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What Users Want. Daniel Weld University of Washington. Variation. 4x. 625x. Two Interface Trends . Usage. Need Increased Customization Beyond changing buttons on the toolbar Overriding inappropriate adaptation High-level functionality Programming by demonstration.

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what users want

What Users Want

Daniel Weld

University of Washington

two interface trends
Variation

4x

625x

Two Interface Trends

Usage

Daniel S. Weld / Univ. Washington

steelcase inspired software
Need Increased Customization
      • Beyond changing buttons on the toolbar
    • Overriding inappropriate adaptation
    • High-level functionality
    • Programming by demonstration
“Steelcase-Inspired Software”

-- David Gelernter

  • One Size Fits All
  • Need Increased Adaptivity
      • Beyond inconsistent defaulting
    • Adapt to available devices, connectivity, …
    • Adapt to user location
    • Adapt to user tasks & goals
    • Adapt to user calendar & current time

(Some overlap with Contextual Computing)

Daniel S. Weld / Univ. Washington

adaptivity customization
Adaptivity & Customization
  • Deep Deployment ~ OS layer
  • Consistent Across Applications
    • Adaptation in every ‘dialog’
  • Bridging Applications
    • Data gathering
    • Transformations

Daniel S. Weld / Univ. Washington

outline
High-level

Customization

Pure

Adaptation

Outline
  • Motivation
  • Deliberative Software Agents
  • Programming by Demonstration
  • Adaptive Websites
  • Adaptive User Interfaces

Daniel S. Weld / Univ. Washington

genesis internet softbots
Genesis: Internet Softbots

[Etzioni & Weld CACM 94]

  • Interface Principles:
  • Goal-Oriented
  • Integrated
  • Balanced
  • Safe
  • User says what she wants
    • Agent determines how & whento achieve it
  •  planning-basedsoftbots

Single, expressive & uniform interface

E.g., printing files vs.

FTPing them

DT-lite: “Softbot must balance the cost of finding information on its own with the nuisance of asking the user.”

Asimov’s first law…

Safety, tidiness & thriftiness

constraints in planner

Daniel S. Weld / Univ. Washington

uw softbot family tree
Softbot Project Observations

Rodney

BargainFinder

ILA

Simon

MetaCrawler

Grouper

Ahoy

Info Manifold

  • Natural language interfaces
    • Popescu et al.
    • Yates et al.
  • Programming by demonstration

ShopBot

Occam

Wrapper Induction

}

LSD

Wed 10:15

Razor

Mulder

Tukwila

GLUE

Piazza

Gplex

UW Softbot Family Tree
  • Specifying goals is bottleneck
    • Logical spec. language [email protected]#!
    • Forms interface
      • Limited to anticipated use
      • Not scalable
    • Often easier to just do task!
      • Except: data integration tasks

Daniel S. Weld / Univ. Washington

outline8
High-level

Customization

Pure

Adaptation

Outline
  • Motivation
  • Deliberative Software Agents
  • Programming by Demonstration
  • Adaptive Websites
  • Adaptive User Interfaces

Daniel S. Weld / Univ. Washington

programming by demonstration
Programming by Demonstration

[Lau & Weld IUI-99]

[Wolfman et al. IUI-01]

  • If it’s too hard for users to specify goals
  • Let’s watch them…
    • And try to help
    • Like plan recognition
  • Initial domains:
    • Email
    • Text editing
    • Cross domain

[Lau et al. ICML-01]

Daniel S. Weld / Univ. Washington

gentle slope systems
Gentle-Slope Systems

C

Click’nCreate

(Multimedia Fusion)

VBasic

Hypercard

MFC

C

C Plugins

kCmds

Difficulty

of use

Hypertalk

Ideal

Basic

Sophistication of what can be created

Adapted from Myers et al.

Daniel S. Weld / Univ. Washington

objectives
VBasic

Turing Cplt

Robust

Programing

None

Previous PBD

Little

Varying

Varying

Lots

SmartEdit

SmartPython

On / Off

Loops,Cond

Robust

Minimal

Objectives

Demand on user

Expressiveness

Domain engr

Robustness

Macro Record

None

On / Off

Straight Ln

Brittle

Daniel S. Weld / Univ. Washington

smartedit 1
SmartEdit 1

[Lau et al. ICML-01]

Daniel S. Weld / Univ. Washington

slide13
2

Daniel S. Weld / Univ. Washington

slide14
3

Daniel S. Weld / Univ. Washington

slide15
4

Daniel S. Weld / Univ. Washington

slide16
5

Daniel S. Weld / Univ. Washington

slide17
6

Daniel S. Weld / Univ. Washington

pbd as a learning problem
Move to next

PBD as a learning problem
  • Action is function : input state ® output state
    • Editor state: text buffer, cursor position, etc.
    • Actions: move, select, delete, insert, cut, paste,…
  • Given a state sequence, infer actions
    • Many actions may be consistent with one example
  • Challenge: Weak bias + low sample complexity

Daniel S. Weld / Univ. Washington

version space algebra
Version Space Algebra
  • Version space = set of complex functions
  • Define version space hierarchically
    • Combine simpler version spaces with algebraic operators
      • Union È: analogous to set union
      • Join : cross product with consistency predicate
      • Transform: convert functions to different types
    • Can factor a version space

Daniel S. Weld / Univ. Washington

smartedit s version space

SMARTedit's version space

Daniel S. Weld / Univ. Washington

fast consistency
Action

Move

Paste

Insert

Copy

Cut

Select

Delete

Fast Consistency
  • Test consistency of example against entire version space
  • Quickly prune subtrees
  • Innovations:
    • Independent join allows BSR representation

Daniel S. Weld / Univ. Washington

preliminary user study
Preliminary User Study
  • 6 undergrad CS majors
  • 7 repetitive tasks with & later w/out SMARTedit
  • Tasks: 4 to 27 iterations, 1-5 min to complete
  • Evaluation metrics:
    • Time saved completing task with SMARTedit's help
    • % user actions (keyboard + mouse) saved
    • User feedback

Daniel S. Weld / Univ. Washington

time saved using smartedit
Time saved using SMARTedit

Time (sec)

Cost Saved

Six

Users

X X

Task Number

Daniel S. Weld / Univ. Washington

action savings with smartedit
Action Savings with SMARTedit

Six

Users

Percent of Actions

Cost Saved

XXXX

X X

Task Number

Daniel S. Weld / Univ. Washington

observations from pbd
Observations from PBD
  • Overhead of macro recorder UI is high
    • Most repetitive tasks short
    • How many shell scripts do you write / day?
  • Focus on pure adaptivity
    • E.g., automatic segmentation

Daniel S. Weld / Univ. Washington

outline26
High-level

Customization

Pure

Adaptation

Outline
  • Motivation
  • Deliberative Software Agents
  • Programming by Demonstration
  • Adaptive Websites
  • Adaptive User Interfaces

Daniel S. Weld / Univ. Washington

early adaptation mitchell maes
Early Adaptation: Mitchell,Maes
  • Predict: Email message priorities

Meeting locations, durations

  • Principle 1: Defaults minimize cost of errors
  • Principle 2: Allow users to adjust thresholds

Daniel S. Weld / Univ. Washington

adaptation in lookout horvitz
Adaptation in Lookout: Horvitz

Adapted from Horvitz

Daniel S. Weld / Univ. Washington

resulting principles
Resulting Principles

[Horvitz CHI-99]

  • Decision-Theoretic Framework
    • Graceful degradation of service precision
    • Use dialogs to disambiguate

(Considering cost of user time, attention)

Adapted from Horvitz

Daniel S. Weld / Univ. Washington

principles about invocation
Principles About Invocation
  • Allow efficient invocation & dismissal
  • Timeouts minimize cost of prediction errors

Daniel S. Weld / Univ. Washington

adapting to small screens
Adapting to Small Screens

Daniel S. Weld / Univ. Washington

web site adaptation in proteus
Visitor

Proteus

Web server

Web Site Adaptation in Proteus

[Anderson et al. WWW-01]

  • Architecture
  • Personalizing in two steps:

1. Learn model of visitor from access logs

    • Transform content per learned model
  • Hill-climbing thru space of websites
    • Transforms: shortcuts & elision
    • Decision-theoretic guidance

Daniel S. Weld / Univ. Washington

guiding the search
Guiding the Search
  • Expected utility based on model of visitor
    • Model learned by mining server access logs
  • Sum value of each screen of each page
  • Discount by difficulty of reaching screen from p
    • Depends on how manylinks followed and howmuch scrolling required

= p

Daniel S. Weld / Univ. Washington

proteus empirical study
Proteus Empirical Study
  • Observe real users on the desktop
    • Info-seeking goals drawn from random distribution
  • Personalize based on observations
  • Measure performance on mobile device
    • Number of links and scrolls, amount of time
    • Compare unmodified and personalized sites
      • Half users did unmodified first, others vice versa

Daniel S. Weld / Univ. Washington

average number links followed
Average number links followed

Daniel S. Weld / Univ. Washington

analysis of proteus
Analysis of Proteus
  • Why Proteus worked well
    • Suggested useful shortcuts
    • Elided mostly unnecessary content
  • Why Proteus worked poorly
    • Sometimes elided useful content
    • Users didn’t find shortcut, tho it existed
    • Flaws with implementation more than concept

Daniel S. Weld / Univ. Washington

principles
Principles
  • Saliency of new UI operations is crucial
    • How name shortcuts?
  • Eliminating features is dangerous
  • Must partition dynamicity
    • Maintain separate dynamic & static “areas”
    • Always allow previous navigational methods
    • Duplicate functionality if necessary
  • Accurate prediction also crucial

Daniel S. Weld / Univ. Washington

partitioned dynamism
Partitioned Dynamism

Daniel S. Weld / Univ. Washington

partitioned dynamism39
Partitioned Dynamism

Daniel S. Weld / Univ. Washington

partitioning failure
Partitioning Failure

Daniel S. Weld / Univ. Washington

principles41
Principles
  • Must partition dynamicity
  • Accurate prediction also crucial

Daniel S. Weld / Univ. Washington

predicting user behavior
Predicting User Behavior

[Anderson et al. IJCAI-01]

  • Model as Sequential Process
  • Markov Models
  • Mixtures of Markov Models
  • Second-Order…
  • Conditioning on Position in Trace
  • Etc.

# times sd was followed

P(sd) =

Total # visits to s

Daniel S. Weld / Univ. Washington

weakness of markov models
Weakness of Markov Models
  • Each state is trained independently
    • Abundant training data at one state cannot improve prediction at another state
    • Large state models require vast training data
  • Problematic since Web trace data is sparse
    • A single visitor views ~0% of any site
    • New & dynamic content not in training data

Daniel S. Weld / Univ. Washington

reasoning about uncertainty
DPRM

RMM

Reasoning about Uncertainty

PRM

Bayes Net

DBN

Structure

Relational

Sequence

MM

Daniel S. Weld / Univ. Washington

relational markov models
Relational Markov Models

[Anderson et al. KDD02]

  • Domains often contain relational structure
    • Each state is a tuple in relational DB sense
  • Structure enables state generalization
  • Which allows learning from sparse data

ProductPage

ProductName

StockLevel

Apple_iMac

in_stock

Palm_m505

backorder

Daniel S. Weld / Univ. Washington

defn markov model
D: a set of hierarchical domains Defn: Markov Model

Relational

  • Q: set of states
    • Pages in a web site
  • Each state ~ a relation

ProductPage(Apple_iMac, in_stock)

  • p: init prob distribution
  • A: transition probability matrix
  • R: a set of relations

Daniel S. Weld / Univ. Washington

domain hierarchies
AllProducts

AllComputers

AllPDAs

AllDesktops

AppleDesktops

iMac

Domain Hierarchies

ProductName

Instance of relation

with leaf values is a

state, e.g.

ProductPage(iMac, in_stock)

Daniel S. Weld / Univ. Washington

domain hierarchies48
AllProducts

AllComputers

AllPDAs

AllDesktops

AppleDesktops

iMac

Domain Hierarchies

ProductName

Instance of relation with

non-leaf values is a set of

states: an abstraction, e.g.

ProductPage(AllComputers, in_stock)

Daniel S. Weld / Univ. Washington

e commerce site markov ver
RMM

MainEntryPage()

ProductPage(AllProducts,

AllStockLevels)

CheckoutPage()

ProductPage(AllProducts,

backorder)

ProductPage(AllProducts,

instock)

E-commerce Site: Markov Ver.

m505_backorder.html

main.html

checkout.html

iMac_instock.html

dell4100_instock.html

Daniel S. Weld / Univ. Washington

rmm generalization

s

d

s

d

RMM generalization
  • Want to estimate P(sd) … but no data!
    • Use shrinkage
  • Can do this with abstractions of d and s
    • Let  be an abstraction of s and  of d

?

Daniel S. Weld / Univ. Washington

calculating shrinkage weights
Calculating Shrinkage Weights
  • Intuitively, the lab should be large when
    • Abstractions are more specific
    • Training data is abundant
  • Three methods for assigning weights
    • Uniform
    • Heuristic (Based on lattice depth and number of examples)
    • EM (Data intensive)

Daniel S. Weld / Univ. Washington

gazelle
Gazelle

Daniel S. Weld / Univ. Washington

outline53
High-level

Customization

Pure

Adaptation

Outline
  • Motivation
  • Deliberative Software Agents
  • Programming by Demonstration
  • Adaptive Websites
  • Adaptive User Interfaces

Daniel S. Weld / Univ. Washington

the google generation
The Google Generation
  • Most WWW traces very short
    • Can’t beat |trace| = 2
  • Not true in desktop apps

Daniel S. Weld / Univ. Washington

striving for duplex
Striving for Duplex

Daniel S. Weld / Univ. Washington

still striving for duplex
Still Striving for Duplex

Daniel S. Weld / Univ. Washington

finally
Finally!

Daniel S. Weld / Univ. Washington

confirm twice
Confirm (Twice!)

Daniel S. Weld / Univ. Washington

state machine partial
State machine (partial)

Six clicks required!

Daniel S. Weld / Univ. Washington

remember partitioned dynamicity
Remember: Partitioned Dynamicity
  • Why Proteus worked poorly
    • Users didn’t find shortcut, tho it existed
  • Saliency of new UI operations is crucial
  • Must partition dynamicity
    • Maintain separate dynamic & static “areas”
    • Duplicate functionality

Daniel S. Weld / Univ. Washington

with controlled adaptation
With Controlled Adaptation

Maintain

Stable

Navigation

Optimize

For

User

Behavior

Daniel S. Weld / Univ. Washington

or rather
Or Rather…

Curry to Boolean

Daniel S. Weld / Univ. Washington

future work
Future Work
  • Conceptual user study
    • What do users want?
  • Interface description language
    • Enhance Pebbles representation?
  • Transformation algorithms
  • Implementation & experiments

Daniel S. Weld / Univ. Washington

conclusion
Conclusion

High-level

Customization

  • Goal-oriented softbots
  • Programming by demonstration
  • Adaptive interfaces / websites

Pure

Adaptation

  • Principles
    • Partitioned Dynamicity
  • Techniques
    • Version-Space Algebra
    • Relational Markov Models

Daniel S. Weld / Univ. Washington

acknowledgements
Acknowledgements
  • Corin Anderson
  • Oren Etzioni
  • Pedro Domingos
  • Keith Golden
  • Cody Kwok
  • Tessa Lau
  • UW AI Group
  • NSF, ONR, NASA, DARPA

Daniel S. Weld / Univ. Washington

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