Intelligence: Supporting Informal Communication,

boorishadamantAI and Robotics

Oct 29, 2013 (4 years and 12 days ago)

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Augmenting Groupware with
Intelligence:

Supporting Informal Communication,
Trust, and Persona Management




Joe Tullio


Dissertation Proposal

May 1, 2003

2

Overview

Introduction/Motivation

Informal communication and calendars

Intersection of CSCW, IUI, Ubiquitous computing

Thesis statement/Contributions

Related work

Proposed work:

Augmenting calendars with attendance predictions

Effects of augmented calendars on user attitudes and
behaviors

Strategies for managing persona in groupware


a taxonomy

Timeline for completion

3

Calendars as tools for informal
communication

Definitions: GCS, Informal communication

Studies:

Palen, L. (1999) "Social, Individual & Technological Issues for
Groupware Calendar Systems",
CHI'99
.

Grudin, J. and Palen, L. (1997) "Emerging Groupware Successes in
Major Corporations: Studies of Adoption and Adaptation",
WWCA'97.

“Calendar work” +


Locating colleagues


Assessing availability


Regulating privacy

4

CSCW framework
(Dix 1994)


Two people and a shared
artifact


People interact with one
another
and

with the
artifact


People even communicate
through

the artifact

5

Calendars in the CSCW
framework

Calendar as the shared
artifact

People communicate
informally

People maintain and
browse calendars

People communicate a
persona
through the
calendar

IUI/User modeling

Calendars can be inaccurate

Wrong recurrence boundaries

Conflicting events

Infrequently attended events

We have a basis for modeling

Domain knowledge

Attendance history

There is
uncertainty

in event attendance

Inherent error & misrepresentation

Bayesian networks

7

Ubicomp

Leverage mobile devices

Individual practices

Ubiquity of calendars

Calendar itself can be used as a
sensor

in
context
-
aware applications

8

At the intersection

Calendar is an artifact supporting informal
communication

Mobile, individual calendars can exhibit
inaccuracies

Inaccuracies can be mitigated with intelligent
assistance

Calendar representation can be seen as
persona

Persona


One’s representation through a shared
artifact

Intelligent systems can diminish control over
this persona

9

Proposed research

Build a prototype groupware calendar that
incorporates attendance prediction

Use this prototype to explore:

Feasibility of a Bayesian model

Effect on user communication practices

Effect on user attitudes toward adoption, trust

Develop a framework for persona management

Groupware augmented with intelligence

Focus on learning

10

Thesis statement

The GCS’s role as a tool for computer
-
supported
cooperative work can be better supported through the
application of predictive user models. These models can
improve it as a predictor of user activity and
consequently as a facilitator of informal communication.
I can validate this claim through an exploration of its
use in a real
-
world setting. I can then develop a
taxonomy of techniques for managing the persona
conveyed by such artifacts along dimensions of the
broader class of intelligent groupware applications.

11

Research contributions

Technological solution to the problem of
inaccurate calendars

Impacts many context
-
aware applications

Analysis of feasibility

Socio
-
technical effects of this solution

Identify changes in communication patterns

Examine user attitudes toward intelligent assistance

Persona management

Framework for designers and researchers

Ground intelligent groupware to the social needs of
the workplace

12

Related work:

Studying calendars

Aforementioned work by Palen, Grudin

Academic environment


Mitchell

Ubicomp systems with calendars:

Horvitz et al
-

Priorities

Tang et al


Awarenex

Marx et al
-

CLUES



13

Related work:

Intelligent groupware

These systems use different representations, UIs, and
learning algorithms…

Challenging in terms of evaluating their

Effects on communication practices

Influence on adoption

User trust, especially in early stages of learning

Horvitz et al
-

Coordinate

Begole et al
-

Rhythm Modeling

Ashbrook & Starner


GPS, Markov models

Hudson et al


Predicting availability with sensors


14

Related work:

Learning/Trust/Persona

Mechanisms to support trust

Some initiated by users, others implemented by designers

Assist in learning
and
to manage appearance

Plausible deniability

Can use impoverished information, system error to justify
absence

Maes


Agents for email, meeting scheduling

Tiernan/Czerwinski


Notification agents

Farnham


Social networks

De Angeli et al


Biometric verification


15

Overview

Introduction/Motivation

Informal communication and calendars

CSCW, IUI, Ubiquitous computing

Thesis statement/Contributions

Related work

Proposed work:

Augmenting calendars with attendance predictions

Effects of augmented calendars on user attitudes and
behaviors

Strategies for managing persona in groupware


a taxonomy

Timeline for completion

16

Augur: A probabilistic shared
calendar
(Goecks, Nguyen)

Calendars shared from personal mobile devices

Support individual practices

Probabilistic model predicts future attendance at co
-
scheduled events

Make the calendar a better predictor of activity for both
workgroups and context
-
aware applications

Visualize predictions in a browsable calendar

Awareness for informal communication

From Ambush: Support for “ambushing”

17

Representation: Bayesian
networks

Compact, descriptive representation of a
domain with uncertainty

Need domain knowledge, some structure

Capable of learning over time

Capable of generating explanations if
needed

Augur Bayesian network

Augur system

architecture

Augmented personal calendar

21

Results to date

Ambush: Stabilization on routine events

SVMs used to identify role, location, event type.

Event Type 80%

Location 82%

Role


more participants needed

Publications:

Tullio, J., Goecks, J., Mynatt, E., Nguyen, D.


Augmenting
Shared Personal Calendars.


UIST 2002.

Mynatt, E. and Tullio, J.


Inferring Calendar Event
Attendance.


IUI 2001.

22

Feasibility of Augur

Do predictions converge with actual
attendance over time?

What type(s) of events perform better?

Is the model’s structure appropriate?

Completeness

SVMs for event classification


23

User attitudes and behaviors

Study Augur’s ability to support informal communication

Study attitudes toward trust, adoption


Building on the work of corporate calendar studies at
Sun, Microsoft, Boeing and others

Also designing to the practices of our academic
environment

Ambushing

Personal (PDA
-
based) calendar practices

Noisy calendars

24

Evaluating attitudes/behaviors:

Proposed activities

Four deployment phases

1.
Preliminary (Summer/Early Fall 2003)


Initial attitudes and practices


Interviews

2.
Calendar deployment (Early Fall 2003)


Collecting training data


Let users become accustomed

3.
Intelligent calendar deployment (Late Fall 2003)


Investigate changes in attitudes/practices over time


Collect measures in accuracy

4.
Persona management (Spring 2004)


25

Participants

Study group

20 participants from several FCE labs

Both “readers” and “writers”

Some working closely, others infrequently

Periodic interviews before, during, after deployment

Larger pool of readers

Expecting advisees, students in courses to read

Log accesses to identify browsing patterns

Limit reading to school machines

26

Challenges

No existing GCS infrastructure

Ramp up by first using shared calendar without
predictive features

Must design for possibly several common tools

Dynamic schedules and personnel

Some learned patterns are incompatible with
changes in term/personnel

Attitude changes versus behavior changes

Opinions may change without measurable changes
in activity


27

Why FCE is promising

Open environment

Not subject to closed calendars at higher positions in
the hierarchy

Existing calendar habits

Personal, “noisy” calendars the norm

Individual calendars demonstrate need for intelligent
assistance

Abundance of events/activities

Ambushing

Seems to be a common practice

28

Method
-

Interviews

Augur as a communication tool (behavior):

How often did you check your calendar/others’
calendars? To what purpose, if any?

How often did you use the predictive features?

Did you change your calendaring habits?

Trust in intelligent assistance (attitude):

How accurate were the predictions for others?

Did they seem to improve or degrade?

Were you represented accurately?

Did you attempt to change your own predictions?

29

Observing behavior

Log calendar accesses

Browsing up/down the hierarchy

Confidentiality

Semi
-
controlled situations

Present interviewees with task using the
calendar, see how they would accomplish it

Control for same calendar information

Think
-
aloud

30

Success metrics

System sees use

Neglect over the first weeks may necessitate
new incentives (or setting)

Changes in behavior observed through
logs and interviews

Changes in attitude evidenced through
interviews

31

Persona management

Persona


One’s representation
through a shared artifact

Management implies
negotiation with the system

Common property of groupware
in general

Complicated by intelligence

Important in early stages of
learning

32

Management strategies

33

User dimensions

34

Application dimensions

35

Method

Populate framework with existing systems

In particular, look for examples of
complex, intelligent groupware

Augur system: support persona
management using framework

Deploy after term change to explore use

Mitigate misrepresentation from error

36

Success metrics

Dimensions of framework

Does the body of intelligent groupware divide this
way?

Is the problem much more complex?

Utility

Does the taxonomy identify new research areas?

Does it provide design guidance?

Are Augur users able to successfully manage their
representation through the shared calendar?

Interviews from previous phase


37

Timeline

Fall/Spring 2000:

Development/Test of Ambush system (published at IUI 2001)

Fall/Spring 2001:

Development/Test of Augur system (published at UIST 2002)

Summer 2003:

Prepare Augur for deployment, develop interview questions, solicit participants

Literature review/development of framework

Fall 2003:

Deployment/Evaluation of Augur

Begin design/implementation of persona management for Augur

Spring 2004:

Re
-
deploy Augur with persona management features

Take advantage of schedule change

Summer 2004:

Writing and defense

Acknowledgments




Thanks to Elaine Huang, Jeremy Goecks, David Nguyen,
my committee, the Everyday Computing Lab, and
everyone else who discussed or critiqued this work with
me.


Thanks also to the National Science Foundation

CAREER Award #0092971.