Algebraic Meta-Theory of Processes with Data

Gebler, Daniel; Goriac, Eugen-Ioan; Mousavi, Mohammad Reza

doi:10.4204/eptcs.120.6

Cited by 5 publications

(7 citation statements)

References 32 publications

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“…This is even desired when we want the rules to be in a standard rule format [5,46]. However, rule formats for DSOS are deferred to future work, discussed in Section 8.1, where one would need to look at more recent works on formats for data [47,22] and for MSOS [15]. The rules for assignment are similar.…”

Section: Read-only Label Categories and A Let Constructmentioning

confidence: 99%

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Dynamic structural operational semantics

Johansen

Owe

2019

Journal of Logical and Algebraic Methods in Programming

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We introduce Dynamic SOS as a framework for describing semantics of programming languages that include dynamic software upgrades, for upgrading software code during run-time. Dynamic SOS (DSOS) is built on top of the Modular SOS of P. Mosses, with an underlying category theory formalization. The idea of Dynamic SOS is to bring out the essential differences between dynamic upgrade constructs and program execution constructs. The important feature of Modular SOS (MSOS) that we exploit in DSOS is the sharp separation of the program execution code from the additional (data) structures needed at run-time. In DSOS we aim to achieve the same modularity and decoupling for dynamic software upgrades. This is partly motivated by the long term goal of having machine-checkable proofs for general results like type safety.We exemplify Dynamic SOS on two languages supporting dynamic software upgrades, namely the C-like PROTEUS, which supports updating of variables, functions, records, or types at specific program points, and CREOL, which supports dynamic class upgrades in the setting of concurrent objects. Existing type analyses for software upgrades can be done on top of DSOS too, as we illustrate for PROTEUS.As a side result we define of a general encapsulating construction on Modular SOS useful in situations where a form of encapsulation of the execution is needed. We use encapsulation in the CREOL setting of concurrent object-oriented programming with active objects and asynchronous method calls.

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Section: Read-only Label Categories and A Let Constructmentioning

confidence: 99%

“…1. generating algebraic semantics [4,6,22] from specific forms of the transition rules; 2. compositional reasoning results wrt. dynamic logic [51,24] using specific forms of transition rules in the style of [21]; or 3. expressiveness results of the programming constructs specified within various rule formats.…”

Section: Possible Continuationsmentioning

confidence: 99%

Dynamic structural operational semantics

Johansen

Owe

2019

Journal of Logical and Algebraic Methods in Programming

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“…6.5 as entities in their own right, while in Hybrid χ the traces include 5-tuple configurations and 3-tuple transition labels. This complexity of rule configurations is arguably an impediment to proving equivalences based on the operational semantics, as there are relatively few properties of the language given in [12], however their format has been analysed for meta-theoretical results which we have not attempted here [86,87]. Although Pann is designed to describe specific neural networks and is not as general as Hybrid χ , we intend that the semantic framework is flexible enough to cope with extensions that would be required for other types of behaviour found in realistic neural networks, such as the growth of new neurons (neurogenesis; [88]).…”

Section: Formal Hybrid Languagesmentioning

confidence: 99%

Modelling and analysing neural networks using a hybrid process algebra

Colvin

2016

Theoretical Computer Science

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Please cite this article in press as: R.J. Colvin, Modeling and analysing neural networks using a hybrid process algebra, Theoret. Comput. Sci. (2015), http://dx.doi.org/10.1016/j.tcs. 2015.08.019 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.Modeling and analysing neural networks using a hybrid process algebra Robert J. ColvinThe University of Queensland, Queensland Brain Institute, Brisbane, Australia, 4072 AbstractResearch involving artificial neural networks has tended to be driven towards efficient computation, especially in the domain of pattern recognition, or towards elucidating biological processes in the brain. Models have become more detailed as our understanding of the biology of the brain has increased, incorporating real-time behaviour of individual neurons interacting within complex system structures and dynamics. There are few examples of abstract and fully formal models of biologically plausible neural networks: in the neural networks literature models are often presented as a mixture of mathematical equations and natural language, supported by simulation code and associated experimental results. The informality often hides or obscures important aspects of a particular model, and leaves a large conceptual gap between the model descriptions and the usually low-level programming code used to simulate them.The main contribution of this paper is formally modelling and analysing a biologically plausible neural network model from the literature that exhibits complex neuron-level behaviour and network-level structure. To achieve this a modelling language 'Pann' is developed, based on the process algebras CSP and Hybrid χ . It is designed to be convenient for mixing the behaviour of discrete events (such as a neuron spike) with mutable continuous and discrete variables (representing chemical properties of a neuron, for instance). Its behaviour is defined using an operational semantics, from which a set of general properties of the language is proved.The groundwork for the biological model is laid by first formalising some well-known concepts from the artificial neural networks domain, such as feedforward behaviour, backpropagation, and recurrent neural networks. The Pann model of a feedforward network, comprising a set of communicating processes representing individual neurons, is proved equivalent to the standard one-line calculation of feedforward behaviour.

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“…There has been a notable amount of effort put into developing algorithms for axiomatizations for GSOS-like systems [2,3,11], yet all involve several transformations of the original system before deriving the axioms. After implementing one such algorithm in the tool PREG Axiomatizer [4], a simpler method was developed in [18]. We slightly adapt that approach here by using an extended version of the prefix operation and by also showing how to axiomatize operations defined using rules with negative premises, not just positive ones.…”

Section: Axiom Schema Derivermentioning

confidence: 99%

“…In [18] we show how to use labels for operating with the data component. For Linda, the set of labels L is extended to triples of the from t D , −, t D ′ , where t D , t D ′ are open data terms from T(Σ D ), standing for the store before and, respectively, after the transition.…”

Section: Linda -Integrating Componentsmentioning

confidence: 99%

Meta SOS - A Maude Based SOS Meta-Theory Framework

Aceto

Goriac

Ingólfsdóttir

2013

Electron. Proc. Theor. Comput. Sci.

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Meta SOS is a software framework designed to integrate the results from the meta-theory of structural operational semantics (SOS). These results include deriving semantic properties of language constructs just by syntactically analyzing their rule-based definition, as well as automatically deriving sound and ground-complete axiomatizations for languages, when considering a notion of behavioural equivalence. This paper describes the Meta SOS framework by blending aspects from the meta-theory of SOS, details on their implementation in Maude, and running examples.

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Algebraic Meta-Theory of Processes with Data

Cited by 5 publications

References 32 publications

Dynamic structural operational semantics

Dynamic structural operational semantics

Modelling and analysing neural networks using a hybrid process algebra

Meta SOS - A Maude Based SOS Meta-Theory Framework

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