35,607 research outputs found
On Skolem-Hardness and Saturation Points in Markov Decision Processes
The Skolem problem and the related Positivity problem for linear recurrence sequences are outstanding number-theoretic problems whose decidability has been open for many decades. In this paper, the inherent mathematical difficulty of a series of optimization problems on Markov decision processes (MDPs) is shown by a reduction from the Positivity problem to the associated decision problems which establishes that the problems are also at least as hard as the Skolem problem as an immediate consequence. The optimization problems under consideration are two non-classical variants of the stochastic shortest path problem (SSPP) in terms of expected partial or conditional accumulated weights, the optimization of the conditional value-at-risk for accumulated weights, and two problems addressing the long-run satisfaction of path properties, namely the optimization of long-run probabilities of regular co-safety properties and the model-checking problem of the logic frequency-LTL. To prove the Positivity- and hence Skolem-hardness for the latter two problems, a new auxiliary path measure, called weighted long-run frequency, is introduced and the Positivity-hardness of the corresponding decision problem is shown as an intermediate step. For the partial and conditional SSPP on MDPs with non-negative weights and for the optimization of long-run probabilities of constrained reachability properties (aU b), solutions are known that rely on the identification of a bound on the accumulated weight or the number of consecutive visits to certain sates, called a saturation point, from which on optimal schedulers behave memorylessly. In this paper, it is shown that also the optimization of the conditional value-at-risk for the classical SSPP and of weighted long-run frequencies on MDPs with non-negative weights can be solved in pseudo-polynomial time exploiting the existence of a saturation point. As a consequence, one obtains the decidability of the qualitative model-checking problem of a frequency-LTL formula that is not included in the fragments with known solutions
Parameterized Linear Temporal Logics Meet Costs: Still not Costlier than LTL
We continue the investigation of parameterized extensions of Linear Temporal
Logic (LTL) that retain the attractive algorithmic properties of LTL: a
polynomial space model checking algorithm and a doubly-exponential time
algorithm for solving games. Alur et al. and Kupferman et al. showed that this
is the case for Parametric LTL (PLTL) and PROMPT-LTL respectively, which have
temporal operators equipped with variables that bound their scope in time.
Later, this was also shown to be true for Parametric LDL (PLDL), which extends
PLTL to be able to express all omega-regular properties.
Here, we generalize PLTL to systems with costs, i.e., we do not bound the
scope of operators in time, but bound the scope in terms of the cost
accumulated during time. Again, we show that model checking and solving games
for specifications in PLTL with costs is not harder than the corresponding
problems for LTL. Finally, we discuss PLDL with costs and extensions to
multiple cost functions.Comment: In Proceedings GandALF 2015, arXiv:1509.0685
Weighted Branching Simulation Distance for Parametric Weighted Kripke Structures
This paper concerns branching simulation for weighted Kripke structures with
parametric weights. Concretely, we consider a weighted extension of branching
simulation where a single transitions can be matched by a sequence of
transitions while preserving the branching behavior. We relax this notion to
allow for a small degree of deviation in the matching of weights, inducing a
directed distance on states. The distance between two states can be used
directly to relate properties of the states within a sub-fragment of weighted
CTL. The problem of relating systems thus changes to minimizing the distance
which, in the general parametric case, corresponds to finding suitable
parameter valuations such that one system can approximately simulate another.
Although the distance considers a potentially infinite set of transition
sequences we demonstrate that there exists an upper bound on the length of
relevant sequences, thereby establishing the computability of the distance.Comment: In Proceedings Cassting'16/SynCoP'16, arXiv:1608.0017
Bayesian Verification under Model Uncertainty
Machine learning enables systems to build and update domain models based on
runtime observations. In this paper, we study statistical model checking and
runtime verification for systems with this ability. Two challenges arise: (1)
Models built from limited runtime data yield uncertainty to be dealt with. (2)
There is no definition of satisfaction w.r.t. uncertain hypotheses. We propose
such a definition of subjective satisfaction based on recently introduced
satisfaction functions. We also propose the BV algorithm as a Bayesian solution
to runtime verification of subjective satisfaction under model uncertainty. BV
provides user-definable stochastic bounds for type I and II errors. We discuss
empirical results from an example application to illustrate our ideas.Comment: Accepted at SEsCPS @ ICSE 201
Chaining Test Cases for Reactive System Testing (extended version)
Testing of synchronous reactive systems is challenging because long input
sequences are often needed to drive them into a state at which a desired
feature can be tested. This is particularly problematic in on-target testing,
where a system is tested in its real-life application environment and the time
required for resetting is high. This paper presents an approach to discovering
a test case chain---a single software execution that covers a group of test
goals and minimises overall test execution time. Our technique targets the
scenario in which test goals for the requirements are given as safety
properties. We give conditions for the existence and minimality of a single
test case chain and minimise the number of test chains if a single test chain
is infeasible. We report experimental results with a prototype tool for C code
generated from Simulink models and compare it to state-of-the-art test suite
generators.Comment: extended version of paper published at ICTSS'1
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Strategic Behaviour under Regulation Benchmarking
Liberalisation of generation and supply activities in the electricity sectors is often followed by regulatory reform of distribution networks. In order to improve the efficiency of distribution utilities, some regulators have adopted incentive regulation schemes that rely on performance benchmarking. Although regulation benchmarking can influence the �regulation game�, the subject has received limited attention. This paper discusses how strategic behaviour can result in inefficient behaviour by firms. We also present a survey of issues encountered by electricity regulators. We then use the Data Envelopment Analysis (DEA) method with US utility data to examine implications of selected cases of strategic behaviour. The results show that gaming can have significant effects on the measured performance and profitability of firms
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