Smart Home Application

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Smart Home Application

• Intelligent TV Viewing
• Vince Guerin

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Glorified House Controller

• NSF funded research project on Smart Home
  technologies
• UTA / TCU Smart Home Project
• - Glorified House Controller (GHC), a remote
  control system, will be able to operate any
  electronic device in a home. It will also be
  able to change the status of different
  appliances, save settings of all devices for a
  quick change, and have the ability to learn
  television viewing habits.

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Smart TV Recommender Goal

• Intelligent program(s) will predict, according
  to a persons likes and dislikes, whether it
  should record a television program or not.
• This will be similar to what Amazon.com does
  for books.

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TV Recommender

• Product must be
• Accurate
• Easy to use
• Able to build trust in the recommendations
  delivered

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Agenda

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Data Needs

• Learning Algorithms

• http//tvlistings2.zap2it.com
• - Online TV Guide
• - Current online guides
• lack info needed for some
• learning methods
• (keywords, etc)

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TV Recommender Two Ways to Learn

• Program reads various keywords inputted by the
  user (such as comedy, horses, horror,
  etc..). Program then picks out television shows
  that contain those words in the description
• Program monitors how often the user watches
  certain types of shows decides based on past
  viewings.

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AI Project Keyword Matching
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Other types of Keywords
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Scenario 1

• 1 User watches at least 2 hours of TV per
  night.
• 2 Program monitors viewing and gathers keywords
  and names of programs most watched.
• 3 After 3 weeks of viewing, user takes vacation
  and turns on program to record shows most
  watched.
• 4 User returns from vacation and views recorded
  shows.

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Scenario 2

• 1 User watches at least 2 hours of TV per
  night.
• 2 Program monitors viewing and gathers keywords
  and names of programs most watched.
• 3 After 3 weeks of viewing, user takes vacation
  and turns on program to record shows most
  watched, as well as programs he/she might enjoy.
• 4 User returns from vacation and views recorded
  shows.

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Scenario 3

• User manually inputs keywords, channels, and
  television programs to guide the system as to
  which programs to record.
• User lets system run all day.
• According to specifications, system records
  appropriate programs.
• User returns and watches pre-selected viewing
  material.

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Java Expert System Shell (JESS)

• What is JESS?
• - Java rule based expert system from Sandia
  National Laboratories (http//herzberg.ca.sandia.g
  ov/jess)
• - Stores rules and facts
• - Ability to reason given rules, and assert
  actions based on facts
• - Similar to a relational database

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JESS Cont

• Why is JESS important to Smart Home
  Technologies?
• Continuously changing data
• Unambiguous language to represent rules
• References and method invocations of Java object
• Seamless interaction between rule evaluation and
  framework

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JESS in Action KM Project

• 2 Phases Rank Record
• Rank
• rules with decreasing salience fire, with each
  rule looking for something different each time
• The highest salience rules fire first, and they
  assign the highest rankings based on the criteria
  for which they check
• When none of those rules can fire any more, then
  the phase change rule fires and changes the phase
  from ranking shows to recording shows

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JESS in Action cont

• Record Phase
• Iterates through the rankings in the same fashion
  (using decreasing salience)
• It will keep recording shows with decreasing
  rank, so long as there isn't a time conflict, and
  there is enough tape left.

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Phillips USA solution

• Kaushal Kurapatis ideas for capturing
  preferences
• Using stereotypes from which the user can
  choose (clusters of TV shows that are similar to
  one another)
• Create a user Profile according to the
  stereotypes

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Phillips USA solution cont

• Calculating the distance between networks/shows
• Example

Calculating the distance between FOX and NBC
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Cont

• Computing Distances

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Cont

• Deriving Stereotypes from Clustering Algorithm

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Phillips Solution conclusion

• Tested in Manor, New York area on 10 users
• Users contributed TV viewing histories for
  periods ranging from 5 months to 2 years
• Average initial error rate was around 40
• (best was 30, worst was 62.6)
• Need to improve out-of-box error rates
• Future work deeper pool of user data

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Summary

• 2 solutions presented somewhat solve the problem,
  but for the final product, we need more.
• Java, perhaps, as the language of choice
• Implementation of keywords for online TV guides
• Overall, these ideas are a good start on working
  toward a useful, functional product

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References

• http//www.cs.umbc.edu/skaush1/IASTED_2002.pdf
  
• TV-Learning paper 1
• http//www.csee.umbc.edu/skaush1/TV02_Ease_of_Use
  _Trust_Accuracy.pdf
• TV-Learning paper 2
• http//tvlistings2.zap2it.com/
• television guide site
• http//www.captions.org/
  
• closed captioning information site

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References Cont

• http//red.cs.tcu.edu/crescent.html_Work_Informat
  ion
• Crescent Home