pwc-archive/papers-with-abstracts huggingface dataset:
Bayesian Inference in Model-Based Machine Vision
Authors: Thomas O. Binford, Tod S. Levitt, Wallace B. Mann
Date: 2013-03-27 00:00:00
Tasks: Bayesian Inference, model

This is a preliminary version of visual interpretation integrating multiple
sensors in SUCCESSOR, an intelligent, model-based vision system. We pursue a
thorough integration of hierarchical Bayesian inference with comprehensive
physical representation of objects and their relations in a system for
reasoning with geometry, surface materials and sensor models in machine vision.
Bayesian inference provides a framework for accruing_ probabilities to rank
order hypotheses.
An Interesting Uncertainty-Based Combinatoric Problem in Spare Parts Forecasting: The FRED System
Authors: John B. Bacon
Date: 2013-03-27 00:00:00

The domain of spare parts forecasting is examined, and is found to present
unique uncertainty based problems in the architectural design of a
knowledge-based system. A mixture of different uncertainty paradigms is
required for the solution, with an intriguing combinatoric problem arising from
an uncertain choice of inference engines. Thus, uncertainty in the system is
manifested in two different meta-levels. The different uncertainty paradigms
and meta-levels must be integrated into a functioning whole. FRED is an example
of a difficult real-world domain to which no existing uncertainty approach is
completely appropriate. This paper discusses the architecture of FRED,
highlighting: the points of uncertainty and other interesting features of the
domain, the specific implications of those features on the system design
(including the combinatoric explosions), their current implementation & future
plans,and other problems and issues with the architecture.
Can Evidence Be Combined in the Dempster-Shafer Theory
Authors: John Yen
Date: 2013-03-27 00:00:00

Dempster's rule of combination has been the most controversial part of the
Dempster-Shafer (D-S) theory. In particular, Zadeh has reached a conjecture on
the noncombinability of evidence from a relational model of the D-S theory. In
this paper, we will describe another relational model where D-S masses are
represented as conditional granular distributions. By comparing it with Zadeh's
relational model, we will show how Zadeh's conjecture on combinability does not
affect the applicability of Dempster's rule in our model.
Bayesian Prediction for Artificial Intelligence
Authors: Matthew Self, Peter Cheeseman
Date: 2013-03-27 00:00:00
Tasks: Prediction

This paper shows that the common method used for making predictions under
uncertainty in A1 and science is in error. This method is to use currently
available data to select the best model from a given class of models-this
process is called abduction-and then to use this model to make predictions
about future data. The correct method requires averaging over all the models to
make a prediction-we call this method transduction. Using transduction, an AI
system will not give misleading results when basing predictions on small
amounts of data, when no model is clearly best. For common classes of models we
show that the optimal solution can be given in closed form.
Do We Need Higher-Order Probabilities and, If So, What Do They Mean?
Authors: Judea Pearl
Date: 2013-03-27 00:00:00

The apparent failure of individual probabilistic expressions to distinguish
uncertainty about truths from uncertainty about probabilistic assessments have
prompted researchers to seek formalisms where the two types of uncertainties
are given notational distinction. This paper demonstrates that the desired
distinction is already a built-in feature of classical probabilistic models,
thus, specialized notations are unnecessary.
Belief in Belief Functions: An Examination of Shafer's Canonical Examples
Authors: Kathryn Blackmond Laskey
Date: 2013-03-27 00:00:00

In the canonical examples underlying Shafer-Dempster theory, beliefs over the
hypotheses of interest are derived from a probability model for a set of
auxiliary hypotheses. Beliefs are derived via a compatibility relation
connecting the auxiliary hypotheses to subsets of the primary hypotheses. A
belief function differs from a Bayesian probability model in that one does not
condition on those parts of the evidence for which no probabilities are
specified. The significance of this difference in conditioning assumptions is
illustrated with two examples giving rise to identical belief functions but
different Bayesian probability distributions.
Higher Order Probabilities
Authors: Henry E. Kyburg Jr
Date: 2013-03-27 00:00:00

A number of writers have supposed that for the full specification of belief,
higher order probabilities are required. Some have even supposed that there may
be an unending sequence of higher order probabilities of probabilities of
probabilities.... In the present paper we show that higher order probabilities
can always be replaced by the marginal distributions of joint probability
distributions. We consider both the case in which higher order probabilities
are of the same sort as lower order probabilities and that in which higher
order probabilities are distinct in character, as when lower order
probabilities are construed as frequencies and higher order probabilities are
construed as subjective degrees of belief. In neither case do higher order
probabilities appear to offer any advantages, either conceptually or
computationally.
Dempster-Shafer vs. Probabilistic Logic
Authors: Daniel Hunter
Date: 2013-03-27 00:00:00

The combination of evidence in Dempster-Shafer theory is compared with the
combination of evidence in probabilistic logic. Sufficient conditions are
stated for these two methods to agree. It is then shown that these conditions
are minimal in the sense that disagreement can occur when any one of them is
removed. An example is given in which the traditional assumption of conditional
independence of evidence on hypotheses holds and a uniform prior is assumed,
but probabilistic logic and Dempster's rule give radically different results
for the combination of two evidence events.
Modifiable Combining Functions
Authors: Paul Cohen, Glenn Shafer, Prakash P. Shenoy
Date: 2013-03-27 00:00:00

Modifiable combining functions are a synthesis of two common approaches to
combining evidence. They offer many of the advantages of these approaches and
avoid some disadvantages. Because they facilitate the acquisition,
representation, explanation, and modification of knowledge about combinations
of evidence, they are proposed as a tool for knowledge engineers who build
systems that reason under uncertainty, not as a normative theory of evidence.
Is Shafer General Bayes?
Authors: Paul K. Black
Date: 2013-03-27 00:00:00

This paper examines the relationship between Shafer's belief functions and
convex sets of probability distributions. Kyburg's (1986) result showed that
belief function models form a subset of the class of closed convex probability
distributions. This paper emphasizes the importance of Kyburg's result by
looking at simple examples involving Bernoulli trials. Furthermore, it is shown
that many convex sets of probability distributions generate the same belief
function in the sense that they support the same lower and upper values. This
has implications for a decision theoretic extension. Dempster's rule of
combination is also compared with Bayes' rule of conditioning.
Generalizing the Dempster-Shafer Theory to Fuzzy Sets
Authors: John Yen
Date: 2013-03-27 00:00:00

With the desire to apply the Dempster-Shafer theory to complex real world
problems where the evidential strength is often imprecise and vague, several
attempts have been made to generalize the theory. However, the important
concept in the D-S theory that the belief and plausibility functions are lower
and upper probabilities is no longer preserved in these generalizations. In
this paper, we describe a generalized theory of evidence where the degree of
belief in a fuzzy set is obtained by minimizing the probability of the fuzzy
set under the constraints imposed by a basic probability assignment. To
formulate the probabilistic constraint of a fuzzy focal element, we decompose
it into a set of consonant non-fuzzy focal elements. By generalizing the
compatibility relation to a possibility theory, we are able to justify our
generalization to Dempster's rule based on possibility distribution. Our
generalization not only extends the application of the D-S theory but also
illustrates a way that probability theory and fuzzy set theory can be combined
to deal with different kinds of uncertain information in AI systems.
Predicting the Likely Behaviors of Continuous Nonlinear Systems in Equilibrium
Authors: Alexander Yeh
Date: 2013-03-27 00:00:00

This paper introduces a method for predicting the likely behaviors of
continuous nonlinear systems in equilibrium in which the input values can vary.
The method uses a parameterized equation model and a lower bound on the input
joint density to bound the likelihood that some behavior will occur, such as a
state variable being inside a given numeric range. Using a bound on the density
instead of the density itself is desirable because often the input density's
parameters and shape are not exactly known. The new method is called SAB after
its basic operations: split the input value space into smaller regions, and
then bound those regions' possible behaviors and the probability of being in
them. SAB finds rough bounds at first, and then refines them as more time is
given. In contrast to other researchers' methods, SAB can (1) find all the
possible system behaviors, and indicate how likely they are, (2) does not
approximate the distribution of possible outcomes without some measure of the
error magnitude, (3) does not use discretized variable values, which limit the
events one can find probability bounds for, (4) can handle density bounds, and
(5) can handle such criteria as two state variables both being inside a numeric
range.
Nonmonotonic Reasoning via Possibility Theory
Authors: Ronald R. Yager
Date: 2013-03-27 00:00:00
Tasks: Relation

We introduce the operation of possibility qualification and show how. this
modal-like operator can be used to represent "typical" or default knowledge in
a theory of nonmonotonic reasoning. We investigate the representational power
of this approach by looking at a number of prototypical problems from the
nonmonotonic reasoning literature. In particular we look at the so called Yale
shooting problem and its relation to priority in default reasoning.
MCE Reasoning in Recursive Causal Networks
Authors: Wilson X. Wen
Date: 2013-03-27 00:00:00

A probabilistic method of reasoning under uncertainty is proposed based on
the principle of Minimum Cross Entropy (MCE) and concept of Recursive Causal
Model (RCM). The dependency and correlations among the variables are described
in a special language BNDL (Belief Networks Description Language). Beliefs are
propagated among the clauses of the BNDL programs representing the underlying
probabilistic distributions. BNDL interpreters in both Prolog and C has been
developed and the performance of the method is compared with those of the
others.
Causal Networks: Semantics and Expressiveness
Authors: Tom S. Verma, Judea Pearl
Date: 2013-03-27 00:00:00

Dependency knowledge of the form "x is independent of y once z is known"
invariably obeys the four graphoid axioms, examples include probabilistic and
database dependencies. Often, such knowledge can be represented efficiently
with graphical structures such as undirected graphs and directed acyclic graphs
(DAGs). In this paper we show that the graphical criterion called d-separation
is a sound rule for reading independencies from any DAG based on a causal input
list drawn from a graphoid. The rule may be extended to cover DAGs that
represent functional dependencies as well as conditional dependencies.
Handling uncertainty in a system for text-symbol context analysis
Authors: Bjornar Tessem, Lars Johan Ersland
Date: 2013-03-27 00:00:00

In pattern analysis, information regarding an object can often be drawn from
its surroundings. This paper presents a method for handling uncertainty when
using context of symbols and texts for analyzing technical drawings. The method
is based on Dempster-Shafer theory and possibility theory.
Updating Probabilities in Multiply-Connected Belief Networks
Authors: Jaap Suermondt, Gregory F. Cooper
Date: 2013-03-27 00:00:00

This paper focuses on probability updates in multiply-connected belief
networks. Pearl has designed the method of conditioning, which enables us to
apply his algorithm for belief updates in singly-connected networks to
multiply-connected belief networks by selecting a loop-cutset for the network
and instantiating these loop-cutset nodes. We discuss conditions that need to
be satisfied by the selected nodes. We present a heuristic algorithm for
finding a loop-cutset that satisfies these conditions.
Generating Decision Structures and Causal Explanations for Decision Making
Authors: Spencer Star
Date: 2013-03-27 00:00:00
Tasks: Decision Making

This paper examines two related problems that are central to developing an
autonomous decision-making agent, such as a robot. Both problems require
generating structured representafions from a database of unstructured
declarative knowledge that includes many facts and rules that are irrelevant in
the problem context. The first problem is how to generate a well structured
decision problem from such a database. The second problem is how to generate,
from the same database, a well-structured explanation of why some possible
world occurred. In this paper it is shown that the problem of generating the
appropriate decision structure or explanation is intractable without
introducing further constraints on the knowledge in the database. The paper
proposes that the problem search space can be constrained by adding knowledge
to the database about causal relafions between events. In order to determine
the causal knowledge that would be most useful, causal theories for
deterministic and indeterministic universes are proposed. A program that uses
some of these causal constraints has been used to generate explanations about
faulty plans. The program shows the expected increase in efficiency as the
causal constraints are introduced.
An Axiomatic Framework for Bayesian and Belief-function Propagation
Authors: Prakash P. Shenoy, Glenn Shafer
Date: 2013-03-27 00:00:00

In this paper, we describe an abstract framework and axioms under which exact
local computation of marginals is possible. The primitive objects of the
framework are variables and valuations. The primitive operators of the
framework are combination and marginalization. These operate on valuations. We
state three axioms for these operators and we derive the possibility of local
computation from the axioms. Next, we describe a propagation scheme for
computing marginals of a valuation when we have a factorization of the
valuation on a hypertree. Finally we show how the problem of computing
marginals of joint probability distributions and joint belief functions fits
the general framework.
A Linear Approximation Method for Probabilistic Inference
Authors: Ross D. Shachter
Date: 2013-03-27 00:00:00

An approximation method is presented for probabilistic inference with
continuous random variables. These problems can arise in many practical
problems, in particular where there are "second order" probabilities. The
approximation, based on the Gaussian influence diagram, iterates over linear
approximations to the inference problem.
Maintenance in Probabilistic Knowledge-Based Systems
Authors: Thomas F. Reid, Gregory S. Parnell
Date: 2013-03-27 00:00:00

Recent developments using directed acyclical graphs (i.e., influence diagrams
and Bayesian networks) for knowledge representation have lessened the problems
of using probability in knowledge-based systems (KBS). Most current research
involves the efficient propagation of new evidence, but little has been done
concerning the maintenance of domain-specific knowledge, which includes the
probabilistic information about the problem domain. By making use of
conditional independencies represented in she graphs, however, probability
assessments are required only for certain variables when the knowledge base is
updated. The purpose of this study was to investigate, for those variables
which require probability assessments, ways to reduce the amount of new
knowledge required from the expert when updating probabilistic information in a
probabilistic knowledge-based system. Three special cases (ignored outcome,
split outcome, and assumed constraint outcome) were identified under which many
of the original probabilities (those already in the knowledge-base) do not need
to be reassessed when maintenance is required.
Decision Making with Linear Constraints on Probabilities
Authors: Michael Pittarelli
Date: 2013-03-27 00:00:00
Tasks: Decision Making

Techniques for decision making with knowledge of linear constraints on
condition probabilities are examined. These constraints arise naturally in many
situations: upper and lower condition probabilities are known; an ordering
among the probabilities is determined; marginal probabilities or bounds on such
probabilities are known, e.g., data are available in the form of a
probabilistic database (Cavallo and Pittarelli, 1987a); etc. Standard
situations of decision making under risk and uncertainty may also be
characterized by linear constraints. Each of these types of information may be
represented by a convex polyhedron of numerically determinate condition
probabilities. A uniform approach to decision making under risk, uncertainty,
and partial uncertainty based on a generalized version of a criterion of
Hurwicz is proposed, Methods for processing marginal probabilities to improve
decision making using any of the criteria discussed are presented.
Probabilistic Semantics and Defaults
Authors: Eric Neufeld, David L. Poole
Date: 2013-03-27 00:00:00

There is much interest in providing probabilistic semantics for defaults but
most approaches seem to suffer from one of two problems: either they require
numbers, a problem defaults were intended to avoid, or they generate peculiar
side effects. Rather than provide semantics for defaults, we address the
problem defaults were intended to solve: that of reasoning under uncertainty
where numeric probability distributions are not available. We describe a
non-numeric formalism called an inference graph based on standard probability
theory, conditional independence and sentences of favouring where a favours b -
favours(a, b) - p(a|b) > p(a). The formalism seems to handle the examples from
the nonmonotonic literature. Most importantly, the sentences of our system can
be verified by performing an appropriate experiment in the semantic domain.
Justifying the Principle of Interval Constraints
Authors: Richard E. Neapolitan, James Kenevan
Date: 2013-03-27 00:00:00

When knowledge is obtained from a database, it is only possible to deduce
confidence intervals for probability values. With confidence intervals
replacing point values, the results in the set covering model include interval
constraints for the probabilities of mutually exclusive and exhaustive
explanations. The Principle of Interval Constraints ranks these explanations by
determining the expected values of the probabilities based on distributions
determined from the interval, constraints. This principle was developed using
the Classical Approach to probability. This paper justifies the Principle of
Interval Constraints with a more rigorous statement of the Classical Approach
and by defending the concept of probabilities of probabilities.
Evidential Reasoning in a Network Usage Prediction Testbed
Authors: Ronald P. Loui
Date: 2013-03-27 00:00:00
Tasks: Prediction

This paper reports on empirical work aimed at comparing evidential reasoning
techniques. While there is prima facie evidence for some conclusions, this i6
work in progress; the present focus is methodology, with the goal that
subsequent results be meaningful. The domain is a network of UNIX* cycle
servers, and the task is to predict properties of the state of the network from
partial descriptions of the state. Actual data from the network are taken and
used for blindfold testing in a betting game that allows abstention. The focal
technique has been Kyburg's method for reasoning with data of varying relevance
to a particular query, though the aim is to be able eventually to compare
various uncertainty calculi. The conclusions are not novel, but are
instructive. 1. All of the calculi performed better than human subjects, so
unbiased access to sample experience is apparently of value. 2. Performance
depends on metric: (a) when trials are repeated, net = gains - losses favors
methods that place many bets, if the probability of placing a correct bet is
sufficiently high; that is, it favors point-valued formalisms; (b) yield =
gains/(gains + lossee) favors methods that bet only when sure to bet correctly;
that is, it favors interval-valued formalisms. 3. Among the calculi, there were
no clear winners or losers. Methods are identified for eliminating the bias of
the net as a performance criterion and for separating the calculi effectively:
in both cases by posting odds for the betting game in the appropriate way.
Utility-Based Control for Computer Vision
Authors: Tod S. Levitt, Thomas O. Binford, Gil J. Ettinger, Patrice Gelband
Date: 2013-03-27 00:00:00
Tasks: Computational Efficiency

Several key issues arise in implementing computer vision recognition of world
objects in terms of Bayesian networks. Computational efficiency is a driving
force. Perceptual networks are very deep, typically fifteen levels of
structure. Images are wide, e.g., an unspecified-number of edges may appear
anywhere in an image 512 x 512 pixels or larger. For efficiency, we dynamically
instantiate hypotheses of observed objects. The network is not fixed, but is
created incrementally at runtime. Generation of hypotheses of world objects and
indexing of models for recognition are important, but they are not considered
here [4,11]. This work is aimed at near-term implementation with parallel
computation in a radar surveillance system, ADRIES [5, 15], and a system for
industrial part recognition, SUCCESSOR [2]. For many applications, vision must
be faster to be practical and so efficiently controlling the machine vision
process is critical. Perceptual operators may scan megapixels and may require
minutes of computation time. It is necessary to avoid unnecessary sensor
actions and computation. Parallel computation is available at several levels of
processor capability. The potential for parallel, distributed computation for
high-level vision means distributing non-homogeneous computations. This paper
addresses the problem of task control in machine vision systems based on
Bayesian probability models. We separate control and inference to extend the
previous work [3] to maximize utility instead of probability. Maximizing
utility allows adopting perceptual strategies for efficient information
gathering with sensors and analysis of sensor data. Results of controlling
machine vision via utility to recognize military situations are presented in
this paper. Future work extends this to industrial part recognition for
SUCCESSOR.
Epistemological Relevance and Statistical Knowledge
Authors: Henry E. Kyburg Jr
Date: 2013-03-27 00:00:00

For many years, at least since McCarthy and Hayes (1969), writers have
lamented, and attempted to compensate for, the alleged fact that we often do
not have adequate statistical knowledge for governing the uncertainty of
belief, for making uncertain inferences, and the like. It is hardly ever
spelled out what "adequate statistical knowledge" would be, if we had it, and
how adequate statistical knowledge could be used to control and regulate
epistemic uncertainty.