pwc-archive/papers-with-abstracts huggingface dataset:
Probabilistic and Non-Monotonic Inference
Authors: Henry E. Kyburg Jr
Date: 2013-03-27 00:00:00
Tasks: Common Sense Reasoning, Decision Making, Sentence

(l) I have enough evidence to render the sentence S probable. (la) So,
relative to what I know, it is rational of me to believe S. (2) Now that I have
more evidence, S may no longer be probable. (2a) So now, relative to what I
know, it is not rational of me to believe S. These seem a perfectly ordinary,
common sense, pair of situations. Generally and vaguely, I take them to embody
what I shall call probabilistic inference. This form of inference is clearly
non-monotonic. Relatively few people have taken this form of inference, based
on high probability, to serve as a foundation for non-monotonic logic or for a
logical or defeasible inference. There are exceptions: Jane Nutter [16] thinks
that sometimes probability has something to do with non-monotonic reasoning.
Judea Pearl [ 17] has recently been exploring the possibility. There are any
number of people whom one might call probability enthusiasts who feel that
probability provides all the answers by itself, with no need of help from
logic. Cheeseman [1], Henrion [5] and others think it useful to look at a
distribution of probabilities over a whole algebra of statements, to update
that distribution in the light of new evidence, and to use the latest updated
distribution of probability over the algebra as a basis for planning and
decision making. A slightly weaker form of this approach is captured by Nilsson
[15], where one assumes certain probabilities for certain statements, and
infers the probabilities, or constraints on the probabilities of other
statement. None of this corresponds to what I call probabilistic inference. All
of the inference that is taking place, either in Bayesian updating, or in
probabilistic logic, is strictly deductive. Deductive inference, particularly
that concerned with the distribution of classical probabilities or chances, is
of great importance. But this is not to say that there is no important role for
what earlier logicians have called "ampliative" or "inductive" or "scientific"
inference, in which the conclusion goes beyond the premises, asserts more than
do the premises. This depends on what David Israel [6] has called "real rules
of inference". It is characteristic of any such logic or inference procedure
that it can go wrong: that statements accepted at one point may be rejected at
a later point. Research underlying the results reported here has been partially
supported by the Signals Warfare Center of the United States Army.
Probabilistic Inference and Probabilistic Reasoning
Authors: Henry E. Kyburg Jr
Date: 2013-03-27 00:00:00

Uncertainty enters into human reasoning and inference in at least two ways.
It is reasonable to suppose that there will be roles for these distinct uses of
uncertainty also in automated reasoning.
Multiple decision trees
Authors: Suk Wah Kwok, Chris Carter
Date: 2013-03-27 00:00:00

This paper describes experiments, on two domains, to investigate the effect
of averaging over predictions of multiple decision trees, instead of using a
single tree. Other authors have pointed out theoretical and commonsense reasons
for preferring the multiple tree approach. Ideally, we would like to consider
predictions from all trees, weighted by their probability. However, there is a
vast number of different trees, and it is difficult to estimate the probability
of each tree. We sidestep the estimation problem by using a modified version of
the ID3 algorithm to build good trees, and average over only these trees. Our
results are encouraging. For each domain, we managed to produce a small number
of good trees. We find that it is best to average across sets of trees with
different structure; this usually gives better performance than any of the
constituent trees, including the ID3 tree.
A Comparison of Decision Analysis and Expert Rules for Sequential Diagnosis
Authors: Jayant Kalagnanam, Max Henrion
Date: 2013-03-27 00:00:00
Tasks: Fault Diagnosis, Sequential Diagnosis

There has long been debate about the relative merits of decision theoretic
methods and heuristic rule-based approaches for reasoning under uncertainty. We
report an experimental comparison of the performance of the two approaches to
troubleshooting, specifically to test selection for fault diagnosis. We use as
experimental testbed the problem of diagnosing motorcycle engines. The first
approach employs heuristic test selection rules obtained from expert mechanics.
We compare it with the optimal decision analytic algorithm for test selection
which employs estimated component failure probabilities and test costs. The
decision analytic algorithm was found to reduce the expected cost (i.e. time)
to arrive at a diagnosis by an average of 14% relative to the expert rules.
Sensitivity analysis shows the results are quite robust to inaccuracy in the
probability and cost estimates. This difference suggests some interesting
implications for knowledge acquisition.
Rational Nonmonotonic Reasoning
Authors: Carl Kadie
Date: 2013-03-27 00:00:00

Nonmonotonic reasoning is a pattern of reasoning that allows an agent to make
and retract (tentative) conclusions from inconclusive evidence. This paper
gives a possible-worlds interpretation of the nonmonotonic reasoning problem
based on standard decision theory and the emerging probability logic. The
system's central principle is that a tentative conclusion is a decision to make
a bet, not an assertion of fact. The system is rational, and as sound as the
proof theory of its underlying probability log.
A Representation of Uncertainty to Aid Insight into Decision Models
Authors: Holly B. Jimison
Date: 2013-03-27 00:00:00

Many real world models can be characterized as weak, meaning that there is
significant uncertainty in both the data input and inferences. This lack of
determinism makes it especially difficult for users of computer decision aids
to understand and have confidence in the models. This paper presents a
representation for uncertainty and utilities that serves as a framework for
graphical summary and computer-generated explanation of decision models. The
application described that tests the methodology is a computer decision aid
designed to enhance the clinician-patient consultation process for patients
with angina (chest pain due to lack of blood flow to the heart muscle). The
angina model is represented as a Bayesian decision network. Additionally, the
probabilities and utilities are treated as random variables with probability
distributions on their range of possible values. The initial distributions
represent information on all patients with anginal symptoms, and the approach
allows for rapid tailoring to more patientspecific distributions. This
framework provides a metric for judging the importance of each variable in the
model dynamically.
Stochastic Sensitivity Analysis Using Fuzzy Influence Diagrams
Authors: Pramod Jain, Alice M. Agogino
Date: 2013-03-27 00:00:00
Tasks: Decision Making, Diagnostic, Sensitivity

The practice of stochastic sensitivity analysis described in the decision
analysis literature is a testimonial to the need for considering deviations
from precise point estimates of uncertainty. We propose the use of Bayesian
fuzzy probabilities within an influence diagram computational scheme for
performing sensitivity analysis during the solution of probabilistic inference
and decision problems. Unlike other parametric approaches, the proposed scheme
does not require resolving the problem for the varying probability point
estimates. We claim that the solution to fuzzy influence diagrams provides as
much information as the classical point estimate approach plus additional
information concerning stochastic sensitivity. An example based on diagnostic
decision making in microcomputer assembly is used to illustrate this idea. We
claim that the solution to fuzzy influence diagrams provides as much
information as the classical point estimate approach plus additional interval
information that is useful for stochastic sensitivity analysis.
Parallel Belief Revision
Authors: Daniel Hunter
Date: 2013-03-27 00:00:00
Tasks: Relation

This paper describes a formal system of belief revision developed by Wolfgang
Spohn and shows that this system has a parallel implementation that can be
derived from an influence diagram in a manner similar to that in which Bayesian
networks are derived. The proof rests upon completeness results for an
axiomatization of the notion of conditional independence, with the Spohn system
being used as a semantics for the relation of conditional independence.
The Optimality of Satisficing Solutions
Authors: Othar Hansson, Andy Mayer
Date: 2013-03-27 00:00:00
Tasks: Heuristic Search

This paper addresses a prevailing assumption in single-agent heuristic search
theory- that problem-solving algorithms should guarantee shortest-path
solutions, which are typically called optimal. Optimality implies a metric for
judging solution quality, where the optimal solution is the solution with the
highest quality. When path-length is the metric, we will distinguish such
solutions as p-optimal.
On the Logic of Causal Models
Authors: Dan Geiger, Judea Pearl
Date: 2013-03-27 00:00:00
Tasks: valid

This paper explores the role of Directed Acyclic Graphs (DAGs) as a
representation of conditional independence relationships. We show that DAGs
offer polynomially sound and complete inference mechanisms for inferring
conditional independence relationships from a given causal set of such
relationships. As a consequence, d-separation, a graphical criterion for
identifying independencies in a DAG, is shown to uncover more valid
independencies then any other criterion. In addition, we employ the Armstrong
property of conditional independence to show that the dependence relationships
displayed by a DAG are inherently consistent, i.e. for every DAG D there exists
some probability distribution P that embodies all the conditional
independencies displayed in D and none other.
Bayesian Assessment of a Connectionist Model for Fault Detection
Authors: Stephen I. Gallant
Date: 2013-03-27 00:00:00
Tasks: Fault Detection

A previous paper [2] showed how to generate a linear discriminant network
(LDN) that computes likely faults for a noisy fault detection problem by using
a modification of the perceptron learning algorithm called the pocket
algorithm. Here we compare the performance of this connectionist model with
performance of the optimal Bayesian decision rule for the example that was
previously described. We find that for this particular problem the
connectionist model performs about 97% as well as the optimal Bayesian
procedure. We then define a more general class of noisy single-pattern boolean
(NSB) fault detection problems where each fault corresponds to a single
:pattern of boolean instrument readings and instruments are independently
noisy. This is equivalent to specifying that instrument readings are
probabilistic but conditionally independent given any particular fault. We
prove:
  1. The optimal Bayesian decision rule for every NSB fault detection problem
is representable by an LDN containing no intermediate nodes. (This slightly
extends a result first published by Minsky & Selfridge.) 2. Given an NSB fault
detection problem, then with arbitrarily high probability after sufficient
iterations the pocket algorithm will generate an LDN that computes an optimal
Bayesian decision rule for that problem. In practice we find that a reasonable
number of iterations of the pocket algorithm produces a network with good, but
not optimal, performance.
Truth Maintenance Under Uncertainty
Authors: Li-Min Fu
Date: 2013-03-27 00:00:00

This paper addresses the problem of resolving errors under uncertainty in a
rule-based system. A new approach has been developed that reformulates this
problem as a neural-network learning problem. The strength and the fundamental
limitations of this approach are explored and discussed. The main result is
that neural heuristics can be applied to solve some but not all problems in
rule-based systems.
Probability as a Modal Operator
Authors: Alan M. Frisch, Peter Haddawy
Date: 2013-03-27 00:00:00

This paper argues for a modal view of probability. The syntax and semantics
of one particularly strong probability logic are discussed and some examples of
the use of the logic are provided. We show that it is both natural and useful
to think of probability as a modal operator. Contrary to popular belief in AI,
a probability ranging between 0 and 1 represents a continuum between
impossibility and necessity, not between simple falsity and truth. The present
work provides a clear semantics for quantification into the scope of the
probability operator and for higher-order probabilities. Probability logic is a
language for expressing both probabilistic and logical concepts.
Uncertainty Management for Fuzzy Decision Support Systems
Authors: Christoph F. Eick
Date: 2013-03-27 00:00:00
Tasks: Management

A new approach for uncertainty management for fuzzy, rule based decision
support systems is proposed: The domain expert's knowledge is expressed by a
set of rules that frequently refer to vague and uncertain propositions. The
certainty of propositions is represented using intervals [a, b] expressing that
the proposition's probability is at least a and at most b. Methods and
techniques for computing the overall certainty of fuzzy compound propositions
that have been defined by using logical connectives 'and', 'or' and 'not' are
introduced. Different inference schemas for applying fuzzy rules by using modus
ponens are discussed. Different algorithms for combining evidence that has been
received from different rules for the same proposition are provided. The
relationship of the approach to other approaches is analyzed and its problems
of knowledge acquisition and knowledge representation are discussed in some
detail. The basic concepts of a rule-based programming language called PICASSO,
for which the approach is a theoretical foundation, are outlined.
A Temporal Logic for Uncertain Events and An Outline of A Possible Implementation in An Extension of PROLOG
Authors: Soumitra Dutta
Date: 2013-03-27 00:00:00

There is uncertainty associated with the occurrence of many events in real
life. In this paper we develop a temporal logic to deal with such uncertain
events and outline a possible implementation in an extension of PROLOG. Events
are represented as fuzzy sets with the membership function giving the
possibility of occurrence of the event in a given interval of time. The
developed temporal logic is simple but powerful. It can determine effectively
the various temporal relations between uncertain events or their combinations.
PROLOG provides a uniform substrate on which to effectively implement such a
temporal logic for uncertain events
Modeling uncertain and vague knowledge in possibility and evidence theories
Authors: Didier Dubois, Henri Prade
Date: 2013-03-27 00:00:00

This paper advocates the usefulness of new theories of uncertainty for the
purpose of modeling some facets of uncertain knowledge, especially vagueness,
in AI. It can be viewed as a partial reply to Cheeseman's (among others)
defense of probability.
Probabilistic Causal Reasoning
Authors: Thomas L. Dean, Keiji Kanazawa
Date: 2013-03-27 00:00:00
Tasks: Decision Making

Predicting the future is an important component of decision making. In most
situations, however, there is not enough information to make accurate
predictions. In this paper, we develop a theory of causal reasoning for
predictive inference under uncertainty. We emphasize a common type of
prediction that involves reasoning about persistence: whether or not a
proposition once made true remains true at some later time. We provide a
decision procedure with a polynomial-time algorithm for determining the
probability of the possible consequences of a set events and initial
conditions. The integration of simple probability theory with temporal
projection enables us to circumvent problems that nonmonotonic temporal
reasoning schemes have in dealing with persistence. The ideas in this paper
have been implemented in a prototype system that refines a database of causal
rules in the course of applying those rules to construct and carry out plans in
a manufacturing domain.
Process, Structure, and Modularity in Reasoning with Uncertainty
Authors: Bruce D'Ambrosio
Date: 2013-03-27 00:00:00
Tasks: Decision Making, Management, Two-sample testing

Computational mechanisms for uncertainty management must support interactive
and incremental problem formulation, inference, hypothesis testing, and
decision making. However, most current uncertainty inference systems
concentrate primarily on inference, and provide no support for the larger
issues. We present a computational approach to uncertainty management which
provides direct support for the dynamic, incremental aspect of this task, while
at the same time permitting direct representation of the structure of
evidential relationships. At the same time, we show that this approach responds
to the modularity concerns of Heckerman and Horvitz [Heck87]. This paper
emphasizes examples of the capabilities of this approach. Another paper
[D'Am89] details the representations and algorithms involved.
A Method for Using Belief Networks as Influence Diagrams
Authors: Gregory F. Cooper
Date: 2013-03-27 00:00:00

This paper demonstrates a method for using belief-network algorithms to solve
influence diagram problems. In particular, both exact and approximation
belief-network algorithms may be applied to solve influence-diagram problems.
More generally, knowing the relationship between belief-network and
influence-diagram problems may be useful in the design and development of more
efficient influence diagram algorithms.
KNET: Integrating Hypermedia and Bayesian Modeling
Authors: R. Martin Chavez, Gregory F. Cooper
Date: 2013-03-27 00:00:00
Tasks: Management, Retrieval

KNET is a general-purpose shell for constructing expert systems based on
belief networks and decision networks. Such networks serve as graphical
representations for decision models, in which the knowledge engineer must
define clearly the alternatives, states, preferences, and relationships that
constitute a decision basis. KNET contains a knowledge-engineering core written
in Object Pascal and an interface that tightly integrates HyperCard, a
hypertext authoring tool for the Apple Macintosh computer, into a novel
expert-system architecture. Hypertext and hypermedia have become increasingly
important in the storage management, and retrieval of information. In broad
terms, hypermedia deliver heterogeneous bits of information in dynamic,
extensively cross-referenced packages. The resulting KNET system features a
coherent probabilistic scheme for managing uncertainty, an objectoriented
graphics editor for drawing and manipulating decision networks, and HyperCard's
potential for quickly constructing flexible and friendly user interfaces. We
envision KNET as a useful prototyping tool for our ongoing research on a
variety of Bayesian reasoning problems, including tractable representation,
inference, and explanation.
Induction and Uncertainty Management Techniques Applied to Veterinary Medical Diagnosis
Authors: M. Cecile, Mary McLeish, P. Pascoe, W. Taylor
Date: 2013-03-27 00:00:00
Tasks: Diagnostic, Management, Medical Diagnosis

This paper discusses a project undertaken between the Departments of
Computing Science, Statistics, and the College of Veterinary Medicine to design
a medical diagnostic system. On-line medical data has been collected in the
hospital database system for several years. A number of induction methods are
being used to extract knowledge from the data in an attempt to improve upon
simple diagnostic charts used by the clinicians. They also enhance the results
of classical statistical methods - finding many more significant variables. The
second part of the paper describes an essentially Bayesian method of evidence
combination using fuzzy events at an initial step. Results are presented and
comparisons are made with other methods.
Decision-Theoretic Control of Problem Solving: Principles and Architecture
Authors: John S. Breese, Michael R. Fehling
Date: 2013-03-27 00:00:00
Tasks: Scheduling

This paper presents an approach to the design of autonomous, real-time
systems operating in uncertain environments. We address issues of problem
solving and reflective control of reasoning under uncertainty in terms of two
fundamental elements: l) a set of decision-theoretic models for selecting among
alternative problem-solving methods and 2) a general computational architecture
for resource-bounded problem solving. The decisiontheoretic models provide a
set of principles for choosing among alternative problem-solving methods based
on their relative costs and benefits, where benefits are characterized in terms
of the value of information provided by the output of a reasoning activity. The
output may be an estimate of some uncertain quantity or a recommendation for
action. The computational architecture, called Schemer-ll, provides for
interleaving of and communication among various problem-solving subsystems.
These subsystems provide alternative approaches to information gathering,
belief refinement, solution construction, and solution execution. In
particular, the architecture provides a mechanism for interrupting the
subsystems in response to critical events. We provide a decision theoretic
account for scheduling problem-solving elements and for critical-event-driven
interruption of activities in an architecture such as Schemer-II.
Hierarchical Evidence and Belief Functions
Authors: Paul K. Black, Kathryn Blackmond Laskey
Date: 2013-03-27 00:00:00

Dempster/Shafer (D/S) theory has been advocated as a way of representing
incompleteness of evidence in a system's knowledge base. Methods now exist for
propagating beliefs through chains of inference. This paper discusses how rules
with attached beliefs, a common representation for knowledge in automated
reasoning systems, can be transformed into the joint belief functions required
by propagation algorithms. A rule is taken as defining a conditional belief
function on the consequent given the antecedents. It is demonstrated by example
that different joint belief functions may be consistent with a given set of
rules. Moreover, different representations of the same rules may yield
different beliefs on the consequent hypotheses.
Probability Distributions Over Possible Worlds
Authors: Fahiem Bacchus
Date: 2013-03-27 00:00:00

In Probabilistic Logic Nilsson uses the device of a probability distribution
over a set of possible worlds to assign probabilities to the sentences of a
logical language. In his paper Nilsson concentrated on inference and associated
computational issues. This paper, on the other hand, examines the probabilistic
semantics in more detail, particularly for the case of first-order languages,
and attempts to explain some of the features and limitations of this form of
probability logic. It is pointed out that the device of assigning probabilities
to logical sentences has certain expressive limitations. In particular,
statistical assertions are not easily expressed by such a device. This leads to
certain difficulties with attempts to give probabilistic semantics to default
reasoning using probabilities assigned to logical sentences.
Summary of A New Normative Theory of Probabilistic Logic
Authors: Romas Aleliunas
Date: 2013-03-27 00:00:00

By probabilistic logic I mean a normative theory of belief that explains how
a body of evidence affects one's degree of belief in a possible hypothesis. A
new axiomatization of such a theory is presented which avoids a finite
additivity axiom, yet which retains many useful inference rules. Many of the
examples of this theory--its models do not use numerical probabilities. Put
another way, this article gives sharper answers to the two questions: 1.What
kinds of sets can used as the range of a probability function? 2.Under what
conditions is the range set of a probability function isomorphic to the set of
real numbers in the interval 10,1/ with the usual arithmetical operations?