pwc-archive/papers-with-abstracts huggingface dataset:
The structure of Bayes nets for vision recognition
Authors: John Mark Agosta
Date: 2013-03-27 00:00:00

This paper is part of a study whose goal is to show the effciency of using
Bayes networks to carry out model based vision calculations. [Binford et al.
1987] Recognition proceeds by drawing up a network model from the object's
geometric and functional description that predicts the appearance of an object.
Then this network is used to find the object within a photographic image. Many
existing and proposed techniques for vision recognition resemble the
uncertainty calculations of a Bayes net. In contrast, though, they lack a
derivation from first principles, and tend to rely on arbitrary parameters that
we hope to avoid by a network model. The connectedness of the network depends
on what independence considerations can be identified in the vision problem.
Greater independence leads to easier calculations, at the expense of the net's
expressiveness. Once this trade-off is made and the structure of the network is
determined, it should be possible to tailor a solution technique for it. This
paper explores the use of a network with multiply connected paths, drawing on
both techniques of belief networks [Pearl 86] and influence diagrams. We then
demonstrate how one formulation of a multiply connected network can be solved.
Normalization and the Representation of Nonmonotonic Knowledge in the Theory of Evidence
Authors: Ronald R. Yager
Date: 2013-03-27 00:00:00

We discuss the Dempster-Shafer theory of evidence. We introduce a concept of
monotonicity which is related to the diminution of the range between belief and
plausibility. We show that the accumulation of knowledge in this framework
exhibits a nonmonotonic property. We show how the belief structure can be used
to represent typical or commonsense knowledge.
Can Uncertainty Management be Realized in a Finite Totally Ordered Probability Algebra?
Authors: Yang Xiang, Michael P. Beddoes, David L. Poole
Date: 2013-03-27 00:00:00
Tasks: Management

In this paper, the feasibility of using finite totally ordered probability
models under Alelinnas's Theory of Probabilistic Logic [Aleliunas, 1988] is
investigated. The general form of the probability algebra of these models is
derived and the number of possible algebras with given size is deduced. Based
on this analysis, we discuss problems of denominator-indifference and
ambiguity-generation that arise in reasoning by cases and abductive reasoning.
An example is given that illustrates how these problems arise. The
investigation shows that a finite probability model may be of very limited
usage.
Directed Cycles in Belief Networks
Authors: Wilson X. Wen
Date: 2013-03-27 00:00:00

The most difficult task in probabilistic reasoning may be handling directed
cycles in belief networks. To the best knowledge of this author, there is no
serious discussion of this problem at all in the literature of probabilistic
reasoning so far.
Comparing Expert Systems Built Using Different Uncertain Inference Systems
Authors: David S. Vaughan, Bruce M. Perrin, Robert M. Yadrick
Date: 2013-03-27 00:00:00
Tasks: regression

This study compares the inherent intuitiveness or usability of the most
prominent methods for managing uncertainty in expert systems, including those
of EMYCIN, PROSPECTOR, Dempster-Shafer theory, fuzzy set theory, simplified
probability theory (assuming marginal independence), and linear regression
using probability estimates. Participants in the study gained experience in a
simple, hypothetical problem domain through a series of learning trials. They
were then randomly assigned to develop an expert system using one of the six
Uncertain Inference Systems (UISs) listed above. Performance of the resulting
systems was then compared. The results indicate that the systems based on the
PROSPECTOR and EMYCIN models were significantly less accurate for certain types
of problems compared to systems based on the other UISs. Possible reasons for
these differences are discussed.
Efficient Parallel Estimation for Markov Random Fields
Authors: Michael J. Swain, Lambert E. Wixson, Paul B. Chou
Date: 2013-03-27 00:00:00

We present a new, deterministic, distributed MAP estimation algorithm for
Markov Random Fields called Local Highest Confidence First (Local HCF). The
algorithm has been applied to segmentation problems in computer vision and its
performance compared with stochastic algorithms. The experiments show that
Local HCF finds better estimates than stochastic algorithms with much less
computation.
Making Decisions with Belief Functions
Authors: Thomas M. Strat
Date: 2013-03-27 00:00:00

A primary motivation for reasoning under uncertainty is to derive decisions
in the face of inconclusive evidence. However, Shafer's theory of belief
functions, which explicitly represents the underconstrained nature of many
reasoning problems, lacks a formal procedure for making decisions. Clearly,
when sufficient information is not available, no theory can prescribe actions
without making additional assumptions. Faced with this situation, some
assumption must be made if a clearly superior choice is to emerge. In this
paper we offer a probabilistic interpretation of a simple assumption that
disambiguates decision problems represented with belief functions. We prove
that it yields expected values identical to those obtained by a probabilistic
analysis that makes the same assumption. In addition, we show how the decision
analysis methodology frequently employed in probabilistic reasoning can be
extended for use with belief functions.
Automated Construction of Sparse Bayesian Networks from Unstructured Probabilistic Models and Domain Information
Authors: Sampath Srinivas, Stuart Russell, Alice M. Agogino
Date: 2013-03-27 00:00:00

An algorithm for automated construction of a sparse Bayesian network given an
unstructured probabilistic model and causal domain information from an expert
has been developed and implemented. The goal is to obtain a network that
explicitly reveals as much information regarding conditional independence as
possible. The network is built incrementally adding one node at a time. The
expert's information and a greedy heuristic that tries to keep the number of
arcs added at each step to a minimum are used to guide the search for the next
node to add. The probabilistic model is a predicate that can answer queries
about independencies in the domain. In practice the model can be implemented in
various ways. For example, the model could be a statistical independence test
operating on empirical data or a deductive prover operating on a set of
independence statements about the domain.
Assessment, Criticism and Improvement of Imprecise Subjective Probabilities for a Medical Expert System
Authors: David J. Spiegelhalter, Rodney C. Franklin, Kate Bull
Date: 2013-03-27 00:00:00
Tasks: scoring rule

Three paediatric cardiologists assessed nearly 1000 imprecise subjective
conditional probabilities for a simple belief network representing congenital
heart disease, and the quality of the assessments has been measured using
prospective data on 200 babies. Quality has been assessed by a Brier scoring
rule, which decomposes into terms measuring lack of discrimination and
reliability. The results are displayed for each of 27 diseases and 24
questions, and generally the assessments are reliable although there was a
tendency for the probabilities to be too extreme. The imprecision allows the
judgements to be converted to implicit samples, and by combining with the
observed data the probabilities naturally adapt with experience. This appears
to be a practical procedure even for reasonably large expert systems.
Freedom: A Measure of Second-order Uncertainty for Intervalic Probability Schemes
Authors: Michael Smithson
Date: 2013-03-27 00:00:00

This paper discusses a new measure that is adaptable to certain intervalic
probability frameworks, possibility theory, and belief theory. As such, it has
the potential for wide use in knowledge engineering, expert systems, and
related problems in the human sciences. This measure (denoted here by F) has
been introduced in Smithson (1988) and is more formally discussed in Smithson
(1989a)o Here, I propose to outline the conceptual basis for F and compare its
properties with other measures of second-order uncertainty. I will argue that F
is an indicator of nonspecificity or alternatively, of freedom, as
distinguished from either ambiguity or vagueness.
Expectation Propagation for Neural Networks with Sparsity-promoting Priors
Authors: Pasi Jylänki, Aapo Nummenmaa, Aki Vehtari
Date: 2013-03-27 00:00:00

We propose a novel approach for nonlinear regression using a two-layer neural
network (NN) model structure with sparsity-favoring hierarchical priors on the
network weights. We present an expectation propagation (EP) approach for
approximate integration over the posterior distribution of the weights, the
hierarchical scale parameters of the priors, and the residual scale. Using a
factorized posterior approximation we derive a computationally efficient
algorithm, whose complexity scales similarly to an ensemble of independent
sparse linear models. The approach enables flexible definition of weight priors
with different sparseness properties such as independent Laplace priors with a
common scale parameter or Gaussian automatic relevance determination (ARD)
priors with different relevance parameters for all inputs. The approach can be
extended beyond standard activation functions and NN model structures to form
flexible nonlinear predictors from multiple sparse linear models. The effects
of the hierarchical priors and the predictive performance of the algorithm are
assessed using both simulated and real-world data. Comparisons are made to two
alternative models with ARD priors: a Gaussian process with a NN covariance
function and marginal maximum a posteriori estimates of the relevance
parameters, and a NN with Markov chain Monte Carlo integration over all the
unknown model parameters.
Decision under Uncertainty
Authors: Philippe Smets
Date: 2013-03-27 00:00:00
Tasks: Form

We derive axiomatically the probability function that should be used to make
decisions given any form of underlying uncertainty.
Simulation Approaches to General Probabilistic Inference on Belief Networks
Authors: Ross D. Shachter, Mark Alan Peot
Date: 2013-03-27 00:00:00

A number of algorithms have been developed to solve probabilistic inference
problems on belief networks. These algorithms can be divided into two main
groups: exact techniques which exploit the conditional independence revealed
when the graph structure is relatively sparse, and probabilistic sampling
techniques which exploit the "conductance" of an embedded Markov chain when the
conditional probabilities have non-extreme values. In this paper, we
investigate a family of "forward" Monte Carlo sampling techniques similar to
Logic Sampling [Henrion, 1988] which appear to perform well even in some
multiply connected networks with extreme conditional probabilities, and thus
would be generally applicable. We consider several enhancements which reduce
the posterior variance using this approach and propose a framework and criteria
for choosing when to use those enhancements.
Evidence Absorption and Propagation through Evidence Reversals
Authors: Ross D. Shachter
Date: 2013-03-27 00:00:00
Tasks: ARC

The arc reversal/node reduction approach to probabilistic inference is
extended to include the case of instantiated evidence by an operation called
"evidence reversal." This not only provides a technique for computing posterior
joint distributions on general belief networks, but also provides insight into
the methods of Pearl [1986b] and Lauritzen and Spiegelhalter [1988]. Although
it is well understood that the latter two algorithms are closely related, in
fact all three algorithms are identical whenever the belief network is a
forest.
Strategies for Generating Micro Explanations for Bayesian Belief Networks
Authors: Peter Sember, Ingrid Zukerman
Date: 2013-03-27 00:00:00

Bayesian Belief Networks have been largely overlooked by Expert Systems
practitioners on the grounds that they do not correspond to the human inference
mechanism. In this paper, we introduce an explanation mechanism designed to
generate intuitive yet probabilistically sound explanations of inferences drawn
by a Bayesian Belief Network. In particular, our mechanism accounts for the
results obtained due to changes in the causal and the evidential support of a
node.
A Logical Interpretation of Dempster-Shafer Theory, with Application to Visual Recognition
Authors: Gregory M. Provan
Date: 2013-03-27 00:00:00

We formulate Dempster Shafer Belief functions in terms of Propositional Logic
using the implicit notion of provability underlying Dempster Shafer Theory.
Given a set of propositional clauses, assigning weights to certain
propositional literals enables the Belief functions to be explicitly computed
using Network Reliability techniques. Also, the logical procedure corresponding
to updating Belief functions using Dempster's Rule of Combination is shown.
This analysis formalizes the implementation of Belief functions within an
Assumption-based Truth Maintenance System (ATMS). We describe the extension of
an ATMS-based visual recognition system, VICTORS, with this logical formulation
of Dempster Shafer theory. Without Dempster Shafer theory, VICTORS computes all
possible visual interpretations (i.e. all logical models) without determining
the best interpretation(s). Incorporating Dempster Shafer theory enables
optimal visual interpretations to be computed and a logical semantics to be
maintained.
Maximum Uncertainty Procedures for Interval-Valued Probability Distributions
Authors: Michael Pittarelli
Date: 2013-03-27 00:00:00

Measures of uncertainty and divergence are introduced for interval-valued
probability distributions and are shown to have desirable mathematical
properties. A maximum uncertainty inference procedure for marginal interval
distributions is presented. A technique for reconstruction of interval
distributions from projections is developed based on this inference procedure
Conditioning on Disjunctive Knowledge: Defaults and Probabilities
Authors: Eric Neufeld, J. D. Horton
Date: 2013-03-27 00:00:00

Many writers have observed that default logics appear to contain the "lottery
paradox" of probability theory. This arises when a default "proof by
contradiction" lets us conclude that a typical X is not a Y where Y is an
unusual subclass of X. We show that there is a similar problem with default
"proof by cases" and construct a setting where we might draw a different
conclusion knowing a disjunction than we would knowing any particular disjunct.
Though Reiter's original formalism is capable of representing this distinction,
other approaches are not. To represent and reason about this case, default
logicians must specify how a "typical" individual is selected. The problem is
closely related to Simpson's paradox of probability theory. If we accept a
simple probabilistic account of defaults based on the notion that one
proposition may favour or increase belief in another, the "multiple extension
problem" for both conjunctive and disjunctive knowledge vanishes.
Shootout-89: A Comparative Evaluation of Knowledge-based Systems that Forecast Severe Weather
Authors: W. R. Moninger, J. A. Flueck, C. Lusk, W. F. Roberts
Date: 2013-03-27 00:00:00

During the summer of 1989, the Forecast Systems Laboratory of the National
Oceanic and Atmospheric Administration sponsored an evaluation of artificial
intelligence-based systems that forecast severe convective storms. The
evaluation experiment, called Shootout-89, took place in Boulder, and focussed
on storms over the northeastern Colorado foothills and plains (Moninger, et
al., 1990). Six systems participated in Shootout-89. These included traditional
expert systems, an analogy-based system, and a system developed using methods
from the cognitive science/judgment analysis tradition. Each day of the
exercise, the systems generated 2 to 9 hour forecasts of the probabilities of
occurrence of: non significant weather, significant weather, and severe
weather, in each of four regions in northeastern Colorado. A verification
coordinator working at the Denver Weather Service Forecast Office gathered
ground-truth data from a network of observers. Systems were evaluated on the
basis of several measures of forecast skill, and on other metrics such as
timeliness, ease of learning, and ease of use. Systems were generally easy to
operate, however the various systems required substantially different levels of
meteorological expertise on the part of their users--reflecting the various
operational environments for which the systems had been designed. Systems
varied in their statistical behavior, but on this difficult forecast problem,
the systems generally showed a skill approximately equal to that of persistence
forecasts and climatological (historical frequency) forecasts. The two systems
that appeared best able to discriminate significant from non significant
weather events were traditional expert systems. Both of these systems required
the operator to make relatively sophisticated meteorological judgments. We are
unable, based on only one summer's worth of data, to determine the extent to
which the greater skill of the two systems was due to the content of their
knowledge bases, or to the subjective judgments of the operator. A follow-on
experiment, Shootout-91, is currently being planned. Interested potential
participants are encouraged to contact the author at the address above.
Experiments Using Belief Functions and Weights of Evidence incorporating Statistical Data and Expert Opinions
Authors: Mary McLeish, P. Yao, M. Cecile, T. Stirtzinger
Date: 2013-03-27 00:00:00
Tasks: Management

This paper presents some ideas and results of using uncertainty management
methods in the presence of data in preference to other statistical and machine
learning methods. A medical domain is used as a test-bed with data available
from a large hospital database system which collects symptom and outcome
information about patients. Data is often missing, of many variable types and
sample sizes for particular outcomes is not large. Uncertainty management
methods are useful for such domains and have the added advantage of allowing
for expert modification of belief values originally obtained from data.
Methodological considerations for using belief functions on statistical data
are dealt with in some detail. Expert opinions are Incorporated at various
levels of the project development and results are reported on an application to
liver disease diagnosis. Recent results contrasting the use of weights of
evidence and logistic regression on another medical domain are also presented.
Defeasible Decisions: What the Proposal is and isn't
Authors: Ronald P. Loui
Date: 2013-03-27 00:00:00
Tasks: Heuristic Search

In two recent papers, I have proposed a description of decision analysis that
differs from the Bayesian picture painted by Savage, Jeffrey and other classic
authors. Response to this view has been either overly enthusiastic or unduly
pessimistic. In this paper I try to place the idea in its proper place, which
must be somewhere in between. Looking at decision analysis as defeasible
reasoning produces a framework in which planning and decision theory can be
integrated, but work on the details has barely begun. It also produces a
framework in which the meta-decision regress can be stopped in a reasonable
way, but it does not allow us to ignore meta-level decisions. The heuristics
for producing arguments that I have presented are only supposed to be
suggestive; but they are not open to the egregious errors about which some have
worried. And though the idea is familiar to those who have studied heuristic
search, it is somewhat richer because the control of dialectic is more
interesting than the deepening of search.
Model-based Influence Diagrams for Machine Vision
Authors: Tod S. Levitt, John Mark Agosta, Thomas O. Binford
Date: 2013-03-27 00:00:00
Tasks: Bayesian Inference, model

We show an approach to automated control of machine vision systems based on
incremental creation and evaluation of a particular family of influence
diagrams that represent hypotheses of imagery interpretation and possible
subsequent processing decisions. In our approach, model-based machine vision
techniques are integrated with hierarchical Bayesian inference to provide a
framework for representing and matching instances of objects and relationships
in imagery and for accruing probabilities to rank order conflicting scene
interpretations. We extend a result of Tatman and Shachter to show that the
sequence of processing decisions derived from evaluating the diagrams at each
stage is the same as the sequence that would have been derived by evaluating
the final influence diagram that contains all random variables created during
the run of the vision system.
Inference Policies
Authors: Paul E. Lehner
Date: 2013-03-27 00:00:00

It is suggested that an AI inference system should reflect an inference
policy that is tailored to the domain of problems to which it is applied -- and
furthermore that an inference policy need not conform to any general theory of
rational inference or induction. We note, for instance, that Bayesian reasoning
about the probabilistic characteristics of an inference domain may result in
the specification of an nonBayesian procedure for reasoning within the
inference domain. In this paper, the idea of an inference policy is explored in
some detail. To support this exploration, the characteristics of some standard
and nonstandard inference policies are examined.
When Should a Decision Maker Ignore the Advice of a Decision Aid?
Authors: Paul E. Lehner, Theresa M. Mullin, Marvin S. Cohen
Date: 2013-03-27 00:00:00

This paper argues that the principal difference between decision aids and
most other types of information systems is the greater reliance of decision
aids on fallible algorithms--algorithms that sometimes generate incorrect
advice. It is shown that interactive problem solving with a decision aid that
is based on a fallible algorithm can easily result in aided performance which
is poorer than unaided performance, even if the algorithm, by itself, performs
significantly better than the unaided decision maker. This suggests that unless
certain conditions are satisfied, using a decision aid as an aid is
counterproductive. Some conditions under which a decision aid is best used as
an aid are derived.