IBM Research June 14, 2007 An IP Continuum for Adaptive Interface Design Jeff Pierce [email protected] © 2007 IBM Corporation

IBM Research

June 14, 2007

An IP Continuum forAdaptive Interface Design

Jeff [email protected]

© 2007 IBM Corporation

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IBM Research

An IP Continuum for Adaptive Interface Design | Jeff Pierce © 2007 IBM Corporation

Two preliminary comments

Unindicted co-conspirator: Charles Isbell– Assistant Professor in College of Computing @ Georgia Tech

– AI researcher, primarily machine learning

– Co-director of APIE lab

– Adaptive Personalized Information Environments

Questions, few answers– Primarily work done at Georgia Tech

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Design of Adaptive (“Intelligent”) Interfaces

Common goal: “right” information / action at the “right” time in the “right” way

Traditional approaches:– Provide people with access to information / commands

– Use AI to present information / act automatically

Challenges:– People

– AI

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People are…

Forgetful Easily distracted Idiosyncratic Inconsistent Short-sighted

… but still surprisingly good at many tasks

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Artificial Intelligence is…

Short on intelligence Easily mistaken Slow to learn Limited by available information Dependent on feedback

…but surprisingly good at finding patterns given sufficient data

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Challenge

Draw on the strengths of people and AI while mitigating their weaknesses

– Combine HCI + Machine Learning

Obvious questions: how?

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One answer

Mixed-initiative user interfaces– No Action – Dialog – Action

Horvitz ’99

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Example Case: Helping users make better decisions in the morning

Hunt ‘02Wesveen et al ‘01

Approach: next-generation alarm clock No Action – Dialog – Action doesn’t work particularly

well here

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An Alternative: The IP Continuum

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Night Before

Decisions: alarm time, what to wear Displays unusual: schedule, weather

Unusual Usual

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During Alarm

Decisions: when to get up Displays unusual: schedule, weather, traffic

Unusual Usual

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After Alarm

Decisions: what to wear, when to leave Displays unusual: schedule, weather, traffic

Unusual Usual

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IP Continuum

Who is responsible for accomplishing a particular task?

HCI AIAdaptive / Intelligent User Interfaces

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IP Continuum

Particular labels (content and position) not important; relative responsibility is

Tool for inspiring and comparing alternative designs

Not really intended to be prescriptive (at least not yet)

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IP Continuum

Associated metrics– Benefit to user (when right)

– Cost to user (when wrong)

– Attention requirement

– Likelihood of success

– Frequency of action

– Quantity and quality of data required

– Quantity and quality of feedback required

– Cost of gathering data

– Resource costs (e.g. storage, processing)

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IP Continuum

Benefit to user (when right)vs.

Cost to user (when wrong)

No benefit,no cost

Much benefit,much cost

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IP Continuum

– Attention requirement

– Likelihood of success

– Frequency of (user) action

Higher Lower

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IP Continuum

– Quantity and quality of data required

– Quantity and quality of feedback required

– Cost of gathering data

– Resource costs (e.g. storage, processing)

Lower Higher

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Deployed examples: simple assistance

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Deployed examples: independent action

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Deployed example: act with recovery

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Research project examples

Alarm ClockNext-generation calendarEmail attachments reminderNext-generation remoteSWIMM: personalized “radio stations”

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Next-Generation Calendar

Draw attention to infrequent, uncommon meetings

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Attachments Reminder

Help user remember to add attachments

Problem: 85% accuracy

Small study: 19 participants

Most popular:

Highlight attach button (D)

Two-step send (E)

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Next-Generation Remote

Allow a single remoteto control multiple devices

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Next-Generation Remote

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SWIMM

Provides, in essence, personal radio stations

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Some open questions

What determines where an application fits?– Where a particular feature of an application fits?

How much variation is there between users?– Spam filters

How much variation is there over time?– In most cases probably shift from left to right, but how much?

Can we be more rigorous about the relative positions of potential interface designs?

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Comment: care and feeding of learning systems

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Explicit vs. implicit feedback

Explicit– Intentional, high quality

– Costly (to user), low quantity

Implicit– Side effect, low(er) quality

– Free, high quantity

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Designing interfaces for feedback

Alternate focus for interface design– Instead of “usability”, “efficiency”

Maximize the quality and quantity of feedback you extract

– Use feedback to drive improvements in usability, efficiency

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SWIMM

Explicit– Never

– Not now

– Not here

– Rating

Implicit– Song completion

– Song reordering

– Change volume

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Current work: Personal Information Environments

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Personal Information Environments

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Examples Services

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Information, commands, activities

How can your devices collaborate to improve your interaction experience?

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Conclusions & Future Work

The IP continuum is a tool to inform / guide the design of adaptive / intelligent UIs

Possible next steps:– Amass experience from a variety of application domains

– Distill design principles

– How to guide choice of region for particular application– …and possibly user(s)

– Quantify trade-offs along continuum (?)

– Explore approaches to designing for feedback

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Questions?

Documents

IBM Research June 14, 2007 An IP Continuum for Adaptive Interface Design Jeff Pierce [email protected] © 2007 IBM Corporation