pwc-archive/papers-with-abstracts huggingface dataset:
An Odds Ratio Based Inference Engine
Authors: David S. Vaughan, Bruce M. Perrin, Robert M. Yadrick, Peter D. Holden, Karl G. Kempf
Date: 2013-03-27 00:00:00

Expert systems applications that involve uncertain inference can be
represented by a multidimensional contingency table. These tables offer a
general approach to inferring with uncertain evidence, because they can embody
any form of association between any number of pieces of evidence and
conclusions. (Simpler models may be required, however, if the number of pieces
of evidence bearing on a conclusion is large.) This paper presents a method of
using these tables to make uncertain inferences without assumptions of
conditional independence among pieces of evidence or heuristic combining rules.
As evidence is accumulated, new joint probabilities are calculated so as to
maintain any dependencies among the pieces of evidence that are found in the
contingency table. The new conditional probability of the conclusion is then
calculated directly from these new joint probabilities and the conditional
probabilities in the contingency table.
Induction, of and by Probability
Authors: Larry Rendell
Date: 2013-03-27 00:00:00
Tasks: Heuristic Search, Incremental Learning, Management

This paper examines some methods and ideas underlying the author's successful
probabilistic learning systems(PLS), which have proven uniquely effective and
efficient in generalization learning or induction. While the emerging
principles are generally applicable, this paper illustrates them in heuristic
search, which demands noise management and incremental learning. In our
approach, both task performance and learning are guided by probability.
Probabilities are incrementally normalized and revised, and their errors are
located and corrected.
Machine Learning, Clustering, and Polymorphy
Authors: Stephen Jose Hanson, Malcolm Bauer
Date: 2013-03-27 00:00:00
Tasks: BIG-bench Machine Learning, Clustering, Information Retrieval, Retrieval

This paper describes a machine induction program (WITT) that attempts to
model human categorization. Properties of categories to which human subjects
are sensitive includes best or prototypical members, relative contrasts between
putative categories, and polymorphy (neither necessary or sufficient features).
This approach represents an alternative to usual Artificial Intelligence
approaches to generalization and conceptual clustering which tend to focus on
necessary and sufficient feature rules, equivalence classes, and simple search
and match schemes. WITT is shown to be more consistent with human
categorization while potentially including results produced by more traditional
clustering schemes. Applications of this approach in the domains of expert
systems and information retrieval are also discussed.
Inductive Inference and the Representation of Uncertainty
Authors: Norman C. Dalkey
Date: 2013-03-27 00:00:00
Tasks: Decision Making

The form and justification of inductive inference rules depend strongly on
the representation of uncertainty. This paper examines one generic
representation, namely, incomplete information. The notion can be formalized by
presuming that the relevant probabilities in a decision problem are known only
to the extent that they belong to a class K of probability distributions. The
concept is a generalization of a frequent suggestion that uncertainty be
represented by intervals or ranges on probabilities. To make the representation
useful for decision making, an inductive rule can be formulated which
determines, in a well-defined manner, a best approximation to the unknown
probability, given the set K. In addition, the knowledge set notion entails a
natural procedure for updating -- modifying the set K given new evidence.
Several non-intuitive consequences of updating emphasize the differences
between inference with complete and inference with incomplete information.
A Framework for Comparing Uncertain Inference Systems to Probability
Authors: Ben P. Wise, Max Henrion
Date: 2013-03-27 00:00:00

Several different uncertain inference systems (UISs) have been developed for
representing uncertainty in rule-based expert systems. Some of these, such as
Mycin's Certainty Factors, Prospector, and Bayes' Networks were designed as
approximations to probability, and others, such as Fuzzy Set Theory and
DempsterShafer Belief Functions were not. How different are these UISs in
practice, and does it matter which you use? When combining and propagating
uncertain information, each UIS must, at least by implication, make certain
assumptions about correlations not explicily specified. The maximum entropy
principle with minimum cross-entropy updating, provides a way of making
assumptions about the missing specification that minimizes the additional
information assumed, and thus offers a standard against which the other UISs
can be compared. We describe a framework for the experimental comparison of the
performance of different UISs, and provide some illustrative results.
Probability Judgement in Artificial Intelligence
Authors: Glenn Shafer
Date: 2013-03-27 00:00:00

This paper is concerned with two theories of probability judgment: the
Bayesian theory and the theory of belief functions. It illustrates these
theories with some simple examples and discusses some of the issues that arise
when we try to implement them in expert systems. The Bayesian theory is well
known; its main ideas go back to the work of Thomas Bayes (1702-1761). The
theory of belief functions, often called the Dempster-Shafer theory in the
artificial intelligence community, is less well known, but it has even older
antecedents; belief-function arguments appear in the work of George Hooper
(16401723) and James Bernoulli (1654-1705). For elementary expositions of the
theory of belief functions, see Shafer (1976, 1985).
Implementing Probabilistic Reasoning
Authors: Matthew L. Ginsberg
Date: 2013-03-27 00:00:00

General problems in analyzing information in a probabilistic database are
considered. The practical difficulties (and occasional advantages) of storing
uncertain data, of using it conventional forward- or backward-chaining
inference engines, and of working with a probabilistic version of resolution
are discussed. The background for this paper is the incorporation of uncertain
reasoning facilities in MRS, a general-purpose expert system building tool.
Metaprobability and Dempster-Shafer in Evidential Reasoning
Authors: Robert Fung, Chee Yee Chong
Date: 2013-03-27 00:00:00

Evidential reasoning in expert systems has often used ad-hoc uncertainty
calculi. Although it is generally accepted that probability theory provides a
firm theoretical foundation, researchers have found some problems with its use
as a workable uncertainty calculus. Among these problems are representation of
ignorance, consistency of probabilistic judgements, and adjustment of a priori
judgements with experience. The application of metaprobability theory to
evidential reasoning is a new approach to solving these problems.
Metaprobability theory can be viewed as a way to provide soft or hard
constraints on beliefs in much the same manner as the Dempster-Shafer theory
provides constraints on probability masses on subsets of the state space. Thus,
we use the Dempster-Shafer theory, an alternative theory of evidential
reasoning to illuminate metaprobability theory as a theory of evidential
reasoning. The goal of this paper is to compare how metaprobability theory and
Dempster-Shafer theory handle the adjustment of beliefs with evidence with
respect to a particular thought experiment. Sections 2 and 3 give brief
descriptions of the metaprobability and Dempster-Shafer theories.
Metaprobability theory deals with higher order probabilities applied to
evidential reasoning. Dempster-Shafer theory is a generalization of probability
theory which has evolved from a theory of upper and lower probabilities.
Section 4 describes a thought experiment and the metaprobability and
DempsterShafer analysis of the experiment. The thought experiment focuses on
forming beliefs about a population with 6 types of members {1, 2, 3, 4, 5, 6}.
A type is uniquely defined by the values of three features: A, B, C. That is,
if the three features of one member of the population were known then its type
could be ascertained. Each of the three features has two possible values, (e.g.
A can be either "a0" or "al"). Beliefs are formed from evidence accrued from
two sensors: sensor A, and sensor B. Each sensor senses the corresponding
defining feature. Sensor A reports that half of its observations are "a0" and
half the observations are 'al'. Sensor B reports that half of its observations
are ``b0,' and half are "bl". Based on these two pieces of evidence, what
should be the beliefs on the distribution of types in the population? Note that
the third feature is not observed by any sensor.
A Framework for Non-Monotonic Reasoning About Probabilistic Assumptions
Authors: Marvin S. Cohen
Date: 2013-03-27 00:00:00

Attempts to replicate probabilistic reasoning in expert systems have
typically overlooked a critical ingredient of that process. Probabilistic
analysis typically requires extensive judgments regarding interdependencies
among hypotheses and data, and regarding the appropriateness of various
alternative models. The application of such models is often an iterative
process, in which the plausibility of the results confirms or disconfirms the
validity of assumptions made in building the model. In current expert systems,
by contrast, probabilistic information is encapsulated within modular rules
(involving, for example, "certainty factors"), and there is no mechanism for
reviewing the overall form of the probability argument or the validity of the
judgments entering into it.
Selecting Uncertainty Calculi and Granularity: An Experiment in Trading-Off Precision and Complexity
Authors: Piero P. Bonissone, Keith S. Decker
Date: 2013-03-27 00:00:00
Tasks: Management, Negation

The management of uncertainty in expert systems has usually been left to ad
hoc representations and rules of combinations lacking either a sound theory or
clear semantics. The objective of this paper is to establish a theoretical
basis for defining the syntax and semantics of a small subset of calculi of
uncertainty operating on a given term set of linguistic statements of
likelihood. Each calculus is defined by specifying a negation, a conjunction
and a disjunction operator. Families of Triangular norms and conorms constitute
the most general representations of conjunction and disjunction operators.
These families provide us with a formalism for defining an infinite number of
different calculi of uncertainty. The term set will define the uncertainty
granularity, i.e. the finest level of distinction among different
quantifications of uncertainty. This granularity will limit the ability to
differentiate between two similar operators. Therefore, only a small finite
subset of the infinite number of calculi will produce notably different
results. This result is illustrated by two experiments where nine and eleven
different calculi of uncertainty are used with three term sets containing five,
nine, and thirteen elements, respectively. Finally, the use of context
dependent rule set is proposed to select the most appropriate calculus for any
given situation. Such a rule set will be relatively small since it must only
describe the selection policies for a small number of calculi (resulting from
the analyzed trade-off between complexity and precision).
Foundations of Probability Theory for AI - The Application of Algorithmic Probability to Problems in Artificial Intelligence
Authors: Ray Solomonoff
Date: 2013-03-27 00:00:00

This paper covers two topics: first an introduction to Algorithmic Complexity
Theory: how it defines probability, some of its characteristic properties and
past successful applications. Second, we apply it to problems in A.I. - where
it promises to give near optimum search procedures for two very broad classes
of problems.
Relative Entropy, Probabilistic Inference and AI
Authors: John E. Shore
Date: 2013-03-27 00:00:00

Various properties of relative entropy have led to its widespread use in
information theory. These properties suggest that relative entropy has a role
to play in systems that attempt to perform inference in terms of probability
distributions. In this paper, I will review some basic properties of relative
entropy as well as its role in probabilistic inference. I will also mention
briefly a few existing and potential applications of relative entropy to
so-called artificial intelligence (AI).
A Constraint Propagation Approach to Probabilistic Reasoning
Authors: Judea Pearl
Date: 2013-03-27 00:00:00
Tasks: Diagnostic

The paper demonstrates that strict adherence to probability theory does not
preclude the use of concurrent, self-activated constraint-propagation
mechanisms for managing uncertainty. Maintaining local records of
sources-of-belief allows both predictive and diagnostic inferences to be
activated simultaneously and propagate harmoniously towards a stable
equilibrium.
Independence and Bayesian Updating Methods
Authors: Rodney W. Johnson
Date: 2013-03-27 00:00:00

Duda, Hart, and Nilsson have set forth a method for rule-based inference
systems to use in updating the probabilities of hypotheses on the basis of
multiple items of new evidence. Pednault, Zucker, and Muresan claimed to give
conditions under which independence assumptions made by Duda et al. preclude
updating-that is, prevent the evidence from altering the probabilities of the
hypotheses. Glymour refutes Pednault et al.'s claim with a counterexample of a
rather special form (one item of evidence is incompatible with all but one of
the hypotheses); he raises, but leaves open, the question whether their result
would be true with an added assumption to rule out such special cases. We show
that their result does not hold even with the added assumption, but that it can
nevertheless be largely salvaged. Namely, under the conditions assumed by
Pednault et al., at most one of the items of evidence can alter the probability
of any given hypothesis; thus, although updating is possible, multiple updating
for any of the hypotheses is precluded.
Uncertain Reasoning Using Maximum Entropy Inference
Authors: Daniel Hunter
Date: 2013-03-27 00:00:00

The use of maximum entropy inference in reasoning with uncertain information
is commonly justified by an information-theoretic argument. This paper
discusses a possible objection to this information-theoretic justification and
shows how it can be met. I then compare maximum entropy inference with certain
other currently popular methods for uncertain reasoning. In making such a
comparison, one must distinguish between static and dynamic theories of degrees
of belief: a static theory concerns the consistency conditions for degrees of
belief at a given time; whereas a dynamic theory concerns how one's degrees of
belief should change in the light of new information. It is argued that maximum
entropy is a dynamic theory and that a complete theory of uncertain reasoning
can be gotten by combining maximum entropy inference with probability theory,
which is a static theory. This total theory, I argue, is much better grounded
than are other theories of uncertain reasoning.
Probabilistic Interpretations for MYCIN's Certainty Factors
Authors: David Heckerman
Date: 2013-03-27 00:00:00
Tasks: Negation

This paper examines the quantities used by MYCIN to reason with uncertainty,
called certainty factors. It is shown that the original definition of certainty
factors is inconsistent with the functions used in MYCIN to combine the
quantities. This inconsistency is used to argue for a redefinition of certainty
factors in terms of the intuitively appealing desiderata associated with the
combining functions. It is shown that this redefinition accommodates an
unlimited number of probabilistic interpretations. These interpretations are
shown to be monotonic transformations of the likelihood ratio p(EIH)/p(El H).
The construction of these interpretations provides insight into the assumptions
implicit in the certainty factor model. In particular, it is shown that if
uncertainty is to be propagated through an inference network in accordance with
the desiderata, evidence must be conditionally independent given the hypothesis
and its negation and the inference network must have a tree structure. It is
emphasized that assumptions implicit in the model are rarely true in practical
applications. Methods for relaxing the assumptions are suggested.
An Inequality Paradigm for Probabilistic Knowledge
Authors: Benjamin N. Grosof
Date: 2013-03-27 00:00:00

We propose an inequality paradigm for probabilistic reasoning based on a
logic of upper and lower bounds on conditional probabilities. We investigate a
family of probabilistic logics, generalizing the work of Nilsson [14]. We
develop a variety of logical notions for probabilistic reasoning, including
soundness, completeness justification; and convergence: reduction of a theory
to a simpler logical class. We argue that a bound view is especially useful for
describing the semantics of probabilistic knowledge representation and for
describing intermediate states of probabilistic inference and updating. We show
that the Dempster-Shafer theory of evidence is formally identical to a special
case of our generalized probabilistic logic. Our paradigm thus incorporates
both Bayesian "rule-based" approaches and avowedly non-Bayesian "evidential"
approaches such as MYCIN and DempsterShafer. We suggest how to integrate the
two "schools", and explore some possibilities for novel synthesis of a variety
of ideas in probabilistic reasoning.
On the Combinality of Evidence in the Dempster-Shafer Theory
Authors: Lotfi Zadeh, Anca Ralescu
Date: 2013-03-27 00:00:00

In the current versions of the Dempster-Shafer theory, the only essential
restriction on the validity of the rule of combination is that the sources of
evidence must be statistically independent. Under this assumption, it is
permissible to apply the Dempster-Shafer rule to two or mere distinct
probability distributions.
On Implementing Usual Values
Authors: Ronald R. Yager
Date: 2013-03-27 00:00:00

In many cases commonsense knowledge consists of knowledge of what is usual.
In this paper we develop a system for reasoning with usual information. This
system is based upon the fact that these pieces of commonsense information
involve both a probabilistic aspect and a granular aspect. We implement this
system with the aid of possibility-probability granules.
Evaluation of Uncertain Inference Models I: PROSPECTOR
Authors: Robert M. Yadrick, Bruce M. Perrin, David S. Vaughan, Peter D. Holden, Karl G. Kempf
Date: 2013-03-27 00:00:00

This paper examines the accuracy of the PROSPECTOR model for uncertain
reasoning. PROSPECTOR's solutions for a large number of computer-generated
inference networks were compared to those obtained from probability theory and
minimum cross-entropy calculations. PROSPECTOR's answers were generally
accurate for a restricted subset of problems that are consistent with its
assumptions. However, even within this subset, we identified conditions under
which PROSPECTOR's performance deteriorates.
Experimentally Comparing Uncertain Inference Systems to Probability
Authors: Ben P. Wise
Date: 2013-03-27 00:00:00

This paper examines the biases and performance of several uncertain inference
systems: Mycin, a variant of Mycin. and a simplified version of probability
using conditional independence assumptions. We present axiomatic arguments for
using Minimum Cross Entropy inference as the best way to do uncertain
inference. For Mycin and its variant we found special situations where its
performance was very good, but also situations where performance was worse than
random guessing, or where data was interpreted as having the opposite of its
true import We have found that all three of these systems usually gave accurate
results, and that the conditional independence assumptions gave the most robust
results. We illustrate how the Importance of biases may be quantitatively
assessed and ranked. Considerations of robustness might be a critical factor is
selecting UlS's for a given application.
Qualitative Probabilistic Networks for Planning Under Uncertainty
Authors: Michael P. Wellman
Date: 2013-03-27 00:00:00

Bayesian networks provide a probabilistic semantics for qualitative
assertions about likelihood. A qualitative reasoner based on an algebra over
these assertions can derive further conclusions about the influence of actions.
While the conclusions are much weaker than those computed from complete
probability distributions, they are still valuable for suggesting potential
actions, eliminating obviously inferior plans, identifying important tradeoffs,
and explaining probabilistic models.
Generalizing Fuzzy Logic Probabilistic Inferences
Authors: Silvio Ursic
Date: 2013-03-27 00:00:00

Linear representations for a subclass of boolean symmetric functions selected
by a parity condition are shown to constitute a generalization of the linear
constraints on probabilities introduced by Boole. These linear constraints are
necessary to compute probabilities of events with relations between the.
arbitrarily specified with propositional calculus boolean formulas.
A General Purpose Inference Engine for Evidential Reasoning Research
Authors: Richard M. Tong, Lee A. Appelbaum, D. G. Shapiro
Date: 2013-03-27 00:00:00
Tasks: Retrieval

The purpose of this paper is to report on the most recent developments in our
ongoing investigation of the representation and manipulation of uncertainty in
automated reasoning systems. In our earlier studies (Tong and Shapiro, 1985) we
described a series of experiments with RUBRIC (Tong et al., 1985), a system for
full-text document retrieval, that generated some interesting insights into the
effects of choosing among a class of scalar valued uncertainty calculi. [n
order to extend these results we have begun a new series of experiments with a
larger class of representations and calculi, and to help perform these
experiments we have developed a general purpose inference engine.
A VLSI Design and Implementation for a Real-Time Approximate Reasoning
Authors: Masaki Togai, Hiroyuki Watanabe
Date: 2013-03-27 00:00:00
Tasks: Decision Making

The role of inferencing with uncertainty is becoming more important in
rule-based expert systems (ES), since knowledge given by a human expert is
often uncertain or imprecise. We have succeeded in designing a VLSI chip which
can perform an entire inference process based on fuzzy logic. The design of the
VLSI fuzzy inference engine emphasizes simplicity, extensibility, and
efficiency (operational speed and layout area). It is fabricated in 2.5 um CMOS
technology. The inference engine consists of three major components; a rule set
memory, an inference processor, and a controller. In this implementation, a
rule set memory is realized by a read only memory (ROM). The controller
consists of two counters. In the inference processor, one data path is laid out
for each rule. The number of the inference rule can be increased adding more
data paths to the inference processor. All rules are executed in parallel, but
each rule is processed serially. The logical structure of fuzzy inference
proposed in the current paper maps nicely onto the VLSI structure. A two-phase
nonoverlapping clocking scheme is used. Timing tests indicate that the
inference engine can operate at approximately 20.8 MHz. This translates to an
execution speed of approximately 80,000 Fuzzy Logical Inferences Per Second
(FLIPS), and indicates that the inference engine is suitable for a demanding
real-time application. The potential applications include decision-making in
the area of command and control for intelligent robot systems, process control,
missile and aircraft guidance, and other high performance machines.