527,811 research outputs found

    The Automatic Inference of State Invariants in TIM

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    As planning is applied to larger and richer domains the effort involved in constructing domain descriptions increases and becomes a significant burden on the human application designer. If general planners are to be applied successfully to large and complex domains it is necessary to provide the domain designer with some assistance in building correctly encoded domains. One way of doing this is to provide domain-independent techniques for extracting, from a domain description, knowledge that is implicit in that description and that can assist domain designers in debugging domain descriptions. This knowledge can also be exploited to improve the performance of planners: several researchers have explored the potential of state invariants in speeding up the performance of domain-independent planners. In this paper we describe a process by which state invariants can be extracted from the automatically inferred type structure of a domain. These techniques are being developed for exploitation by STAN, a Graphplan based planner that employs state analysis techniques to enhance its performance

    Colored-Gaussian Multiple Descriptions: Spectral and Time-Domain Forms

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    It is well known that Shannon's rate-distortion function (RDF) in the colored quadratic Gaussian (QG) case can be parametrized via a single Lagrangian variable (the "water level" in the reverse water filling solution). In this work, we show that the symmetric colored QG multiple-description (MD) RDF in the case of two descriptions can be parametrized in the spectral domain via two Lagrangian variables, which control the trade-off between the side distortion, the central distortion, and the coding rate. This spectral-domain analysis is complemented by a time-domain scheme-design approach: we show that the symmetric colored QG MD RDF can be achieved by combining ideas of delta-sigma modulation and differential pulse-code modulation. Specifically, two source prediction loops, one for each description, are embedded within a common noise shaping loop, whose parameters are explicitly found from the spectral-domain characterization.Comment: Accepted for publications in the IEEE Transactions on Information Theory. Title have been shortened, abstract clarified, and paper significantly restructure

    Combining Expression and Content in Domains for Dialog Managers

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    We present work in progress on abstracting dialog managers from their domain in order to implement a dialog manager development tool which takes (among other data) a domain description as input and delivers a new dialog manager for the described domain as output. Thereby we will focus on two topics; firstly, the construction of domain descriptions with description logics and secondly, the interpretation of utterances in a given domain.Comment: 5 pages, uses conference.st

    Remarks on logic for process descriptions in ontological reasoning: A Drug Interaction Ontology case study

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    We present some ideas on logical process descriptions, using relations from the DIO (Drug Interaction Ontology) as examples and explaining how these relations can be naturally decomposed in terms of more basic structured logical process descriptions using terms from linear logic. In our view, the process descriptions are able to clarify the usual relational descriptions of DIO. In particular, we discuss the use of logical process descriptions in proving linear logical theorems. Among the types of reasoning supported by DIO one can distinguish both (1) basic reasoning about general structures in reality and (2) the domain-specific reasoning of experts. We here propose a clarification of this important distinction between (realist) reasoning on the basis of an ontology and rule-based inferences on the basis of an expert’s view

    Power spectra methods for a stochastic description of diffusion on deterministically growing domains

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    A central challenge in developmental biology is understanding the creation of robust spatiotemporal heterogeneity. Generally, the mathematical treatments of biological systems have used continuum, mean-field hypotheses for their constituent parts, which ignores any sources of intrinsic stochastic effects. In this paper we consider a stochastic space-jump process as a description of diffusion, i.e., particles are able to undergo a random walk on a discretized domain. By developing analytical Fourier methods we are able to probe this probabilistic framework, which gives us insight into the patterning potential of diffusive systems. Further, an alternative description of domain growth is introduced, with which we are able to rigorously link the mean-field and stochastic descriptions. Finally, through combining these ideas, it is shown that such stochastic descriptions of diffusion on a deterministically growing domain are able to support the nucleation of states that are far removed from the deterministic mean-field steady state

    On an evaluation of transformation languages in a fully XML-driven framework for video content adaptation

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    Bitstream Structure Descriptions (BSDs) allow taking the complexity of transforming scalable bitstreams from the compressed domain to the semantic domain. These descriptions are an essential part of an XUL-driven video adaptation framework. The performance of a BSD transformation engine is very important in such an architecture. This paper evaluates the efficiency of XML-based transformation languages in our video adaptation framework. XSLT, STX, and a hybrid solution are compared to each other in terms of execution times, memory consumption, and user-friendliness. Our experiments show that STX is the preferred solution when speed and low-memory are important. The hybrid solution is competitive in terms of memory consumption and is more user-friendly than STX. Although XSLT is relative fast, its memory consumption is very high
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