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Intelligent User Interfaces and User

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AI's Application to UIs. What's Not Mentioned? Affect. of course. IUI's Roots. Turing Test ... Electronic Transactions on AI. http://www.dfki.de ... – PowerPoint PPT presentation

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Title: Intelligent User Interfaces and User


1
Intelligent User Interfaces and User
  • IUI Overview
  • User Modeling via Stereotypes
  • Models of Users Emotions

2
Intelligent User Interfaces An Introduction
3
Two Major Areas of Discussion
  • Introduction to Intelligent User Interfaces
    (IUI)
  • Overview of the collection of readings contained
    in Readings in Intelligent User Interfaces

4
Motivation
  • WIMP (windows, icons, menus, and pointing) now
    standard for most apps
  • IUIs will provide adaptivity, context
    sensitivity, and task assistance
  • IUIs should be learnable, usable, and transparent

5
Definition
  • Intelligent User Interfaces are human-machine
    interfaces that aim to improve the effiency,
    effectiveness, and naturalness of human-machine
    interaction by representing, reasoning, and
    acting on models of the user, domain, task,
    discourse, and media.

6
Definitions
  • Mode or Modality refers primarily to the human
    senses vision, audition, olfaction, touch, and
    taste.
  • Medium refers to the material object used for
    presenting or saving information (I/O devices)
  • Code refers to a system of symbols (natural
    language, pictorial language, gestural language)

7
Relationship Between Medium, Mode, and Code
8
Whats Not Mentioned?
  • Human mediums
  • eyes, ears, skin, etc.

9
IUI Architecture
10
Current Interface Pratice Related to IUI
11
No Generation w/o Representation
  • Various constiuents of multimodal
    communication should be generated on the fly from
    a common representation of what is to be conveyed
    w/o using any preplanned text or images.

12
AIs Application to UIs
13
Whats Not Mentioned?
  • Affect
  • of course

14
IUIs Roots
  • Turing Test
  • Pattern matching from a conversational database
  • Intelligent Tutoring
  • Automated Interface Design
  • Natural Language Interfaces
  • Standard Reference Model (SRM) for Intelligent
    Multimodal Presentation Systems (IMMPS)
  • Email Filters
  • Bayesian-based user models

15
Major Topics Covered by Readings
  • Analysis of Input
  • Generation of Output
  • User and Discourse Models
  • Model-Based Interfaces
  • Agent-Based Interaction
  • Empirical Evaluation

16
Resources
  • http//www.mitre.org/resources/centers/it/maybury/
    iui99/sld001.htm
  • Intelligent User Interfaces An Introduction
  • http//cslu.cse.ogi.edu/HLTsurvey/
  • Survey of the State of the Art in Human Language
    Technology (1996)
  • http//search.nap.edu/readingroom/books/screen/
  • More Than Screen Deep Toward Every-Citizen
    Interfaces to the Nation's
  • Information Infrastructure

17
Resources
  • http//degraaff.org/hci/
  • HCI Index (news and a list of references)
  • http//www.etaij.org/
  • Electronic Transactions on AI
  • http//www.dfki.de/
  • The German Research Center for Artificial
    Intelligence
  • http//www.dfki.de/sigmedia/
  • Multi-Language Processing
  • http//www.i3net.org/
  • European Network for Intelligent Information
    Interfaces

18
User Modeling via Stereotypes
  • Elaine Rich
  • University of Texas

19
Why User Modeling?
  • People need to form a model of the person with
    whom they are dealing before they can behave
    properly.
  • Examples
  • PS2 Controller size, Auto Insurance Quote

20
User Modeling via Stereotypes
  • A stereotype is a cluster of characteristics
  • e.g.
  • Profession Stereotype
  • income level, dress code at work, degree, etc
  • Ethnic group stereotype
  • Food preference, spacial distance

21
Stereotypes Are Based on Domain
  • Entertainment Industry
  • caremovie, music preference, education level
  • dont dress, weight, height
  • Car Sales Industry
  • care income, family size
  • dont education level, religion

22
Stereotypes Are Based on Probability
  • How confident are we ?
  • Computer Science Professor - NON_TV 85
  • College Graduates - over 20 90
  • Probability may result in unfairness.

23
So, a Stereotype is a set of triples
  • (Attribute, Value, Rating)
  • Medical Doctor
  • (Income, 4, 900)
  • (Education, 5, 900)
  • (Afraid-of-Blood, -5, 900)
  • (Watch-TV, -3, 800)

24
A Stereotype Contains Triggers
  • A trigger is a hint that instantiate other
    stereotypes.
  • Medical Doctor
  • High_Education_Trigger -gt NonTV_StereoType
  • High_Income_Trigger -gt House_Owner
  • A trigger also has rating as confidence level.

25
Over View of Grundy the Librarian
  • Has pre-built stereotypes
  • Hierarchical memory
  • Global, individual, dialogue
  • Algorithm for activating stereotypes
  • Adaptation of stereotypes

26
Activating Stereotypes (by triggers)
  • If trigger already instantiated, do nothing, if
    not, instantiate it.
  • Example
  • Name John - Man_trigger (instantiate)
  • Father - Man_trigger (ignore)

27
Activating Stereotypes (by triggers)
  • If stereotype has not been activated before, it
    is activated now.
  • Name John -gt Man_trigger -gt Man_stereotype
  • If stereotype has been activated before and still
    active (confirmation)
  • Father -gt Man_trigger_Rating up -gt
    Man_Stereotype_Rating up

28
Activating Stereotypes (by triggers)
  • If stereotype has been activated before, but its
    not activated now. The situation must be
    re-examined on the basis of the balance of the
    evidence is in favor or opposed to the
    stereotype.
  • How to calculate the balance of evidence???
  • Whats the definition of in favor or oppose
    ?

29
Adaptation of stereotypes
  • Why?
  • Lack of real data at construction time results
    in the need of adaptation of stereotypes.
  • Concern
  • Prevent bias because of frequent usage by a few
    users, need weighted constant of the overall
    values.

30
Adaptation of stereotypes
  • Confirmation Case
  • increase VALUE
  • increase RATING
  • M.D. -gt (Income, 3, 800)
  • Drive Ferrari -gt (Income, 4, 900)
  • New_Value (3 100 4) / (100 1) 3.01 gt 3
  • New_Rating 800 900/800 801.125 gt 800

31
Adaptation of Stereotypes
  • Conflict case
  • decrease VALUE
  • decrease RATING
  • M.D. -gt (Non_TV_Person, 4, 800)
  • Subscribe to TV_Guide -gt (Non_TV_Person, -5,
    900)
  • New_Value (4 100 - 5) / (100 1) 3.91 lt 4
  • New_Rating 800 - 900/800 799.875 lt 800

32
Results of Grundy
Controlled Random
GOOD 102 54
BAD 39 60
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