Intelligent travel planning: a multiagent planning system to solve web problems in the e-tourism domain

This paper presents Intelligent Travel Planning (ITP), a multiagent planning system to solve Web electronic problems in the Web, whose main goal is to search for useful solutions in the electronic-Tourism domain to system users. The system uses different types of intelligent autonomous agents whose main characteristics are cooperation, negotiation, learning, planning and knowledge sharing. Obviously the information used by the intelligent agents is heterogeneous and geographically distributed, since the main information source of the system is Internet. Other information sources are agent knowledge bases in the distributed system. The process to obtain, filter, and store the information is performed automatically by agents. This information is translated into a homogeneous format for high-level reasoning in order to obtain different partial solutions. Partial solutions are reconstructed into a general solution (or solutions) to be presented to the user. The system will show a set of solutions to the users that can be evaluated by them.Publicad

Improving group recommendations using personality, dynamic clustering and Multi-Agent microServices

The complexity associated to group recommendations needs strategies to mitigate several problems, such as the group's heterogeinity and conflicting preferences, the emotional contagion phenomenon, the cold-start problem, and the group members' needs and concerns while providing recommendations that satisfy all members at once. In this demonstration, we show how we implemented a Multi-Agent Microservice to model the tourists in a mobile Group Recommender System for Tourism prototype and a novel dynamic clustering process to help minimize the group's heterogeneity and conflicting preferences. To help solve the cold-start problem, the preliminary tourist attractions preference and travel-related preferences & concerns are predicted using the tourists' personality, considering the tourists' disabilities and fears/phobias. Although there is no need for data from previous interactions to build the tourists' profile since we predict the tourists' preferences, the tourist agents learn with each other by using association rules to find patterns in the tourists' profile and in the ratings given to Points of Interest to refine the recommendations.FCT -Fundação para a Ciência e a Tecnologia(UIDB/00319/2020

Learning in Multi-Agent Information Systems - A Survey from IS Perspective

Multiagent systems (MAS), long studied in artificial intelligence, have recently become popular in mainstream IS research. This resurgence in MAS research can be attributed to two phenomena: the spread of concurrent and distributed computing with the advent of the web; and a deeper integration of computing into organizations and the lives of people, which has led to increasing collaborations among large collections of interacting people and large groups of interacting machines. However, it is next to impossible to correctly and completely specify these systems a priori, especially in complex environments. The only feasible way of coping with this problem is to endow the agents with learning, i.e., an ability to improve their individual and/or system performance with time. Learning in MAS has therefore become one of the important areas of research within MAS. In this paper we present a survey of important contributions made by IS researchers to the field of learning in MAS, and present directions for future research in this area

A tourism recommender agent: From theory to practice

In this paper a multiagent Tourism Recommender System is presented. This system has a multiagent architecture and one of its main agents, The Travel Assistant Agent (T-Agent), is modelled as a graded BDI agent. The graded BDI agent model allows to specify an agent’s architecture able to deal with the environment uncertainty and with graded mental attitudes. We focus on the implementational aspects of the multiagent system and specially on the T-Agent development, going from the theoric agent model to the concrete agent implementation.Red de Universidades con Carreras en Informática (RedUNCI

Group Fairness for Content Creators: the Role of Human and Algorithmic Biases under Popularity-based Recommendations

The Creator Economy faces concerning levels of unfairness. Content creators (CCs) publicly accuse platforms of purposefully reducing the visibility of their content based on protected attributes, while platforms place the blame on viewer biases. Meanwhile, prior work warns about the “rich-get-richer” effect perpetuated by existing popularity biases in recommender systems: Any initial advantage in visibility will likely be exacerbated over time. What remains unclear is how the biases based on protected attributes from platforms and viewers interact and contribute to the observed inequality in the context of popularity-biased recommender systems. The difficulty of the question lies in the complexity and opacity of the system. To overcome this challenge, we design a simple agent-based model (ABM) that unifies the platform systems which allocate the visibility of CCs (e.g., recommender systems, moderation) into a single popularity-based function, which we call the visibility allocation system (VAS). Through simulations, we find that although viewer homophilic biases do alone create inequalities, small levels of additional biases in VAS are more harmful. From the perspective of interventions, our results suggest that (a) attempts to reduce attribute-biases in moderation and recommendations should precede those reducing viewers’ homophilic tendencies, (b) decreasing the popularity-biases in VAS decreases but not eliminates inequalities, (c) boosting the visibility of protected CCs to overcome viewers’ homophily with respect to one fairness metric is unlikely to produce fair outcomes with respect to all metrics, and (d) the process is also unfair for viewers and this unfairness could be overcome through the same interventions. More generally, this work demonstrates the potential of using ABMs to better understand the causes and effects of biases and interventions within complex sociotechnical systems

A tourism recommender agent: From theory to practice

In this paper a multiagent Tourism Recommender System is presented. This system has a multiagent architecture and one of its main agents, The Travel Assistant Agent (T-Agent), is modelled as a graded BDI agent. The graded BDI agent model allows to specify an agent’s architecture able to deal with the environment uncertainty and with graded mental attitudes. We focus on the implementational aspects of the multiagent system and specially on the T-Agent development, going from the theoric agent model to the concrete agent implementation.Red de Universidades con Carreras en Informática (RedUNCI

CARS – A Spatio-Temporal BDI Recommender System: Time, Space and Uncertainty

International audienceAgent-based recommender systems have been exploited in the last years to provide informative suggestions to users, showing the advantage of exploiting components like beliefs, goals and trust in the recommenda-tions' computation. However, many real-world scenarios, like the traffic one, require the additional feature of representing and reasoning about spatial and temporal knowledge, considering also their vague connotation. This paper tackles this challenge and introduces CARS, a spatio-temporal agent-based recommender system based on the Belief-Desire-Intention (BDI) architecture. Our approach extends the BDI model with spatial and temporal information to represent and reason about fuzzy beliefs and desires dynamics. An experimental evaluation about spatio-temporal reasoning in the traffic domain is carried out using the NetLogo platform, showing the improvements our recommender system introduces to support agents in achieving their goals

Recuperación personalizada de recursos educativos

Esta línea de investigación trata el problema de la recuperación personalizada inteligente de recursos de aprendizaje. Tiene como objetivo diseñar e implementar un sistema de recomendación que ayude a un usuario a encontrar los recursos educativos electrónicos que le sean más apropiados de acuerdo a sus necesidades y preferencias. Como hipótesis de trabajo se considera que se tienen diferentes repositorios de objetos de aprendizaje, donde cada objeto tiene metadatos descriptivos. Se propone utilizar estos metadatos para recuperar aquellos objetos que satisfagan no sólo el tema de la consulta, sino también el perfil de usuario, teniendo en cuenta sus características y preferencias. Esto engloba el establecimiento de una estrategia de búsqueda adecuada y la definición de metadatos educacionales adecuados. También abarca el diseño de una arquitectura multiagente acorde a las diferentes funcionalidades del sistema y las arquitecturas de los agentes que la componen. Mediante la implementación de un prototipo se espera poder experimentar la arquitectura del sistema y la metodología de la recuperación propuesta.Eje: Tecnología Informática Aplicada en EducaciónRed de Universidades con Carreras en Informática (RedUNCI

Making better recommendations with online profiling agents

Master'sMASTER OF SCIENC