pwc-archive/papers-with-abstracts huggingface dataset:
The Role of Calculi in Uncertain Inference Systems
Authors: Michael P. Wellman, David Heckerman
Date: 2013-03-27 00:00:00
Tasks: Decision Making

Much of the controversy about methods for automated decision making has
focused on specific calculi for combining beliefs or propagating uncertainty.
We broaden the debate by (1) exploring the constellation of secondary tasks
surrounding any primary decision problem, and (2) identifying knowledge
engineering concerns that present additional representational tradeoffs. We
argue on pragmatic grounds that the attempt to support all of these tasks
within a single calculus is misguided. In the process, we note several
uncertain reasoning objectives that conflict with the Bayesian ideal of
complete specification of probabilities and utilities. In response, we advocate
treating the uncertainty calculus as an object language for reasoning
mechanisms that support the secondary tasks. Arguments against Bayesian
decision theory are weakened when the calculus is relegated to this role.
Architectures for uncertainty handling that take statements in the calculus as
objects to be reasoned about offer the prospect of retaining normative status
with respect to decision making while supporting the other tasks in uncertain
reasoning.
Problem Structure and Evidential Reasoning
Authors: Richard M. Tong, Lee A. Appelbaum
Date: 2013-03-27 00:00:00
Tasks: Retrieval

In our previous series of studies to investigate the role of evidential
reasoning in the RUBRIC system for full-text document retrieval (Tong et al.,
1985; Tong and Shapiro, 1985; Tong and Appelbaum, 1987), we identified the
important role that problem structure plays in the overall performance of the
system. In this paper, we focus on these structural elements (which we now call
"semantic structure") and show how explicit consideration of their properties
reduces what previously were seen as difficult evidential reasoning problems to
more tractable questions.
Towards Solving the Multiple Extension Problem: Combining Defaults and Probabilities
Authors: Eric Neufeld, David L. Poole
Date: 2013-03-27 00:00:00
Tasks: Diagnostic

The multiple extension problem arises frequently in diagnostic and default
inference. That is, we can often use any of a number of sets of defaults or
possible hypotheses to explain observations or make Predictions. In default
inference, some extensions seem to be simply wrong and we use qualitative
techniques to weed out the unwanted ones. In the area of diagnosis, however,
the multiple explanations may all seem reasonable, however improbable. Choosing
among them is a matter of quantitative preference. Quantitative preference
works well in diagnosis when knowledge is modelled causally. Here we suggest a
framework that combines probabilities and defaults in a single unified
framework that retains the semantics of diagnosis as construction of
explanations from a fixed set of possible hypotheses. We can then compute
probabilities incrementally as we construct explanations. Here we describe a
branch and bound algorithm that maintains a set of all partial explanations
while exploring a most promising one first. A most probable explanation is
found first if explanations are partially ordered.
A Knowledge Engineer's Comparison of Three Evidence Aggregation Methods
Authors: Donald H. Mitchell, Steven A. Harp, David K. Simkin
Date: 2013-03-27 00:00:00

The comparisons of uncertainty calculi from the last two Uncertainty
Workshops have all used theoretical probabilistic accuracy as the sole metric.
While mathematical correctness is important, there are other factors which
should be considered when developing reasoning systems. These other factors
include, among other things, the error in uncertainty measures obtainable for
the problem and the effect of this error on the performance of the resulting
system.
Comparisons of Reasoning Mechanisms for Computer Vision
Authors: Ze-Nian Li
Date: 2013-03-27 00:00:00

An evidential reasoning mechanism based on the Dempster-Shafer theory of
evidence is introduced. Its performance in real-world image analysis is
compared with other mechanisms based on the Bayesian formalism and a simple
weight combination method.
Convergent Deduction for Probabilistic Logic
Authors: Peter Haddawy, Alan M. Frisch
Date: 2013-03-27 00:00:00

This paper discusses the semantics and proof theory of Nilsson's
probabilistic logic, outlining both the benefits of its well-defined model
theory and the drawbacks of its proof theory. Within Nilsson's semantic
framework, we derive a set of inference rules which are provably sound. The
resulting proof system, in contrast to Nilsson's approach, has the important
feature of convergence - that is, the inference process proceeds by computing
increasingly narrow probability intervals which converge from above and below
on the smallest entailed probability interval. Thus the procedure can be
stopped at any time to yield partial information concerning the smallest
entailed interval.
A Measure-Free Approach to Conditioning
Authors: I. R. Goodman
Date: 2013-03-27 00:00:00
Tasks: Negation

In an earlier paper, a new theory of measurefree "conditional" objects was
presented. In this paper, emphasis is placed upon the motivation of the theory.
The central part of this motivation is established through an example involving
a knowledge-based system. In order to evaluate combination of evidence for this
system, using observed data, auxiliary at tribute and diagnosis variables, and
inference rules connecting them, one must first choose an appropriate algebraic
logic description pair (ALDP): a formal language or syntax followed by a
compatible logic or semantic evaluation (or model). Three common choices- for
this highly non-unique choice - are briefly discussed, the logics being
Classical Logic, Fuzzy Logic, and Probability Logic. In all three,the key
operator representing implication for the inference rules is interpreted as the
often-used disjunction of a negation (b => a) = (b'v a), for any events a,b.
  However, another reasonable interpretation of the implication operator is
through the familiar form of probabilistic conditioning. But, it can be shown -
quite surprisingly - that the ALDP corresponding to Probability Logic cannot be
used as a rigorous basis for this interpretation! To fill this gap, a new ALDP
is constructed consisting of "conditional objects", extending ordinary
Probability Logic, and compatible with the desired conditional probability
interpretation of inference rules. It is shown also that this choice of ALDP
leads to feasible computations for the combination of evidence evaluation in
the example. In addition, a number of basic properties of conditional objects
and the resulting Conditional Probability Logic are given, including a
characterization property and a developed calculus of relations.
A Study of Associative Evidential Reasoning
Authors: Yizong Cheng, Rangasami L. Kashyap
Date: 2013-03-27 00:00:00
Tasks: Relation

Evidential reasoning is cast as the problem of simplifying the
evidence-hypothesis relation and constructing combination formulas that possess
certain testable properties. Important classes of evidence as identifiers,
annihilators, and idempotents and their roles in determining binary operations
on intervals of reals are discussed. The appropriate way of constructing
formulas for combining evidence and their limitations, for instance, in
robustness, are presented.
Using T-Norm Based Uncertainty Calculi in a Naval Situation Assessment Application
Authors: Piero P. Bonissone
Date: 2013-03-27 00:00:00

RUM (Reasoning with Uncertainty Module), is an integrated software tool based
on a KEE, a frame system implemented in an object oriented language. RUM's
architecture is composed of three layers: representation, inference, and
control. The representation layer is based on frame-like data structures that
capture the uncertainty information used in the inference layer and the
uncertainty meta-information used in the control layer. The inference layer
provides a selection of five T-norm based uncertainty calculi with which to
perform the intersection, detachment, union, and pooling of information. The
control layer uses the meta-information to select the appropriate calculus for
each context and to resolve eventual ignorance or conflict in the information.
This layer also provides a context mechanism that allows the system to focus on
the relevant portion of the knowledge base, and an uncertain-belief revision
system that incrementally updates the certainty values of well-formed formulae
(wffs) in an acyclic directed deduction graph. RUM has been tested and
validated in a sequence of experiments in both naval and aerial situation
assessment (SA), consisting of correlating reports and tracks, locating and
classifying platforms, and identifying intents and threats. An example of naval
situation assessment is illustrated. The testbed environment for developing
these experiments has been provided by LOTTA, a symbolic simulator implemented
in Flavors. This simulator maintains time-varying situations in a multi-player
antagonistic game where players must make decisions in light of uncertain and
incomplete data. RUM has been used to assist one of the LOTTA players to
perform the SA task.
Theory-Based Inductive Learning: An Integration of Symbolic and Quantitative Methods
Authors: Spencer Star
Date: 2013-03-27 00:00:00
Tasks: Inductive Learning

The objective of this paper is to propose a method that will generate a
causal explanation of observed events in an uncertain world and then make
decisions based on that explanation. Feedback can cause the explanation and
decisions to be modified. I call the method Theory-Based Inductive Learning
(T-BIL). T-BIL integrates deductive learning, based on a technique called
Explanation-Based Generalization (EBG) from the field of machine learning, with
inductive learning methods from Bayesian decision theory. T-BIL takes as inputs
(1) a decision problem involving a sequence of related decisions over time, (2)
a training example of a solution to the decision problem in one period, and (3)
the domain theory relevant to the decision problem. T-BIL uses these inputs to
construct a probabilistic explanation of why the training example is an
instance of a solution to one stage of the sequential decision problem. This
explanation is then generalized to cover a more general class of instances and
is used as the basis for making the next-stage decisions. As the outcomes of
each decision are observed, the explanation is revised, which in turn affects
the subsequent decisions. A detailed example is presented that uses T-BIL to
solve a very general stochastic adaptive control problem for an autonomous
mobile robot.
A Heuristic Bayesian Approach to Knowledge Acquisition: Application to Analysis of Tissue-Type Plasminogen Activator
Authors: Ross D. Shachter, David M. Eddy, Vic Hasselblad, Robert Wolpert
Date: 2013-03-27 00:00:00

This paper describes a heuristic Bayesian method for computing probability
distributions from experimental data, based upon the multivariate normal form
of the influence diagram. An example illustrates its use in medical technology
assessment. This approach facilitates the integration of results from different
studies, and permits a medical expert to make proper assessments without
considerable statistical training.
The Recovery of Causal Poly-Trees from Statistical Data
Authors: George Rebane, Judea Pearl
Date: 2013-03-27 00:00:00

Poly-trees are singly connected causal networks in which variables may arise
from multiple causes. This paper develops a method of recovering ply-trees from
empirically measured probability distributions of pairs of variables. The
method guarantees that, if the measured distributions are generated by a causal
process structured as a ply-tree then the topological structure of such tree
can be recovered precisely and, in addition, the causal directionality of the
branches can be determined up to the maximum extent possible. The method also
pinpoints the minimum (if any) external semantics required to determine the
causal relationships among the variables considered.
Automated Generation of Connectionist Expert Systems for Problems Involving Noise and Redundancy
Authors: Stephen I. Gallant
Date: 2013-03-27 00:00:00

When creating an expert system, the most difficult and expensive task is
constructing a knowledge base. This is particularly true if the problem
involves noisy data and redundant measurements. This paper shows how to modify
the MACIE process for generating connectionist expert systems from training
examples so that it can accommodate noisy and redundant data. The basic idea is
to dynamically generate appropriate training examples by constructing both a
'deep' model and a noise model for the underlying problem. The use of
winner-take-all groups of variables is also discussed. These techniques are
illustrated with a small example that would be very difficult for standard
expert system approaches.
The Inductive Logic of Information Systems
Authors: Norman C. Dalkey
Date: 2013-03-27 00:00:00

An inductive logic can be formulated in which the elements are not
propositions or probability distributions, but information systems. The logic
is complete for information systems with binary hypotheses, i.e., it applies to
all such systems. It is not complete for information systems with more than two
hypotheses, but applies to a subset of such systems. The logic is inductive in
that conclusions are more informative than premises. Inferences using the
formalism have a strong justification in terms of the expected value of the
derived information system.
The Automatic Training of Rule Bases that Use Numerical Uncertainty Representations
Authors: Richard A. Caruana
Date: 2013-03-27 00:00:00

The use of numerical uncertainty representations allows better modeling of
some aspects of human evidential reasoning. It also makes knowledge acquisition
and system development, test, and modification more difficult. We propose that
where possible, the assignment and/or refinement of rule weights should be
performed automatically. We present one approach to performing this training -
numerical optimization - and report on the results of some preliminary tests in
training rule bases. We also show that truth maintenance can be used to make
training more efficient and ask some epistemological questions raised by
training rule weights.
Decision Tree Induction Systems: A Bayesian Analysis
Authors: Wray L. Buntine
Date: 2013-03-27 00:00:00

Decision tree induction systems are being used for knowledge acquisition in
noisy domains. This paper develops a subjective Bayesian interpretation of the
task tackled by these systems and the heuristic methods they use. It is argued
that decision tree systems implicitly incorporate a prior belief that the
simpler (in terms of decision tree complexity) of two hypotheses be preferred,
all else being equal, and that they perform a greedy search of the space of
decision rules to find one in which there is strong posterior belief. A number
of improvements to these systems are then suggested.
Implementing Evidential Reasoning in Expert Systems
Authors: John Yen
Date: 2013-03-27 00:00:00
Tasks: Explanation Generation

The Dempster-Shafer theory has been extended recently for its application to
expert systems. However, implementing the extended D-S reasoning model in
rule-based systems greatly complicates the task of generating informative
explanations. By implementing GERTIS, a prototype system for diagnosing
rheumatoid arthritis, we show that two kinds of knowledge are essential for
explanation generation: (l) taxonomic class relationships between hypotheses
and (2) pointers to the rules that significantly contribute to belief in the
hypothesis. As a result, the knowledge represented in GERTIS is richer and more
complex than that of conventional rule-based systems. GERTIS not only
demonstrates the feasibility of rule-based evidential-reasoning systems, but
also suggests ways to generate better explanations, and to explicitly represent
various useful relationships among hypotheses and rules.
Structuring Causal Tree Models with Continuous Variables
Authors: Lei Xu, Judea Pearl
Date: 2013-03-27 00:00:00

This paper considers the problem of invoking auxiliary, unobservable
variables to facilitate the structuring of causal tree models for a given set
of continuous variables. Paralleling the treatment of bi-valued variables in
[Pearl 1986], we show that if a collection of coupled variables are governed by
a joint normal distribution and a tree-structured representation exists, then
both the topology and all internal relationships of the tree can be uncovered
by observing pairwise dependencies among the observed variables (i.e., the
leaves of the tree). Furthermore, the conditions for normally distributed
variables are less restrictive than those governing bi-valued variables. The
result extends the applications of causal tree models which were found useful
in evidential reasoning tasks.
Satisfaction of Assumptions is a Weak Predictor of Performance
Authors: Ben P. Wise
Date: 2013-03-27 00:00:00

This paper demonstrates a methodology for examining the accuracy of uncertain
inference systems (UIS), after their parameters have been optimized, and does
so for several common UIS's. This methodology may be used to test the accuracy
when either the prior assumptions or updating formulae are not exactly
satisfied. Surprisingly, these UIS's were revealed to be no more accurate on
the average than a simple linear regression. Moreover, even on prior
distributions which were deliberately biased so as give very good accuracy,
they were less accurate than the simple probabilistic model which assumes
marginal independence between inputs. This demonstrates that the importance of
updating formulae can outweigh that of prior assumptions. Thus, when UIS's are
judged by their final accuracy after optimization, we get completely different
results than when they are judged by whether or not their prior assumptions are
perfectly satisfied.
Coefficients of Relations for Probabilistic Reasoning
Authors: Silvio Ursic
Date: 2013-03-27 00:00:00

Definitions and notations with historical references are given for some
numerical coefficients commonly used to quantify relations among collections of
objects for the purpose of expressing approximate knowledge and probabilistic
reasoning.
Objective Probability
Authors: Henry E. Kyburg Jr
Date: 2013-03-27 00:00:00
Tasks: All

A distinction is sometimes made between "statistical" and "subjective"
probabilities. This is based on a distinction between "unique" events and
"repeatable" events. We argue that this distinction is untenable, since all
events are "unique" and all events belong to "kinds", and offer a conception of
probability for A1 in which (1) all probabilities are based on -- possibly
vague -- statistical knowledge, and (2) every statement in the language has a
probability. This conception of probability can be applied to very rich
languages.
NAIVE: A Method for Representing Uncertainty and Temporal Relationships in an Automated Reasoner
Authors: Michael C. Higgins
Date: 2013-03-27 00:00:00

This paper describes NAIVE, a low-level knowledge representation language and
inferencing process. NAIVE has been designed for reasoning about
nondeterministic dynamic systems like those found in medicine. Knowledge is
represented in a graph structure consisting of nodes, which correspond to the
variables describing the system of interest, and arcs, which correspond to the
procedures used to infer the value of a variable from the values of other
variables. The value of a variable can be determined at an instant in time,
over a time interval or for a series of times. Information about the value of a
variable is expressed as a probability density function which quantifies the
likelihood of each possible value. The inferencing process uses these
probability density functions to propagate uncertainty. NAIVE has been used to
develop medical knowledge bases including over 100 variables.
Practical Issues in Constructing a Bayes' Belief Network
Authors: Max Henrion
Date: 2013-03-27 00:00:00
Tasks: Sensitivity

Bayes belief networks and influence diagrams are tools for constructing
coherent probabilistic representations of uncertain knowledge. The process of
constructing such a network to represent an expert's knowledge is used to
illustrate a variety of techniques which can facilitate the process of
structuring and quantifying uncertain relationships. These include some
generalizations of the "noisy OR gate" concept. Sensitivity analysis of generic
elements of Bayes' networks provides insight into when rough probability
assessments are sufficient and when greater precision may be important.
A Perspective on Confidence and Its Use in Focusing Attention During Knowledge Acquisition
Authors: David Heckerman, Holly B. Jimison
Date: 2013-03-27 00:00:00

We present a representation of partial confidence in belief and preference
that is consistent with the tenets of decision-theory. The fundamental insight
underlying the representation is that if a person is not completely confident
in a probability or utility assessment, additional modeling of the assessment
may improve decisions to which it is relevant. We show how a traditional
decision-analytic approach can be used to balance the benefits of additional
modeling with associated costs. The approach can be used during knowledge
acquisition to focus the attention of a knowledge engineer or expert on parts
of a decision model that deserve additional refinement.
Temporal Reasoning About Uncertain Worlds
Authors: Steve Hanks
Date: 2013-03-27 00:00:00

We present a program that manages a database of temporally scoped beliefs.
The basic functionality of the system includes maintaining a network of
constraints among time points, supporting a variety of fetches, mediating the
application of causal rules, monitoring intervals of time for the addition of
new facts, and managing data dependencies that keep the database consistent. At
this level the system operates independent of any measure of belief or belief
calculus. We provide an example of how an application program mi9ght use this
functionality to implement a belief calculus.
Stochastic Simulation of Bayesian Belief Networks
Authors: Homer L. Chin, Gregory F. Cooper
Date: 2013-03-27 00:00:00
Tasks: ARC

This paper examines Bayesian belief network inference using simulation as a
method for computing the posterior probabilities of network variables.
Specifically, it examines the use of a method described by Henrion, called
logic sampling, and a method described by Pearl, called stochastic simulation.
We first review the conditions under which logic sampling is computationally
infeasible. Such cases motivated the development of the Pearl's stochastic
simulation algorithm. We have found that this stochastic simulation algorithm,
when applied to certain networks, leads to much slower than expected
convergence to the true posterior probabilities. This behavior is a result of
the tendency for local areas in the network to become fixed through many
simulation cycles. The time required to obtain significant convergence can be
made arbitrarily long by strengthening the probabilistic dependency between
nodes. We propose the use of several forms of graph modification, such as graph
pruning, arc reversal, and node reduction, in order to convert some networks
into formats that are computationally more efficient for simulation.
Using the Dempster-Shafer Scheme in a Diagnostic Expert System Shell
Authors: Gautam Biswas, Teywansh S. Anand
Date: 2013-03-27 00:00:00
Tasks: Diagnostic

This paper discusses an expert system shell that integrates rule-based
reasoning and the Dempster-Shafer evidence combination scheme. Domain knowledge
is stored as rules with associated belief functions. The reasoning component
uses a combination of forward and backward inferencing mechanisms to allow
interaction with users in a mixed-initiative format.