The rapid growth of data presents a significant challenge to the capability of traditional storage technologies to collect and manage data. Furthermore, we investigate variants of MSL with alternative semantics and we build bridges with interval temporal logics and with logics equipped with sabotage operators.
#Logicworks z meaning full#
We establish that the full logic MSL admits an undecidable satisfiability problem. For example, the satisfiability problem for the fragment of MSL with $\Diamond $, the difference modality $\langle \neq \rangle $ and separating conjunction $\ast $ is shown Tower-complete whereas the restriction either to $\Diamond $ and $\ast $ or to $\langle \neq \rangle $ and $\ast $ is only NP-complete. We analyse the decidability status and the computational complexity of several fragments of MSL, obtaining surprising results by design of proof methods that take into account the modal and separation features of MSL. With such a combination of operators, some fragments of MSL can be seen as genuine modal logics whereas some others capture standard separation logics, leading to an original language to speak about memory states. We introduce a modal separation logic MSL whose models are memory states from separation logic and the logical connectives include modal operators as well as separating conjunction and implication from separation logic. This result is beneficial because it opens up the opportunity of genuinely comparing analyst processes without revealing sensitive system details, as well as opening an opportunity towards improved decision-support via limited automation. The result is that we can express, in machine-readable and precise language, the abductive hypotheses than an analyst makes, and the results of evaluating them. With these examples in mind, we design a logic capable of expressing decision-making during incident analysis. It is the only logical operation which introduces any new idea.” We reference canonical examples as paradigms of decision-making during analysis. As Charles Peirce coined the term in 1900, “Abduction is the process of forming an explanatory hypothesis.
#Logicworks z meaning verification#
To reach this goal, we adapt tools from program verification that can capture and test abductive reasoning.
Such tools are lacking, and are a necessary (though not sufficient) component of a more scientific analysis process. Our goal is to provide the tools necessary for specifying precisely the reasoning process in incident analysis. Incident analysis is a good target for additional rigor because it is the most human-intensive part of incident response. We provide three contributions: an exploration of the extent of models merging that is necessary for success in computer science an introduction to the technical details of Separation Logic, which can be used for reasoning about other exhaustible resources and an introduction to (a subset of) the problems, process, and results of computer scientists for those outside the field.Ī scientific incident analysis is one with a methodical, justifiable approach to the human decision-making process.
#Logicworks z meaning software#
Furthermore, the history of Separation Logic for analysing programs provides a novel case for philosophers of science of how software engineers and computer scientists develop models and the components of such models. Seeking this intersection of two different senses of model provides a strategy for how computer scientists and logicians may be successful. When this occurs, both the logic and engineering benefit greatly. For these two senses of model-the engineering/conceptual and the logical-to merge in a genuine sense, each must maintain their norms of use from their home disciplines. Separation Logic is an interesting case because of its widespread success in verification tools. Scalability is a central problem, and some would even say the central problem, in appli- cations of logic in computer science. Separation Logic works because it merges the software engineer’s conceptual model of a program’s manipulation of computer memory with the logical model that interprets what sentences in the logic are true, and because it has a proof theory which aids in the crucial problem of scaling the reasoning task. Through the case of the history of Separation Logic, we explore how this assertion is more than idle poetry. One might poetically muse that computers have the essence both of logic and machines.
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