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This paper is concerned with constructions that express probability and their interaction with the indicative and subjunctive mood, as well as with other contextual elements. In detail, the paper deals with the constructions [sin duda + indicative/subjunctive], [tal vez + indicative/subjunctive], [probablemente + indicative/subjunctive] and [posiblemente + indicative/subjunctive]. In their interaction with mood, the constructions are understood as different microconstructions. For example, [sin duda + indicative] and [sin duda + subjunctive] are seen as different microconstructions of the superordinate
mesoconstruction [modal construction (of probability) + verb]. In a qualitative analysis examples from the CREA, CORPES XXI, and CdE corpora are examined regarding the interaction of [expression of probability] + [mood]. Following the Principle of No Synonymy of Grammatical Forms, the analysis confirms that the use of mood additionally influences the expressed degree of probability of the constructions. For instance, while probablemente generally expresses a slightly higher probability than posiblemente, a fine-tuned analysis
shows that the expressed degrees of probability of [probablemente + subjunctive] and [posiblemente + indicative] are highly similar. This is also often confirmed by further contextual information. In summary, the paper shows that Cognitive Construction Grammar is
a very suitable background against which to investigate modal phenomena, as cognitive approaches generally deal with the ways in which language users conceptualize the world from their own point of view, and as expressions of modality, more precisely, probability,
are also closely related to speakers’ attitudes or perspectives.
The analysis of behavioral models is of high importance for cyber-physical systems, as the systems often encompass complex behavior based on e.g. concurrent components with mutual exclusion or probabilistic failures on demand. The rule-based formalism of probabilistic timed graph transformation systems is a suitable choice when the models representing states of the system can be understood as graphs and timed and probabilistic behavior is important. However, model checking PTGTSs is limited to systems with rather small state spaces.
We present an approach for the analysis of large scale systems modeled as probabilistic timed graph transformation systems by systematically decomposing their state spaces into manageable fragments. To obtain qualitative and quantitative analysis results for a large scale system, we verify that results obtained for its fragments serve as overapproximations for the corresponding results of the large scale system. Hence, our approach allows for the detection of violations of qualitative and quantitative safety properties for the large scale system under analysis. We consider a running example in which we model shuttles driving on tracks of a large scale topology and for which we verify that shuttles never collide and are unlikely to execute emergency brakes. In our evaluation, we apply an implementation of our approach to the running example.