Reachability Under Uncertainty

In this paper we introduce a new network reachability problem where the goal is to find the most reliable path between two nodes in a network, represented as a directed acyclic graph. Individual edges within this network may fail according to certain probabilities, and these failure probabilities may depend on the values of one or more hidden variables. This problem may be viewed as a generalization of shortest-path problems for finding minimum cost paths or Viterbi-type problems for finding highest-probability sequences of states, where the addition of the hidden variables introduces correlations that are not handled by previous algorithms. We give theoretical results characterizing this problem including an NP-hardness proof. We also give an exact algorithm and a more efficient approximation algorithm for this problem.

[1]  Francisco Casacuberta,et al.  Probabilistic finite-state machines - part II , 2005, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[2]  Francisco Casacuberta,et al.  Probabilistic finite-state machines - part I , 2005, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[3]  Prabhakar Raghavan,et al.  Randomized rounding: A technique for provably good algorithms and algorithmic proofs , 1985, Comb..

[4]  Ariel Orda Routing with end-to-end QoS guarantees in broadband networks , 1999, TNET.

[5]  Sartaj Sahni,et al.  General Techniques for Combinatorial Approximation , 1977, Oper. Res..

[6]  Matthew Brand,et al.  Stochastic Shortest Paths Via Quasi-convex Maximization , 2006, ESA.

[7]  Funda Ergün,et al.  An improved FPTAS for Restricted Shortest Path , 2002, Inf. Process. Lett..

[8]  Danny Raz,et al.  A simple efficient approximation scheme for the restricted shortest path problem , 2001, Oper. Res. Lett..

[9]  Leo Liberti,et al.  Introduction to Global Optimization , 2006 .

[10]  U. Rothblum,et al.  Integer Convex Maximization , 2006 .

[11]  Ronald Prescott Loui,et al.  Optimal paths in graphs with stochastic or multidimensional weights , 1983, Commun. ACM.

[12]  Noah A. Smith,et al.  Contrastive Estimation: Training Log-Linear Models on Unlabeled Data , 2005, ACL.

[13]  Reiner Horst,et al.  Introduction to Global Optimization (Nonconvex Optimization and Its Applications) , 2002 .

[14]  Fernando A. Kuipers,et al.  An overview of constraint-based path selection algorithms for QoS routing , 2002 .

[15]  R. Ravi,et al.  Bicriteria Network Design Problems , 1994, J. Algorithms.

[16]  Refael Hassin,et al.  Approximation Schemes for the Restricted Shortest Path Problem , 1992, Math. Oper. Res..