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National Research University Higher School of EconomicsStudent ThesesCompositional Process Model Synthesis based on Interface Patterns

Student
Title
Supervisor
Faculty
Educational Programme
Final Grade
Year of Graduation
Roman Nesterov
Compositional Process Model Synthesis based on Interface Patterns
System and Software Engineering
(Master’s programme)
2017
The primary purpose of this study is to develop a compositional approach to model and analyze multi-agent systems regarding the behavior of interacting agents.

Correct coordination of several multi-agent system components is an error-prone task since the interaction of several simple components can generate rather sophisticated behavior. Verification of such systems is very difficult or even impossible because of the so-called state space explosion problem when the size of the system reachability set grows exponentially on the number of interacting agents. To overcome this problem, several approaches to the construction of correct models for interacting agents in a compositional way have been proposed in the literature. Their authors define different properties and conditions to ensure correct behavior of interacting agents. Checking these conditions may be in its turn quite a problem. In this paper, we propose patterns for correct composition of component models. For justifying these patterns, we use morphisms on Petri nets. However, to apply patterns the user does not need to be familiar with the underlying theory.

The report has three main parts. The first chapter gives preliminary definitions which are used in the paper as well as provides a review of existing compositional approaches. The second chapter contains the formal framework for the proposed compositional approach to multi-agent system model synthesis. The last chapter describes a series of experiments on using our compositional approach in the context of automated multi-agent process discovery.

The primary results show that using patterns allows us to obtain structured multi-agent system models which let distinguish the behavior of agents and a scheme of their intercommunication. Moreover, this method preserves agent correctness properties in a complete multi-agent model.

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