Operations Research
Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Decision analysis and expert systems
AI Magazine
Computers and Intractability: A Guide to the Theory of NP-Completeness
Computers and Intractability: A Guide to the Theory of NP-Completeness
A Guide to the Literature on Learning Probabilistic Networks from Data
IEEE Transactions on Knowledge and Data Engineering
Evidence Propagation and Value of Evidence on Influence Diagrams
Operations Research
A review of explanation methods for heuristic expert systems
The Knowledge Engineering Review
The effects of structural characteristics of explanations on use of a DSS
Decision Support Systems
Processing partially specified queries over high-dimensional databases
Data & Knowledge Engineering
Explaining clinical decisions by extracting regularity patterns
Decision Support Systems
A PGM framework for recursive modeling of players in simple sequential Bayesian games
International Journal of Approximate Reasoning
Explanation of Bayesian Networks and Influence Diagrams in Elvira
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
| Hi-index | 0.00 |
In decision-making problems under uncertainty, a decision table consists of a set of attributes indicating what is the optimal decision (response) within the different scenarios defined by the attributes. We recently introduced a method to give explanations of these responses. In this paper, the method is extended. To do this, it is combined with a query system to answer expert questions about the preferred action for a given instantiation of decision table attributes. The main difficulty is to accurately answer queries associated with incomplete instantiations. Incomplete instantiations are the result of the evaluation of a partial model outputting decision tables that only include a subset of the whole problem, leading to uncertain responses. Our proposal establishes an automatic and interactive dialogue between the decision-support system and the expert to elicit information from the expert to reduce uncertainty. Typically, the process involves learning a Bayesian network structure from a relevant part of the decision table and computing some interesting conditional probabilities that are revised accordingly.