Beyond Intelligent Interfaces: Exploring, Analyzing, and Creating Success Models of Cooperative...

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Beyond Intelligent Interfaces: Exploring, Analyzing, and Creating Success Models of Cooperative Problem Solving Gerhard Fischer & Brent Reeves

Transcript of Beyond Intelligent Interfaces: Exploring, Analyzing, and Creating Success Models of Cooperative...

Page 1: Beyond Intelligent Interfaces: Exploring, Analyzing, and Creating Success Models of Cooperative Problem Solving Gerhard Fischer & Brent Reeves.

Beyond Intelligent Interfaces:Exploring, Analyzing, and Creating Success Models of

Cooperative Problem Solving

Gerhard Fischer & Brent Reeves

Page 2: 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”• As description of particular problem/solution• Overview of situated cognition literature

Page 3: Beyond Intelligent Interfaces: Exploring, Analyzing, and Creating Success Models of Cooperative Problem Solving Gerhard Fischer & Brent Reeves.

Research Approach

Look at shortcomingsand successes

Look at successin other contexts

Understand humanlimitations and opportunities

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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

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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

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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

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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

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High-Functionality Systems (HFS)

• Remember discussion of Microsoft Word …

Page 9: Beyond Intelligent Interfaces: Exploring, Analyzing, and Creating Success Models of Cooperative Problem Solving Gerhard Fischer & Brent Reeves.

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.

Page 10: Beyond Intelligent Interfaces: Exploring, Analyzing, and Creating Success Models of Cooperative Problem Solving Gerhard Fischer & Brent Reeves.

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

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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

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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)

solution

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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

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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

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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

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Page 17: Beyond Intelligent Interfaces: Exploring, Analyzing, and Creating Success Models of Cooperative Problem Solving Gerhard Fischer & Brent Reeves.
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Integrated, Domain-oriented, Knowledge-based Design Environments

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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