Collaborating Agents for Adaptation to Mobile Users

Collaborating Agents for Adaptation to Mobile Users

Angela Carrillo-Ramos (Pontificia Universidad Javeriana, Colombia), Manuele Kirsch Pinheiro (Université Paris 1 Panthéon-Sorbonne, France), Marlène Villanova-Oliver (Grenoble Computer Science Laboratory, France), Jérôme Gensel (Grenoble Computer Science Laboratory, France) and Yolande Berbers (Katholieke Universiteit Leuven, Belgium)
DOI: 10.4018/978-1-60566-306-7.ch012
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Abstract

The authors of this chapter present a two-fold approach for adapting content information delivered to a group of mobile users. This approach is based on a filtering process which considers both the user’s current context and her/his preferences for this context. The authors propose an object-based context representation, which takes into account the user’s physical and collaborative contexts, including elements related to collaboration tasks and group work in which the user is involved. They define the notion of preference for an individual or a group of people that develops a collaborative task and give a typology of preferences before proposing a formalism to represent them. This representation is exploited by a context matching algorithm in order to select only user preferences which can be applied according to the context of use. This chapter also presents the framework PUMAS which adopts a Multi-Agent System approach to support our propositions.
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Introduction

Nowadays, through the Web or wireless networks, Mobile Devices (MD), such as cellular phones, PDA, etc., can be used, to access distant Information Systems (IS), which allows mobile users to share and to collaborate with communities of users anytime, anywhere. This freedom of keeping connected and keeping the contact with the colleagues in any situations represents an opportunity for collaborating groups. Users are not anymore constrained to work isolated at their offices; they can work and interact with other users at different places and in unpredictable situations. For instance, a user can keep collaborating and use a wiki system to write a document with her/his colleagues even when she/he (or her/his colleagues) is traveling, may prepare a meeting with these colleagues being kilometers far away from the meeting place, share her/his impressions about a photo with her/his friends, etc. All these opportunities to collaborate are rendered possible through new mobile technologies.

However, mobile technologies present several physical and technical constraints, such as a limited battery lifetime and display size, for common used MD, and intermittent and poor quality connections, for wireless networks. In addition to these constraints, mobile users are often confronted to environmental constraints, like noisy and uncomfortable environments. Moreover, mobile users typically use these MD in brief time intervals, in order to perform activities and to consult small, but relevant, amount of information. All these constraints significantly affect mobile user’s expectations regarding the content supplied by IS. This content should, for instance, match the capacities of the client device and the quality of the network connection used by the user. If this content corresponds to a video, it should use a format that is accepted by the client device and a quality acceptable for a network transmission. And even if these conditions are satisfied, the video should match the environmental conditions (e.g., no sound if the user is in a noisy environment) and social aspects of the current situation, having, for example, a duration that matches the user’s current activity. Indeed, mobile user’s interests and needs change according to the user’s activities. The supplied content should match these interests in order to satisfy the users and help them in their own activities and when they are working (collaborating) with other users.

Through the example presented above, one can note that mobile users have multiple needs regarding content adaptation. More than traditional users, mobiles users need an informational content that suits her/his current context of use, which provides in particular a description of the (changing) conditions (temporal, spatial, hardware, physical and environmental) under which a user accesses one or several IS. In the remainder of this chapter we use “context” and “user’s context” like synonyms of “context of use”.

In this chapter, we propose to study how a collaborative and social technology such as Multi-Agent System (MAS) can be used for adapting services and information supplied to mobile users belonging to a social community of people. Adaptation is performed according to the user’s profile (essentially here her/his preferences in terms of activity, content, and presentation) and according to the contexts (environmental but also collaborative) in which she/he uses the system.

We aim at providing mobile users who access an IS through a MD with the most relevant information according to the characteristics of the context of use. In a previous work (Carrillo-Ramos et al., 2006), we have defined PUMAS (acronym of Peer Ubiquitous Multi-Agent System), a framework for retrieving information distributed among several IS and/or accessed through different types of MD. The architecture of PUMAS relies on four MAS (a Connection MAS, a Communication MAS, an Information MAS and an Adaptation MAS), each one encompassing several ubiquitous software agents which collaborate in order to achieve the different tasks handled by PUMAS (e.g., MD connection/disconnection, information storage and retrieval, content and presentation adaptation, etc.). Beyond the management of accesses to IS through MD, PUMAS is also in charge of performing an adaptation process over information.

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Table of Contents
Foreword
Bamshad Mobasher
Acknowledgment
Max Chevalier, Christine Julien, Chantal Soule-Dupuy
Chapter 1
Laurent Candillier, Kris Jack, Françoise Fessant, Frank Meyer
The aim of Recommender Systems is to help users to find items that they should appreciate from huge catalogues. In that field, collaborative... Sample PDF
State-of-the-Art Recommender Systems
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Chapter 2
Neal Lathia
Recommender systems generate personalized content for each of its users, by relying on an assumption reflected in the interaction between people... Sample PDF
Computing Recommendations with Collaborative Filtering
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Chapter 3
Edwin Simpson, Mark H. Butler
The increasing amount of available information has created a demand for better, more automated methods of finding and organizing different types of... Sample PDF
Analyzing Communal Tag Relationships for Enhanced Navigation and User Modeling
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Chapter 4
Adaptive User Profiles  (pages 65-87)
Steve Cayzer, Elke Michlmayr
A major opportunity for collaborative knowledge management is the construction of user models which can be exploited to provide relevant... Sample PDF
Adaptive User Profiles
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Chapter 5
Eugene Santos Jr., Hien Nguyen
In this chapter, we study and present our results on the problem of employing a cognitive user model for Information Retrieval (IR) in which a... Sample PDF
Modeling Users for Adaptive Information Retrieval by Capturing User Intent
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Chapter 6
Mihaela Brut, Florence Sedes, Corinne Zayani
Inside the e-learning platforms, it is important to manage the user competencies profile and to recommend to each user the most suitable documents... Sample PDF
Ontology-Based User Competencies Modeling for E-Learning Recommender Systems
$37.50
Chapter 7
Colum Foley, Alan F. Smeaton, Gareth J.F. Jones
Traditionally information retrieval (IR) research has focussed on a single user interaction modality, where a user searches to satisfy an... Sample PDF
Combining Relevance Information in a Synchronous Collaborative Information Retrieval Environment
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Chapter 8
Charles Delalonde, Eddie Soulier
This research leverages information retrieval activity in order to build a network of organizational expertise in a distributed R&D laboratory. The... Sample PDF
DemonD: A Social Search Engine Built Upon the Actor-Network Theory
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Chapter 9
Hager Karoui
In this chapter, the authors propose a case-based reasoning recommender system called COBRAS: a Peer-to-Peer (P2P) bibliographical reference... Sample PDF
COBRAS: Cooperative CBR Bibliographic Recommender System
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Chapter 10
Zehra Cataltepe, Berna Altinel
As the amount, availability, and use of online music increase, music recommendation becomes an important field of research. Collaborative... Sample PDF
Music Recommendation by Modeling User's Preferred Perspectives of Content, Singer/Genre and Popularity
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Chapter 11
Nima Taghipour, Ahmad Kardan
Information overload is no longer news; the explosive growth of the Internet has made this issue increasingly serious for Web users. Recommender... Sample PDF
Web Content Recommendation Methods Based on Reinforcement Learning
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Chapter 12
Angela Carrillo-Ramos, Manuele Kirsch Pinheiro, Marlène Villanova-Oliver, Jérôme Gensel, Yolande Berbers
The authors of this chapter present a two-fold approach for adapting content information delivered to a group of mobile users. This approach is... Sample PDF
Collaborating Agents for Adaptation to Mobile Users
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Chapter 13
Cristina Gena, Liliana Ardissono
This chapter describes the user-centered design approach we adopted in the development and evaluation of an adaptive Web site. The development of... Sample PDF
A User-Centered Approach to the Retrieval of Information in an Adaptive Web Site
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Chapter 14
Antonella Carbonaro, Rodolfo Ferrini
Active learning is the ability of learners to carry out learning activities in such a way that they will be able to effectively and efficiently... Sample PDF
Personalized Information Retrieval in a Semantic-Based Learning Environment
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Chapter 15
Hanh Huu Hoang, Tho Manh Nguyen, A Min Tjoa
Formulating unambiguous queries in the Semantic Web applications is a challenging task for users. This article presents a new approach in guiding... Sample PDF
A Semantic Web Based Approach for Context-Aware User Query Formulation and Information Retrieval
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About the Contributors