Search results for "Presentation"

A hybrid architecture for autonomous agents

1997

A new hybrid approach for autonomous agents is described. The approach integrates in a principled way the functional and the behavioral approaches of agent design. The integration is based on the introduction of a conceptual space representation that links the subsymbolic level, which is a repository of reactive modules, with the symbolic level, in which rich symbolic descriptions of the agent environment take place. Results are reported obtained by an experimental implementation of the agent.

Phase Fourier vector model for scale invariant three-dimensional image detection.

2009

A scale invariant 3D object detection method based on phase Fourier transform (PhFT) is addressed. Three-dimensionality is expressed in terms of range images. The PhFT of a range image gives information about the orientations of the surfaces in the 3D object. When the object is scaled, the PhFT becomes a distribution multiplied by a constant factor which is related to the scale factor. Then 3D scale invariant detection can be solved as illumination invariant detection process. Several correlation operations based on vector space representation are applied. Results show the tolerance of detection method to scale besides discrimination against false objects.

QUALITATIVE MODELING OF CELL GROWTH PROCESSES

1988

In this paper we present a qualitative physics model to reason about cell growth processes and cell-drug interactions, to be used in the knowledge base of NEWCHEM, an expert system intended to guide experimentation in the design of new optimal protocols in cancer treatment, After a brief discussion of the contributions that artificial intelligence techniques could make in cancer research and a brief presentation of some currently developed expert systems, some details of the proposed model based on the Forbus and Kuipers approaches to qualitative physics are given and its implementation as a LISP program is briefly discussed.

A Cognitive Framework for Imitation Learning

2006

Abstract In order to have a robotic system able to effectively learn by imitation, and not merely reproduce the movements of a human teacher, the system should have the capabilities of deeply understanding the perceived actions to be imitated. This paper deals with the development of cognitive architecture for learning by imitation in which a rich conceptual representation of the observed actions is built. The purpose of the following discussion is to show how this Conceptual Area can be employed to efficiently organize perceptual data, to learn movement primitives from human demonstration and to generate complex actions by combining and sequencing simpler ones. The proposed architecture ha…

Chapter 11. Computational representation of FrameNet for multilingual natural language generation

2021

Erratum to: A New Feature Selection Methodology for K-mers Representation of DNA Sequences

2017

Advances in the statistical methodology for the selection of image descriptors for visual pattern representation and classification

1995

Recent advances in the statistical methodology for selecting optimal subsets of features (image descriptors) for visual pattern representation and classification are presented. The paper attempts to provide a guideline about which approach to choose with respect to the a priori knowledge of the problem. Two basic approaches are reviewed and the conditions under which they should be used are specified. References to more detailed material about each one of the methods are given and experimental results supporting the main conclusions are briefly outlined.

Adding Synthetic Detail to Natural Terrain Using a Wavelet Approach

2002

Terrain representation is a basic topic in the field of interactive graphics. The amount of data required for good quality terrain representation offers an important challenge to developers of such systems. For users of these applications the accuracy of geographical data is less important than their natural visual appearance. This makes it possible to mantain a limited geographical data base for the system and to extend it generating synthetic data.In this paper we combine fractal and wavelet theories to provide extra data which keeps the natural essence of actual information available. The new levels of detail(LOD) for the terrain are obtained applying an inverse Wavelet Transform (WT) to…

Machine Learning Techniques for Automatic Depression Assessment

2018

Depression is one of the most common mood disorder that is inherently related to emotions, involving bad mood, low self-esteem and loss of interest in normal pleasurable activities. The aim of this work is to develop a framework based on the dataset provided by AVEC'14 for depression assessment. The proposed work presents two different motion representation methods: a) Gabor Motion History Image (GMHI), and b) Motion History Image (MHI). Several combinations of appearance-based low level features are extracted from both motion representations. These features were further combined with statistically derived features, and used for training and testing with several machine learning techniques.…

Gaussian Process Regression (GPR) Representation in Predictive Model Markup Language (PMML)

2017

International audience; This paper describes Gaussian process regression (GPR) models presented in predictive model markup language (PMML). PMML is an extensible-markup-language (XML) -based standard language used to represent data-mining and predictive analytic models, as well as pre- and post-processed data. The previous PMML version, PMML 4.2, did not provide capabilities for representing probabilistic (stochastic) machine-learning algorithms that are widely used for constructing predictive models taking the associated uncertainties into consideration. The newly released PMML version 4.3, which includes the GPR model, provides new features: confidence bounds and distribution for the pred…