Invariant Analysis for Multi-Agent Graph Transformation Systems using k-Induction

Invariant Analysis for Multi-Agent Graph Transformation Systems using k-Induction
Author :
Publisher : Universitätsverlag Potsdam
Total Pages : 44
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ISBN-10 : 9783869565316
ISBN-13 : 3869565314
Rating : 4/5 (314 Downloads)

Book Synopsis Invariant Analysis for Multi-Agent Graph Transformation Systems using k-Induction by : Sven Schneider

Download or read book Invariant Analysis for Multi-Agent Graph Transformation Systems using k-Induction written by Sven Schneider and published by Universitätsverlag Potsdam. This book was released on 2022-11-17 with total page 44 pages. Available in PDF, EPUB and Kindle. Book excerpt: The analysis of behavioral models such as Graph Transformation Systems (GTSs) is of central importance in model-driven engineering. However, GTSs often result in intractably large or even infinite state spaces and may be equipped with multiple or even infinitely many start graphs. To mitigate these problems, static analysis techniques based on finite symbolic representations of sets of states or paths thereof have been devised. We focus on the technique of k-induction for establishing invariants specified using graph conditions. To this end, k-induction generates symbolic paths backwards from a symbolic state representing a violation of a candidate invariant to gather information on how that violation could have been reached possibly obtaining contradictions to assumed invariants. However, GTSs where multiple agents regularly perform actions independently from each other cannot be analyzed using this technique as of now as the independence among backward steps may prevent the gathering of relevant knowledge altogether. In this paper, we extend k-induction to GTSs with multiple agents thereby supporting a wide range of additional GTSs. As a running example, we consider an unbounded number of shuttles driving on a large-scale track topology, which adjust their velocity to speed limits to avoid derailing. As central contribution, we develop pruning techniques based on causality and independence among backward steps and verify that k-induction remains sound under this adaptation as well as terminates in cases where it did not terminate before.


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