pwc-archive/papers-with-abstracts huggingface dataset:
Estimating Uncertain Spatial Relationships in Robotics
Authors: Randall Smith, Matthew Self, Peter Cheeseman
Date: 2013-03-27 00:00:00
Tasks: State Estimation

In this paper, we describe a representation for spatial information, called
the stochastic map, and associated procedures for building it, reading
information from it, and revising it incrementally as new information is
obtained. The map contains the estimates of relationships among objects in the
map, and their uncertainties, given all the available information. The
procedures provide a general solution to the problem of estimating uncertain
relative spatial relationships. The estimates are probabilistic in nature, an
advance over the previous, very conservative, worst-case approaches to the
problem. Finally, the procedures are developed in the context of
state-estimation and filtering theory, which provides a solid basis for
numerous extensions.
Appropriate and Inappropriate Estimation Techniques
Authors: David Sher
Date: 2013-03-27 00:00:00

Mode {also called MAP} estimation, mean estimation and median estimation are
examined here to determine when they can be safely used to derive {posterior)
cost minimizing estimates. (These are all Bayes procedures, using the mode.
mean. or median of the posterior distribution). It is found that modal
estimation only returns cost minimizing estimates when the cost function is
0-t. If the cost function is a function of distance then mean estimation only
returns cost minimizing estimates when the cost function is squared distance
from the true value and median estimation only returns cost minimizing
estimates when the cost function ts the distance from the true value. Results
are presented on the goodness or modal estimation with non 0-t cost functions
Propagation of Belief Functions: A Distributed Approach
Authors: Prakash P. Shenoy, Glenn Shafer, Khaled Mellouli
Date: 2013-03-27 00:00:00
Tasks: Diagnostic

In this paper, we describe a scheme for propagating belief functions in
certain kinds of trees using only local computations. This scheme generalizes
the computational scheme proposed by Shafer and Logan1 for diagnostic trees of
the type studied by Gordon and Shortliffe, and the slightly more general scheme
given by Shafer for hierarchical evidence. It also generalizes the scheme
proposed by Pearl for Bayesian causal trees (see Shenoy and Shafer). Pearl's
causal trees and Gordon and Shortliffe's diagnostic trees are both ways of
breaking the evidence that bears on a large problem down into smaller items of
evidence that bear on smaller parts of the problem so that these smaller
problems can be dealt with one at a time. This localization of effort is often
essential in order to make the process of probability judgment feasible, both
for the person who is making probability judgments and for the machine that is
combining them. The basic structure for our scheme is a type of tree that
generalizes both Pearl's and Gordon and Shortliffe's trees. Trees of this
general type permit localized computation in Pearl's sense. They are based on
qualitative judgments of conditional independence. We believe that the scheme
we describe here will prove useful in expert systems. It is now clear that the
successful propagation of probabilities or certainty factors in expert systems
requires much more structure than can be provided in a pure production-system
framework. Bayesian schemes, on the other hand, often make unrealistic demands
for structure. The propagation of belief functions in trees and more general
networks stands on a middle ground where some sensible and useful things can be
done. We would like to emphasize that the basic idea of local computation for
propagating probabilities is due to Judea Pearl. It is a very innovative idea;
we do not believe that it can be found in the Bayesian literature prior to
Pearl's work. We see our contribution as extending the usefulness of Pearl's
idea by generalizing it from Bayesian probabilities to belief functions. In the
next section, we give a brief introduction to belief functions. The notions of
qualitative independence for partitions and a qualitative Markov tree are
introduced in Section III. Finally, in Section IV, we describe a scheme for
propagating belief functions in qualitative Markov trees.
DAVID: Influence Diagram Processing System for the Macintosh
Authors: Ross D. Shachter
Date: 2013-03-27 00:00:00

Influence diagrams are a directed graph representation for uncertainties as
probabilities. The graph distinguishes between those variables which are under
the control of a decision maker (decisions, shown as rectangles) and those
which are not (chances, shown as ovals), as well as explicitly denoting a goal
for solution (value, shown as a rounded rectangle.
A Backwards View for Assessment
Authors: Ross D. Shachter, David Heckerman
Date: 2013-03-27 00:00:00

Much artificial intelligence research focuses on the problem of deducing the
validity of unobservable propositions or hypotheses from observable evidence.!
Many of the knowledge representation techniques designed for this problem
encode the relationship between evidence and hypothesis in a directed manner.
Moreover, the direction in which evidence is stored is typically from evidence
to hypothesis.
A Causal Bayesian Model for the Diagnosis of Appendicitis
Authors: Stanley M. Schwartz, Jonathan Baron, John R. Clarke
Date: 2013-03-27 00:00:00

The causal Bayesian approach is based on the assumption that effects (e.g.,
symptoms) that are not conditionally independent with respect to some causal
agent (e.g., a disease) are conditionally independent with respect to some
intermediate state caused by the agent, (e.g., a pathological condition). This
paper describes the development of a causal Bayesian model for the diagnosis of
appendicitis. The paper begins with a description of the standard Bayesian
approach to reasoning about uncertainty and the major critiques it faces. The
paper then lays the theoretical groundwork for the causal extension of the
Bayesian approach, and details specific improvements we have developed. The
paper then goes on to describe our knowledge engineering and implementation and
the results of a test of the system. The paper concludes with a discussion of
how the causal Bayesian approach deals with the criticisms of the standard
Bayesian model and why it is superior to alternative approaches to reasoning
about uncertainty popular in the Al community.
The Rational and Computational Scope of Probabilistic Rule-Based Expert Systems
Authors: Shimon Schocken
Date: 2013-03-27 00:00:00

Belief updating schemes in artificial intelligence may be viewed as three
dimensional languages, consisting of a syntax (e.g. probabilities or certainty
factors), a calculus (e.g. Bayesian or CF combination rules), and a semantics
(i.e. cognitive interpretations of competing formalisms). This paper studies
the rational scope of those languages on the syntax and calculus grounds. In
particular, the paper presents an endomorphism theorem which highlights the
limitations imposed by the conditional independence assumptions implicit in the
CF calculus. Implications of the theorem to the relationship between the CF and
the Bayesian languages and the Dempster-Shafer theory of evidence are
presented. The paper concludes with a discussion of some implications on
rule-based knowledge engineering in uncertain domains.
Approximate Deduction in Single Evidential Bodies
Authors: Enrique H. Ruspini
Date: 2013-03-27 00:00:00

Results on approximate deduction in the context of the calculus of evidence
of Dempster-Shafer and the theory of interval probabilities are reported.
Approximate conditional knowledge about the truth of conditional propositions
was assumed available and expressed as sets of possible values (actually
numeric intervals) of conditional probabilities. Under different
interpretations of this conditional knowledge, several formulas were produced
to integrate unconditioned estimates (assumed given as sets of possible values
of unconditioned probabilities) with conditional estimates. These formulas are
discussed together with the computational characteristics of the methods
derived from them. Of particular importance is one such evidence integration
formulation, produced under a belief oriented interpretation, which
incorporates both modus ponens and modus tollens inferential mechanisms, allows
integration of conditioned and unconditioned knowledge without resorting to
iterative or sequential approximations, and produces elementary mass
distributions as outputs using similar distributions as inputs.
Learning Link-Probabilities in Causal Trees
Authors: Igor Roizer, Judea Pearl
Date: 2013-03-27 00:00:00

A learning algorithm is presented which given the structure of a causal tree,
will estimate its link probabilities by sequential measurements on the leaves
only. Internal nodes of the tree represent conceptual (hidden) variables
inaccessible to observation. The method described is incremental, local,
efficient, and remains robust to measurement imprecisions.
Distributed Revision of Belief Commitment in Multi-Hypothesis Interpretations
Authors: Judea Pearl
Date: 2013-03-27 00:00:00
Tasks: Medical Diagnosis

This paper extends the applications of belief-networks to include the
revision of belief commitments, i.e., the categorical acceptance of a subset of
hypotheses which, together, constitute the most satisfactory explanation of the
evidence at hand. A coherent model of non-monotonic reasoning is established
and distributed algorithms for belief revision are presented. We show that, in
singly connected networks, the most satisfactory explanation can be found in
linear time by a message-passing algorithm similar to the one used in belief
updating. In multiply-connected networks, the problem may be exponentially hard
but, if the network is sparse, topological considerations can be used to render
the interpretation task tractable. In general, finding the most probable
combination of hypotheses is no more complex than computing the degree of
belief for any individual hypothesis. Applications to medical diagnosis are
illustrated.
An Explanation Mechanism for Bayesian Inferencing Systems
Authors: Steven W. Norton
Date: 2013-03-27 00:00:00

Explanation facilities are a particularly important feature of expert system
frameworks. It is an area in which traditional rule-based expert system
frameworks have had mixed results. While explanations about control are well
handled, facilities are needed for generating better explanations concerning
knowledge base content. This paper approaches the explanation problem by
examining the effect an event has on a variable of interest within a symmetric
Bayesian inferencing system. We argue that any effect measure operating in this
context must satisfy certain properties. Such a measure is proposed. It forms
the basis for an explanation facility which allows the user of the Generalized
Bayesian Inferencing System to question the meaning of the knowledge base. That
facility is described in detail.
An Uncertainty Management Calculus for Ordering Searches in Distributed Dynamic Databases
Authors: Uttam Mukhopadhyay
Date: 2013-03-27 00:00:00
Tasks: Management, Retrieval

MINDS is a distributed system of cooperating query engines that customize,
document retrieval for each user in a dynamic environment. It improves its
performance and adapts to changing patterns of document distribution by
observing system-user interactions and modifying the appropriate certainty
factors, which act as search control parameters. It argued here that the
uncertainty management calculus must account for temporal precedence,
reliability of evidence, degree of support for a proposition, and saturation
effects. The calculus presented here possesses these features. Some results
obtained with this scheme are discussed.
Computing Reference Classes
Authors: Ronald P. Loui
Date: 2013-03-27 00:00:00

For any system with limited statistical knowledge, the combination of
evidence and the interpretation of sampling information require the
determination of the right reference class (or of an adequate one). The present
note (1) discusses the use of reference classes in evidential reasoning, and
(2) discusses implementations of Kyburg's rules for reference classes. This
paper contributes the first frank discussion of how much of Kyburg's system is
needed to be powerful, how much can be computed effectively, and how much is
philosophical fat.
Evidential Reasoning in Parallel Hierarchical Vision Programs
Authors: Ze-Nian Li, Leonard Uhr
Date: 2013-03-27 00:00:00

This paper presents an efficient adaptation and application of the
Dempster-Shafer theory of evidence, one that can be used effectively in a
massively parallel hierarchical system for visual pattern perception. It
describes the techniques used, and shows in an extended example how they serve
to improve the system's performance as it applies a multiple-level set of
processes.
Bayesian Inference for Radar Imagery Based Surveillance
Authors: Tod S. Levitt
Date: 2013-03-27 00:00:00
Tasks: Bayesian Inference

We are interested in creating an automated or semi-automated system with the
capability of taking a set of radar imagery, collection parameters and a priori
map and other tactical data, and producing likely interpretations of the
possible military situations given the available evidence. This paper is
concerned with the problem of the interpretation and computation of certainty
or belief in the conclusions reached by such a system.
An Application of Non-Monotonic Probabilistic Reasoning to Air Force Threat Correlation
Authors: Kathryn Blackmond Laskey, Marvin S. Cohen
Date: 2013-03-27 00:00:00

Current approaches to expert systems' reasoning under uncertainty fail to
capture the iterative revision process characteristic of intelligent human
reasoning. This paper reports on a system, called the Non-monotonic
Probabilist, or NMP (Cohen, et al., 1985). When its inferences result in
substantial conflict, NMP examines and revises the assumptions underlying the
inferences until conflict is reduced to acceptable levels. NMP has been
implemented in a demonstration computer-based system, described below, which
supports threat correlation and in-flight route replanning by Air Force pilots.
Knowledge and Uncertainty
Authors: Henry E. Kyburg Jr
Date: 2013-03-27 00:00:00
Tasks: Decision Making

One purpose -- quite a few thinkers would say the main purpose -- of seeking
knowledge about the world is to enhance our ability to make good decisions. An
item of knowledge that can make no conceivable difference with regard to
anything we might do would strike many as frivolous. Whether or not we want to
be philosophical pragmatists in this strong sense with regard to everything we
might want to enquire about, it seems a perfectly appropriate attitude to adopt
toward artificial knowledge systems. If is granted that we are ultimately
concerned with decisions, then some constraints are imposed on our measures of
uncertainty at the level of decision making. If our measure of uncertainty is
real-valued, then it isn't hard to show that it must satisfy the classical
probability axioms. For example, if an act has a real-valued utility U(E) if
the event E obtains, and the same real-valued utility if the denial of E
obtains, so that U(E) = U(-E), then the expected utility of that act must be
U(E), and that must be the same as the uncertainty-weighted average of the
returns of the act, p-U(E) + q-U('E), where p and q represent the uncertainty
of E and-E respectively. But then we must have p + q = 1.
Decision Under Uncertainty in Diagnosis
Authors: Charles I. Kalme
Date: 2013-03-27 00:00:00
Tasks: Diagnostic

This paper describes the incorporation of uncertainty in diagnostic reasoning
based on the set covering model of Reggia et. al. extended to what in the
Artificial Intelligence dichotomy between deep and compiled (shallow, surface)
knowledge based diagnosis may be viewed as the generic form at the compiled end
of the spectrum. A major undercurrent in this is advocating the need for a
strong underlying model and an integrated set of support tools for carrying
such a model in order to deal with uncertainty.
Evidence as Opinions of Experts
Authors: Robert Hummel, Michael Landy
Date: 2013-03-27 00:00:00

We describe a viewpoint on the Dempster/Shafer 'Theory of Evidence', and
provide an interpretation which regards the combination formulas as statistics
of the opinions of "experts". This is done by introducing spaces with binary
operations that are simpler to interpret or simpler to implement than the
standard combination formula, and showing that these spaces can be mapped
homomorphically onto the Dempster/Shafer theory of evidence space. The experts
in the space of "opinions of experts" combine information in a Bayesian
fashion. We present alternative spaces for the combination of evidence
suggested by this viewpoint.
Imprecise Meanings as a Cause of Uncertainty in Medical Knowledge-Based Systems
Authors: Steven J. Henkind
Date: 2013-03-27 00:00:00

There has been a considerable amount of work on uncertainty in
knowledge-based systems. This work has generally been concerned with
uncertainty arising from the strength of inferences and the weight of evidence.
In this paper we discuss another type of uncertainty: that which is due to
imprecision in the underlying primitives used to represent the knowledge of the
system. In particular, a given word may denote many similar but not identical
entities. Such words are said to be lexically imprecise. Lexical imprecision
has caused widespread problems in many areas. Unless this phenomenon is
recognized and appropriately handled, it can degrade the performance of
knowledge-based systems. In particular, it can lead to difficulties with the
user interface, and with the inferencing processes of these systems. Some
techniques are suggested for coping with this phenomenon.
An Axiomatic Framework for Belief Updates
Authors: David Heckerman
Date: 2013-03-27 00:00:00

In the 1940's, a physicist named Cox provided the first formal justification
for the axioms of probability based on the subjective or Bayesian
interpretation. He showed that if a measure of belief satisfies several
fundamental properties, then the measure must be some monotonic transformation
of a probability. In this paper, measures of change in belief or belief updates
are examined. In the spirit of Cox, properties for a measure of change in
belief are enumerated. It is shown that if a measure satisfies these
properties, it must satisfy other restrictive conditions. For example, it is
shown that belief updates in a probabilistic context must be equal to some
monotonic transformation of a likelihood ratio. It is hoped that this formal
explication of the belief update paradigm will facilitate critical discussion
and useful extensions of the approach.
The Myth of Modularity in Rule-Based Systems
Authors: David Heckerman, Eric J. Horvitz
Date: 2013-03-27 00:00:00
Tasks: valid

In this paper, we examine the concept of modularity, an often cited advantage
of the ruled-based representation methodology. We argue that the notion of
modularity consists of two distinct concepts which we call syntactic modularity
and semantic modularity. We argue that when reasoning under certainty, it is
reasonable to regard the rule-based approach as both syntactically and
semantically modular. However, we argue that in the case of plausible
reasoning, rules are syntactically modular but are rarely semantically modular.
To illustrate this point, we examine a particular approach for managing
uncertainty in rule-based systems called the MYCIN certainty factor model. We
formally define the concept of semantic modularity with respect to the
certainty factor model and discuss logical consequences of the definition. We
show that the assumption of semantic modularity imposes strong restrictions on
rules in a knowledge base. We argue that such restrictions are rarely valid in
practical applications. Finally, we suggest how the concept of semantic
modularity can be relaxed in a manner that makes it appropriate for plausible
reasoning.
Flexible Interpretations: A Computational Model for Dynamic Uncertainty Assessment
Authors: Shohara L. Hardt
Date: 2013-03-27 00:00:00

The investigations reported in this paper center on the process of dynamic
uncertainty assessment during interpretation tasks in real domain. In
particular, we are interested here in the nature of the control structure of
computer programs that can support multiple interpretation and smooth
transitions between them, in real time. Each step of the processing involves
the interpretation of one input item and the appropriate re-establishment of
the system's confidence of the correctness of its interpretation(s).
Information and Multi-Sensor Coordination
Authors: Greg Hager, Hugh F. Durrant-Whyte
Date: 2013-03-27 00:00:00
Tasks: Decision Making, Decision Making Under Uncertainty

The control and integration of distributed, multi-sensor perceptual systems
is a complex and challenging problem. The observations or opinions of different
sensors are often disparate incomparable and are usually only partial views.
Sensor information is inherently uncertain and in addition the individual
sensors may themselves be in error with respect to the system as a whole. The
successful operation of a multi-sensor system must account for this uncertainty
and provide for the aggregation of disparate information in an intelligent and
robust manner. We consider the sensors of a multi-sensor system to be members
or agents of a team, able to offer opinions and bargain in group decisions. We
will analyze the coordination and control of this structure using a theory of
team decision-making. We present some new analytic results on multi-sensor
aggregation and detail a simulation which we use to investigate our ideas. This
simulation provides a basis for the analysis of complex agent structures
cooperating in the presence of uncertainty. The results of this study are
discussed with reference to multi-sensor robot systems, distributed Al and
decision making under uncertainty.
Non-Monotonicity in Probabilistic Reasoning
Authors: Benjamin N. Grosof
Date: 2013-03-27 00:00:00

We start by defining an approach to non-monotonic probabilistic reasoning in
terms of non-monotonic categorical (true-false) reasoning. We identify a type
of non-monotonic probabilistic reasoning, akin to default inheritance, that is
commonly found in practice, especially in "evidential" and "Bayesian"
reasoning. We formulate this in terms of the Maximization of Conditional
Independence (MCI), and identify a variety of applications for this sort of
default. We propose a formalization using Pointwise Circumscription. We compare
MCI to Maximum Entropy, another kind of non-monotonic principle, and conclude
by raising a number of open questions
Deriving And Combining Continuous Possibility Functions in the Framework of Evidential Reasoning
Authors: Pascal Fua
Date: 2013-03-27 00:00:00

To develop an approach to utilizing continuous statistical information within
the Dempster- Shafer framework, we combine methods proposed by Strat and by
Shafero We first derive continuous possibility and mass functions from
probability-density functions. Then we propose a rule for combining such
evidence that is simpler and more efficiently computed than Dempster's rule. We
discuss the relationship between Dempster's rule and our proposed rule for
combining evidence over continuous frames.