OF THE THESIS Partial Order Reduction for Planning by You Xu Master of Science in Computer Science Washington University in St. Louis, 2009 Research Advisor: Professor Yixin Chen Partial Order Reduction (POR) is a technique that reduces the search space by recognizing interchangeable orders between actions and expanding only a subset of all possible orders during the search. It has been extensi...

Partial order reduction (POR) and net unfoldings are two alternative methods to tackle statespace explosion caused by concurrency. In this paper, we propose the combination of both approaches in an effort to combine their strengths. We first define, for an abstract execution model, unfolding semantics parameterized over an arbitrary independence relation. Based on it, our main contribution is a...

In this first section we present a high level discussion on computational science, and the need for compact models of phenomena observed in nature and industry. We argue that much more complex problems can be addressed by making use of current computing technology and advanced algorithms, but that there is a need for model order reduction in order to cope with even more complex problems. We als...

Partial Order Reduction (POR) techniques improve the basic model checking algorithm by reducing the numbers of states and transitions explored in verifying a property of the model. In the “ample set” POR framework for the verification of an LTL −X formula φ, one associates to each state s a subset Ts of the set of all transitions enabled at s. The approach requires that whenever Ts is a proper ...

Several recently developed model order reduction methods for fast simulation of large-scale dynamical systems with two or more parameters are reviewed. Besides, an alternative approach for linear parameter system model reduction as well as a more efficient method for nonlinear parameter system model reduction are proposed in this paper. Comparison between different methods from theoretical eleg...

Systematic testing, first demonstrated in small, specialized cases 15 years ago, has matured sufficiently for large-scale systems developers to begin to put it into practice. With actual deployment come new, pragmatic challenges to the usefulness of the techniques. In this paper we are concerned with scaling dynamic partial order reduction, a key technique for mitigating the state space explosi...

A dynamic partial order reduction (DPOR) algorithm is optimal when it always explores at most one representative per Mazurkiewicz trace. Existing literature suggests that the reduction obtained by the non-optimal, state-of-the-art Source-DPOR (SDPOR) algorithm is comparable to optimal DPOR. We show the first program with O(n) Mazurkiewicz traces where SDPOR explores O(2 n) redundant schedules a...

We discuss linear time-invariant systems (LTI systems) of type ẋ = Ax + Bu, y = Cx + Du, where u is an input function, x is the state variable and y is the output of the dynamical system. Often one is only interested in the input-output properties of this system, which are of small size, whereas the state variable is typically large. Model order reduction (MOR) methods deal with these kind of s...

model order reduction is known as the problem of minimizing the -norm of the difference between the transfer function of the original system and the reduced one. in many papers, linear matrix inequality (lmi) approach is utilized to address the minimization problem. this paper deals with defining an extra matrix inequality constraint to guarantee that the minimum phase characteristic of the sys...