Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Decomposition of multiattribute expected-utility functions
Annals of Operations Research
Modular utility representation for decision-theoretic planning
Proceedings of the first international conference on Artificial intelligence planning systems
An efficient algorithm for finding the M most probable configurationsin probabilistic expert systems
Statistics and Computing
UCP-Networks: A Directed Graphical Representation of Conditional Utilities
UAI '01 Proceedings of the 17th Conference in Uncertainty in Artificial Intelligence
Decision Analysis
Reasoning with conditional ceteris paribus preference statements
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
Graphical models for preference and utility
UAI'95 Proceedings of the Eleventh conference on Uncertainty in artificial intelligence
Weighted Description Logics Preference Formulas for Multiattribute Negotiation
SUM '09 Proceedings of the 3rd International Conference on Scalable Uncertainty Management
Directional Decomposition of Multiattribute Utility Functions
ADT '09 Proceedings of the 1st International Conference on Algorithmic Decision Theory
Preferences in AI: An overview
Artificial Intelligence
The local geometry of multiattribute tradeoff preferences
Artificial Intelligence
Computing utility from weighted description logic preference formulas
DALT'09 Proceedings of the 7th international conference on Declarative Agent Languages and Technologies
| Hi-index | 0.00 |
We introduce CUI networks, a compact graphical representation of utility functions over multiple attributes. CUI networks model multiattribute utility functions using the well-studied and widely applicable utility independence concept. We show how conditional utility independence leads to an effective functional decomposition that can be exhibited graphically, and how local, compact data at the graph nodes can be used to calculate joint utility. We discuss aspects of elicitation, network construction, and optimization, and contrast our new representation with previous graphical preference modeling.