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Providing Expert Advice by Analogy for OnLine Help

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Title: Providing Expert Advice by Analogy for OnLine Help


1
Providing Expert Advice by Analogy for On-Line
Help
  • Henry Lieberman and Ashwani Kumar
  • Media Laboratory
  • Massachusetts Institute of Technology
  • Cambridge, MA, USA
  • http//www.media.mit.edu/lieber/

2
What happens when interaction with the Web goes
wrong?
  • Increasingly, people use the Web to perform
    procedures (buy things, vote, date, seek jobs) as
    well as just browse
  • What happens when things go wrong?
  • Debug it yourself
  • Get help
  • Increasingly, confidence in Web interactions
    depends on effectiveness of help

3
How do you get help?
  • Look up things in problem-solution database
  • Telephone support
  • On-line chat

4
Elicititation Explanation
  • Elicitation Helper asks user what is wrong. Get
    enough information from user to determine problem
    and choose solution.
  • Explanation Helper tells user how to fix the
    problem. Explain why the solution worked, and
    how to avoid such problems in the future.

5
Mismatch between expert and novice models
  • The helper is an expert the user is a novice
  • Novice may lack technical vocabulary to
    understand the elicitation questions
  • Novice may lack background knowledge to
    understand explanation of soultion
  • Expert may be unable to empathize with novice

6
Problem/Solution Database
7
AI Expert Systems
  • Expert Systems are the traditional way to encode
    expert behavior
  • Knowledge Engineer experts to get their knowledge
    and procedures
  • Encode Expert Model
  • Deep but narrow knowledge
  • If expert and novice dont share vocabulary and
    knowledge, its difficult for the novice to
    interact directly with the Expert Model

8
We need a model of novice knowledge
  • How do we model the knowledge of someone who is
    not supposed to know very much?
  • Traditionally, novice is modeled as subset of
    expert knowledge
  • Intelligent Tutoring Systems, Qualitative
    Physics
  • But how do you model novice knowledge in
    general?

9
Open Mind - Push Singh
  • Common Sense statements collected from volunteer
    contributors on the Web
  • 770,000 English sentences
  • Conventional data/knowledge bases Do a lot of
    organizing work up front, so you can get the
    stuff out easily
  • OM Get the stuff in any which way, then do a lot
    of work on the back end to interpret it
  • Query expansion, Parser, Semantic net miner

10
Open Mind - Push Singh
11
How do human helpers succeed in bridging
Expert/Novice gap?
  • We interviewed helpers on the America Online
    (internet service provider) help desk
  • Helpers tend to provide cookbook solutions
  • We asked for successful interactions where users
    came to understand the problem and solution
  • Helpers best explained things by making analogies
    to everyday life situations!

12
Problem/Solution Database
  • Problem My browser can see my companys home
    page, but it wont let me access my workgroups
    Web site
  • Solution Procedure Check if cipher strength is
    '0. Upgrade Browser to 128 bit Encryption.
  • Explanation Generally, Websites require 128-bit
    encryption in order to process information
    securely. If the cipher strength of your browser
    is inadequate, you will not get into secure
    Websites. Upgrading your browser's encryption may
    help it better handle secure Websites.
  • NOTE You only need to do this when unable to get
    to secure Websites.

13
Better Explain by Analogy
  • Encryption in a browser is like security
    clearance to enter a building. If you don't have
    the proper security clearance, you may be able to
    get into the building, but not into certain
    areas. You must upgrade your security clearance
    status to go further. So without the proper
    encryption, your browser may be able to access a
    website, but not log in.

14
Introducing SuggestDesk
  • SuggestDesk is an agent that listens to the
    helper/user chat
  • Provides suggestions to helper (unseen to novice
    user)
  • Tries to recognize opportunities for making
    analogies between problem/solution DB and Common
    Sense knowledge

15
SuggestDesk
16
Example
  • User My browser runs slowly
  • Problem/Solution DB
  • Browser might run slowly because of network
    congestion
  • Browser might run slowly because the browser is
    infected by a virus

17
Elicitation Explanation
  • Elicitation
  • (Traffic) When did you try to log in?
  • (Virus) Have you downloaded any new applications
    lately?
  • Solution
  • (Traffic) Try again at a later time.
  • (Virus) Run an anti-virus program.

18
SuggestDesks help (Traffic)
  • What makes things slow down?
  • Traffic runs slowly at rush hour.
  • Analogy
  • The AOL service is like a road.
  • The users are like cars.
  • If too many users try to use the service at the
    same time, it slows down.
  • The solution is to try at a time when things are
    less crowded or find an alternative route.

19
SuggestDesks help (Virus)
  • What makes things slow down?
  • People slow down when they are tired.
  • Being sick can make you tired.
  • Analogy
  • A computer virus is like a biological virus
  • If you have the flu, you cant do things as fast
    as you normally would
  • An antivirus program is like medicine.

20
SuggestDesk Architecture
  • The Natural Language Processor (NLP),
  • The Commonsense Processor (CP),
  • The Expert Analyzer (EA),
  • The Analogy Mapping Engine (AME), and
  • The Elicitation and Explanation Processor (EEP).

21
Natural Language Processing
  • Via MontyLingua, Common Sense part-of-speech
    tagger

Result prep_phrases_tagged,
verb_phrases_taggedis/VBZ running/VBG,
verb_arg_structures_concise("run" "browser"
"slow"), noun_phrasesbrowser,
noun_phrases_taggedbrowser/NN, adj_phrases_tagg
edslow/JJ, verb_arg_structuresis/VBZ running
/VBG, browser/NN, slow/JJ, modifiers_taggeds
low/JJ, prep_phrases, verb_phrasesis
running, parameterized_predicatesrun,
past_tense, passive_voice, browser, ,
slow, , modifiersslow,
adj_phrasesslow
22
ExpertNet
(EffectOf 'surf internet' 'download
files') (EffectOf 'surf internet' 'download
applications') (EffectOf 'download files'
'browser cache is large') (EffectOf 'download
applications' 'browser infected by
virus') (EffectOf 'PC infected by virus'
'browser run slow')
23
Analogy Mapping Engine
Analogiescomputer, UsedFor, surf internet,
1.1887218755408673, CapableOfReceivingAction,
run slow, 1.1887218755408673, CapableOfReceiving
Action, crash, 1.1887218755408673, CapableOfRece
ivingAction, start, 1.1887218755408673, 6.188721
8755408675, car, CapableOfReceivingAction,
damage, 1.1887218755408673, CapableOfReceivingAc
tion, crash, 1.1887218755408673,
CapableOfReceivingAction, start, 1.18872187554086
73, 5.930167946706389, software, CapableOfRe
ceivingAction, run slow, 1.1887218755408673, Cap
ableOfReceivingAction, crash, 1.1887218755408673,
CapableOfReceivingAction, install,
1.1887218755408673, CapableOfReceivingAction,
install, 1.1887218755408673, 5.855516191543203
24
Weighting
  • log(f0.5i4),
  • where foutgoing edges and i incoming edges

25
SuggestDesk Architecture
  • The Natural Language Processor (NLP),
  • The Commonsense Processor (CP),
  • The Expert Analyzer (EA),
  • The Analogy Mapping Engine (AME), and
  • The Elicitation and Explanation Processor (EEP).

26
User Testing
27
Woodstein (with Earl Wagner)
  • Debugger for Web procedures
  • By analogy to debugger for programming
  • Provides visualization, explanation, tools for
    incremental exploration
  • Self-help debugging
  • Co-operative debugging between expert and novice

28
Woodstein
29
Conclusion
  • The effectiveness of online help is key to the
    success of Web interactions
  • Help needs to bridge the gap between expert and
    novice knowledge
  • Common Sense Reasoning can help find analogies
    that allow expert and novice to communicate
  • We need debuggers that help us systematically
    explore the causes of problems
  • Dont worry, help is on the way!
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