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Intelligent User Interfaces

Intelligent User Interfaces. Frank Shipman Department of Computer Science Texas A&M University E-mail: shipman@cs.tamu.edu. What this is about. Designing, building, and evaluating intelligent user interfaces Particular technologies used to create intelligent user interfaces

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Intelligent User Interfaces

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  1. Intelligent User Interfaces Frank Shipman Department of Computer Science Texas A&M University E-mail: shipman@cs.tamu.edu

  2. What this is about • Designing, building, and evaluating intelligent user interfaces • Particular technologies used to create intelligent user interfaces • Issues concerning applicability of intelligent user interfaces

  3. Intelligent user interfaces (IUI)? • Systems that provide interactive support based on embedded AI mechanisms • Interfaces to AI functionality and knowledge representations • Adaptive systems • Cooperative problem-solving systems

  4. Technologies I User models,Situation models, and Programming by Demonstration

  5. Adaptive Interfaces • Requirements: • interface that can be adapted • user or situation model • adaptation strategy • Frequently used for providing assistance or training to user

  6. User Model • “any information which a program has which is specific to a particular user.The information itself could range from a simple count of errors, to some complicated data structure which purports to represent relevant parts of the user’s knowledge of the problem domain.”

  7. Stereotyping vs. Individual • Stereotyping (canonical user modeling) • provide interfaces for classes of users • classes might be based on skill (novices, midrange users, experts) or role • Individual approach • dynamically adapt to suit each user • can be based on observed use of system or self assessment

  8. Representations for User Models • Descriptive method • modeling the user’s observed behavior • describes what system has seen user do • Skill-based or cognitive method • attempt to model the internal cognitive models and processes of the user • represents background knowledge, goals, plans, preferences, misconceptions

  9. Acquisition of User Model • Model based on a combination of: • Observations of system use • statistical history • chronological history • Self-assessment • Testing • How can model evolve over time? • Any of the above

  10. Berkeley UNIX Consultant • Goal: Provide help to new UNIX users • Generates user model based on “successful” use of UNIX commands • Explanations of difficult commands can make use of student’s knowledge.

  11. Intelligent Tutoring Systems • Task is generally well-known • assignments given to student by system • systems track partial completion • Systems keep record of student’s success and failure. • used to determine future assignments • used to determine how to help when student has difficulties

  12. Situation Models • Components of situation:users, system, environment • Users • multiple user models • System • hardware constraints and load • device / resource availability

  13. Representing the Environment • Identifying environmental influences • anticipating use situations • classes of use vs. detailed model of environment • Monitoring environment • direct input devices • user description

  14. Mars Medical Assistant • Goal: Provide medical support for astronauts on three year trip to Mars • Consider educational, consultation, and emergency situations • Models of user and patient • limited highly-trained user community • no new users joining during mission

  15. Other Adaptive Systems • Typing completion • suggests completions for partial terms based on prior use • Emacs suggestions • notifies user when more efficient method available to complete task • Computer Chess Game • determines quality of own play based on perceived level of opponent

  16. Programming by Demonstration • Generalizing from demonstrated action and situation sequences to programs • Difficulties: • knowing what must stay the same • knowing what are variables and their types • connecting to programmed application code

  17. Programming by Demonstration Systems • Peridot -- demonstration of simple interface • Marquise -- demonstration of graphical editors including palettes and modes • DEMO -- demonstrating dynamically created objects • DEMO2 -- refinement by system based on multiple demonstrations

  18. Pavlov • Focus on programming animation • Includes: • graphical objects • models of motion and time • Stimulus-response demonstration • modes for creating objects and behaviors • mode for demonstrating interaction

  19. Technologies II Presentation generation,Design Environments, and Interface agents

  20. Presentation Generation • Generating dynamic links to information • enabling user-controlled flow • Generating presentations based on current situation and/or user • Use of user or situation model • Generating rhetorical structure/transition • Scripting events • Media-based decisions

  21. Presentations and Explanations • Examples: • Explainer (Redmiles) • Explainable Expert System (Moore) • Story Presentation System (Sgouros, …)

  22. Explainer • Domain: Graphical program explanation for software reuse • Creates links between perspectives on software including source code, documentation, execution information, application domain view • Provides user multiple points of access to better inform about software

  23. Explainable Expert System (EES) • Explains different outcomes in an expert system / planner • Generates natural language to answer user’s questions • Keeps dialog history to provide differential descriptions

  24. EES Architecture for Explanation Knowledge Base Plan Operators User Question Response Query analyzer Text planner Sentence generator User goals Dialog history Focus Information

  25. EES Example User: “Describe Inderal” System: “Inderal a drug that …” User: “Describe Elavil” System: “Like Inderal, Elavil is used …” User: “Describe Cafergot” System: “Cafergot is very different from the drugs we have been talking about. …”

  26. Story Presentation System • A dynamic dramatization method for narrative presentations • Architecture: Symbolic Plot Description Dramatic Effects Library Original Story Material Plot Analysis Dramatization Presentation Manager Story Presentation

  27. Story Presentation Analysis • Plot analysis models: • physical and emotional state changes • positive and negative interference among characters • Dramatization uses plot analysis to determine dramatic events in story • Lifeline, Rising complication, Reversal of fortune, Dramatic irony, Happy end

  28. Story Presentation Results • Presentation manager adds dramatic effects to original story material to emphasize dramatic events in story • Effects include • audio: selection of noises or music • images and video: flashbacks, flashforwards, images of other characters

  29. Design Environments • Provide a software environment supporting the activities part of design. • specification, construction • argumentation, documentation, communication • Examples: • Framer (Lemke, Fischer) • JANUS (Morch, McCall, Fischer , ...)

  30. Framer • Knowledge-based support for interface design • Approach: direct manipulation interface builder Framer 1 -- construction kit approach Framer 2 -- design environment

  31. Design Environment Components (1) • Checklist • system provides decomposition of task, • user identifies current focus • Palette & Workspace • system provides primitive components • user identifies components used and organization of components in design

  32. Design Environment Components (2) • Specification sheets • system brings design issues to user’s attention, presents potential answers, and explains significance and consequences of design choices • user symbolically specifies answers to design issues

  33. Design Environment Components (3) • Critics • system points out sub-optimal design decisions, explains why this is believed, and provides heuristics for making decisions • users may accept or reject the system’s critique

  34. Design Environment Components (4) • Catalog • system provides examples • user selects designs to reuse and modify • Code generator • system generates an executable representation of designed interface

  35. Other Design Environments • JANUS -- kitchen design • designed for non-technical users • XNetwork -- computer network design • identified need for simulation component • VDDE -- voice dialog design • another type of interface design with interesting constraints

  36. Software Agents • One view:Software processes that have non-trivial tasks delegated to them which require independent action and a report on the results.

  37. Issues for Software Agents (1) • Personification • Should agents be represented as a living or animated character? • Does it improve adoption of software? • Does it create inflated expectations? • Is it just too annoying?

  38. Issues for Software Agents (2) • Trust and Competence • How does user develop an informed level of trust? • Can agent give self-assessment on likely outcome of task? • Delegation • How can user delegate tasks? • How can user check on status of delegated tasks?

  39. Issues for Software Agents (3) • Control • How does user set limits on the agent’s activity? • When does the agent get to interrupt the user (mixed-initiative dialog)? • Dealing with multiple agents • How can the user manage many agents? • How can interactions between agents be predicted?

  40. Information Retrieval Agents • Watch user patterns to infer interests or goals which are used to classify, rank, or suggest new information • Examples: • INFOSCOPE: patterns in Netnews use • BASAR: patterns in Web access • Issue: the “cold start” problem • must watch a while before suggesting

  41. Social Filtering • Finding elements liked by others (with similar preferences) • requires some notion of preferences • improves with more users • Examples • Tapestry -- rating of documents • GroupLens -- collaboration & user profiles • Amazon.com and CD-NOW

  42. Technologies III Knowledge manipulation and Using recognized structure

  43. Interacting with Knowledge • User tasks • Adding knowledge • Editing rule bases and object hierarchies • Examples • HITS Knowledge Editor (Terveen) • Modifier (Girgensohn) • Hyper-Object Substrate (Shipman)

  44. Knowledge Representations • Informal • text, graphics, audio, video • Semi-formal • hypertext, argumentation • Formal • frames, semantic nets, scripts, rules, inheritance hierarchies,

  45. HITS Knowledge Editor • Knowledge editor for CYC project • Difficulties of knowledge representation formalization - articulation in precise detail comprehension - complex vocabulary, size modification - location and consistency

  46. Terveen’s Design Principle #1 • Provide a workspace in which users and systems can jointly construct and manipulate a context for problem-solving, and in which the state of the problem-solving is represented visibly.

  47. Terveen’s Design Principle #2 • Deliver intelligent assistance through critics.

  48. Terveen’s Design Principle #3 • Exploit the interactive potential of computational media to manage the user-system interaction according to conventions that are appropriate to the role of each party in the interaction.

  49. Support Provided by HKE • Inferences -- information inferred from workspace and existing KB • Troubles -- inconsistencies between workspace and KB • Suggestions -- relevant representational issues for users to consider

  50. Modifier • Support for End-User Modifiability • Users are not knowledge engineers • Example: • Adding new object class to existing system • Support: suggestions based on similarity of features and efficiency of representation

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