A compositional approach to probabilistic knowledge compilation

Reducing the Cost of Probabilistic Knowledge Compilation

Bayesian networks (BN) are a popular representation for reasoning under uncertainty. The computational complexity of inference, however, hinders its applicability to many real-world domains that in principle can be modeled by BNs. Inference methods based on Weighted Model Counting (WMC) reduce the cost of inference by exploiting patterns exhibited by the probabilities associated with BN nodes. ...

Aggregation in Probabilistic Databases via Knowledge Compilation

This paper presents a query evaluation technique for positive relational algebra queries with aggregates on a representation system for probabilistic data based on the algebraic structures of semiring and semimodule. The core of our evaluation technique is a procedure that compiles semimodule and semiring expressions into so-called decomposition trees, for which the computation of the probabili...

A Probabilistic Approach to Remote Compositional Analysis of Planetary Surfaces

A Probabilistic Approach to Knowledge Translation

In this paper, we focus on a novel knowledge reuse scenario where the knowledge in the source schema needs to be translated to a semantically heterogeneous target schema. We refer to this task as “knowledge translation” (KT). Unlike data translation and transfer learning, KT does not require any data from the source or target schema. We adopt a probabilistic approach to KT by representing the k...

Strategic knowledge in design: a compositional approach

In interactive design processes, strategic decisions are made at different levels. To support designers, design support systems need to include corresponding strategic knowledge at these levels. In this paper three levels of strategic interaction and strategic knowledge are identified within a compositional model of design. These levels are identified in reasoning about the manipulation of requ...