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AI in End-User Software Engineering

AI in End-User Software Engineering. Henry Lieberman, MIT (presenting) Raphael Hoffmann, Michael Toomim, U. Washington, Seattle. Roles for AI in End-User SoftEng. Supply knowledge end-user doesn't have Recognize user intent Plan generation and plan recognition

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AI in End-User Software Engineering

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  1. AI in End-User Software Engineering • Henry Lieberman, MIT (presenting) • Raphael Hoffmann, Michael Toomim, U. Washington, Seattle

  2. Roles for AI in End-User SoftEng • Supply knowledge end-user doesn't have • Recognize user intent • Plan generation and plan recognition • Automate common patterns of use • Generalize & Specialize • Manage multiple representations • Error checking and sanity checking

  3. U. Washington SoftBots & more • Planning-based software agents • Version-space Programming by Demonstration • Sequence prediction via Logic & Probability • Recommendation from software libraries

  4. SmartEdit - Lau

  5. Version Spaces

  6. Linked Editing • Alternative to content abstraction facilities • Powerpoint Master Slides • Style Sheets • "Symbols" vs. instances in Flash • Web templates, components • User stays within WYSIWYG paradigm

  7. Linked Editing • End-users "underuse" abstractions • Linked Editing • User edits concrete example • Generalizing over content rather than actions • Finds other instances of description • Performs "analogous" edits • Program code, Web pages, Vector graphics, Sheets

  8. Linked Editing

  9. Linked Editing

  10. Assieme - Customization Recommendation • Goal: Recommending scripts for application extensions: Emacs, Firefox • Collect keystrokes, file names, commands • Provide Web service for recommendation • Collab filtering, learning classifier

  11. Assieme - Customization Recommendation

  12. The crazy dream: Programming in English • Programming languages are too difficult for human beings • People can express procedures in English (or other natural language) • Why not program in natural language? • That was the original idea behind Cobol • (whereas Fortran tried to make programming like math)

  13. Childrens’ description of Pac-Man

  14. But what can you do if you can only partially understand English? • Need to make the interaction fail-soft • Rely on dialogue to resolve ambiguities and errors • Maybe don’t eliminate code entirely • Emphasize correspondence between language and code • An "outliner" for code • Application: Programming MOOs

  15. New resources for Common Sense Reasoning • Open Mind Common Sense knowledge base • ConceptNet Semantic Net • MontyLingua/LangUtils

  16. Metafor Interface

  17. Programmatic Semantics • English to “scaffolding” code • Roughly, • Turns nouns into classes and/or instances; • Verbs into functions • Adjectives into instance variables • Resolves anaphora • Untangles conditional and loops • Propagates context

  18. Narrative stances • I want to make a bar with a customer. (1st person programmer) • There is a customer in the bar. (3rd person objective) • I am a customer sitting on a stool. (1st person program) • The bartender said, “Here is a customer” (3rd person subjective)

  19. Refactoring • There is a bar. (atom)The bar contains two customers. (unimorphic list)It also contains a waiter. (unimorphic wrt. persons)It also contains some stools. (polymorphic list)The bar opens and closes. (class / agent)The bar is a kind of store. (inheritance class)Some bars close at 6pm. (subclass or instantiatable)

  20. Shameless Plug

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