pwc-archive/papers-with-abstracts huggingface dataset:
A Decision-Theoretic Model for Using Scientific Data
Authors: Harold P. Lehmann
Date: 2013-03-27 00:00:00
Tasks: model

Many Artificial Intelligence systems depend on the agent's updating its
beliefs about the world on the basis of experience. Experiments constitute one
type of experience, so scientific methodology offers a natural environment for
examining the issues attendant to using this class of evidence. This paper
presents a framework which structures the process of using scientific data from
research reports for the purpose of making decisions, using decision analysis
as the basis for the structure and using medical research as the general
scientific domain. The structure extends the basic influence diagram for
updating belief in an object domain parameter of interest by expanding the
parameter into four parts: those of the patient, the population, the study
sample, and the effective study sample. The structure uses biases to perform
the transformation of one parameter into another, so that, for instance,
selection biases, in concert with the population parameter, yield the study
sample parameter. The influence diagram structure provides decision theoretic
justification for practices of good clinical research such as randomized
assignment and blindfolding of care providers. The model covers most research
designs used in medicine: case-control studies, cohort studies, and controlled
clinical trials, and provides an architecture to separate clearly between
statistical knowledge and domain knowledge. The proposed general model can be
the basis for clinical epidemiological advisory systems, when coupled with
heuristic pruning of irrelevant biases; of statistical workstations, when the
computational machinery for calculation of posterior distributions is added;
and of meta-analytic reviews, when multiple studies may impact on a single
population parameter.
Hierarchical Evidence Accumulation in the Pseiki System and Experiments in Model-Driven Mobile Robot Navigation
Authors: A. C. Kak, K. M. Andress, C. Lopez-Abadia, M. S. Carroll, J. R. Lewis
Date: 2013-03-27 00:00:00
Tasks: Autonomous Navigation, Robot Navigation

In this paper, we will review the process of evidence accumulation in the
PSEIKI system for expectation-driven interpretation of images of 3-D scenes.
Expectations are presented to PSEIKI as a geometrical hierarchy of
abstractions. PSEIKI's job is then to construct abstraction hierarchies in the
perceived image taking cues from the abstraction hierarchies in the
expectations. The Dempster-Shafer formalism is used for associating belief
values with the different possible labels for the constructed abstractions in
the perceived image. This system has been used successfully for autonomous
navigation of a mobile robot in indoor environments.
Bounded Conditioning: Flexible Inference for Decisions under Scarce Resources
Authors: Eric J. Horvitz, Jaap Suermondt, Gregory F. Cooper
Date: 2013-03-27 00:00:00

We introduce a graceful approach to probabilistic inference called bounded
conditioning. Bounded conditioning monotonically refines the bounds on
posterior probabilities in a belief network with computation, and converges on
final probabilities of interest with the allocation of a complete resource
fraction. The approach allows a reasoner to exchange arbitrary quantities of
computational resource for incremental gains in inference quality. As such,
bounded conditioning holds promise as a useful inference technique for
reasoning under the general conditions of uncertain and varying reasoning
resources. The algorithm solves a probabilistic bounding problem in complex
belief networks by breaking the problem into a set of mutually exclusive,
tractable subproblems and ordering their solution by the expected effect that
each subproblem will have on the final answer. We introduce the algorithm,
discuss its characterization, and present its performance on several belief
networks, including a complex model for reasoning about problems in
intensive-care medicine.
A Tractable Inference Algorithm for Diagnosing Multiple Diseases
Authors: David Heckerman
Date: 2013-03-27 00:00:00

We examine a probabilistic model for the diagnosis of multiple diseases. In the model, diseases and findings are represented as binary variables. Also, diseases are marginally independent, features are conditionally independent given disease instances, and diseases interact to produce findings via a noisy OR-gate. An algorithm for computing the posterior probability of each disease, given a set of observed findings, called quickscore, is presented. The time complexity of the algorithm is O(nm-2m+), where n is the number of diseases, m+ is the number of positive findings and m- is the number of negative findings. Although the time complexity of quickscore i5 exponential in the number of positive findings, the algorithm is useful in practice because the number of observed positive findings is usually far less than the number of diseases under consideration. Performance results for quickscore applied to a probabilistic version of Quick Medical Reference (QMR) are provided.
The Compilation of Decision Models
Authors: David Heckerman, John S. Breese, Eric J. Horvitz
Date: 2013-03-27 00:00:00

We introduce and analyze the problem of the compilation of decision models
from a decision-theoretic perspective. The techniques described allow us to
evaluate various configurations of compiled knowledge given the nature of
evidential relationships in a domain, the utilities associated with alternative
actions, the costs of run-time delays, and the costs of memory. We describe
procedures for selecting a subset of the total observations available to be
incorporated into a compiled situation-action mapping, in the context of a
binary decision with conditional independence of evidence. The methods allow us
to incrementally select the best pieces of evidence to add to the set of
compiled knowledge in an engineering setting. After presenting several
approaches to compilation, we exercise one of the methods to provide insight
into the relationship between the distribution over weights of evidence and the
preferred degree of compilation.
Heuristic Search as Evidential Reasoning
Authors: Othar Hansson, Andy Mayer
Date: 2013-03-27 00:00:00
Tasks: Bayesian Inference, Heuristic Search

BPS, the Bayesian Problem Solver, applies probabilistic inference and
decision-theoretic control to flexible, resource-constrained problem-solving.
This paper focuses on the Bayesian inference mechanism in BPS, and contrasts it
with those of traditional heuristic search techniques. By performing sound
inference, BPS can outperform traditional techniques with significantly less
computational effort. Empirical tests on the Eight Puzzle show that after only
a few hundred node expansions, BPS makes better decisions than does the best
existing algorithm after several million node expansions
The Relationship between Knowledge, Belief and Certainty
Authors: Joseph Y. Halpern
Date: 2013-03-27 00:00:00

We consider the relation between knowledge and certainty, where a fact is
known if it is true at all worlds an agent considers possible and is certain if
it holds with probability 1. We identify certainty with probabilistic belief.
We show that if we assume one fixed probability assignment, then the logic
KD45, which has been identified as perhaps the most appropriate for belief,
provides a complete axiomatization for reasoning about certainty. Just as an
agent may believe a fact although phi is false, he may be certain that a fact
phi, is true although phi is false. However, it is easy to see that an agent
can have such false (probabilistic) beliefs only at a set of worlds of
probability 0. If we restrict attention to structures where all worlds have
positive probability, then S5 provides a complete axiomatization. If we
consider a more general setting, where there might be a different probability
assignment at each world, then by placing appropriate conditions on the support
of the probability function (the set of worlds which have non-zero
probability), we can capture many other well-known modal logics, such as T and
S4. Finally, we consider which axioms characterize structures satisfying
Miller's principle.
Deciding Consistency of Databases Containing Defeasible and Strict Information
Authors: Moises Goldszmidt, Judea Pearl
Date: 2013-03-27 00:00:00
Tasks: Sentence

We propose a norm of consistency for a mixed set of defeasible and strict
sentences, based on a probabilistic semantics. This norm establishes a clear
distinction between knowledge bases depicting exceptions and those containing
outright contradictions. We then define a notion of entailment based also on
probabilistic considerations and provide a characterization of the relation
between consistency and entailment. We derive necessary and sufficient
conditions for consistency, and provide a simple decision procedure for testing
consistency and deciding whether a sentence is entailed by a database. Finally,
it is shown that if al1 sentences are Horn clauses, consistency and entailment
can be tested in polynomial time.
The Effects of Perfect and Sample Information on Fuzzy Utilities in Decision-Making
Authors: Maria Angeles Gil, Pramod Jain
Date: 2013-03-27 00:00:00
Tasks: Decision Making

In this paper, we first consider a Bayesian framework and model the "utility
function" in terms of fuzzy random variables. On the basis of this model, we
define the "prior (fuzzy) expected utility" associated with each action, and
the corresponding "posterior (fuzzy) expected utility given sample information
from a random experiment". The aim of this paper is to analyze how sample
information can affect the expected utility. In this way, by using some fuzzy
preference relations, we conclude that sample information allows a decision
maker to increase the expected utility on the average. The upper bound on the
value of the expected utility is when the decision maker has perfect
information. Applications of this work to the field of artificial intelligence
are presented through two examples.
d-Separation: From Theorems to Algorithms
Authors: Dan Geiger, Tom S. Verma, Judea Pearl
Date: 2013-03-27 00:00:00

An efficient algorithm is developed that identifies all independencies
implied by the topology of a Bayesian network. Its correctness and maximality
stems from the soundness and completeness of d-separation with respect to
probability theory. The algorithm runs in time O (l E l) where E is the number
of edges in the network.
Weighing and Integrating Evidence for Stochastic Simulation in Bayesian Networks
Authors: Robert Fung, Kuo-Chu Chang
Date: 2013-03-27 00:00:00

Stochastic simulation approaches perform probabilistic inference in Bayesian
networks by estimating the probability of an event based on the frequency that
the event occurs in a set of simulation trials. This paper describes the
evidence weighting mechanism, for augmenting the logic sampling stochastic
simulation algorithm [Henrion, 1986]. Evidence weighting modifies the logic
sampling algorithm by weighting each simulation trial by the likelihood of a
network's evidence given the sampled state node values for that trial. We also
describe an enhancement to the basic algorithm which uses the evidential
integration technique [Chin and Cooper, 1987]. A comparison of the basic
evidence weighting mechanism with the Markov blanket algorithm [Pearl, 1987],
the logic sampling algorithm, and the evidence integration algorithm is
presented. The comparison is aided by analyzing the performance of the
algorithms in a simple example network.
Interval Influence Diagrams
Authors: Kenneth W. Fertig, John S. Breese
Date: 2013-03-27 00:00:00
Tasks: ARC, Sensitivity

We describe a mechanism for performing probabilistic reasoning in influence
diagrams using interval rather than point valued probabilities. We derive the
procedures for node removal (corresponding to conditional expectation) and arc
reversal (corresponding to Bayesian conditioning) in influence diagrams where
lower bounds on probabilities are stored at each node. The resulting bounds for
the transformed diagram are shown to be optimal within the class of constraints
on probability distributions that can be expressed exclusively as lower bounds
on the component probabilities of the diagram. Sequences of these operations
can be performed to answer probabilistic queries with indeterminacies in the
input and for performing sensitivity analysis on an influence diagram. The
storage requirements and computational complexity of this approach are
comparable to those for point-valued probabilistic inference mechanisms, making
the approach attractive for performing sensitivity analysis and where
probability information is not available. Limited empirical data on an
implementation of the methodology are provided.
Positive and Negative Explanations of Uncertain Reasoning in the Framework of Possibility Theory
Authors: Henri Farrency, Henri Prade
Date: 2013-03-27 00:00:00

This paper presents an approach for developing the explanation capabilities
of rule-based expert systems managing imprecise and uncertain knowledge. The
treatment of uncertainty takes place in the framework of possibility theory
where the available information concerning the value of a logical or numerical
variable is represented by a possibility distribution which restricts its more
or less possible values. We first discuss different kinds of queries asking for
explanations before focusing on the two following types : i) how, a particular
possibility distribution is obtained (emphasizing the main reasons only) ; ii)
why in a computed possibility distribution, a particular value has received a
possibility degree which is so high, so low or so contrary to the expectation.
The approach is based on the exploitation of equations in max-min algebra. This
formalism includes the limit case of certain and precise information.
How Much More Probable is "Much More Probable"? Verbal Expressions for Probability Updates
Authors: Christopher Elsaesser, Max Henrion
Date: 2013-03-27 00:00:00
Tasks: Bayesian Inference

Bayesian inference systems should be able to explain their reasoning to
users, translating from numerical to natural language. Previous empirical work
has investigated the correspondence between absolute probabilities and
linguistic phrases. This study extends that work to the correspondence between
changes in probabilities (updates) and relative probability phrases, such as
"much more likely" or "a little less likely." Subjects selected such phrases to
best describe numerical probability updates. We examined three hypotheses about
the correspondence, and found the most descriptively accurate of these three to
be that each such phrase corresponds to a fixed difference in probability
(rather than fixed ratio of probabilities or of odds). The empirically derived
phrase selection function uses eight phrases and achieved a 72% accuracy in
correspondence with the subjects' actual usage.
Automated Reasoning Using Possibilistic Logic: Semantics, Belief Revision and Variable Certainty Weights
Authors: Didier Dubois, Jerome Lang, Henri Prade
Date: 2013-03-27 00:00:00

In this paper an approach to automated deduction under uncertainty,based on
possibilistic logic, is proposed ; for that purpose we deal with clauses
weighted by a degree which is a lower bound of a necessity or a possibility
measure, according to the nature of the uncertainty. Two resolution rules are
used for coping with the different situations, and the refutation method can be
generalized. Besides the lower bounds are allowed to be functions of variables
involved in the clause, which gives hypothetical reasoning capabilities. The
relation between our approach and the idea of minimizing abnormality is briefly
discussed. In case where only lower bounds of necessity measures are involved,
a semantics is proposed, in which the completeness of the extended resolution
principle is proved. Moreover deduction from a partially inconsistent knowledge
base can be managed in this approach and displays some form of
non-monotonicity.
Decision Making "Biases" and Support for Assumption-Based Higher-Order Reasoning
Authors: Marvin S. Cohen
Date: 2013-03-27 00:00:00
Tasks: Decision Making

Unaided human decision making appears to systematically violate consistency
constraints imposed by normative theories; these biases in turn appear to
justify the application of formal decision-analytic models. It is argued that
both claims are wrong. In particular, we will argue that the "confirmation
bias" is premised on an overly narrow view of how conflicting evidence is and
ought to be handled. Effective decision aiding should focus on supporting the
contral processes by means of which knowledge is extended into novel situations
and in which assumptions are adopted, utilized, and revised. The Non- Monotonic
Probabilist represents initial work toward such an aid.
An Empirical Evaluation of a Randomized Algorithm for Probabilistic Inference
Authors: R. Martin Chavez, Gregory F. Cooper
Date: 2013-03-27 00:00:00
Tasks: Medical Diagnosis

In recent years, researchers in decision analysis and artificial intelligence
(Al) have used Bayesian belief networks to build models of expert opinion.
Using standard methods drawn from the theory of computational complexity,
workers in the field have shown that the problem of probabilistic inference in
belief networks is difficult and almost certainly intractable. K N ET, a
software environment for constructing knowledge-based systems within the
axiomatic framework of decision theory, contains a randomized approximation
scheme for probabilistic inference. The algorithm can, in many circumstances,
perform efficient approximate inference in large and richly interconnected
models of medical diagnosis. Unlike previously described stochastic algorithms
for probabilistic inference, the randomized approximation scheme computes a
priori bounds on running time by analyzing the structure and contents of the
belief network. In this article, we describe a randomized algorithm for
probabilistic inference and analyze its performance mathematically. Then, we
devote the major portion of the paper to a discussion of the algorithm's
empirical behavior. The results indicate that the generation of good trials
(that is, trials whose distribution closely matches the true distribution),
rather than the computation of numerous mediocre trials, dominates the
performance of stochastic simulation. Key words: probabilistic inference,
belief networks, stochastic simulation, computational complexity theory,
randomized algorithms.
Plan Recognition in Stories and in Life
Authors: Eugene Charniak, Robert P. Goldman
Date: 2013-03-27 00:00:00

Plan recognition does not work the same way in stories and in "real life"
(people tend to jump to conclusions more in stories). We present a theory of
this, for the particular case of how objects in stories (or in life) influence
plan recognition decisions. We provide a Bayesian network formalization of a
simple first-order theory of plans, and show how a particular network parameter
seems to govern the difference between "life-like" and "story-like" response.
We then show why this parameter would be influenced (in the desired way) by a
model of speaker (or author) topic selection which assumes that facts in
stories are typically "relevant".
BaRT: A Bayesian Reasoning Tool for Knowledge Based Systems
Authors: Lashon B. Booker, Naveen Hota, Connie Loggia Ramsey
Date: 2013-03-27 00:00:00

As the technology for building knowledge based systems has matured, important
lessons have been learned about the relationship between the architecture of a
system and the nature of the problems it is intended to solve. We are
implementing a knowledge engineering tool called BART that is designed with
these lessons in mind. BART is a Bayesian reasoning tool that makes belief
networks and other probabilistic techniques available to knowledge engineers
building classificatory problem solvers. BART has already been used to develop
a decision aid for classifying ship images, and it is currently being used to
manage uncertainty in systems concerned with analyzing intelligence reports.
This paper discusses how state-of-the-art probabilistic methods fit naturally
into a knowledge based approach to classificatory problem solving, and
describes the current capabilities of BART.
Uncertainty and Incompleteness
Authors: Piero P. Bonissone, David A. Cyrluk, James W. Goodwin, Jonathan Stillman
Date: 2013-03-27 00:00:00

Two major difficulties in using default logics are their intractability and
the problem of selecting among multiple extensions. We propose an approach to
these problems based on integrating nommonotonic reasoning with plausible
reasoning based on triangular norms. A previously proposed system for reasoning
with uncertainty (RUM) performs uncertain monotonic inferences on an acyclic
graph. We have extended RUM to allow nommonotonic inferences and cycles within
nonmonotonic rules. By restricting the size and complexity of the nommonotonic
cycles we can still perform efficient inferences. Uncertainty measures provide
a basis for deciding among multiple defaults. Different algorithms and
heuristics for finding the optimal defaults are discussed.
Now that I Have a Good Theory of Uncertainty, What Else Do I Need?
Authors: Piero P. Bonissone
Date: 2013-03-27 00:00:00
Tasks: General Classification

Rather than discussing the isolated merits of a nominative theory of
uncertainty, this paper focuses on a class of problems, referred to as Dynamic
Classification Problem (DCP), which requires the integration of many theories,
including a prescriptive theory of uncertainty. We start by analyzing the
Dynamic Classification Problem and by defining its induced requirements on a
supporting (plausible) reasoning system. We provide a summary of the underlying
theory (based on the semantics of many-valed logics) and illustrate the
constraints imposed upon it to ensure the modularity and computational
performance required by the applications. We describe the technologies used for
knowledge engineering (such as object-based simulator to exercise requirements,
and development tools to build the Knowledge Base and functionally validate
it). We emphasize the difference between development environment and run-time
system, describe the rule cross-compiler, and the real-time inference engine
with meta-reasoning capabilities. Finally, we illustrate how our proposed
technology satisfies the pop's requirements and analyze some of the lessons
reamed from its applications to situation assessment problems for Pilot's
Associate and Submarine Commander Associate.
Temporal Reasoning with Probabilities
Authors: Carlo Berzuini, Riccardo Bellazzi, Silvana Quaglini
Date: 2013-03-27 00:00:00

In this paper we explore representations of temporal knowledge based upon the
formalism of Causal Probabilistic Networks (CPNs). Two different
?continuous-time? representations are proposed. In the first, the CPN includes
variables representing ?event-occurrence times?, possibly on different time
scales, and variables representing the ?state? of the system at these times. In
the second, the CPN describes the influences between random variables with
values in () representing dates, i.e. time-points associated with the
occurrence of relevant events. However, structuring a system of inter-related
dates as a network where all links commit to a single specific notion of cause
and effect is in general far from trivial and leads to severe difficulties. We
claim that we should recognize explicitly different kinds of relation between
dates, such as ?cause?, ?inhibition?, ?competition?, etc., and propose a method
whereby these relations are coherently embedded in a CPN using additional
auxiliary nodes corresponding to "instrumental" variables. Also discussed,
though not covered in detail, is the topic concerning how the quantitative
specifications to be inserted in a temporal CPN can be learned from specific
data.