Advisor

Hansen, Eric

Committee Member

Banicescu, Ioana

Committee Member

Swan, J. Edward, II

Date of Degree

1-1-2014

Document Type

Graduate Thesis - Open Access

Major

Computer Science

Degree Name

Master of Science

Department

Department of Computer Science and Engineering

Abstract

A stochastic shortest path problem is an undiscounted infinite-horizon Markov decision process with an absorbing and costree target state, where the objective is to reach the target state while optimizing total expected cost. In almost all cases, the objective in solving a stochastic shortest path problem is to minimize the total expected cost to reach the target state. But in probabilistic model checking, it is also useful to solve a problem where the objective is to maximize the expected cost to reach the target state. This thesis considers the maximum-time stochastic shortest path problem, which is a special case of the maximum-cost stochastic shortest path problem where actions have unit cost. The contribution is an efficient approach to computing high-quality bounds on the optimal solution for this problem. The bounds are useful in themselves, but can also be used by other algorithms to accelerate search for an optimal solution.

URI

https://hdl.handle.net/11668/20375

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