Languages Representable by Vertex-Labeled Graphs

Grunsky, Igor; Kurganskyy, Oleksiy; Potapov, Igor

doi:10.1007/11549345_38

Cited by 3 publications

(1 citation statement)

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“…Development of the models and methods for the automata theory and experiments, determining the general methodology of the behavioral test analysis, removes the upper bounds of applicability of the test automata models [13,14]. Complexity reduction, first of all, the reduction of the time of behavioral tests synthesis in a class of errors of mapping with the substantially saved values of the check completeness, is reached by:  the distributed network [15,16,17] and throughhierarchical [18] methods, reducing polynomially the dimension of the test synthesis and its duration due to the decomposition;  the evolutionary-genetic methods [19][20][21], which have the upper bounds of complexity of the determined methods and give polynomially smaller experimental values of complexity of the tests synthesis for the majority of cases [22]. Nevertheless, the synthesis of behavioral tests of the acceptable check completeness is possible for DIS with an average degree of complexity; it refers to the test of NP-complex class despite this reduction.…”

Section: Analysis Of Existing Solutionsmentioning

confidence: 99%

The Hybrid Agent Model of Behavioral Testing

Sugak¹,

Martynyuk²,

Drozd³

2015

IJC

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Operation testing and diagnostic tests, applied for distributed information systems, inherit and employ the properties of distribution, autonomy, goal formation and cooperation, natural for the multi-agent systems. This paper presents the behavioral diagnostics agent model, based on the evolutionary organization of component tests in the automata network environment. The model can be used to construct a multi-agent diagnostics system. A hybrid agent model provides a combination of reactive operation testing and deliberative diagnostic tests, based on the deterministic and evolutionary methods of synthesis of behavioral tests. An agent model consists of the component models of allocation environment, functioning goals and strategies, operations of observation, enforcement strategy and adaptation, initial component models, goals and strategies for ensuring the autonomy. Agent intelligence is based on a locally-exhaustive deterministic and pseudorandom targeted evolutionary synthesis of behavioral tests, providing and accumulating the results. Cooperation of the agents involves their deterministic and evolutionary interactions under the conditions of test feasibility and portability.

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