16,183 research outputs found
Heuristic search for equivalence checking
Equivalence checking plays a crucial role in formal verification since it is a natural relation for expressing the matching of a system implementation against its specification. In this paper, we present an efficient procedure, based on heuristic search, for checking well-known bisimulation equivalences for concurrent systems specified through process algebras. The method tries to improve, with respect to other solutions, both the memory occupation and the time required for proving the equivalence of systems. A prototype has been developed to evaluate the approach on several examples of concurrent system specifications
Symmetry reduction and heuristic search for error detection in model checking
The state explosion problem is the main limitation of model checking. Symmetries in the system being verified can be exploited in order to avoid this problem by defining an equivalence (symmetry) relation on the states of the system, which induces a semantically equivalent quotient system of smaller size. On the other hand, heuristic search algorithms can be applied to improve the bug finding capabilities of model checking. Such algorithms use
heuristic functions to guide the exploration. Bestfirst
is used for accelerating the search, while A* guarantees optimal error trails if combined with admissible estimates. We analyze some aspects of combining both approaches, concentrating on the problem of finding the optimal path to the equivalence class of a given error state. Experimental
results evaluate our approach
Taming Numbers and Durations in the Model Checking Integrated Planning System
The Model Checking Integrated Planning System (MIPS) is a temporal least
commitment heuristic search planner based on a flexible object-oriented
workbench architecture. Its design clearly separates explicit and symbolic
directed exploration algorithms from the set of on-line and off-line computed
estimates and associated data structures. MIPS has shown distinguished
performance in the last two international planning competitions. In the last
event the description language was extended from pure propositional planning to
include numerical state variables, action durations, and plan quality objective
functions. Plans were no longer sequences of actions but time-stamped
schedules. As a participant of the fully automated track of the competition,
MIPS has proven to be a general system; in each track and every benchmark
domain it efficiently computed plans of remarkable quality. This article
introduces and analyzes the most important algorithmic novelties that were
necessary to tackle the new layers of expressiveness in the benchmark problems
and to achieve a high level of performance. The extensions include critical
path analysis of sequentially generated plans to generate corresponding optimal
parallel plans. The linear time algorithm to compute the parallel plan bypasses
known NP hardness results for partial ordering by scheduling plans with respect
to the set of actions and the imposed precedence relations. The efficiency of
this algorithm also allows us to improve the exploration guidance: for each
encountered planning state the corresponding approximate sequential plan is
scheduled. One major strength of MIPS is its static analysis phase that grounds
and simplifies parameterized predicates, functions and operators, that infers
knowledge to minimize the state description length, and that detects domain
object symmetries. The latter aspect is analyzed in detail. MIPS has been
developed to serve as a complete and optimal state space planner, with
admissible estimates, exploration engines and branching cuts. In the
competition version, however, certain performance compromises had to be made,
including floating point arithmetic, weighted heuristic search exploration
according to an inadmissible estimate and parameterized optimization
Weighted Automata Extraction from Recurrent Neural Networks via Regression on State Spaces
We present a method to extract a weighted finite automaton (WFA) from a
recurrent neural network (RNN). Our algorithm is based on the WFA learning
algorithm by Balle and Mohri, which is in turn an extension of Angluin's
classic \lstar algorithm. Our technical novelty is in the use of
\emph{regression} methods for the so-called equivalence queries, thus
exploiting the internal state space of an RNN to prioritize counterexample
candidates. This way we achieve a quantitative/weighted extension of the recent
work by Weiss, Goldberg and Yahav that extracts DFAs. We experimentally
evaluate the accuracy, expressivity and efficiency of the extracted WFAs.Comment: AAAI 2020. We are preparing to distribute the implementatio
Graph Subsumption in Abstract State Space Exploration
In this paper we present the extension of an existing method for abstract
graph-based state space exploration, called neighbourhood abstraction, with a
reduction technique based on subsumption. Basically, one abstract state
subsumes another when it covers more concrete states; in such a case, the
subsumed state need not be included in the state space, thus giving a
reduction. We explain the theory and especially also report on a number of
experiments, which show that subsumption indeed drastically reduces both the
state space and the resources (time and memory) needed to compute it.Comment: In Proceedings GRAPHITE 2012, arXiv:1210.611
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