2012/02/14 by Radu Marinescu, Marinescu, Radu, Nic Wilson +1
Computer Science · Decision Sciences · #Artificial Intelligence (cs.AI) #Bayesian Modeling and Causal Inference #Data Management and Algorithms #FOS: Computer and information sciences #Multi-Criteria Decision Making
paper · pdf · doi:10.48550/arxiv.1202.3745
openalex publication_date 2012/02/14 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
In this paper, we develop a qualitative theory of influence diagrams that can be used to model and solve sequential decision making tasks when only qualitative (or imprecise) information is available. Our approach is based on an order-of-magnitude approximation of both probabilities and utilities and allows for specifying partially ordered preferences via sets of utility values. We also propose a dedicated variable elimination algorithm that can be applied for solving order-of-magnitude influence diagrams.