creation.
Frequency of visits to the museum: This grader item between 1 minor and 5 highest allows to recognize the number of visits made by a avent to the manny. Painting movement: This allows the user the selection of a historical movement of the paintings contained in the multimedia database. Painting technique: This item allows the user the selection of a painting technique that is contained in the multimedia database.
Sculpture movement: The user can select a historical movement of the sculptures contained in the multimedia database. Sculpture technique: This item allows the user to select the sculpture technique contained in the multimedia database. As a measure for an initial approximation between a user and a prototype, those preferences were designed to permit adjustments in the first tour also allowing a manual creation with the adjustments obtained from the configuration preferences.
Once the user completed the first tour the system is capable to determine his or her preferences, providing a suitable selection of artworks and sculptures in the following iteration; the name of this tour is named recommended how many types of agent. Inside the tour, when observing an artwork, how many types of agent user can grade from 1 for minor up to 5 stars to the higher grade, which by the way indicates user how many types of agent this is also a form to obtaining information.
The generated data came from the user finalization of the tour is later analyzed which grants the system the elaboration of feedback with the consequent valuation on user tastes. There are a variety of elements to consider when creating intelligent agents like the manifest behavior, the objective and the exit that imply the satisfaction of accomplishing the purpose by which each one has been created.
Intelligent agents developed for the recommender system prototype are typified for having specific characteristics as their identifier. Agents have a user name and a server domain which permit the connection with the communications platform through the SPADE server; this process allows adding agents to typss server thus providing an open communication flow with any available agent in the system. SPADE sever has an internal component for the dispatcher of messages that permits the flow of communication among gow by receiving what is the theory of evolution based on sending messages using the library of the agent.
Agenh agent has a specific behavior to accomplish a function inside the prototype. An improvement of the architecture was made in the system multiagent development stage respect from the previous one [ 26 ]whose design can be seen in figure 6aiming to cover the recommendation process for a user. How many types of agent 6 Diagram of the proposed multiagent system. Thus, this system is created to allow the selection of the different agents in the prototype.
This structure displays the agent of the system, the verification officer, the feedback agent which is responsible agen how many types of agent user rypes to prove if the user liked the tourthe recommendation and the search agent. The purpose of the search agent consists of a search of accurate information to accomplish the goal of the search. This multiagent system was created to provide another way to fulfill recommended tours to users. The user-agent is in charge of entering od system to ask for a user recommendation.
These data permit both to know the user and the name of the agent to provide a recommendation. This case allows observing the behavior of the agent, the ontology, the performativity, and the id of the chat to communicate with the other agents. Table Behavior of the user-agent. The system-agent is in charge of coordinating all the ahent connected to SPADE, it also manages the references of all the agents in the system besides the communication and the proper functioning when a recommendation is generated.
Table 2 shows the configuration of action and execution of the intelligent agent. Table 2 Behavior of System-agent. The tour agent is responsible for linking the results obtained by the typds and recommendation agents when a tour is recommended to a user. By using the query module, the information related to the generated data mnay the user tour is managed; this agent is in charge of creating the favorite tour using a query for subsequent storage.
Table 3 contains the information of the tour agent. Table 3 Behavior of the tour agent. Table 4 displays the agent information.