90,828 research outputs found
Unifying Practical Uncertainty Representations: II. Clouds
There exist many simple tools for jointly capturing variability and
incomplete information by means of uncertainty representations. Among them are
random sets, possibility distributions, probability intervals, and the more
recent Ferson's p-boxes and Neumaier's clouds, both defined by pairs of
possibility distributions. In the companion paper, we have extensively studied
a generalized form of p-box and situated it with respect to other models . This
paper focuses on the links between clouds and other representations.
Generalized p-boxes are shown to be clouds with comonotonic distributions. In
general, clouds cannot always be represented by random sets, in fact not even
by 2-monotone (convex) capacities.Comment: 30 pages, 7 figures, Pre-print of journal paper to be published in
International Journal of Approximate Reasoning (with expanded section
concerning clouds and probability intervals
Degenerate Gaussian factors for probabilistic inference
In this paper, we propose a parametrised factor that enables inference on
Gaussian networks where linear dependencies exist among the random variables.
Our factor representation is effectively a generalisation of traditional
Gaussian parametrisations where the positive-definite constraint of the
covariance matrix has been relaxed. For this purpose, we derive various
statistical operations and results (such as marginalisation, multiplication and
affine transformations of random variables) that extend the capabilities of
Gaussian factors to these degenerate settings. By using this principled factor
definition, degeneracies can be accommodated accurately and automatically at
little additional computational cost. As illustration, we apply our methodology
to a representative example involving recursive state estimation of cooperative
mobile robots.Comment: Accepted by International Journal of Approximate Reasoning on 17
January 202
Binary Join Trees
A longer and updated version of this paper appears in: Shenoy, P. P., "Binary Join Trees for Computing Marginals in the Shenoy-Shafer Architecture," International Journal of Approximate Reasoning, 17(2--3), 1997, 239--263 (available from .The main goal of this paper is to describe a datastructure called binary join trees that are useful incomputing multiple marginals efficiently usingthe Shenoy-Shafer architecture. We define binaryjoin trees, describe their utility, and sketch a procedure for constructing them
Reasoning about norms under uncertainty in dynamic environments
The behaviour of norm-autonomous agents is determined by their goals and the
norms that are explicitly represented inside their minds. Thus, they require
mechanisms for acquiring and accepting norms, determining when norms are
relevant to their case, and making decisions about norm compliance. Up un-
til now the existing proposals on norm-autonomous agents assume that agents
interact within a deterministic environment that is certainly perceived. In prac-
tice, agents interact by means of sensors and actuators under uncertainty with
non-deterministic and dynamic environments. Therefore, the existing propos-
als are unsuitable or, even, useless to be applied when agents have a physical
presence in some real-world environment. In response to this problem we have
developed the n-BDI architecture. In this paper, we propose a multi -context
graded BDI architecture (called n-BDI) that models norm-autonomous agents
able to deal with uncertainty in dynamic environments. The n-BDI architecture
has been experimentally evaluated and the results are shown in this paper.This paper was partially funded by the Spanish government under Grant CONSOLIDER-INGENIO 2010 CSD2007-00022 and the Valencian government under Project PROMETEOH/2013/019.Criado Pacheco, N.; Argente, E.; Noriega, P.; Botti Navarro, VJ. (2014). Reasoning about norms under uncertainty in dynamic environments. International Journal of Approximate Reasoning. 55(9):2049-2070. https://doi.org/10.1016/j.ijar.2014.02.004S2049207055
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