Learning with a context-aware multiagent system

New technological developments have made it possible to interact with computer systems and applications anywhere and anytime. It is vital that these applications are able to adapt to the user, as a person, and to its current situation, whatever that is. Therefore, the premises for evolution towards a learning society and a knowledge economy are present. Hence, there is a stringent demand for new learner-centered frameworks that allow active participation of learners in knowledge creation within communities, organizations, territories and society, at large. This paper presents the multi-agent architecture of our context-aware system and the learning scenarios within multi-dimensional learning spaces that the system provides support for. This architecture is the outcome of our endeavor to develop ePH, a system for sharing public interest information and knowledge, which is accessible through always-on, context-aware services.

[1]  Monica Vladoiu,et al.  FACE - A Knowledge-Intensive Case-Based Architecture for Context-Aware Services , 2010, NDT.

[2]  Monica Vladoiu,et al.  Driving Style Analysis Using Data Mining Techniques , 2010, Int. J. Comput. Commun. Control.

[3]  Eduardo Mena,et al.  Location-dependent queries in mobile contexts: distributed processing using mobile agents , 2006, IEEE Transactions on Mobile Computing.

[4]  Agnar Aamodt,et al.  Case-Based Reasoning: Foundational Issues, Methodological Variations, and System Approaches , 1994, AI Commun..

[5]  Arthur C. Graesser,et al.  Is it an Agent, or Just a Program?: A Taxonomy for Autonomous Agents , 1996, ATAL.

[6]  Guanling Chen,et al.  A Survey of Context-Aware Mobile Computing Research , 2000 .

[7]  Agnar Aamodt,et al.  A context model for knowledge-intensive case-based reasoning , 1998, Int. J. Hum. Comput. Stud..

[8]  Ayse Göker,et al.  User Context and Personalisation , 2002, ECCBR Workshops.

[9]  Pierre De Loor,et al.  Context's Modeling for Participative Simulation , 2006, FLAIRS Conference.

[10]  Eduardo Mena,et al.  Location-dependent query processing: Where we are and where we are heading , 2010, CSUR.

[11]  Jane Yung-jen Hsu,et al.  A Multi-agent Context-aware Service Platform in a Smart Space , 2007 .

[12]  Anind K. Dey,et al.  Understanding and Using Context , 2001, Personal and Ubiquitous Computing.

[13]  Elaine Rich,et al.  User Modeling via Stereotypes , 1998, Cogn. Sci..

[14]  Marius Mikalsen,et al.  Context: Representation and Reasoning. Representing and Reasoning about Context in a Mobile Environment , 2005, Rev. d'Intelligence Artif..

[15]  Anders Kofod-Petersen,et al.  Using Activity Theory to Model Context Awareness: A Qualitative Case Study , 2006, FLAIRS.

[16]  Franziska Klügl.  From agent theory to agent implementation , 2001 .

[17]  Keith Cheverst,et al.  UTILIZING CONTEXT HISTORY TO PROVIDE DYNAMIC ADAPTATIONS , 2004, Appl. Artif. Intell..

[18]  Charles Tijus,et al.  Contextual Categorization: A Mechanism Linking Perception and Knowledge in Modeling and Simulating Perceived Events as Actions , 2001, CONTEXT.

[19]  Toward Location-based Services using GPS-based Devices , 2022 .

[20]  A. Roadmapof.  A Roadmap of Agent Research and Development , 1995 .

[21]  Gregory D. Abowd,et al.  Cyberguide: A mobile context‐aware tour guide , 1997, Wirel. Networks.

[22]  Maureen Layte,et al.  Rethinking quality for building a learning society , 2006 .

[23]  José M. Molina López,et al.  A Multi-Agent architecture for intelligent gathering systems , 2005, AI Communications.

[24]  Frédérique Laforest,et al.  A comprehensive approach to model and use context for adapting applications in pervasive environments , 2007, J. Syst. Softw..

[25]  Euiho Suh,et al.  Context-aware systems: A literature review and classification , 2009, Expert Syst. Appl..

[26]  Monica Vladoiu,et al.  Framework for Building of a Dynamic User Community (EPH) - Sharing of Context-Aware, Public Interest Information or Knowledge through Always-on Services , 2008, ICEIS.