Conflict-Driven Diagnosis using Relational Aggregations

Conflict-driven problem solvers such as GDE use previously discovered conflicts to guide further search through the candidate space. To do so, ATMS-based problem solvers employ an inference engine that performs two fundamentally different tasks: Checking a given assumption set for consistency and predicting values for system variables under given assumptions. In this paper, we show how separating the tasks of conflict search and prediction of values leads to a problem solver that can guarantee completeness and correctness of the consistency check for a given assumption set, and how this can be used to effectively guide the search for minimal conflicts. We develop an inference engine based on aggregation of relational models that can be shown to have the above properties, provided that three basic operations on relations are available. As a consequence, we can complement conflict-driven diagnosis by a new paradigm called consistency-driven search for conflicts. To illustrate these points, we present a diagnostic algorithm that exploits and implements these ideas by operating on binary aggregation trees. We argue that this algorithm is especially suited for the application in on-board diagnostic environments, where the set of observable variables remains fixed.