On the complexity of the maximum satisfiability problem for Horn formulas
Information Processing Letters
Finding MAPs for belief networks is NP-hard
Artificial Intelligence
Artificial intelligence: a modern approach
Artificial intelligence: a modern approach
Linear least-squares algorithms for temporal difference learning
Machine Learning - Special issue on reinforcement learning
The independent choice logic for modelling multiple agents under uncertainty
Artificial Intelligence - Special issue on economic principles of multi-agent systems
Context-specific multiagent coordination and planning with factored MDPs
Eighteenth national conference on Artificial intelligence
Visual exploration and incremental utility elicitation
Eighteenth national conference on Artificial intelligence
Constraint Processing
Semirings for Soft Constraint Solving and Programming (LECTURE NOTES IN COMPUTER SCIENCE)
Semirings for Soft Constraint Solving and Programming (LECTURE NOTES IN COMPUTER SCIENCE)
Compiling propositional weighted bases
Artificial Intelligence - Special issue on nonmonotonic reasoning
Compact value-function representations for qualitative preferences
UAI '04 Proceedings of the 20th conference on Uncertainty in artificial intelligence
Machine Learning
Preference-Based Organization Interfaces: Aiding User Critiques in Recommender Systems
UM '07 Proceedings of the 11th international conference on User Modeling
Practical solution techniques for first-order MDPs
Artificial Intelligence
Multi-Sensor Data Fusion: An Introduction
Multi-Sensor Data Fusion: An Introduction
Winning the DARPA grand challenge with an AI robot
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
Journal of Artificial Intelligence Research
First order decision diagrams for relational MDPs
Journal of Artificial Intelligence Research
Semiring-based constraint logic programming
IJCAI'97 Proceedings of the 15th international joint conference on Artifical intelligence - Volume 1
First-order probabilistic inference
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Symbolic dynamic programming for first-order MDPs
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 1
Lifted first-order probabilistic inference
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
SPOOK: a system for probabilistic object-oriented knowledge representation
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
UCP-networks: a directed graphical representation of conditional utilities
UAI'01 Proceedings of the Seventeenth conference on Uncertainty in artificial intelligence
Graphical models for preference and utility
UAI'95 Proceedings of the Eleventh conference on Uncertainty in artificial intelligence
UAI'97 Proceedings of the Thirteenth conference on Uncertainty in artificial intelligence
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Value functions are defined over a fixed set of outcomes. In work on preference handling in AI, these outcomes are usually a set of assignments over a fixed set of state variables. If the set of variables changes, a new value function must be elicited. Given that in most applications the state variables are properties (attributes) of objects in the world, this implies that the introduction of new objects requires re-elicitation of preferences. However, often, the user has in mind preferential information that is much more generic, and which is relevant to a given type of domain regardless of the precise number of objects of each kind and their properties. Such information requires the introduction of relational models. Following in the footsteps of work on probabilistic relational models (PRMs), we suggest in this work a rule-based, relational language of preferences. This language extends regular rule-based languages and leads to a much more flexible approach for specifying control rules for autonomous systems. It also extends standard generalized-additive value functions to handle a dynamic universe of objects. Given any specific set of objects this specification induces a generalized-additive value function over assignments to the controllable attributes associated with these objects. We then describe a prototype of a decision support system for command and control centers we developed to illustrate and study the use of these rules.