High-level action programming languages such as Golog have successfully been used to model the behavior of autonomous agents. In addition to a logic-based action formalism for describing the environment and the effect...
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ISBN:
(纸本)9783642408854;9783642408847
High-level action programming languages such as Golog have successfully been used to model the behavior of autonomous agents. In addition to a logic-based action formalism for describing the environment and the effects of basic actions, they enable the construction of complex actions using typical programming language constructs. To ensure that the execution of such complex actions leads to the desired behavior of the agent, one needs to specify the required properties in a formal way, and then verify that these requirements are met by any execution of the program. Due to the expressiveness of the action formalism underlying Golog (Situation Calculus), the verification problem for Golog programs is in general undecidable. Action formalisms based on Description logic (DL) try to achieve decidability of inference problems such as the projection problem by restricting the expressiveness of the underlying base logic. However, until now these formalisms have not been used within Golog programs. In the present paper, we introduce a variant of Golog where basic actions are defined using such a DL-based formalism, and show that the verification problem for such programs is decidable. this improves on our previous work on verifying properties of infinite sequences of DL actions in that it considers (finite and infinite) sequences of DL actions that correspond to (terminating and non-terminating) runs of a Golog program rather than just infinite sequences accepted by a Buchi automaton abstracting the program.
functional decomposition is the main step in the FPGA-oriented logic synthesis, where a function is decomposed into a set of functions, each of which must be simple enough to be implementable in one logic cell. this p...
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ISBN:
(数字)9783642314643
ISBN:
(纸本)9783642314636;9783642314643
functional decomposition is the main step in the FPGA-oriented logic synthesis, where a function is decomposed into a set of functions, each of which must be simple enough to be implementable in one logic cell. this paper presents a method of searching for the best decomposition strategy for logical functions specified by cubes. the strategy is represented by a decision tree, where each node corresponds to a single decomposition step. In that way the multistage decomposition of complex logical functions may be specified. the tree evolves using the parallel developmental genetic programming. the goal of the evolution is to find a decomposition strategy for which the cost of FPGA implementation of a given function is minimal. Experimental results show that our approach gives significantly better results than other existing methods.
Factor Analysis (FA) is a well established method for factors separation in analysis of dynamic medical imaging. However, its assumptions are valid only in limited regions of interest (ROI) in the images which must be...
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Factor Analysis (FA) is a well established method for factors separation in analysis of dynamic medical imaging. However, its assumptions are valid only in limited regions of interest (ROI) in the images which must be selected manually or using heuristics. the resulting quality of separation is sensitive to the choice of these ROI. We propose a new probabilistic model for functional analysis with inherent estimation of probabilistic ROI. the model is solved using the Variational Bayes method which provides also automatic relevance determination of the estimated factors. Performance of the method is demonstrated on data from renal scintigraphy, where a significant improvement is achieved. Since there are no scintigraphy-related assumptions, the method can be used in any other imaging modality.
Understanding the relationship between structure and function is a major challenge in neuroscience. Diffusion MRI (dMRI) in the only non-invasive modality allowing to have access to the neural structure. Magnetoenceph...
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Understanding the relationship between structure and function is a major challenge in neuroscience. Diffusion MRI (dMRI) in the only non-invasive modality allowing to have access to the neural structure. Magnetoencephalography (MEG) is another non-invasive modality that allows a direct access to the temporal succession of cognitive processes. functional cortex parcellation being one of the most important ways to understand structure-function relationship, we propose an innovative method merging MEG and dMRI to parcellate the cortex. the combination of MEG forward problem and connectivity information reveals cortical areas generating a similar magnetic field at sensors while having a similar connectivity. Results show suitable clusters that forecast interesting studies for inter- and intra-subjects comparisons of the cortex parcellations. the automatic nested cortex parcellation we propose could be a first step to analyse sources that are seeds of long or short range connectivity and to differentiate these connectivities in the white matter.
Automatic fluorescent particle tracking is an essential task to study the dynamics of a large number of biological structures at a sub-cellular level. We have developed a two-step multi-frame association finding algor...
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Automatic fluorescent particle tracking is an essential task to study the dynamics of a large number of biological structures at a sub-cellular level. We have developed a two-step multi-frame association finding algorithm which is based on a temporally semi-global formulation as well as combines a spatially global and a spatially local approach. Using this multi-frame association finding algorithm we have developed a probabilistic tracking approach based on the Kalman filter. We have successfully applied the approach to synthetic as well as real microscopy image sequences of ALV virus particles and quantified the performance. We found that the proposed approach outperforms previous approaches.
there are numerous similarities, but also numerous differences between two types of programmable technical systems - between the advanced computer controlled robotic systems, and the usual computers. these differences...
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this paper proposes a simple high-level programming language, endowed with resources that help encoding self-modifying programs. Withthis purpose, a conventional imperative language syntax (not explicitly stated in t...
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this paper proposes a simple high-level programming language, endowed with resources that help encoding self-modifying programs. Withthis purpose, a conventional imperative language syntax (not explicitly stated in this paper) is incremented with special commands and statements forming an adaptive layer specially designed with focus on the dynamical changes to be applied to the code at run-time. the resulting language allows programmers to easily specify dynamic changes to their own program's code. Such a language succeeds to allow programmers to effortless describe the dynamic logic of their adaptive applications. In this paper, we describe the most important aspects of the design and implementation of such a language. A small example is finally presented for illustration purposes.
there are numerous similarities, but also numerous differences between two types of programmable technical systems - between the advanced computer controlled robotic systems, and the usual computers. these differences...
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there are numerous similarities, but also numerous differences between two types of programmable technical systems - between the advanced computer controlled robotic systems, and the usual computers. these differences are reflected not only in the architectural structures structure and functional behavior of these types of systems but also in their programming. this paper emphasizes some of the differences of the skills necessary for computer programming, and of the programming of robots, and shows some among the approaches recently tested at the Institute of Computer Science of the Silesian University in Opava, Czech Republic.
the Alcoholism is an addictive disorder, which causes social, physical, psychiatric and neurological damages on individuals. In this paper, Global Field Synchronization (GFS) measurements of multi channel ERP (Event R...
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the Alcoholism is an addictive disorder, which causes social, physical, psychiatric and neurological damages on individuals. In this paper, Global Field Synchronization (GFS) measurements of multi channel ERP (Event Related Potential) signals in Delta, theta, Alpha, Beta and Gamma frequency bands are used as discriminating feature vectors in the classification of alcoholic and non-alcoholic control subjects. GFS measurements show the functional connectivity of neurocognitive networks in the patient's brain as a response to a given stimuli type. A channel optimization algorithm that improves recognition accuracy by selecting channels withthe most significant attributes is applied during Global Field Synchronization prior to classification stage. Probabilistic Neural Network is used as the classifier. the proposed system successfully classifies alcoholic and non-alcoholic subjects with accuracy over 80%.
the proceeding contains 23 papers. the topics discussed include: model-based knowledge representation and reasoning via answer set programming;on the algebraic foundation of proof assistants for intuitionistic type th...
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ISBN:
(纸本)3540789685
the proceeding contains 23 papers. the topics discussed include: model-based knowledge representation and reasoning via answer set programming;on the algebraic foundation of proof assistants for intuitionistic type theory;substructural type systems for program analysis;integrating answer set reasoning with constraint solving techniques;optimizing compilation of CHR with rule priorities;certified exact real arithmetic using co-induction in arbitrary integer base;on-demand refinement of dependent types;proving properties about lists using containers;complexity analysis by rewriting;semantics and pragmatics of new shortcut fusion rules;types of hereditary head normalizing terms;typed dynamic control operators for delimited continuations;strictness analysis algorithms based on an inequality system for lazy types;quantitative logicprogramming revisited;and formalizing a constraint deductive database language based on hereditary harrop formulas with negation.
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