Operations Research
Valuation-based systems for Bayesian decision analysis
Operations Research
d-Separation: From Theorems to Algorithms
UAI '89 Proceedings of the Fifth Annual Conference on Uncertainty in Artificial Intelligence
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Probabilistic inference in influence diagrams
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Graphical Models as Languages for Computer Assisted Diagnosis and Decision Making
ECSQARU '01 Proceedings of the 6th European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty
Decomposition of Influence Diagrams
ECSQARU '01 Proceedings of the 6th European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems - New trends in probabilistic graphical models
Node deletion sequences in influence diagrams using genetic algorithms
Statistics and Computing
Learning a decision maker's utility function from (possibly) inconsistent behavior
Artificial Intelligence
A comparison of two approaches for solving unconstrained influence diagrams
International Journal of Approximate Reasoning
Learning a decision maker's utility function from (possibly) inconsistent behavior
Artificial Intelligence
A forward-backward Monte Carlo method for solving influence diagrams
International Journal of Approximate Reasoning
A PGM framework for recursive modeling of players in simple sequential Bayesian games
International Journal of Approximate Reasoning
Variable elimination for influence diagrams with super value nodes
International Journal of Approximate Reasoning
Sequential decision making with partially ordered preferences
Artificial Intelligence
Unconstrained influence diagrams
UAI'02 Proceedings of the Eighteenth conference on Uncertainty in artificial intelligence
Representing and solving asymmetric Bayesian decision problems
UAI'00 Proceedings of the Sixteenth conference on Uncertainty in artificial intelligence
Evaluating influence diagrams using LIMIDs
UAI'00 Proceedings of the Sixteenth conference on Uncertainty in artificial intelligence
| Hi-index | 0.00 |
Influence diagrams serve as a powerful tool for modelling symmetric decision problems. When solving an influence diagram we determine a set of strategies for the decisions involved. A strategy for a decision variable is in principle a function over its past. However, some of the past may be irrelevant for the decision, and for computational reasons it is important not to deal with redundant variables in the strategies. We show that current methods (e.g. the Decision Bayes-ball algorithm [Shachter, 1998]) do not determine the relevant past, and we present a complete algorithm. Actually, this paper takes a more general outset: When formulating a decision scenario as an influence diagram, a linear temporal ordering of the decisions variables is required. This constraint ensures that the decision scenario is welldefined. However, the structure of a decision scenario often yields certain decisions conditionally independent, and it is therefore unnecessary to impose a linear temporal ordering on the decisions. In this paper we deal with partial influence diagrams i.e. influence diagrams with only a partial temporal ordering specified. We present a set of conditions which are necessary and sufficient to ensure that a partial influence diagram is welldefined. These conditions are used as a basis for the construction of an algorithm for determining whether or not a partial influence diagram is welldefined.