62,255 research outputs found
Probabilistic Logic, Probabilistic Regular Expressions, and Constraint Temporal Logic
The classic theorems of BĂŒchi and Kleene state the expressive equivalence of finite automata to monadic second order logic and regular expressions, respectively. These fundamental results enjoy applications in nearly every field of theoretical computer science. Around the same time as BĂŒchi and Kleene, Rabin investigated probabilistic finite automata. This equally well established model has applications ranging from natural language processing to probabilistic model checking.
Here, we give probabilistic extensions BĂŒchi\\\''s theorem and Kleene\\\''s theorem to the probabilistic setting. We obtain a probabilistic MSO logic by adding an expected second order quantifier. In the scope of this quantifier, membership is determined by a Bernoulli process. This approach turns out to be universal and is applicable for finite and infinite words as well as for finite trees. In order to prove the expressive equivalence of this probabilistic MSO logic to probabilistic automata, we show a Nivat-theorem, which decomposes a recognisable function into a regular language, homomorphisms, and a probability measure.
For regular expressions, we build upon existing work to obtain probabilistic regular expressions on finite and infinite words. We show the expressive equivalence between these expressions and probabilistic Muller-automata. To handle Muller-acceptance conditions, we give a new construction from probabilistic regular expressions to Muller-automata. Concerning finite trees, we define probabilistic regular tree expressions using a new iteration operator, called infinity-iteration. Again, we show that these expressions are expressively equivalent to probabilistic tree automata.
On a second track of our research we investigate Constraint LTL over multidimensional data words with data values from the infinite tree. Such LTL formulas are evaluated over infinite words, where every position possesses several data values from the infinite tree. Within Constraint LTL on can compare these values from different positions. We show that the model checking problem for this logic is PSPACE-complete via investigating the emptiness problem of Constraint BĂŒchi automata
LTLf and LDLf Monitoring: A Technical Report
Runtime monitoring is one of the central tasks to provide operational
decision support to running business processes, and check on-the-fly whether
they comply with constraints and rules. We study runtime monitoring of
properties expressed in LTL on finite traces (LTLf) and in its extension LDLf.
LDLf is a powerful logic that captures all monadic second order logic on finite
traces, which is obtained by combining regular expressions and LTLf, adopting
the syntax of propositional dynamic logic (PDL). Interestingly, in spite of its
greater expressivity, LDLf has exactly the same computational complexity of
LTLf. We show that LDLf is able to capture, in the logic itself, not only the
constraints to be monitored, but also the de-facto standard RV-LTL monitors.
This makes it possible to declaratively capture monitoring metaconstraints, and
check them by relying on usual logical services instead of ad-hoc algorithms.
This, in turn, enables to flexibly monitor constraints depending on the
monitoring state of other constraints, e.g., "compensation" constraints that
are only checked when others are detected to be violated. In addition, we
devise a direct translation of LDLf formulas into nondeterministic automata,
avoiding to detour to Buechi automata or alternating automata, and we use it to
implement a monitoring plug-in for the PROM suite
Propositional Dynamic Logic for Message-Passing Systems
We examine a bidirectional propositional dynamic logic (PDL) for finite and
infinite message sequence charts (MSCs) extending LTL and TLC-. By this kind of
multi-modal logic we can express properties both in the entire future and in
the past of an event. Path expressions strengthen the classical until operator
of temporal logic. For every formula defining an MSC language, we construct a
communicating finite-state machine (CFM) accepting the same language. The CFM
obtained has size exponential in the size of the formula. This synthesis
problem is solved in full generality, i.e., also for MSCs with unbounded
channels. The model checking problem for CFMs and HMSCs turns out to be in
PSPACE for existentially bounded MSCs. Finally, we show that, for PDL with
intersection, the semantics of a formula cannot be captured by a CFM anymore
Model Checking Markov Chains with Actions and State Labels
In the past, logics of several kinds have been proposed for reasoning about discrete- or continuous-time Markov chains. Most of these logics rely on either state labels (atomic propositions) or on transition labels (actions). However, in several applications it is useful to reason about both state-properties and action-sequences. For this purpose, we introduce the logic asCSL which provides powerful means to characterize execution paths of Markov chains with actions and state labels. asCSL can be regarded as an extension of the purely state-based logic asCSL (continuous stochastic logic). \ud
In asCSL, path properties are characterized by regular expressions over actions and state-formulas. Thus, the truth value of path-formulas does not only depend on the available actions in a given time interval, but also on the validity of certain state formulas in intermediate states.\ud
We compare the expressive power of CSL and asCSL and show that even the state-based fragment of asCSL is strictly more expressive than CSL if time intervals starting at zero are employed. Using an automaton-based technique, an asCSL formula and a Markov chain with actions and state labels are combined into a product Markov chain. For time intervals starting at zero we establish a reduction of the model checking problem for asCSL to CSL model checking on this product Markov chain. The usefulness of our approach is illustrated by through an elaborate model of a scalable cellular communication system for which several properties are formalized by means of asCSL-formulas, and checked using the new procedure
A Generic Framework for Reasoning about Dynamic Networks of Infinite-State Processes
We propose a framework for reasoning about unbounded dynamic networks of
infinite-state processes. We propose Constrained Petri Nets (CPN) as generic
models for these networks. They can be seen as Petri nets where tokens
(representing occurrences of processes) are colored by values over some
potentially infinite data domain such as integers, reals, etc. Furthermore, we
define a logic, called CML (colored markings logic), for the description of CPN
configurations. CML is a first-order logic over tokens allowing to reason about
their locations and their colors. Both CPNs and CML are parametrized by a color
logic allowing to express constraints on the colors (data) associated with
tokens. We investigate the decidability of the satisfiability problem of CML
and its applications in the verification of CPNs. We identify a fragment of CML
for which the satisfiability problem is decidable (whenever it is the case for
the underlying color logic), and which is closed under the computations of post
and pre images for CPNs. These results can be used for several kinds of
analysis such as invariance checking, pre-post condition reasoning, and bounded
reachability analysis.Comment: 29 pages, 5 tables, 1 figure, extended version of the paper published
in the the Proceedings of TACAS 2007, LNCS 442
Learning Linear Temporal Properties
We present two novel algorithms for learning formulas in Linear Temporal
Logic (LTL) from examples. The first learning algorithm reduces the learning
task to a series of satisfiability problems in propositional Boolean logic and
produces a smallest LTL formula (in terms of the number of subformulas) that is
consistent with the given data. Our second learning algorithm, on the other
hand, combines the SAT-based learning algorithm with classical algorithms for
learning decision trees. The result is a learning algorithm that scales to
real-world scenarios with hundreds of examples, but can no longer guarantee to
produce minimal consistent LTL formulas. We compare both learning algorithms
and demonstrate their performance on a wide range of synthetic benchmarks.
Additionally, we illustrate their usefulness on the task of understanding
executions of a leader election protocol
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