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Levels of Discussion for Fischer/Reeves

Beyond Intelligent Interfaces: Exploring, Analyzing, and Creating Success Models of Cooperative Problem Solving Gerhard Fischer & Brent Reeves. Levels of Discussion for Fischer/Reeves. As contradiction of (some aspects of) Hefley/Murray As method for using “success models”

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Levels of Discussion for Fischer/Reeves

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  1. Beyond Intelligent Interfaces:Exploring, Analyzing, and Creating Success Models ofCooperative Problem SolvingGerhard Fischer & Brent Reeves

  2. Levels of Discussion for Fischer/Reeves • As contradiction of (some aspects of) Hefley/Murray • As method for using “success models” • As description of particular problem/solution • Overview of situated cognition literature

  3. Research Approach Look at successin other contexts Look at shortcomings and successes Understand human limitations and opportunities

  4. Where is the “Intelligence”? • Intelligent interfaces: in the user discourse machine • Interfaces to intelligent systems: in the task machine • Need to put intelligence in both, or bridge the two components • Cooperative problem solving systems integrate interaction mechanisms with domain knowledge

  5. Considerations for Designing Cooperative Problem-Solving Systems • Understanding complex task domains • Users cannot specify their task prior to doing it • Level of cooperation between human and computer • Exploit asymmetry of partners • Impact of communication breakdowns • Cannot design away all miscommunication • Role of background assumptions • Build systems on the premise that background assumptions can never be fully articulated • Semi-formal vs. formal approaches • Combining information delivery with automatic reasoning • Humans enjoy doing and deciding • Automate uninteresting tasks while empowering the user

  6. Knowledge-based System Assumptions • Users can fully articulate their problem in advance • Users will ask for help • Cannot ask for information you do not know exists • A consultation model is acceptable • Studies of physicians attitudes to MYCIN showed this is not always so • General purpose programming environments are sufficient • Too far from the problem space

  7. Earlier Systems • HELGON: retrieval by reformulation • LISP-CRITIC: user asks for help • ACTIVIST: system volunteers information • SYSTEMS’ ASSISTANT: mixed-initiative interaction • FINANZ: end-user (domain expert) modification

  8. High-Functionality Systems (HFS) • Remember discussion of Microsoft Word …

  9. Challenges Posed byHigh-Functionality Systems • Users do not know the existence of tools • Users do not know how to access tools • Users do not know when to use tools • Users cannot combine, adapt, and modify tools according to their specific needs.

  10. Success Model • Idea: Find HFS in “real world” and see why it works • McGuckin’s Hardware • 350,000 different items • 33,000 square feet • Very popular • Study: “tag along” with consumers to see how it works

  11. Results • Knowledgeable sales agents help to • Determine what people need • Locate tools • Explain use of tools • Combine/adapt tools • Elicit problem understanding • Miscommunications were common but resolved

  12. Incremental Problem Specification • “you cannot understand the problem without having a concept of the solution in mind” Horst Rittel • Asymmetry of knowledge Description of ProblemSpace (customer) solution Description of SolutionSpace (sales rep)

  13. Expertise • Not only ability to problem solve • Learn incrementally and restructure one’s knowledge • Knowing when to break the rules • Determine the relevance of information • Degrade gracefully if not in core of expertise

  14. Additional Characteristics • Multiple specification techniques • Descriptions could take multiple forms • Mixed-initiative dialogues • Physical artifacts and feedback • Distributed intelligence • departmental expertise • Setting of problem matters • Carraher et al. found that Brazilian school children who worked as street vendors were 98% accurate for street transactions while only 37% accurate on mathematically identical problems in the classroom

  15. Integrated, Domain-oriented, Knowledge-based Design Environments • Combining • unselfconscious design in construction kit with • mixed-initiative delivery of information about design via knowledge-based critics and argumentation • Requires a combination of structured and semi-structured information about domain • The roles of • specifications • examples

  16. Integrated, Domain-oriented, Knowledge-based Design Environments

  17. Final Thoughts • "High-functionality computer systems offer the same broad functionality as large hardware stores, but they are operated like discount department stores" • Need human-problem domain communication • User modeling might help but is second order term in problem solution

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