Experiments with multiple abstraction heuristics in symbolic verification

Authors:
Kairong Qian;Albert Nymeyer;Steven Susanto
Affiliations:
School of Computer Science & Engineering, The University of New South Wales, Sydney, Australia;School of Computer Science & Engineering, The University of New South Wales, Sydney, Australia;School of Computer Science & Engineering, The University of New South Wales, Sydney, Australia
Venue:
SARA'05 Proceedings of the 6th international conference on Abstraction, Reformulation and Approximation
Year:
2005

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Abstract

In this work we investigate a symbolic heuristic search algorithm in a model checker. The symbolic search algorithm is built on a system that manipulates binary decision diagrams (BDDs). We study the performance of the search algorithm in terms of the number of BDD operations, size of the BDDs, number of nodes they contain and run-time. We study the heuristic distribution of the state space, we measure effort by computing the mean heuristic value, and we compare single and multiple heuristics. In the case of multiple heuristics, we consider admissible and non-admissible merge strategies. We experiment on problems from a variety of domains. We find that multiple heuristics can perform significantly worse than single heuristics in symbolic search in at least one domain. In general, the effect of the heuristics on the symbolic search in the different domains varies markedly, and we conjecture that the different behaviour is caused by intrinsic differences in the characteristics of the state space.