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Interaction Challenges for Agents with Common Sense

Delve into the realm of common sense knowledge and its significance in AI interfaces. Discover interaction challenges, opportunities, and ways to enhance user experience through common sense usage in interfaces.

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Interaction Challenges for Agents with Common Sense

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  1. Interaction Challenges for Agents with Common Sense

  2. "People do not walk on their heads." The assertion comes about 900 statements deep into the 527,308 items that comprise the Open Mind common sense database. It's after "Laws are the rules of society" and before "The sky is blue during the day." This collection of mundane facts, which would take more than 20,000 pages to print out, consists entirely of statements so unremarkable they are barely worth stating. Most of us would correctly dismiss them as common sense. - from the article, Guess who's smarter

  3. What do we mean by “Common Sense”? • Simple statements about everyday life • You go to a restaurant to eat • And…the ability to use that knowledge when appropriate

  4. Agents with Common Sense • Some AI interfaces are now beginning to make use of large knowledge bases of Common Sense • Explicitly collected • Cyc, Open Mind, ThoughtTreasure • Distilled from other sources • Semantic Web, Wikipedia, Web resources, Web mining, other resource mining

  5. Interaction Challenges • Finding opportunities for applying Common Sense in interfaces • Setting users' expectations • Making interfaces fail-soft • Taking advantage of user interaction

  6. Interaction Challenges • Making better mistakes • Get lots of knowledge, but not too much • Common sense inference vs. mathematical inference • Debugging • Evaluating Common Sense interfaces

  7. Interaction challenges for AI/Common Sense • Many interaction challenges for Common Sense interfaces are the same as for AI in general • But some are unique or critical for Common Sense… • Can't be sure what will be known • Reasonable, rather than right • Know a little about everything, not much about anything • Don't miss the obvious • Try not to make stupid mistakes

  8. Opportunities for using Common Sense • Find UI situations that are underconstrained • Ordinary system would either take no action or do something arbitrary • Then, give user some reasonable choices • Provide intelligent defaults • Make the most likely thing easiest to do

  9. Opportunities for using Common Sense • Recognize users' likely goals • Help users map from goals to actions • Sanity checking • In the case of trouble, help users debug

  10. Opportunities for using Common Sense • Find situations where every little bit helps • A little bit of knowledge is better than none • A little bit of knowledge about a lot of things can be more useful than a lot of knowledge about a few things

  11. Setting users' expectations • Avoid direct question-answer interfaces • Right or wrong. Only one shot. • Better to cast system in role of advisor • Making suggestions, help • Adapting interface to most likely uses • Remove unnecessary steps in the interface • The user only expects intelligent behavior only once in a while

  12. Take advantage of user interaction • Repurpose input that the user gives you for other reasons • Every time the user tells the interface something, they're telling you what their interests are -- learn from it

  13. Make Common Sense interfaces "fail-soft" • There should no dire consequences of being wrong or not knowing what to do • Don't interfere if the user wants to use the application without interaction with the agent • If the relevant knowledge is missing, incomplete or wrong, the user is no worse off than without the agent

  14. Make better mistakes • Common Sense approaches have the advantage that when they make mistakes, they tend to make plausible mistakes • Statistical approaches can make arbitrary mistakes • Better mistakes improve user trust in interfaces

  15. Evaluation of Common Sense interfaces • Evaluation is tough because • Depends on what's in the knowledge base • Depends on limited-depth and other kinds of approximate inference • Standardized tasks don't test breadth of coverage • Try to relativize testing to coverage • Start with easier cases, then move to "typical" or "hard" cases

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