Given a set of interacting components with non-deterministic variable update and given safety requirements, the goal of priority synthesis is to restrict, by means of priorities, the set of possible interactions in such a way as to guarantee the given safety conditions for all possible runs. In distributed priority synthesis we are interested in obtaining local sets of priorities, which are deployed in terms of local component controllers sharing intended next moves between components in local neighborhoods only. These possible communication paths between local controllers are specified by means of a communication architecture. We formally define the problem of distributed priority synthesis in terms of a multi-player safety game between players for (angelically) selecting the next transition of the components and an environment for (demonically) updating uncontrollable variables; this problem is NP-complete. We propose several optimizations including a solution-space exploration based on a diagnosis method using a nested extension of the usual attractor computation in games together with a reduction to corresponding SAT problems. When diagnosis fails, the method proposes potential candidates to guide the exploration. These optimized algorithms for solving distributed priority synthesis problems have been integrated into our VissBIP framework. An experimental validation of this implementation is performed using a range of case studies including scheduling in multicore processors and modular robotics.
Lately, the problem of deploying priorities on a given architecture has gained increased recognition @cite_6 @cite_13 ; the advantage of priority synthesis is that the set of synthesized priorities is always known to be deployable. As a continuing toolchain, there are many existing works which try to deploy priorities on a given architecture @cite_6 @cite_13 , but these methods suffer from the fact that the set of priorities may be inherently undeployable. From these failures, our observation is that as priorities are control artifacts used to achieve certain specification, there can be different sets of priorities which achieve the same goal. What is required is to such a priority set respecting the architectural constraints. Therefore, our method reduces efforts in designing algorithms for priority deployment.
@cite_13 @cite_6
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We provide a uniform solution to the problem of synthesizing a finite-state distributed system. An instance of the synthesis problem consists of a system architecture and a temporal specification. The architecture is given as a directed graph, where the nodes represent processes (including the environment as a special process) that communicate synchronously through shared variables attached to the edges. The same variable may occur on multiple outgoing edges of a single node, allowing for the broadcast of data. A solution to the synthesis problem is a collection of finite-state programs for the processes in the architecture, such that the joint behavior of the programs satisfies the specification in an unrestricted environment. We define information forks, a comprehensive criterion that characterizes all architectures with an undecidable synthesis problem. The criterion is effective: for a given architecture with n processes and v variables, it can be determined in O(n sup 2 spl middot v) time whether the synthesis problem is decidable. We give a uniform synthesis algorithm for all decidable cases. Our algorithm works for all spl omega -regular tree specification languages, including the spl mu -calculus. The undecidability proof, on the other hand, uses only LTL or, alternatively, CTL as the specification language. Our results therefore hold for the entire range of specification languages from LTL CTL to the spl mu -calculus. The problem of synthesizing a finite-state distributed reactive system is considered. Given a distributed architecture A, which comprises several processors P sub 1 , . . ., P sub k and their interconnection scheme, and a propositional temporal specification phi , a solution to the synthesis problem consists of finite-state programs Pi sub 1 , . . ., Pi sub k (one for each processor), whose joint (synchronous) behavior maintains phi against all possible inputs from the environment. Such a solution is referred to as the realization of the specification phi over the architecture A. Specifically, it is shown that the problem of realizing a given propositional specification over a given architecture is undecidable, and it is nonelementarily decidable for the very restricted class of hierarchical architectures. An extensive characterization of architecture classes for which the realizability problem is elementarily decidable and of classes for which it is undecidable is given. > In this paper, we present a novel, general, and efficient architecture for addressing computer vision problems that are approached from an 'Analysis by Synthesis' standpoint. Analysis by synthesis involves the minimization of reconstruction error, which is typically a non-convex function of the latent target variables. State-of-the-art methods adopt a hybrid scheme where discriminatively trained predictors like Random Forests or Convolutional Neural Networks are used to initialize local search algorithms. While these hybrid methods have been shown to produce promising results, they often get stuck in local optima. Our method goes beyond the conventional hybrid architecture by not only proposing multiple accurate initial solutions but by also defining a navigational structure over the solution space that can be used for extremely efficient gradient-free local search. We demonstrate the efficacy and generalizability of our approach on tasks as diverse as Hand Pose Estimation, RGB Camera Relocalization, and Image Retrieval. A new exemplar-based framework unifying image completion, texture synthesis and image inpainting is presented in this work. Contrary to existing greedy techniques, these tasks are posed in the form of a discrete global optimization problem with a well defined objective function. For solving this problem a novel optimization scheme, called Priority- BP, is proposed which carries two very important extensions over standard belief propagation (BP): "prioritybased message scheduling" and "dynamic label pruning". These two extensions work in cooperation to deal with the intolerable computational cost of BP caused by the huge number of existing labels. Moreover, both extensions are generic and can therefore be applied to any MRF energy function as well. The effectiveness of our method is demonstrated on a wide variety of image completion examples.