Dynamic programming: deterministic and stochastic models
Dynamic programming: deterministic and stochastic models
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
UCP-Networks: A Directed Graphical Representation of Conditional Utilities
UAI '01 Proceedings of the 17th Conference in Uncertainty in Artificial Intelligence
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
Memory Approaches to Reinforcement Learning in Non-Markovian Domains
Memory Approaches to Reinforcement Learning in Non-Markovian Domains
Algorithms for partially observable markov decision processes
Algorithms for partially observable markov decision processes
Value-function approximations for partially observable Markov decision processes
Journal of Artificial Intelligence Research
On the foundations of expected expected utility
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Approximating optimal policies for partially observable stochastic domains
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
Utility elicitation as a classification problem
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Solving POMDPs by searching in policy space
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Proceedings of the 13th international conference on World Wide Web
Learning User Preferences for Wireless Services Provisioning
AAMAS '04 Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems - Volume 1
Preference Elicitation without Numbers
AAMAS '04 Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems - Volume 3
Compact value-function representations for qualitative preferences
UAI '04 Proceedings of the 20th conference on Uncertainty in artificial intelligence
Preference elicitation for interface optimization
Proceedings of the 18th annual ACM symposium on User interface software and technology
Dynamic preferences in multi-criteria reinforcement learning
ICML '05 Proceedings of the 22nd international conference on Machine learning
Automatic construction of personalized customer interfaces
Proceedings of the 11th international conference on Intelligent user interfaces
Strong planning under partial observability
Artificial Intelligence
Constraint-based optimization and utility elicitation using the minimax decision criterion
Artificial Intelligence
Point-Based Value Iteration for Continuous POMDPs
The Journal of Machine Learning Research
Specifying label layout style by example
Proceedings of the 20th annual ACM symposium on User interface software and technology
Conversational recommenders with adaptive suggestions
Proceedings of the 2007 ACM conference on Recommender systems
Graphically structured value-function compilation
Artificial Intelligence
The permutable POMDP: fast solutions to POMDPs for preference elicitation
Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems - Volume 1
Preference-based search with adaptive recommendations
AI Communications - Recommender Systems
A bayesian reinforcement learning approach for customizing human-robot interfaces
Proceedings of the 14th international conference on Intelligent user interfaces
Eliciting bid taker non-price preferences in (combinatorial) auctions
AAAI'04 Proceedings of the 19th national conference on Artifical intelligence
Stochastic local search for POMDP controllers
AAAI'04 Proceedings of the 19th national conference on Artifical intelligence
Preference elicitation and generalized additive utility
AAAI'06 proceedings of the 21st national conference on Artificial intelligence - Volume 2
Multi-angle view on preference elicitation for negotiation support systems
HuCom '08 Proceedings of the 1st International Working Conference on Human Factors and Computational Models in Negotiation
Optimal recommendation sets: covering uncertainty over user preferences
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 2
Uncertainty in preference elicitation and aggregation
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 1
Purely epistemic markov decision processes
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 2
Indefinite-horizon POMDPs with action-based termination
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 2
Decision-theoretic bidding based on learned density models in simultaneous, interacting auctions
Journal of Artificial Intelligence Research
Preference-based search using example-critiquing with suggestions
Journal of Artificial Intelligence Research
Observation reduction for strong plans
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
On the foundations of expected expected utility
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Incremental utility elicitation with minimax regret decision criterion
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Towards cooperative negotiation for decentralized resource allocation in autonomic computing systems
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Regret-based optimal recommendation sets in conversational recommender systems
Proceedings of the third ACM conference on Recommender systems
Eliciting single-peaked preferences using comparison queries
Journal of Artificial Intelligence Research
Regret-based utility elicitation in constraint-based decision problems
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Strong planning under partial observability
Artificial Intelligence
Constraint-based optimization and utility elicitation using the minimax decision criterion
Artificial Intelligence
Discovering relative importance of skyline attributes
Proceedings of the VLDB Endowment
Eliciting matters: controlling skyline sizes by incremental integration of user preferences
DASFAA'07 Proceedings of the 12th international conference on Database systems for advanced applications
Regret-based reward elicitation for Markov decision processes
UAI '09 Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence
Assessing regret-based preference elicitation with the UTPREF recommendation system
Proceedings of the 11th ACM conference on Electronic commerce
Planning to see: A hierarchical approach to planning visual actions on a robot using POMDPs
Artificial Intelligence
Aggregating value ranges: preference elicitation and truthfulness
Autonomous Agents and Multi-Agent Systems
Preference elicitation in prioritized skyline queries
The VLDB Journal — The International Journal on Very Large Data Bases
Preference elicitation and inverse reinforcement learning
ECML PKDD'11 Proceedings of the 2011 European conference on Machine learning and knowledge discovery in databases - Volume Part III
Cooperative negotiation in autonomic systems using incremental utility elicitation
UAI'03 Proceedings of the Nineteenth conference on Uncertainty in Artificial Intelligence
Active collaborative filtering
UAI'03 Proceedings of the Nineteenth conference on Uncertainty in Artificial Intelligence
Multiattribute bayesian preference elicitation with pairwise comparison queries
ISNN'10 Proceedings of the 7th international conference on Advances in Neural Networks - Volume Part I
Bayesian multitask inverse reinforcement learning
EWRL'11 Proceedings of the 9th European conference on Recent Advances in Reinforcement Learning
Evaluating POMDP rewards for active perception
Proceedings of the 11th International Conference on Autonomous Agents and Multiagent Systems - Volume 3
Generating diverse plans to handle unknown and partially known user preferences
Artificial Intelligence
People, sensors, decisions: Customizable and adaptive technologies for assistance in healthcare
ACM Transactions on Interactive Intelligent Systems (TiiS) - Special issue on highlights of the decade in interactive intelligent systems
Efficiently learning the preferences of people
Machine Learning
Monte carlo methods for preference learning
LION'12 Proceedings of the 6th international conference on Learning and Intelligent Optimization
Observer effect from stateful resources in agent sensing
Autonomous Agents and Multi-Agent Systems
Guessing preferences: a new approach to multi-attribute ranking and selection
Proceedings of the Winter Simulation Conference
A survey of point-based POMDP solvers
Autonomous Agents and Multi-Agent Systems
Dynamic facts in large team information sharing
Proceedings of the 2013 international conference on Autonomous agents and multi-agent systems
Linear fitted-Q iteration with multiple reward functions
The Journal of Machine Learning Research
An active learning approach to home heating in the smart grid
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
MineralMiner: An active sensing simulation environment
Multiagent and Grid Systems
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Preference elicitation is a key problem facing the deployment of intelligent systems that make or recommend decisions on the behalf of users. Since not all aspects of a utility function have the same impact on object-level decision quality, determining which information to extract from a user is itself a sequential decision problem, balancing the amount of elicitation effort and time with decision quality. We formulate this problem as a partially-observable Markov decision process (POMDP). Because of the continuous nature of the state and action spaces of this POMDP, standard techniques cannot be used to solve it. We describe methods that exploit the special structure of preference elicitation to deal with parameterized belief states over the continuous state space, and gradient techniques for optimizing parameterized actions. These methods can be used with a number of different belief state representations, including mixture models.