Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Conditional, hierarchical, multi-agent preferences
TARK '98 Proceedings of the 7th conference on Theoretical aspects of rationality and knowledge
Graphical models for preference and utility
UAI'95 Proceedings of the Eleventh conference on Uncertainty in artificial intelligence
Efficient utility functions for ceteris paribus preferences
Eighteenth national conference on Artificial intelligence
Logical Preference Representation and Combinatorial Vote
Annals of Mathematics and Artificial Intelligence
Decision Analysis
CUI networks: a graphical representation for conditional utility independence
AAAI'06 proceedings of the 21st national conference on Artificial intelligence - Volume 2
Journal of Artificial Intelligence Research
CUI networks: a graphical representation for conditional utility independence
Journal of Artificial Intelligence Research
Directional Decomposition of Multiattribute Utility Functions
ADT '09 Proceedings of the 1st International Conference on Algorithmic Decision Theory
Modeling challenges with influence diagrams: Constructing probability and utility models
Decision Support Systems
Preferences in AI: An overview
Artificial Intelligence
The local geometry of multiattribute tradeoff preferences
Artificial Intelligence
UAI'00 Proceedings of the Sixteenth 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
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We introduce a new class of graphical representations, expected utility networks (EUNs), and discuss some of its properties and potential applications to artificial intelligence and economic theory. In EUNs not only probabilities, but also utilities enjoy a modular representation. EUNs are undirected graphs with two types of arc, representing probability and utility dependencies respectively. The representation of utilities is based on a novel notion of conditional utility independence, which we introduce and discuss in the context of other existing proposals. Just as probabilistic inference involves the computation of conditional probabilities, strategic inference involves the computation of conditional expected utilities for alternative plans of action. We define a new notion of conditional expected utility (EU) independence, and show that in EUNs node separation with respect to the probability and utility subgraphs implies conditional EU independence.