9,835 research outputs found
A Decision Calculus for Belief Functions in Valuation-Based Systems
Valuation-based system (VBS) provides a general framework for representing
knowledge and drawing inferences under uncertainty. Recent studies have shown
that the semantics of VBS can represent and solve Bayesian decision problems
(Shenoy, 1991a). The purpose of this paper is to propose a decision calculus
for Dempster-Shafer (D-S) theory in the framework of VBS. The proposed calculus
uses a weighting factor whose role is similar to the probabilistic
interpretation of an assumption that disambiguates decision problems
represented with belief functions (Strat 1990). It will be shown that with the
presented calculus, if the decision problems are represented in the valuation
network properly, we can solve the problems by using fusion algorithm (Shenoy
1991a). It will also be shown the presented decision calculus can be reduced to
the calculus for Bayesian probability theory when probabilities, instead of
belief functions, are given.Comment: Appears in Proceedings of the Eighth Conference on Uncertainty in
Artificial Intelligence (UAI1992
Lazy Evaluation of Symmetric Bayesian Decision Problems
Solving symmetric Bayesian decision problems is a computationally intensive
task to perform regardless of the algorithm used. In this paper we propose a
method for improving the efficiency of algorithms for solving Bayesian decision
problems. The method is based on the principle of lazy evaluation - a principle
recently shown to improve the efficiency of inference in Bayesian networks. The
basic idea is to maintain decompositions of potentials and to postpone
computations for as long as possible. The efficiency improvements obtained with
the lazy evaluation based method is emphasized through examples. Finally, the
lazy evaluation based method is compared with the hugin and valuation-based
systems architectures for solving symmetric Bayesian decision problems.Comment: Appears in Proceedings of the Fifteenth Conference on Uncertainty in
Artificial Intelligence (UAI1999
Application of Compressive Sensing Techniques in Distributed Sensor Networks: A Survey
In this survey paper, our goal is to discuss recent advances of compressive
sensing (CS) based solutions in wireless sensor networks (WSNs) including the
main ongoing/recent research efforts, challenges and research trends in this
area. In WSNs, CS based techniques are well motivated by not only the sparsity
prior observed in different forms but also by the requirement of efficient
in-network processing in terms of transmit power and communication bandwidth
even with nonsparse signals. In order to apply CS in a variety of WSN
applications efficiently, there are several factors to be considered beyond the
standard CS framework. We start the discussion with a brief introduction to the
theory of CS and then describe the motivational factors behind the potential
use of CS in WSN applications. Then, we identify three main areas along which
the standard CS framework is extended so that CS can be efficiently applied to
solve a variety of problems specific to WSNs. In particular, we emphasize on
the significance of extending the CS framework to (i). take communication
constraints into account while designing projection matrices and reconstruction
algorithms for signal reconstruction in centralized as well in decentralized
settings, (ii) solve a variety of inference problems such as detection,
classification and parameter estimation, with compressed data without signal
reconstruction and (iii) take practical communication aspects such as
measurement quantization, physical layer secrecy constraints, and imperfect
channel conditions into account. Finally, open research issues and challenges
are discussed in order to provide perspectives for future research directions
Distributed Bayesian Detection Under Unknown Observation Statistics
In this paper, distributed Bayesian detection problems with unknown prior
probabilities of hypotheses are considered. The sensors obtain observations
which are conditionally dependent across sensors and their probability density
functions (pdf) are not exactly known. The observations are quantized and are
sent to the fusion center. The fusion center fuses the current quantized
observations and makes a final decision. It also designs (updated) quantizers
to be used at the sensors and the fusion rule based on all previous quantized
observations. Information regarding updated quantizers is sent back to the
sensors for use at the next time. In this paper, the conditional joint pdf is
represented in a parametric form by using the copula framework. The unknown
parameters include dependence parameters and marginal parameters. Maximum
likelihood estimation (MLE) with feedback based on quantized data is proposed
to estimate the unknown parameters. These estimates are iteratively used to
refine the quantizers and the fusion rule to improve distributed detection
performance by using feedback. Numerical examples show that the new detection
method based on MLE with feedback is much better than the usual detection
method based on the assumption of conditionally independent observations.Comment: 17 pages, 6 figures, submitted to journa
A Belief-Function Based Decision Support System
In this paper, we present a decision support system based on belief functions
and the pignistic transformation. The system is an integration of an evidential
system for belief function propagation and a valuation-based system for
Bayesian decision analysis. The two subsystems are connected through the
pignistic transformation. The system takes as inputs the user's "gut feelings"
about a situation and suggests what, if any, are to be tested and in what
order, and it does so with a user friendly interface.Comment: Appears in Proceedings of the Ninth Conference on Uncertainty in
Artificial Intelligence (UAI1993
Closed-loop Bayesian Semantic Data Fusion for Collaborative Human-Autonomy Target Search
In search applications, autonomous unmanned vehicles must be able to
efficiently reacquire and localize mobile targets that can remain out of view
for long periods of time in large spaces. As such, all available information
sources must be actively leveraged -- including imprecise but readily available
semantic observations provided by humans. To achieve this, this work develops
and validates a novel collaborative human-machine sensing solution for dynamic
target search. Our approach uses continuous partially observable Markov
decision process (CPOMDP) planning to generate vehicle trajectories that
optimally exploit imperfect detection data from onboard sensors, as well as
semantic natural language observations that can be specifically requested from
human sensors. The key innovation is a scalable hierarchical Gaussian mixture
model formulation for efficiently solving CPOMDPs with semantic observations in
continuous dynamic state spaces. The approach is demonstrated and validated
with a real human-robot team engaged in dynamic indoor target search and
capture scenarios on a custom testbed.Comment: Final version accepted and submitted to 2018 FUSION Conference
(Cambridge, UK, July 2018
Sequential Hypothesis Test with Online Usage-Constrained Sensor Selection
This work investigates the sequential hypothesis testing problem with online
sensor selection and sensor usage constraints. That is, in a sensor network,
the fusion center sequentially acquires samples by selecting one "most
informative" sensor at each time until a reliable decision can be made. In
particular, the sensor selection is carried out in the online fashion since it
depends on all the previous samples at each time. Our goal is to develop the
sequential test (i.e., stopping rule and decision function) and sensor
selection strategy that minimize the expected sample size subject to the
constraints on the error probabilities and sensor usages. To this end, we first
recast the usage-constrained formulation into a Bayesian optimal stopping
problem with different sampling costs for the usage-contrained sensors. The
Bayesian problem is then studied under both finite- and infinite-horizon
setups, based on which, the optimal solution to the original usage-constrained
problem can be readily established. Moreover, by capitalizing on the structures
of the optimal solution, a lower bound is obtained for the optimal expected
sample size. In addition, we also propose algorithms to approximately evaluate
the parameters in the optimal sequential test so that the sensor usage and
error probability constraints are satisfied. Finally, numerical experiments are
provided to illustrate the theoretical findings, and compare with the existing
methods.Comment: 33 page
Probabilistic Inference in Influence Diagrams
This paper is about reducing influence diagram (ID) evaluation into Bayesian
network (BN) inference problems. Such reduction is interesting because it
enables one to readily use one's favorite BN inference algorithm to efficiently
evaluate IDs. Two such reduction methods have been proposed previously (Cooper
1988, Shachter and Peot 1992). This paper proposes a new method. The BN
inference problems induced by the mew method are much easier to solve than
those induced by the two previous methods.Comment: Appears in Proceedings of the Fourteenth Conference on Uncertainty in
Artificial Intelligence (UAI1998
Reasoning and Facts Explanation in Valuation Based Systems
In the literature, the optimization problem to identify a set of composite
hypotheses H, which will yield the largest where a composite
hypothesis is an instantiation of all the nodes in the network except the
evidence nodes \cite{KSy:93} is of significant interest. This problem is called
"finding the Most Plausible Explanation (MPE) of a given evidence in
a Bayesian belief network".
The problem of finding most probable hypotheses is generally NP-hard
\cite{Cooper:90}. Therefore in the past various simplifications of the task by
restricting (to 1 or 2), restricting the structure (e.g. to singly
connected networks), or shifting the complexity to spatial domain have been
investigated.
A genetic algorithm is proposed in this paper to overcome some of these
restrictions while stepping out from probabilistic domain onto the general
Valuation based System (VBS) framework is also proposed by generalizing the
genetic algorithm approach to the realm of Dempster-Shafer belief calculus.Comment: 12 pasge
Design of a Framework to Facilitate Decisions Using Information Fusion
Information fusion is an advanced research area which can assist decision
makers in enhancing their decisions. This paper aims at designing a new
multi-layer framework that can support the process of performing decisions from
the obtained beliefs using information fusion. Since it is not an easy task to
cross the gap between computed beliefs of certain hypothesis and decisions, the
proposed framework consists of the following layers in order to provide a
suitable architecture (ordered bottom up): 1. A layer for combination of basic
belief assignments using an information fusion approach. Such approach exploits
Dezert-Smarandache Theory, DSmT, and proportional conflict redistribution to
provide more realistic final beliefs. 2. A layer for computation of pignistic
probability of the underlying propositions from the corresponding final
beliefs. 3. A layer for performing probabilistic reasoning using a Bayesian
network that can obtain the probable reason of a proposition from its pignistic
probability. 4. Ranking the system decisions is ultimately used to support
decision making. A case study has been accomplished at various operational
conditions in order to prove the concept, in addition it pointed out that: 1.
The use of DSmT for information fusion yields not only more realistic beliefs
but also reliable pignistic probabilities for the underlying propositions. 2.
Exploiting the pignistic probability for the integration of the information
fusion with the Bayesian network provides probabilistic inference and enable
decision making on the basis of both belief based probabilities for the
underlying propositions and Bayesian based probabilities for the corresponding
reasons. A comparative study of the proposed framework with respect to other
information fusion systems confirms its superiority to support decision making.Comment: 17 pages, 5 figures, Journal of Al Azhar University Engineering
Sector, Vol. 8, No. 28, July 2013, 1237-1250. arXiv admin note: text overlap
with arXiv:cs/0409007 by other author
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