Exploiting System Hierarchy to Compute Repair Plans in Probabilistic Model-based Diagnosis
This addresses the problem of efficiently isolating faults and recommending repairs in complex systems, though it is incremental as it builds on existing model-based diagnosis methods.
The paper tackles the intractability of computing optimal repair policies in model-based diagnosis by introducing a hierarchical system specification to make optimal repair sequence computation tractable for large systems, achieving an approach suitable for offline precomputation with trade-offs in inference time and strategy quality.
The goal of model-based diagnosis is to isolate causes of anomalous system behavior and recommend inexpensive repair actions in response. In general, precomputing optimal repair policies is intractable. To date, investigators addressing this problem have explored approximations that either impose restrictions on the system model (such as a single fault assumption) or compute an immediate best action with limited lookahead. In this paper, we develop a formulation of repair in model-based diagnosis and a repair algorithm that computes optimal sequences of actions. This optimal approach is costly but can be applied to precompute an optimal repair strategy for compact systems. We show how we can exploit a hierarchical system specification to make this approach tractable for large systems. When introducing hierarchy, we also consider the tradeoff between simply replacing a component and decomposing it to repair its subcomponents. The hierarchical repair algorithm is suitable for off-line precomputation of an optimal repair strategy. A modification of the algorithm takes advantage of an iterative deepening scheme to trade off inference time and the quality of the computed strategy.