Classification via sequential testing

The problem of generating the sequence of tests required to reach a diagnostic conclusion with minimum average cost, which is also known as test sequencing problem, is considered. The test sequencing problem is formulated as an optimal binary AND/OR decision tree construction problem, whose solution is known to be NP-complete. The problem can be solved optimally using dynamic programming or AND/OR graph search methods (AO*, CF, and HS). However, for large systems, the associated computational effort with dynamic programming or AND/OR graph search methods is substantial, due to the rapidly increasing number of nodes in AND/OR search graph. In order to prevent the computational explosion, one-step or multistep lookahead heuristic algorithms have been developed to solve the test sequencing problem. Our approach is based on integrating concepts from the one-step lookahead heuristic algorithms and the strategies used in Huffman coding. The effectiveness of the algorithms is demonstrated on several test cases. The traditional test sequencing problem is generalized here to include asymmetrical tests. Our approach to test sequencing can be adapted to solve a wide variety of binary identification problems arising in decision table programming, medical diagnosis, database query processing, quality assurance, and pattern recognition

Minimum power multicasting with delay bound constraints in Ad Hoc wireless networks

In this paper, we design a new heuristic for an important extension of the minimum power multicasting problem in ad hoc wireless networks. Assuming that each transmission takes a fixed amount of time, we impose constraints on the number of hops allowed to reach the destination nodes in the multicasting application. This setting would be applicable in time critical or real time applications, and the relative importance of the nodes may be indicated by these delay bounds. We design a filtered beam search procedure for solving this problem. The performance of our algorithm is demonstrated on numerous test cases by benchmarking it against an optimal algorithm in small problem instances, and against a modified version of the well-known Broadcast Incremental Power (BIP) algorithm 20 for relatively large problems

Bottom-up construction of minimum-cost and/or trees for sequential fault diagnosis

The problem of generating the sequence of tests required to reach a diagnostic conclusion with minimum average cost, which is also known as test sequencing problem, is considered. The traditional test sequencing problem is generalized here to include asymmetrical tests. In general, the next test to execute depends on the results of previous tests. Hence the test sequencing problem can naturally be formulated as an optimal binary AND/OR decision tree construction problem, whose solution is known to be NP-complete. Our approach is based on integrating concepts from one-step look-ahead heuristic algorithms and basic ideas of Huffman coding to construct AND/OR decision tree bottom-up as opposed to heuristics proposed in the literature that construct the AND/OR trees top-down. The performance of the algorithm is demonstrated on numerous test cases, with various properties