1,268 research outputs found
Synthesis of Parametric Programs using Genetic Programming and Model Checking
Formal methods apply algorithms based on mathematical principles to enhance
the reliability of systems. It would only be natural to try to progress from
verification, model checking or testing a system against its formal
specification into constructing it automatically. Classical algorithmic
synthesis theory provides interesting algorithms but also alarming high
complexity and undecidability results. The use of genetic programming, in
combination with model checking and testing, provides a powerful heuristic to
synthesize programs. The method is not completely automatic, as it is fine
tuned by a user that sets up the specification and parameters. It also does not
guarantee to always succeed and converge towards a solution that satisfies all
the required properties. However, we applied it successfully on quite
nontrivial examples and managed to find solutions to hard programming
challenges, as well as to improve and to correct code. We describe here several
versions of our method for synthesizing sequential and concurrent systems.Comment: In Proceedings INFINITY 2013, arXiv:1402.661
Practical Distributed Control Synthesis
Classic distributed control problems have an interesting dichotomy: they are
either trivial or undecidable. If we allow the controllers to fully
synchronize, then synthesis is trivial. In this case, controllers can
effectively act as a single controller with complete information, resulting in
a trivial control problem. But when we eliminate communication and restrict the
supervisors to locally available information, the problem becomes undecidable.
In this paper we argue in favor of a middle way. Communication is, in most
applications, expensive, and should hence be minimized. We therefore study a
solution that tries to communicate only scarcely and, while allowing
communication in order to make joint decision, favors local decisions over
joint decisions that require communication.Comment: In Proceedings INFINITY 2011, arXiv:1111.267
Synthesizing Finite-state Protocols from Scenarios and Requirements
Scenarios, or Message Sequence Charts, offer an intuitive way of describing
the desired behaviors of a distributed protocol. In this paper we propose a new
way of specifying finite-state protocols using scenarios: we show that it is
possible to automatically derive a distributed implementation from a set of
scenarios augmented with a set of safety and liveness requirements, provided
the given scenarios adequately \emph{cover} all the states of the desired
implementation. We first derive incomplete state machines from the given
scenarios, and then synthesis corresponds to completing the transition relation
of individual processes so that the global product meets the specified
requirements. This completion problem, in general, has the same complexity,
PSPACE, as the verification problem, but unlike the verification problem, is
NP-complete for a constant number of processes. We present two algorithms for
solving the completion problem, one based on a heuristic search in the space of
possible completions and one based on OBDD-based symbolic fixpoint computation.
We evaluate the proposed methodology for protocol specification and the
effectiveness of the synthesis algorithms using the classical alternating-bit
protocol.Comment: This is the working draft of a paper currently in submission.
(February 10, 2014
PranCS: A protocol and discrete controller synthesis tool
© 2017, Springer International Publishing AG. PranCS is a tool for synthesizing protocol adapters and discrete controllers. It exploits general search techniques such as simulated annealing and genetic programming for homing in on correct solutions, and evaluates the fitness of candidates by using model-checking results. Our Proctocol and Controller Synthesis (PranCS) tool uses NuSMV as a back-end for the individual model-checking tasks and a simple candidate mutator to drive the search. PranCS is also designed to explore the parameter space of the search techniques it implements. In this paper, we use PranCS to study the influence of turning various parameters in the synthesis process
Parameterized Synthesis
We study the synthesis problem for distributed architectures with a
parametric number of finite-state components. Parameterized specifications
arise naturally in a synthesis setting, but thus far it was unclear how to
detect realizability and how to perform synthesis in a parameterized setting.
Using a classical result from verification, we show that for a class of
specifications in indexed LTL\X, parameterized synthesis in token ring networks
is equivalent to distributed synthesis in a network consisting of a few copies
of a single process. Adapting a well-known result from distributed synthesis,
we show that the latter problem is undecidable. We describe a semi-decision
procedure for the parameterized synthesis problem in token rings, based on
bounded synthesis. We extend the approach to parameterized synthesis in
token-passing networks with arbitrary topologies, and show applicability on a
simple case study. Finally, we sketch a general framework for parameterized
synthesis based on cutoffs and other parameterized verification techniques.Comment: Extended version of TACAS 2012 paper, 29 page
Synthesis of Protocols and Discrete Controllers
In this thesis, a number of search techniques are proposed as a solution for program and discrete controller synthesis (DCS). Classic synthesis techniques facilitate exhaus- tive search, while genetic programming has recently proven the potential of generic search techniques. But is genetic programming the right search technique for the synthesis prob- lem? In this thesis we challenge this belief and argue in favor of simulated annealing, a different class of general search techniques. We show that, in hindsight, the success of genetic programming has drawn from what is arguably a hybrid between simulated annealing and genetic programming, and compare the fitness of classic genetic program- ming, the hybrid form, and pure simulated annealing. Our experimental evaluation suggests that pure simulated annealing offers better results for automated programming than techniques based on genetic programming. Discrete Controller Synthesis (DCS) and Program Synthesis have similar goals: they are automated techniques to infer a control strategy and an implementation, respectively, that is correct by construction. We also investigate the application of the search tech- niques that we have been used for program synthesis for the computation of deterministic strategies solving symbolic Discrete Controller Synthesis (DCS) problems, where a model of the system under control is given along with desired objective behaviours. We experi- mentally confirm that relative performance results are similar to program synthesis, and give a complexity analysis of our simulated annealing algorithm for symbolic DCS. From the performance results we obtain, we draw the conclusion that simulated annealing, when combined with efficient model-checking techniques, is worth further investigating to solve symbolic DCS problems. A tool is designed to explore the parameter space of different synthesis techniques. Besides using it to synthesise a discrete control strategies for reactive systems (controller synthesis) and for protocol adapters for the coordination of different threads (software synthesis), we can also use it to study the influence of turning various screws in the syn- thesis process. For simulated annealing, PranCS allows the user to define the behaviour of the cooling schedule. For genetic programming, the user can select the population size
On the Limits and Practice of Automatically Designing Self-Stabilization
A protocol is said to be self-stabilizing when the distributed system executing it is guaranteed to recover from any fault that does not cause permanent damage. Designing such protocols is hard since they must recover from all possible states, therefore we investigate how feasible it is to synthesize them automatically. We show that synthesizing stabilization on a fixed topology is NP-complete in the number of system states. When a solution is found, we further show that verifying its correctness on a general topology (with any number of processes) is undecidable, even for very simple unidirectional rings. Despite these negative results, we develop an algorithm to synthesize a self-stabilizing protocol given its desired topology, legitimate states, and behavior. By analogy to shadow puppetry, where a puppeteer may design a complex puppet to cast a desired shadow, a protocol may need to be designed in a complex way that does not even resemble its specification. Our shadow/puppet synthesis algorithm addresses this concern and, using a complete backtracking search, has automatically designed 4 new self-stabilizing protocols with minimal process space requirements: 2-state maximal matching on bidirectional rings, 5-state token passing on unidirectional rings, 3-state token passing on bidirectional chains, and 4-state orientation on daisy chains
Towards 40 years of constraint reasoning
Research on constraints started in the early 1970s. We are approaching 40 years since the beginning of this successful field, and it is an opportunity to revise what has been reached. This paper is a personal view of the accomplishments in this field. We summarize the main achievements along three dimensions: constraint solving, modelling and programming. We devote special attention to constraint solving, covering popular topics such as search, inference (especially arc consistency), combination of search and inference, symmetry exploitation, global constraints and extensions to the classical model. For space reasons, several topics have been deliberately omitted.Partially supported by the Spanish project TIN2009-13591-C02-02 and Generalitat de Catalunya grant 2009-SGR-1434.Peer Reviewe
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