Machine Learning Enhancing Adaptivity of Multimodal Mobile Systems

One of the most important features of a mobile device concerns its flexibility and capability to adapt the functionality it provides to the users. However, the main problems of the systems present in literature are their incapability to identify user needs and, more importantly, the insufficient mappings of those needs to available resources/ services. In this paper, we present a two-phase construction of the user model: firstly, an initial static user model is built for the user connecting to the system the first time. Then, the model is revised/adjusted by considering the information collected in the logs of the user interaction with the device/context in order to make the model more adequate to the evolving user's interests/ preferences/behaviour. The initial model is built by exploiting the stereotype concept, its adjustment is performed exploiting machine learning techniques and particularly, sequence mining and pattern discovery strategies.

[1]  Stephen Muggleton,et al.  Learning Programs in the Event Calculus , 1997, ILP.

[2]  Juan Barceló,et al.  Problem Solving in the Brain and by the Machine , 2009 .

[3]  John McCarthy,et al.  SOME PHILOSOPHICAL PROBLEMS FROM THE STANDPOINT OF ARTI CIAL INTELLIGENCE , 1987 .

[4]  Diane J. Cook,et al.  Improving home automation by discovering regularly occurring device usage patterns , 2003, Third IEEE International Conference on Data Mining.

[5]  Mounir Boukadoum,et al.  Enhanced Global Best Particle Swarm Classification , 2014, Int. J. Softw. Sci. Comput. Intell..

[6]  Lilit Hakobyan,et al.  Older Adults with AMD as Co-Designers of an Assistive Mobile Application , 2014, Int. J. Mob. Hum. Comput. Interact..

[7]  Donato Malerba,et al.  Discovering Associations between Spatial Objects: An ILP Application , 2001, ILP.

[8]  Anthony G. Cohn,et al.  Multi-Dimensional Modal Logic as a Framework for Spatio-Temporal Reasoning , 2002, Applied Intelligence.

[9]  Peter Dolog,et al.  Towards the Adaptive Semantic Web , 2003, PPSWR.

[10]  Paramartha Dutta,et al.  Handbook of Research on Computational Intelligence for Engineering, Science, and Business , 2012 .

[11]  C. Randell,et al.  Context awareness by analysing accelerometer data , 2000, Digest of Papers. Fourth International Symposium on Wearable Computers.

[12]  Henrik Boström,et al.  Learning First Order Logic Time Series Classifiers , 2000, ILP Work-in-progress reports.

[13]  Juan Carlos Augusto,et al.  Designing Smart Homes, The Role of Artificial Intelligence , 2006, Designing Smart Homes.

[14]  Sotirios K. Goudos,et al.  Application of Multi-Objective Evolutionary Algorithms to Antenna and Microwave Design Problems , 2012 .

[15]  Hendrik Blockeel,et al.  From Shell Logs to Shell Scripts , 2001, ILP.

[16]  Du Zhang,et al.  A Value-Based Framework for Software Evolutionary Testing , 2011, Int. J. Softw. Sci. Comput. Intell..

[17]  Alfred Kobsa,et al.  Modeling the user's conceptual knowledge in BGP‐MS, a user modeling shell system 1 , 1990, Comput. Intell..

[18]  Jeffrey D. Ullman,et al.  Principles of Database and Knowledge-Base Systems, Volume II , 1988, Principles of computer science series.

[19]  Lubos Popelínský,et al.  Knowledge Discovery in Spatial Data by Means of ILP , 1998, PKDD.

[20]  Yiyu Yao,et al.  On the System Algebra Foundations for Granular Computing , 2009, Int. J. Softw. Sci. Comput. Intell..

[21]  Heikki Mannila,et al.  Fast Discovery of Association Rules , 1996, Advances in Knowledge Discovery and Data Mining.

[22]  Saso Dzeroski,et al.  Inductive Logic Programming: Techniques and Applications , 1993 .

[23]  Alfred Kobsa,et al.  Personalized Digital Television: Targeting Programs to Individual Viewers (Human-Computer Interaction Series, 6) , 2004 .

[24]  Lawrence R. Rabiner,et al.  A tutorial on hidden Markov models and selected applications in speech recognition , 1989, Proc. IEEE.

[25]  Jean Vanderdonckt,et al.  Task Modelling in Multiple Contexts of Use , 2002, DSV-IS.

[26]  James Chen,et al.  Adaptive hypertext navigation based on user goals and context , 1993, User Modeling and User-Adapted Interaction.

[27]  Henry A. Kautz,et al.  Learning and inferring transportation routines , 2004, Artif. Intell..

[28]  Ivan Bratko,et al.  Prolog Programming for Artificial Intelligence , 1986 .

[29]  Ramakrishnan Srikant,et al.  Mining sequential patterns , 1995, Proceedings of the Eleventh International Conference on Data Engineering.

[30]  Hannu Toivonen,et al.  Discovery of frequent DATALOG patterns , 1999, Data Mining and Knowledge Discovery.

[31]  Andreas Krause,et al.  SenSay: a context-aware mobile phone , 2003, Seventh IEEE International Symposium on Wearable Computers, 2003. Proceedings..

[32]  Saul Greenberg,et al.  USING UNIX: COLLECTED TRACES OF 168 USERS , 1988 .

[33]  Luc De Raedt,et al.  Inductive Logic Programming: Theory and Methods , 1994, J. Log. Program..

[34]  Charlie C. Chen,et al.  Understanding Continuance of Using VoIP Applications to Improve Intercultural Communication: Information and System Quality Perspectives , 2012, Int. J. Soc. Organ. Dyn. IT.

[35]  Alfred Kobsa,et al.  The Adaptive Web, Methods and Strategies of Web Personalization , 2007, The Adaptive Web.

[36]  Keith Cheverst,et al.  Developing a context-aware electronic tourist guide: some issues and experiences , 2000, CHI.

[37]  Umeshwar Dayal,et al.  Multi-dimensional sequential pattern mining , 2001, CIKM '01.

[38]  Diane J. Cook,et al.  PREDIcting inhabitant action using action and task models with application to smart homes , 2004, Int. J. Artif. Intell. Tools.

[39]  Thurasamy Ramayah,et al.  Consumer Intention to Use Anti-Spyware Software: An Application of Structural Equation Modeling , 2014, Int. J. Technol. Hum. Interact..

[40]  A. B. M. Shawkat Ali,et al.  Multidisciplinary Computational Intelligence Techniques: Applications in Business, Engineering, and Medicine , 2012 .

[41]  Alfred Kobsa,et al.  The user modeling shell system BGP-MS , 2005, User Modeling and User-Adapted Interaction.

[42]  Diane J. Cook,et al.  Health Monitoring and Assistance to Support Aging in Place , 2006, J. Univers. Comput. Sci..

[43]  Jun Peng,et al.  Image Retrieval based on HSV Feature and Regional Shannon Entropy , 2012, Int. J. Softw. Sci. Comput. Intell..

[44]  Timothy W. Finin.  GUMS — A General User Modeling Shell , 1989 .

[45]  Jon Orwant,et al.  Heterogeneous learning in the Doppelgänger user modeling system , 2005, User Modeling and User-Adapted Interaction.