7,631 research outputs found

    Real Time Animation of Virtual Humans: A Trade-off Between Naturalness and Control

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    Virtual humans are employed in many interactive applications using 3D virtual environments, including (serious) games. The motion of such virtual humans should look realistic (or ‘natural’) and allow interaction with the surroundings and other (virtual) humans. Current animation techniques differ in the trade-off they offer between motion naturalness and the control that can be exerted over the motion. We show mechanisms to parametrize, combine (on different body parts) and concatenate motions generated by different animation techniques. We discuss several aspects of motion naturalness and show how it can be evaluated. We conclude by showing the promise of combinations of different animation paradigms to enhance both naturalness and control

    A Survey of Symbolic Execution Techniques

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    Many security and software testing applications require checking whether certain properties of a program hold for any possible usage scenario. For instance, a tool for identifying software vulnerabilities may need to rule out the existence of any backdoor to bypass a program's authentication. One approach would be to test the program using different, possibly random inputs. As the backdoor may only be hit for very specific program workloads, automated exploration of the space of possible inputs is of the essence. Symbolic execution provides an elegant solution to the problem, by systematically exploring many possible execution paths at the same time without necessarily requiring concrete inputs. Rather than taking on fully specified input values, the technique abstractly represents them as symbols, resorting to constraint solvers to construct actual instances that would cause property violations. Symbolic execution has been incubated in dozens of tools developed over the last four decades, leading to major practical breakthroughs in a number of prominent software reliability applications. The goal of this survey is to provide an overview of the main ideas, challenges, and solutions developed in the area, distilling them for a broad audience. The present survey has been accepted for publication at ACM Computing Surveys. If you are considering citing this survey, we would appreciate if you could use the following BibTeX entry: http://goo.gl/Hf5FvcComment: This is the authors pre-print copy. If you are considering citing this survey, we would appreciate if you could use the following BibTeX entry: http://goo.gl/Hf5Fv

    On-line planning and scheduling: an application to controlling modular printers

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    We present a case study of artificial intelligence techniques applied to the control of production printing equipment. Like many other real-world applications, this complex domain requires high-speed autonomous decision-making and robust continual operation. To our knowledge, this work represents the first successful industrial application of embedded domain-independent temporal planning. Our system handles execution failures and multi-objective preferences. At its heart is an on-line algorithm that combines techniques from state-space planning and partial-order scheduling. We suggest that this general architecture may prove useful in other applications as more intelligent systems operate in continual, on-line settings. Our system has been used to drive several commercial prototypes and has enabled a new product architecture for our industrial partner. When compared with state-of-the-art off-line planners, our system is hundreds of times faster and often finds better plans. Our experience demonstrates that domain-independent AI planning based on heuristic search can flexibly handle time, resources, replanning, and multiple objectives in a high-speed practical application without requiring hand-coded control knowledge

    Concurrency in prolog using threads and a shared database

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    Concurrency in Logic Programming has received much attention in the past. One problem with many proposals, when applied to Prolog, is that they involve large modifications to the standard implementations, and/or the communication and synchronization facilities provided do not fit as naturally within the language model as we feel is possible. In this paper we propose a new mechanism for implementing synchronization and communication for concurrency, based on atomic accesses to designated facts in the (shared) datábase. We argüe that this model is comparatively easy to implement and harmonizes better than previous proposals within the Prolog control model and standard set of built-ins. We show how in the proposed model it is easy to express classical concurrency algorithms and to subsume other mechanisms such as Linda, variable-based communication, or classical parallelism-oriented primitives. We also report on an implementation of the model and provide performance and resource consumption data

    Answer Set Planning Under Action Costs

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    Recently, planning based on answer set programming has been proposed as an approach towards realizing declarative planning systems. In this paper, we present the language Kc, which extends the declarative planning language K by action costs. Kc provides the notion of admissible and optimal plans, which are plans whose overall action costs are within a given limit resp. minimum over all plans (i.e., cheapest plans). As we demonstrate, this novel language allows for expressing some nontrivial planning tasks in a declarative way. Furthermore, it can be utilized for representing planning problems under other optimality criteria, such as computing ``shortest'' plans (with the least number of steps), and refinement combinations of cheapest and fastest plans. We study complexity aspects of the language Kc and provide a transformation to logic programs, such that planning problems are solved via answer set programming. Furthermore, we report experimental results on selected problems. Our experience is encouraging that answer set planning may be a valuable approach to expressive planning systems in which intricate planning problems can be naturally specified and solved

    Scalable Architecture for Integrated Batch and Streaming Analysis of Big Data

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    Thesis (Ph.D.) - Indiana University, Computer Sciences, 2015As Big Data processing problems evolve, many modern applications demonstrate special characteristics. Data exists in the form of both large historical datasets and high-speed real-time streams, and many analysis pipelines require integrated parallel batch processing and stream processing. Despite the large size of the whole dataset, most analyses focus on specific subsets according to certain criteria. Correspondingly, integrated support for efficient queries and post- query analysis is required. To address the system-level requirements brought by such characteristics, this dissertation proposes a scalable architecture for integrated queries, batch analysis, and streaming analysis of Big Data in the cloud. We verify its effectiveness using a representative application domain - social media data analysis - and tackle related research challenges emerging from each module of the architecture by integrating and extending multiple state-of-the-art Big Data storage and processing systems. In the storage layer, we reveal that existing text indexing techniques do not work well for the unique queries of social data, which put constraints on both textual content and social context. To address this issue, we propose a flexible indexing framework over NoSQL databases to support fully customizable index structures, which can embed necessary social context information for efficient queries. The batch analysis module demonstrates that analysis workflows consist of multiple algorithms with different computation and communication patterns, which are suitable for different processing frameworks. To achieve efficient workflows, we build an integrated analysis stack based on YARN, and make novel use of customized indices in developing sophisticated analysis algorithms. In the streaming analysis module, the high-dimensional data representation of social media streams poses special challenges to the problem of parallel stream clustering. Due to the sparsity of the high-dimensional data, traditional synchronization method becomes expensive and severely impacts the scalability of the algorithm. Therefore, we design a novel strategy that broadcasts the incremental changes rather than the whole centroids of the clusters to achieve scalable parallel stream clustering algorithms. Performance tests using real applications show that our solutions for parallel data loading/indexing, queries, analysis tasks, and stream clustering all significantly outperform implementations using current state-of-the-art technologies

    A local and global tour on MOMoT

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    Many model transformation scenarios require flexible execution strategies as they should produce models with the highest possible quality. At the same time, transformation problems often span a very large search space with respect to possible transformation results. Recently, different proposals for finding good transformation results without enumerating the complete search space have been proposed by using meta-heuristic search algorithms. However, determining the impact of the different kinds of search algorithms, such as local search or global search, on the transformation results is still an open research topic. In this paper, we present an extension to MOMoT, which is a search-based model transformation tool, for supporting not only global searchers for model transformation orchestrations, but also local ones. This leads to a model transformation framework that allows as the first of its kind multi-objective local and global search. By this, the advantages and disadvantages of global and local search for model transformation orchestration can be evaluated. This is done in a case-study-based evaluation, which compares different performance aspects of the local- and global-search algorithms available in MOMoT. Several interesting conclusions have been drawn from the evaluation: (1) local-search algorithms perform reasonable well with respect to both the search exploration and the execution time for small input models, (2) for bigger input models, their execution time can be similar to those of global-search algorithms, but global-search algorithms tend to outperform local-search algorithms in terms of search exploration, (3) evolutionary algorithms show limitations in situations where single changes of the solution can have a significant impact on the solution’s fitness.Ministerio de Economia y Competitividad TIN2015-70560-RJunta de Andalucía P12-TIC-186
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