Adaptation and user expertise modelling in AthosMail

This article describes the User Model component of AthosMail, a speech-based interactive e-mail application developed in the context of the EU project DUMAS. The focus is on the system’s adaptive capabilities and user expertise modelling, exemplified through the User Model parameters dealing with initiative and explicitness of the system responses. The purpose of the conducted research was to investigate how the users could interact with a system in a more natural way, and the two aspects that mainly influence the system’s interaction capabilities, and thus the naturalness of the dialogue as a whole, are considered to be the dialogue control and the amount of information provided to the user. The User Model produces recommendations of the system’s appropriate reaction depending on the user’s observed competence level, monitored and computed on the basis of the user’s interaction with the system. The article also discusses methods for the evaluation of adaptive user models and presents results from the AthosMail evaluation.

[1]  Aseel Berglund,et al.  Using speech and dialogue for interactive TV navigation , 2004, Universal Access in the Information Society.

[2]  David N. Chin KNOME: Modeling What the User Knows in UC , 1989 .

[3]  Judy Kay,et al.  Learner Control , 2001, User Modeling and User-Adapted Interaction.

[4]  Kristiina Jokinen,et al.  User Expertise Modeling and Adaptivity in a Speech-Based E-Mail System , 2004, ACL.

[5]  Nicole Yankelovich,et al.  How do users know what to say? , 1996, INTR.

[6]  Emiel Krahmer,et al.  Problem spotting in human-machine interaction , 1999, EUROSPEECH.

[7]  Niels Ole Bernsen,et al.  Designing Co-operativity in Spoken Human-Machine Dialogues , 1997 .

[8]  Kristiina Jokinen,et al.  DUMAS-Adaptation and Robust Information Processing for Mobile Speech Interfaces , 2004 .

[9]  Peter Brusilovsky,et al.  From adaptive hypermedia to the adaptive web , 2002, CACM.

[10]  Kristiina Jokinen Natural Interaction in Spoken Dialogue Systems , .

[11]  Preben Hansen,et al.  Multi-session group scenarios for speech interface design , 2003 .

[12]  Morena Danieli,et al.  Metrics for Evaluating Dialogue Strategies in a Spoken Language System , 1996, ArXiv.

[13]  Marilyn A. Walker,et al.  Learning to Predict Problematic Situations in a Spoken Dialogue System: Experiments with How May I Help You? , 2000, ANLP.

[14]  Kristiina Jokinen Evaluation of adaptivity and user expertise in a speech-based e-mail system , 1996 .

[15]  Marilyn A. Walker,et al.  From novice to expert: the effect of tutorials on user expertise with spoken dialogue systems , 1998, ICSLP.

[16]  Douglas B. Terry,et al.  Using collaborative filtering to weave an information tapestry , 1992, CACM.

[17]  Albert L. Lederer,et al.  Nine management guidelines for better cost estimating , 1992, CACM.

[18]  Thomas W. Malone,et al.  Intelligent Information Sharing Systems , 1986 .

[19]  Ronnie W. Smith,et al.  Current and New Directions in Discourse and Dialogue , 2004 .

[20]  D. Richard Hipp,et al.  Spoken Natural Language Dialog Systems , 1994 .

[21]  Kristiina Jokinen,et al.  Adaptivity and Response Generation in a Spoken Dialogue System , 2003 .

[22]  David R. Traum,et al.  Cooperation, dialogue and ethics , 2000, Int. J. Hum. Comput. Stud..

[23]  Ronnie W. Smith,et al.  Effective Spoken Natural Language Dialog Requires Variable Initiative Behavior: An Empirical Study , 1993 .

[24]  Shimei Pan,et al.  Designing and Evaluating an Adaptive Spoken Dialogue System , 2002, User Modeling and User-Adapted Interaction.

[25]  Jorma Rissanen,et al.  Learning Interaction Patterns for Adaptive User Interfaces , 2002 .

[26]  Markku Turunen,et al.  A Multilingual Adaptive Spoken Dialogue System for the E-mail Domain , 2004 .

[27]  Marilyn A. Walker,et al.  Evaluating spoken dialogue agents with PARADISE: Two case studies , 1998, Comput. Speech Lang..

[28]  Reinhard Oppermann,et al.  Adaptively supported adaptability , 1994, Int. J. Hum. Comput. Stud..

[29]  Sebastian Möller A new Taxonomy for the Quality of Telephone Services Based on Spoken Dialogue Systems , 2002, SIGDIAL Workshop.

[30]  P. R. Chesnais,et al.  The Fishwrap personalized news system , 1995, Proceedings of the Second International Workshop on Community Networking 'Integrated Multimedia Services to the Home'.

[31]  YankelovichNicole How do users know what to say , 1996 .

[32]  Hubert L. Dreyfus,et al.  Mind over Machine: The Power of Human Intuition and Expertise in the Era of the Computer , 1987, IEEE Expert.

[33]  Jennifer Chu-Carroll,et al.  MIMIC: An Adaptive Mixed Initiative Spoken Dialogue System for Information Queries , 2000, ANLP.

[34]  Jyrki Rissanen,et al.  Adaptive User Modelling in AthosMail , 2004, User Interfaces for All.

[35]  Anders Holst,et al.  Random indexing of text samples for latent semantic analysis , 2000 .

[36]  David Benyon,et al.  Developing adaptive systems to fit individual aptitudes , 1993, IUI '93.

[37]  Kristiina Jokinen,et al.  Goal Formulation based on Communicative Principles , 1996, COLING.

[38]  Alexandros Paramythis,et al.  A modular approach to the evaluation of Adaptive User Interfaces , 2001 .

[39]  Silvia Pfleger,et al.  Human Comfort and Security of Information Systems , 1997, Research Reports Esprit.

[40]  Magnus Sahlgren,et al.  From Words to Understanding , 2001 .

[41]  Pontus Johansson User Modeling in Dialogue Systems , 2002 .

[42]  Jennifer Chu-Carroll Evaluating Automatic Dialogue Strategy Adaptation for a Spoken Dialogue System , 2000, ANLP.

[43]  Siobhan Chapman Logic and Conversation , 2005 .

[44]  Kristina Höök,et al.  Steps to take before intelligent user interfaces become real , 2000, Interact. Comput..

[45]  Anthony Jameson,et al.  Pros and Cons of Controllability: An Empirical Study , 2002, AH.

[46]  Yoshinori Uesaka,et al.  Foundations of real-world intelligence , 2001 .

[47]  Pattie Maes,et al.  Amalthaea: An Evolving Multi-Agent Information Filtering and Discovery System for the WWW , 2004, Autonomous Agents and Multi-Agent Systems.