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

levels of discussion for fischer reeves
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
research approach
Research Approach

Look at successin other contexts

Look at shortcomings

and successes

Understand human

limitations and


where is the intelligence
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
considerations for designing cooperative problem solving systems
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
knowledge based system assumptions
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
earlier systems
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
high functionality systems hfs
High-Functionality Systems (HFS)
  • Remember discussion of Microsoft Word …
challenges posed by high functionality systems
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.
success model
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
  • 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
incremental problem specification
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)


Description of SolutionSpace (sales rep)

  • 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
additional characteristics
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
integrated domain oriented knowledge based design environments
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
final thoughts
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