Inference with Idempotent Valuations
This work addresses inference challenges in systems with idempotent valuations, which is incremental as it builds on existing valuation-based frameworks.
The paper tackles the problem of performing inference in valuation-based systems that satisfy an idempotent property by defining a lattice structure and introducing two representation strategies for valuations. It results in efficient computation methods, with specific applications to finite sets and convex polytopes.
Valuation based systems verifying an idempotent property are studied. A partial order is defined between the valuations giving them a lattice structure. Then, two different strategies are introduced to represent valuations: as infimum of the most informative valuations or as supremum of the least informative ones. It is studied how to carry out computations with both representations in an efficient way. The particular cases of finite sets and convex polytopes are considered.