“Intelligent User Interfaces” by Hefley and Murray A 1993 Perspective

IUI: A 1993 Perspective Intelligent User Interfaces brings together work in separate research communities Artificial intelligence Human-computer interaction Research often included Dialog understanding User modeling Presentation generation Tutoring and error remediation

Many Potential Problems

How To Help? Reducing the gulf of execution Good physical designs Recognizing user knowledge and goals Reducing the gulf of evaluation Good visualization Presentation generation Designing for iterative action Dialogue management Error remediation

System Architectures Presentation, sequencing, and representation of semantics are components Consider relation to model, view, controller design pattern

The Problem as Task Manipulation

The Problem as Domain-Oriented Interaction

Classification of Systems Knowledge-based systems Components: Inference engine, consistency enforcer, knowledge base, justifier for explanations Application: Intelligent Tutoring Systems Decision support systems Components: Dialogue management, model base management, database management Application: Where users must make final decisions

Adaptive Intelligent Systems Needs to Learn over time Be aware of unforeseen situations Be “self aware” History of interactions with user could be used To inform user model To adapt domain model

Classes of Intelligent User Interfaces Intelligent Front Ends Natural Language interfaces Intelligent Tutoring Systems Intelligent Help and Support Systems Intelligent Multimedia Presentation Systems Decision Support Systems Adaptive Interfaces Cooperative Intelligent Agents and Dialogue Assistants

Relation to Class Projects So, this is a REALLY OLD paper … what did we learn Hopefully, some ideas about what might count as an intelligent user interface, the activities they might support, and how they might be structured

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

Research Approach Look at success in other contexts Look at shortcomings and successes Understand human limitations and opportunities

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 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 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 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) Remember discussion of Microsoft Word …

Challenges Posed by High-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 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

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

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

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

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 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

Integrated, Domain-oriented, Knowledge-based Design Environments

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

Example of IUI Challenges User modeling in time-critical environments What aspects of the user do we model? What is the representation? How do we get information for the model? When do we act based on the model? More general issues with such models Issues of access and privacy