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Using an Extended Episodic Memory Within a Mobile Companion

Using an Extended Episodic Memory Within a Mobile Companion. Alexander Kröner, Stephan Baldes, Anthony Jameson, and Mathias Bauer. German Research Center for Artificial Intelligence (DFKI) GmbH Stuhlsatzenhausweg 3, 66123 Saarbrücken, Germany BMB+F 524-40001-01 IW C03 (SPECTER).

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Using an Extended Episodic Memory Within a Mobile Companion

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  1. Using an Extended Episodic Memory Within a Mobile Companion Alexander Kröner, Stephan Baldes, Anthony Jameson, and Mathias Bauer German Research Center for Artificial Intelligence (DFKI) GmbH Stuhlsatzenhausweg 3, 66123 Saarbrücken, Germany BMB+F 524-40001-01 IW C03 (SPECTER)

  2. About this Talk • About Specter • Goals, example, challenges • About the personal journal • User interface • Adding value to the journal • Binding services to journal entries

  3. What is Specter? • Specter is about… • … context- and affect-aware personal assistance • … in instrumented environments • … using a long-term memory • Major issues • Extension of perception • Learning about behavior and affect • Augmentation of decision making and effecting • Reflection and introspection

  4. Example Scenario: Shopping Assistant • Technology • RFID enabled shelf and basket • RFID-labeled Products • Integrates software of projects Specter and REAL (Saarland University) • Logging low-level actions • Product in shelf, removed from shelf, etc. • Applying logged information • Proposing alternative products

  5. situation classification ... ... plan recognition location classification object classification abstraction of data ... ... action recognition affect recognition motion profiling ... motion data classification phys. data classification Stream of raw, low-level sensor data Obtaining Data from the Environment reflection/introspection UM decision support PersonalJournal episode retrieval

  6. Obstacles in Obtaining Data • Automatically retrieved input • Sensor input may be incorrect • Sensor input may be completely missing • Inference mechanisms are not perfect • user input required • Manual (user) input • Lack of motivation • manual input has to be minimized • input value has to be maximized • Recorded information may be sensible • user's trust has to be strengthened

  7. Design Principles • Input has to be acquired collaboratively [addresses: manual input, lack of motivation, trust] • filling the journal automatically • asking for additional information if required • providing control of information and bound services • Provision of diverse, multiple benefits [addresses: maximizing value] • providing the option of binding various services to journal content • Support for diverse forms of collaboration [addresses: lack of motivation] • providing various ways of entering data • enabling input/feedback in varying scenarios

  8. Accessing the Personal Journal The personal journal user interface

  9. Browser/Viewer Approach • Goal: browsing the journal in a similar fashion like the Web • Adopted concept of Web browser • Browsed documents are generated on-the-fly by data viewers • Browser and/or viewers can be exchanged Browser Viewers

  10. Navigating the Personal Journal • Content requests • History (forward, backward,…) • Bookmarks • Hyperlinks • Reminder Points • Created by the user during interaction with the instrumented environment • Indicate the need to inspect points within the timeline during introspection

  11. Adding Content • Annotating entries • Free text comment, references, categories, ratings • Grouping entries • A group indicates that some entries are related together • For manual organization, and input for Specter

  12. Collaborative Learning of the User Model Binding services to the personal journal

  13. Example: EC Card Purchase

  14. Example: Assisting an EC Card Purchase • Goal: checking bank account automatically before payment is required • Binding a service • Identifying situation where check is required • Identifying relevant conditions, e.g., shop size, total prize of wares contained in basket • Binding a service “account check” to that situation • To be stored as trigger in the user model • Evoking a service • Comparing given situations with triggers from the user model

  15. Binding Services: Workflow User or Specter chooses a service that should be triggered automatically Specter collects attributes from (potentially) relevant journal entries User discards irrelevant attributes and/or adds relevant ones Specter computes a rule and shows it to the user User accepts or changes the rule Specter adds the new rule as service trigger to the user model

  16. Defining Decision Trees EC CardPurchase Store Price Edeka Hela  112€ > 112€ Price DayOfWeek TimeOfDay TimeOfDay ...  117,5€ > 117,5€ So Sa  1:07pm  5:25pm > 1:07pm > 5:25pm – + – + – + – +

  17. Customizing Decision Trees EC CardPurchase userdef #1 Store Edeka Hela Price DayOfWeek ...  117,5€ > 117,5€ So Sa TimeOfDay TimeOfDay TimeOfDay TimeOfDay [12am,2pm] [12am,2pm] [12am,2pm] [12am,2pm] otherwise otherwise otherwise otherwise + – + – + – + –

  18. Creating a Service Trigger

  19. Conclusion and Outlook • Specter • Overview • Personal journal user interface • Binding services to journal content • Next steps • Journal user interface • Queries • Setting up plans • Additional viewers • Identifying additional services

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