Computing Cost-Optimal Definitely Discriminating Tests

The goal of testing is to discriminate between multiple hypotheses about a system--for example, different fault diagnoses--by applying input patterns and verifying or falsifying the hypotheses from the observed outputs. Definitely discriminating tests (DDTs) are those input patterns that are guaranteed to discriminate between different hypotheses of non-deterministic systems. Finding DDTs is important in practice, but can be very expensive ( p 2-complete). Even more challenging is the problem of finding a DDT that minimizes the cost of the testing process, i.e., an input pattern that can be most cheaply enforced and that is a DDT. This paper addresses both problems. We show how we can transform a given problem into a Boolean structure in decomposable negation normal form (DNNF), and extract from it a Boolean formula whose models correspond to DDTs. This allows us to harness recent advances in both knowledge compilation and satisfiability for efficient and scalable DDT computation in p...

• Type: Conference
• Year: 2010
• Where: AAAI
• Authors: Anika Schumann, Jinbo Huang, Martin Sachenbacher
• Keywords: AAAI 2010, Hypotheses, Input Pattern, Intelligent Agents, Multiple Hypotheses