Common Sense Computing MIT Media Lab Providing Expert Advice by Analogy for On-Line Help Henry Lieberman and Ashwani Kumar Media Laboratory Massachusetts

Common Sense Computing • MIT Media Lab

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/


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


How do you get help?

Look up things in problem-solution database

Telephone support

On-line chat


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.


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


Problem/Solution Database


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 don’t share vocabulary and knowledge, it’s difficult for the novice to interact directly with the Expert Model


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”?


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


Open Mind - Push Singh


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!


Problem/Solution Database

Problem: “My browser can see my company’s home page, but it won’t let me access my workgroup’s 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.


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.


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


SuggestDesk


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


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.


SuggestDesk’s 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.


SuggestDesk’s 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 can’t do things as fast as you normally would

An antivirus program is like medicine.


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).


Natural Language Processing

Via MontyLingua, Common Sense part-of-speech tagger

Result= [{prep_phrases_tagged=[], verb_phrases_tagged=[is/VBZ running/VBG], verb_arg_structures_concise=[("run" "browser" "slow")],noun_phrases=[browser], noun_phrases_tagged=[browser/NN],adj_phrases_tagged=[slow/JJ], verb_arg_structures=[[is/VBZrunning/VBG, browser/NN, [slow/JJ]]], modifiers_tagged=[slow/JJ],prep_phrases=[], verb_phrases=[is running],parameterized_predicates=[[[run, [past_tense, passive_voice]],[browser, []], [slow, []]]], modifiers=[slow], adj_phrases=[slow]}]


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')


Analogy Mapping Engine

Analogies:[[computer, [[UsedFor, surf internet, 1.1887218755408673],[CapableOfReceivingAction, run slow, 1.1887218755408673],[CapableOfReceivingAction, crash, 1.1887218755408673],[CapableOfReceivingAction, start, 1.1887218755408673]],6.1887218755408675], [car, [[CapableOfReceivingAction, damage,1.1887218755408673], [CapableOfReceivingAction, crash,1.1887218755408673], [CapableOfReceivingAction, start,1.1887218755408673]], 5.930167946706389], [software,[[CapableOfReceivingAction, run slow, 1.1887218755408673],[CapableOfReceivingAction, crash, 1.1887218755408673],[CapableOfReceivingAction, install, 1.1887218755408673],[CapableOfReceivingAction, install, 1.1887218755408673]],5.855516191543203]]


Weighting

log(f+0.5*i+4),

where f=outgoing edges and i = incoming edges


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).


User Testing


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


Woodstein


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

Don’t worry, help is on the way!

Documents

Common Sense Computing MIT Media Lab Providing Expert Advice by Analogy for On-Line Help Henry Lieberman and Ashwani Kumar Media Laboratory Massachusetts