Smart Home Application
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CRESCENT TCU Dept. of Computer Science
Smart Home Application
Intelligent TV ViewingVince Guerin
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CRESCENT TCU Dept. of Computer Science
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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CRESCENT TCU Dept. of Computer Science
Smart TV Recommender Goal
• “Intelligent” program(s) will predict, according to a person’s 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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CRESCENT TCU Dept. of Computer Science
TV Recommender
Product must be:• Accurate• Easy to use• Able to build trust in the
recommendations delivered
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CRESCENT TCU Dept. of Computer Science
Agenda
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CRESCENT TCU Dept. of Computer Science
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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CRESCENT TCU Dept. of Computer Science
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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CRESCENT TCU Dept. of Computer Science
AI Project – Keyword Matching
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CRESCENT TCU Dept. of Computer Science
Other types of Keywords
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CRESCENT TCU Dept. of Computer Science
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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CRESCENT TCU Dept. of Computer Science
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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CRESCENT TCU Dept. of Computer Science
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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CRESCENT TCU Dept. of Computer Science
Java Expert System Shell (JESS)
• What is JESS?- Java rule based expert system from Sandia National Laboratories (http://herzberg.ca.sandia.gov/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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CRESCENT TCU Dept. of Computer Science
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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CRESCENT TCU Dept. of Computer Science
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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CRESCENT TCU Dept. of Computer Science
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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CRESCENT TCU Dept. of Computer Science
Phillips USA solution
Kaushal Kurapati’s 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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CRESCENT TCU Dept. of Computer Science
Phillips USA solution cont…
• Calculating the “distance” between networks/shows
Example:
Calculating the “distance” between FOX and NBC
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CRESCENT TCU Dept. of Computer Science
Cont…
Computing Distances:
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CRESCENT TCU Dept. of Computer Science
Cont…
Deriving Stereotypes from Clustering Algorithm:
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CRESCENT TCU Dept. of Computer Science
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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CRESCENT TCU Dept. of Computer Science
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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CRESCENT TCU Dept. of Computer Science
References
• http://www.cs.umbc.edu/~skaush1/IASTED_2002.pdf
TV-Learning paper #1• http://www.csee.umbc.edu/~skaush1/TV02_Ea
se_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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CRESCENT TCU Dept. of Computer Science
References Cont…
• http://red.cs.tcu.edu/crescent.html#_Work_InformationCrescent Home