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Personal Assistants for the Web: An MIT Perspective

Personal Assistants for the Web: An MIT Perspective. Dep. Of Computer Science 95323-016 김광수. Introduction. The problem of information complexity Solution : Intelligent information agent Active assistance in finding and organizing information Like a human assistant

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Personal Assistants for the Web: An MIT Perspective

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  1. Personal Assistants for the Web:An MIT Perspective Dep. Of Computer Science 95323-016 김광수

  2. Introduction • The problem of information complexity • Solution : Intelligent information agent • Active assistance in finding and organizing information • Like a human assistant • The word “agent” : assistant

  3. Intelligent Information Agent • Information in the Web • Highly unstructured • Natural language , pictures • Partial understanding => effective assistance to the user

  4. Information Retrieval - static databases, concentrated , organized in records - a conversational paradigm( query, hits ) • But, on the Web,Information Intelligent Agent - hypertext, distributed, unstructured, non-textual information - active, proactively trying to gather information even without the user’s explicit command

  5. Letizia • information reconnaissance agent • It watches your Web browsing to try to learn what topics you are interested in. • It searches Semantic neighborhood of the current page to discover other pages you might be interested in

  6. Letizia • A co-operative venture between the user and Letizia • While you search “deep” (DFS) , Letizia searches “wide” (BFS)

  7. Remembrance Agent • information reconnaissance agent • RA maintains the user’s personal information ( ex. the user’s e-mail, the set of files in the user’s home directory ) • It shows messages that are relevant to the currently viewed text

  8. An engineer reads email about a project • RA might remind her of project schedules, status reports, and other resources related to the project

  9. Let’s Browse • Allow a group to collaboratively browse together • Ex) business meeting , WebTV for family • By intersecting individual profiles of the users • A Letizia-like scan of a breadth-first neighborhood surrounding each user’s home page, or their organization’s home page

  10. Firefly • Collaborative filtering agent • Every person says what items they like and dislike • New items are recommended to a user based on the opinions of people with similar taste

  11. Yenta • Yenta introduces the users who share similar tastes to each other (match-making) • Yenta indexes e-mail & personal files like RA • Distributed, peer-to-peer communication, no central site

  12. Butterfly • A recommendation system for chat channels • The user converses with the Butterfly “chatterbot” • Butterfly periodically scans the thousands of available chat channels, sampling each only for a short time

  13. ExpertFinder • EF assists with the problem of finding another user who is knowledgeable to answer a question • EF monitors a user’s activity within desktop applications • Ex) for Java programming

  14. Tête-à-Tête • Matchmaking between buyers and sellers in Electronic Commerce • The paradigm of integrative negotiation - multiple dimensions rather than just price

  15. The Footprints Sytem • “history-rich” • visualizing history-of-use in a complex information space • Nodes are documents (from any web site), links are traversals

  16. Information Agents Can be Controversial • It can make mistakes • But - It can be used with conventional direct-manipulation software - feedback between the user and the agent • Intelligent information agents can help the users !

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