Download Automated Technology for Verification and Analysis: 10th by Shuyuan Zhang, Sharad Malik, Rick McGeer (auth.), Supratik PDF

By Shuyuan Zhang, Sharad Malik, Rick McGeer (auth.), Supratik Chakraborty, Madhavan Mukund (eds.)

This e-book constitutes the completely refereed lawsuits of the tenth foreign Symposium on automatic know-how for Verification and research, ATVA 2012, held at Thiruvananthapuram, Kerala, India, in October 2012. The 25 commonplace papers, three invited papers and four software papers offered have been conscientiously chosen from a number of submissions. convention papers are equipped in nine technical classes, masking the subjects of automata thought, logics and proofs, version checking, software program verification, synthesis, verification and parallelism, probabilistic verification, constraint fixing and purposes, and probabilistic systems.

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Read or Download Automated Technology for Verification and Analysis: 10th International Symposium, ATVA 2012, Thiruvananthapuram, India, October 3-6, 2012. Proceedings PDF

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Additional info for Automated Technology for Verification and Analysis: 10th International Symposium, ATVA 2012, Thiruvananthapuram, India, October 3-6, 2012. Proceedings

Example text

Wm , ym )}. We construct an LDFA A = Ln , Q, Σ, δ, Q0, F as described below. – The states Q are defined as follows. Let P be the set of all prefixes of the words w1 , . . , wm . Each prefix w ∈ P induces a state qw . Note that prefixes that are common to more than one word contribute one element to P , and therefore induce one state in Q. In addition, there is a state qsink . – For all w ∈ P and σ ∈ Σ, if wσ ∈ P , then δ(qw , σ, qwσ ) = ; Otherwise, δ(qw , σ, qsink ) = . In addition, δ(qsink , σ, qsink ) = for all σ ∈ Σ.

For each such variable rj we add the equadr tion dtj = 0 to the system of ODEs. This will reflect the fact that once the initial value of a rate constant has been sampled, this value will not change during the process of generating a trajectory. Naturally, different trajectories can have different initial values. A key idea is to compactly store the generated set of trajectories as a DBN. This is achieved by exploiting the network structure and by simple counting. First we specify one random variable Yi for each variable yi of the ODE model For each unknown rate constant rj , we add one random variable Rj .

It is shown in [13] that the minimization problem for LDFAs is NP-complete in general. As good news, it is also shown in [13] that even though it is impossible to define a right congruence even when the LDFA is defined with respect to a fully-ordered lattice (indeed, the LDFA in Figure 2 is defined with respect to L4 ), it is possible to minimize such LDFAs in polynomial time. The multi-valued setting makes it possible to combine reasoning about weighted and lattice automata with approximation.

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