Visual information seeking: tight coupling of dynamic query filters with starfield displays
CHI '94 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Design galleries: a general approach to setting parameters for computer graphics and animation
Proceedings of the 24th annual conference on Computer graphics and interactive techniques
Enriching buyers' experiences: the SmartClient approach
Proceedings of the SIGCHI conference on Human Factors in Computing Systems
An integrated environment for knowledge acquisition
Proceedings of the 6th international conference on Intelligent user interfaces
Smoothed analysis of algorithms: why the simplex algorithm usually takes polynomial time
STOC '01 Proceedings of the thirty-third annual ACM symposium on Theory of computing
Making Rational Decisions Using Adaptive Utility Elicitation
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
Utility elicitation as a classification problem
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Problem-focused incremental elicitation of multi-attribute tility models
UAI'97 Proceedings of the Thirteenth conference on Uncertainty in artificial intelligence
Knowledge-based acquisition of tradeoff preferences for negotiating agents
ICEC '03 Proceedings of the 5th international conference on Electronic commerce
ValueCharts: analyzing linear models expressing preferences and evaluations
Proceedings of the working conference on Advanced visual interfaces
A fuzzy logic based method to acquire user threshold of minimum-support for mining association rules
Information Sciences—Informatics and Computer Science: An International Journal
Compact value-function representations for qualitative preferences
UAI '04 Proceedings of the 20th conference on Uncertainty in artificial intelligence
Availability bars for calendar scheduling
CHI '06 Extended Abstracts on Human Factors in Computing Systems
Proceedings of the working conference on Advanced visual interfaces
International Journal of Human-Computer Studies
Constraint-based optimization and utility elicitation using the minimax decision criterion
Artificial Intelligence
Generating and evaluating evaluative arguments
Artificial Intelligence
Graphically structured value-function compilation
Artificial Intelligence
AI Communications - Constraint Programming for Planning and Scheduling
On graphical modeling of preference and importance
Journal of Artificial Intelligence Research
Incremental utility elicitation with minimax regret decision criterion
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Generating and evaluating evaluative arguments
Artificial Intelligence
Constraint-based optimization and utility elicitation using the minimax decision criterion
Artificial Intelligence
Case-based ranking for decision support systems
ICCBR'03 Proceedings of the 5th international conference on Case-based reasoning: Research and Development
Relational preference rules for control
Artificial Intelligence
Overcoming incomplete user models in recommendation systems via an ontology
WebKDD'05 Proceedings of the 7th international conference on Knowledge Discovery on the Web: advances in Web Mining and Web Usage Analysis
Group decision support for requirements negotiation
UMAP'11 Proceedings of the 19th international conference on Advances in User Modeling
Efficiently learning the preferences of people
Machine Learning
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Incremental utility elicitation (IUE) is a decision-theoretic framework in which tools simultaneously make suggestions to a human decision maker based on an incomplete model of the decision maker's utility function, and update the model based on feedback from the user. Most systems that perform IUE construct and ask questions about a small number of alternatives in order to build a model of the user's preferences. We describe a system called VEIL that is based on visual exploration of the available alternatives and provides visual cues about their estimated utility based on IUE. VEIL uses a linear programming formulation to make fast updates to the utility estimate based on the user's expressed preferences between pairs of alternatives. In experiments, VEIL's update method converges quickly to make good suggestions and help the user form an overall impression of the space of alternatives.