Decision support and information fusion in complex domains requires reasoning about inherently uncertain properties of and relationships among varied and often unknown number of entities interacting in differing and often unspecified ways. Tractable probabilistic reasoning about such complex situations requires combining efficient inference with logical reasoning about which variables to include in a model and what the appropriate probability distributions are. This paper describes the PLASMA architecture for predicate logic based assembly of situation-specific probabilistic models. PLASMA maintains a declarative representation of a decision theoretically coherent first-order probabilistic domain theory. As evidence about a situation is absorbed and queries are processed, PLASMA uses logical inference to reason about which known and/or hypothetical entities to represent explicitly in the situation model, which known and/or uncertain relationships to represent, what functional forms and parameters to specify for the local distributions, and which exact or approximate inference and/or optimization techniques to apply. We report on a prototype implementation of the PLASMA architecture within IET’s Quiddity*Suite, a knowledge-based probabilistic reasoning toolkit. Examples from our application experience are discussed.