Juan C. Burguillo

A hybrid content-based and item-based collaborative filtering approach to recommend TV programs enhanced with singular value decomposition

With the advent of new cable and satellite services, and the next generation of digital TV systems, people are faced with an unprecedented level of program choice. This often means that viewers receive much more information than they can actually manage, which may lead them to believe that they are missing programs that could likely interest them. In this context, TV program recommendation systems allow us to cope with this problem by automatically matching users likes to TV programs and recommending the ones with higher user preference. This paper describes the design, development, and startup of queveo.tv: a Web 2.0 TV program recommendation system. The proposed hybrid approach (which combines content-filtering techniques with those based on collaborative filtering) also provides all typical advantages of any social network, such as supporting communication among users as well as allowing users to add and tag contents, rate and comment the items, etc. To eliminate the most serious limitations of collaborative filtering, we have resorted to a well-known matrix factorization technique in the implementation of the item-based collaborative filtering algorithm, which has shown a good behavior in the TV domain. Every step in the development of this application was taken keeping always in mind the main goal: to simplify as much as possible the user task of selecting what program to watch on TV.

CHARLIE: An AIML-based chatterbot which works as an interface among INES and humans

INES (INtelligent Educational System) is a functional prototype of an online learning platform, which combines three essential capabilities related to e-learning activities. These capabilities are those concerning to a LMS (Learning Management System), a LCMS (Learning Content Management System), and an ITS (Intelligent Tutoring System). To carry out all this functionalities, our system, as a whole, comprises a set different tools and technologies, as follows: semantic managing users (administrators, teachers, students…) and contents tools, an intelligent chatterbot able to communicate with students in natural language, an intelligent agent based on BDI (Believes, Desires, Intentions) technology that acts as the brain of the system, an inference engine based on JESS (a rule engine for the Java platform) and ontologies (to modelate the user, his/her activities, and the learning contents) that contribute with the semantics of the system, etc. At the present paper we will focus on the chatterbot, CHARLIE (CHAtteR Learning Interface Entity), developed and used in the platform, which is an AIML-based (Artificial Intelligence Markup Language) bot. We will specifically address its performance and its contribution to INES.

moreTourism: Mobile recommendations for tourism

This paper introduces a hybrid recommendation platform providing information about tourist resources depending on the user profile, location, schedule and the amount of time for visiting interest points isolated or combined in a route.

A Qualitative Comparison of Techniques for Student Modeling in Intelligent Tutoring Systems

Intelligent tutoring systems (ITS) are interactive learning environments based on instruction assisted by computers. The intelligence of these systems is largely attributed to their ability to adapt to a specific student during the teaching process. In general, the adaptation process can be described by three phases: (i) getting the information about the student, (ii) processing the information to initialize and update a student model, and (iii) using the student model to provide the adaptation. In this paper we studied aspects related with student modeling (SM) in intelligent tutoring systems. First we make a qualitative comparison of two techniques: Bayesian networks (BN) and case-based reasoning (CBR) for SM. We apply both techniques to a case study concerning the development of an ITS for e-learning in the medical domain. Finally, we discuss the results obtained

An intelligent tutoring module controlled by BDI agents for an e-learning platform

INES (INtelligent Educational System) is an operative prototype of an e-learning platform. This platform includes several tools and technologies, such as: (i) semantic management of users and contents; (ii) conversational agents to communicate with students in natural language; (iii) BDI-based (Believes, Desires, Intentions) agents, which shape the tutoring module of the system; (iv) an inference engine; and (v) ontologies, to semantically model the users, their activities, and the learning contents. The main contribution of this paper is the intelligent tutoring module of the system. Briefly, the tasks of this module are to recognize each student (checking his/her system credentials) and to obtain information about his/her learning progress. So, it can be able to suggest to each student specific tasks to achieve his/her particular learning objectives, based on several parameters related to the existing learning paths and the students profile.

Fostering Cooperation through Dynamic Coalition Formation and Partner Switching

In this article we tackle the problem of maximizing cooperation among self-interested agents in a resource exchange environment. Our main concern is the design of mechanisms for maximizing cooperation among self-interested agents in a way that their profits increase by exchanging or trading with resources. Although dynamic coalition formation and partner switching (rewiring) have been shown to promote the emergence and maintenance of cooperation for self-interested agents, no prior work in the literature has investigated whether merging both mechanisms exhibits positive synergies that lead to increase cooperation even further. Therefore, we introduce and analyze a novel dynamic coalition formation mechanism, that uses partner switching, to help self-interested agents to increase their profits in a resource exchange environment. Our experiments show the effectiveness of our mechanism at increasing the agents’ profits, as well as the emergence of trading as the preferred behavior over different types of complex networks.

A transport based clearing system for dynamic carpooling business services

The WiSafeCar (Wireless Traffic Safety Network Between Cars) project aims at creating a comprehensive wireless communication and service platform targeting vehicular networks, striving to reduce accidents and traffic congestion. Within the frame of this project, a dynamic carpooling transport system was designed, reacting in real time to events and user transport requests. In order to allow this system to integrate seamlessly with other transport modes, but also to encourage users to use carpooling, a clearing service has been implemented to solve the inherent compensation issues. Both the carpooling system and the underlying utility services have been prototyped, using the Netlogo simulator, to prove not only the efficiency of WiSafeCar at reducing the congestion in cities; but also to validate the business models to be implemented and more precisely the clearing algorithm.

Dynamic constrained coalition formation among electric vehicles

BackgroundThe use of electric vehicles (EVs) and vehicle-to-grid (V2G) technologies have been advocated as an efficient way to reduce the intermittency of renewable energy sources in smart grids. However, operating on V2G sessions in a cost-effective way is not a trivial task for EVs. The formation of coalitions among EVs has been proposed to tackle this problem.MethodsIn this paper we introduce Dynamic Constrained Coalition Formation (DCCF), which is a distributed heuristic-based method for constrained coalition structure generation (CSG) in dynamic environments. In our approach, coalitions are formed observing constraints imposed by the grid. To this end, EV agents negotiate the formation of feasible coalitions among themselves.ResultsBased on experiments, we show that DCCF is efficient to provide good solutions in a fast way. DCCF provides solutions whose quality approaches 98% of the optimum. In dynamically changing scenarios, DCCF also shows good results, keeping the agents payoff stable along time.ConclusionsEssentially, DCCF’s main advantage over traditional CSG algorithms is that its computational effort is very lower. On the other hand, unlike traditional algorithms, DCCF is suitable only for constraint-based problems.

Learning to cooperate in the Iterated Prisoner's Dilemma by means of social attachments

The Iterated Prisoner’s Dilemma (IPD) has been used as a paradigm for studying the emergence of cooperation among individual agents. Many computer experiments show that cooperation does arise under certain conditions. In particular, the spatial version of the IPD has been used and analyzed to understand the role of local interactions in the emergence and maintenance of cooperation. It is known that individual learning leads players to the Nash equilibrium of the game, which means that cooperation is not selected. Therefore, in this paper we propose that when players have social attachment, learning may lead to a certain rate of cooperation. We perform experiments where agents play the spatial IPD considering social relationships such as belonging to a hierarchy or to coalition. Results show that learners end up cooperating, especially when coalitions emerge.

Evolutionary Algorithms Based on Game Theory and Cellular Automata with Coalitions

Cellular genetic algorithms (cGAs) are a kind of genetic algorithms (GAs) with decentralized population in which interactions among individuals are restricted to the closest ones. The use of decentralized populations in GAs allows to keep the population diversity for longer, usually resulting in a better exploration of the search space and, therefore in a better performance of the algorithm. However, the use of decentralized populations supposes the need of several new parameters that have a major impact on the behavior of the algorithm. In the case of cGAs, these parameters are the population and neighborhood shapes. Hence, in this work we propose a new adaptive technique based in Cellular Automata, Game Theory and Coalitions that allow to manage dynamic neighborhoods. As a result, the new adaptive cGAs (EACO) with coalitions outperform the compared cGA with fixed neighborhood for the selected benchmark of combinatorial optimization problems.