Computation and action under bounded resources
Computation and action under bounded resources
Anytime deduction for probabilistic logic
Artificial Intelligence
AAAI'94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 2)
Planning under time constraints in stochastic domains
Artificial Intelligence - Special volume on planning and scheduling
Graphical models for preference and utility
UAI'95 Proceedings of the Eleventh conference on Uncertainty in artificial intelligence
Elicitation of probabilities for belief networks: combining qualitative and quantitative information
UAI'95 Proceedings of the Eleventh conference on Uncertainty in artificial intelligence
Personal choice point: helping users visualize what it means to buy a BMW
Proceedings of the 8th international conference on Intelligent user interfaces
Visual exploration and incremental utility elicitation
Eighteenth national conference on Artificial intelligence
Designing example-critiquing interaction
Proceedings of the 9th international conference on Intelligent user interfaces
Compact value-function representations for qualitative preferences
UAI '04 Proceedings of the 20th conference on Uncertainty in artificial intelligence
Refining preference-based search results through Bayesian filtering
Proceedings of the 12th international conference on Intelligent user interfaces
What am I gonna wear?: scenario-oriented recommendation
Proceedings of the 12th international conference on Intelligent user interfaces
Adaptive preference elicitation for top-K recommendation tasks using GAI-networks
AIAP'07 Proceedings of the 25th conference on Proceedings of the 25th IASTED International Multi-Conference: artificial intelligence and applications
Eliciting bid taker non-price preferences in (combinatorial) auctions
AAAI'04 Proceedings of the 19th national conference on Artifical intelligence
Eliciting single-peaked preferences using comparison queries
Journal of Artificial Intelligence Research
The complexity of learning separable ceteris paribus preferences
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Aggregating value ranges: preference elicitation and truthfulness
Autonomous Agents and Multi-Agent Systems
A hybrid approach to reasoning with partially elicited preference models
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
Utility elicitation as a classification problem
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Toward case-based preference elicitation: similarity measures on preference structures
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Cooperative negotiation in autonomic systems using incremental utility elicitation
UAI'03 Proceedings of the Nineteenth conference on Uncertainty in 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
| Hi-index | 0.00 |
Decision theory has become widely accepted in the AI community as a useful framework for planning and decision making. Applying the framework typically requires elicitation of some form of probability and utility information. While much work in AI has focused on providing representations and tools for elicitation of probabilities, relatively little work has addressed the elicitation of utility models. This imbalance is not particularly justified considering that probability models are relatively stable across problem instances, while utility models may be different for each instance. Spending large amounts of time on elicitation can be undesirable for interactive systems used in low-stakes decision making and in time-critical decision making. In this paper we investigate the issues of reasoning with incomplete utility models. We identify patterns of problem instances where plans can be proved to be suboptimal if the (unknown) utility function satisfies certain conditions. We present an approach to planning and decision making that performs the utility elicitation incrementally and in a way that is informed by the domain model.