pwc-archive/papers-with-abstracts huggingface dataset:
Planning, Scheduling, and Uncertainty in the Sequence of Future Events
Authors: B. R. Fox, Karl G. Kempf
Date: 2013-03-27 00:00:00
Tasks: Scheduling

Scheduling in the factory setting is compounded by computational complexity
and temporal uncertainty. Together, these two factors guarantee that the
process of constructing an optimal schedule will be costly and the chances of
executing that schedule will be slight. Temporal uncertainty in the task
execution time can be offset by several methods: eliminate uncertainty by
careful engineering, restore certainty whenever it is lost, reduce the
uncertainty by using more accurate sensors, and quantify and circumscribe the
remaining uncertainty. Unfortunately, these methods focus exclusively on the
sources of uncertainty and fail to apply knowledge of the tasks which are to be
scheduled. A complete solution must adapt the schedule of activities to be
performed according to the evolving state of the production world. The example
of vision-directed assembly is presented to illustrate that the principle of
least commitment, in the creation of a plan, in the representation of a
schedule, and in the execution of a schedule, enables a robot to operate
intelligently and efficiently, even in the presence of considerable uncertainty
in the sequence of future events.
Towards a General-Purpose Belief Maintenance System
Authors: Brian Falkenhainer
Date: 2013-03-27 00:00:00

There currently exists a gap between the theories proposed by the probability
and uncertainty and the needs of Artificial Intelligence research. These
theories primarily address the needs of expert systems, using knowledge
structures which must be pre-compiled and remain static in structure during
runtime. Many Al systems require the ability to dynamically add and remove
parts of the current knowledge structure (e.g., in order to examine what the
world would be like for different causal theories). This requires more
flexibility than existing uncertainty systems display. In addition, many Al
researchers are only interested in using "probabilities" as a means of
obtaining an ordering, rather than attempting to derive an accurate
probabilistic account of a situation. This indicates the need for systems which
stress ease of use and don't require extensive probability information when one
cannot (or doesn't wish to) provide such information. This paper attempts to
help reconcile the gap between approaches to uncertainty and the needs of many
AI systems by examining the control issues which arise, independent of a
particular uncertainty calculus. when one tries to satisfy these needs. Truth
Maintenance Systems have been used extensively in problem solving tasks to help
organize a set of facts and detect inconsistencies in the believed state of the
world. These systems maintain a set of true/false propositions and their
associated dependencies. However, situations often arise in which we are unsure
of certain facts or in which the conclusions we can draw from available
information are somewhat uncertain. The non-monotonic TMS 12] was an attempt at
reasoning when all the facts are not known, but it fails to take into account
degrees of belief and how available evidence can combine to strengthen a
particular belief. This paper addresses the problem of probabilistic reasoning
as it applies to Truth Maintenance Systems. It describes a belief Maintenance
System that manages a current set of beliefs in much the same way that a TMS
manages a set of true/false propositions. If the system knows that belief in
fact is dependent in some way upon belief in fact2, then it automatically
modifies its belief in facts when new information causes a change in belief of
fact2. It models the behavior of a TMS, replacing its 3-valued logic (true,
false, unknown) with an infinite valued logic, in such a way as to reduce to a
standard TMS if all statements are given in absolute true/false terms. Belief
Maintenance Systems can, therefore, be thought of as a generalization of Truth
Maintenance Systems, whose possible reasoning tasks are a superset of those for
a TMS.
Models vs. Inductive Inference for Dealing With Probabilistic Knowledge
Authors: Norman C. Dalkey
Date: 2013-03-27 00:00:00

Two different approaches to dealing with probabilistic knowledge are examined
-models and inductive inference. Examples of the first are: influence diagrams
[1], Bayesian networks [2], log-linear models [3, 4]. Examples of the second
are: games-against nature [5, 6] varieties of maximum-entropy methods [7, 8,
9], and the author's min-score induction [10]. In the modeling approach, the
basic issue is manageability, with respect to data elicitation and computation.
Thus, it is assumed that the pertinent set of users in some sense knows the
relevant probabilities, and the problem is to format that knowledge in a way
that is convenient to input and store and that allows computation of the
answers to current questions in an expeditious fashion. The basic issue for the
inductive approach appears at first sight to be very different. In this
approach it is presumed that the relevant probabilities are only partially
known, and the problem is to extend that incomplete information in a reasonable
way to answer current questions. Clearly, this approach requires that some form
of induction be invoked. Of course, manageability is an important additional
concern. Despite their seeming differences, the two approaches have a fair
amount in common, especially with respect to the structural framework they
employ. Roughly speaking, this framework involves identifying clusters of
variables which strongly interact, establishing marginal probability
distributions on the clusters, and extending the subdistributions to a more
complete distribution, usually via a product formalism. The product extension
is justified on the modeling approach in terms of assumed conditional
independence; in the inductive approach the product form arises from an
inductive rule.
Reasoning With Uncertain Knowledge
Authors: A. Julian Craddock, Roger A. Browse
Date: 2013-03-27 00:00:00
Tasks: Descriptive

A model of knowledge representation is described in which propositional facts
and the relationships among them can be supported by other facts. The set of
knowledge which can be supported is called the set of cognitive units, each
having associated descriptions of their explicit and implicit support
structures, summarizing belief and reliability of belief. This summary is
precise enough to be useful in a computational model while remaining
descriptive of the underlying symbolic support structure. When a fact supports
another supportive relationship between facts we call this meta-support. This
facilitates reasoning about both the propositional knowledge. and the support
structures underlying it.
Predicting The Performance of Minimax and Product in Game-Tree
Authors: Ping-Chung Chi, Dana Nau
Date: 2013-03-27 00:00:00

The discovery that the minimax decision rule performs poorly in some games
has sparked interest in possible alternatives to minimax. Until recently, the
only games in which minimax was known to perform poorly were games which were
mainly of theoretical interest. However, this paper reports results showing
poor performance of minimax in a more common game called kalah. For the kalah
games tested, a non-minimax decision rule called the product rule performs
significantly better than minimax.
  This paper also discusses a possible way to predict whether or not minimax
will perform well in a game when compared to product. A parameter called the
rate of heuristic flaw (rhf) has been found to correlate positively with the.
performance of product against minimax. Both analytical and experimental
results are given that appear to support the predictive power of rhf.
Towards The Inductive Acquisition of Temporal Knowledge
Authors: Kaihu Chen
Date: 2013-03-27 00:00:00
Tasks: Time Series, Time Series Analysis

The ability to predict the future in a given domain can be acquired by
discovering empirically from experience certain temporal patterns that tend to
repeat unerringly. Previous works in time series analysis allow one to make
quantitative predictions on the likely values of certain linear variables.
Since certain types of knowledge are better expressed in symbolic forms, making
qualitative predictions based on symbolic representations require a different
approach. A domain independent methodology called TIM (Time based Inductive
Machine) for discovering potentially uncertain temporal patterns from real time
observations using the technique of inductive inference is described here.
Probabilistic Reasoning About Ship Images
Authors: Lashon B. Booker, Naveen Hota
Date: 2013-03-27 00:00:00
Tasks: General Classification

One of the most important aspects of current expert systems technology is the
ability to make causal inferences about the impact of new evidence. When the
domain knowledge and problem knowledge are uncertain and incomplete Bayesian
reasoning has proven to be an effective way of forming such inferences [3,4,8].
While several reasoning schemes have been developed based on Bayes Rule, there
has been very little work examining the comparative effectiveness of these
schemes in a real application. This paper describes a knowledge based system
for ship classification [1], originally developed using the PROSPECTOR updating
method [2], that has been reimplemented to use the inference procedure
developed by Pearl and Kim [4,5]. We discuss our reasons for making this
change, the implementation of the new inference engine, and the comparative
performance of the two versions of the system.
Taxonomy, Structure, and Implementation of Evidential Reasoning
Authors: Moshe Ben-Bassat
Date: 2013-03-27 00:00:00
Tasks: Bayesian Inference, Decision Making

The fundamental elements of evidential reasoning problems are described,
followed by a discussion of the structure of various types of problems.
Bayesian inference networks and state space formalism are used as the tool for
problem representation.
  A human-oriented decision making cycle for solving evidential reasoning
problems is described and illustrated for a military situation assessment
problem. The implementation of this cycle may serve as the basis for an expert
system shell for evidential reasoning; i.e. a situation assessment processor.
Knowledge Engineering Within A Generalized Bayesian Framework
Authors: Stephen W. Barth, Steven W. Norton
Date: 2013-03-27 00:00:00

During the ongoing debate over the representation of uncertainty in
Artificial Intelligence, Cheeseman, Lemmer, Pearl, and others have argued that
probability theory, and in particular the Bayesian theory, should be used as
the basis for the inference mechanisms of Expert Systems dealing with
uncertainty. In order to pursue the issue in a practical setting, sophisticated
tools for knowledge engineering are needed that allow flexible and
understandable interaction with the underlying knowledge representation
schemes. This paper describes a Generalized Bayesian framework for building
expert systems which function in uncertain domains, using algorithms proposed
by Lemmer. It is neither rule-based nor frame-based, and requires a new system
of knowledge engineering tools. The framework we describe provides a
knowledge-based system architecture with an inference engine, explanation
capability, and a unique aid for building consistent knowledge bases.
Application of Evidential Reasoning to Helicopter Flight Path Control
Authors: Shoshana Abel
Date: 2013-03-27 00:00:00

This paper presents a methodology for research and development of the
inferencing and knowledge representation aspects of an Expert System approach
for performing reasoning under uncertainty in support of a real time vehicle
guidance and navigation system. Such a system could be of major benefit for
non-terrain following low altitude flight systems operating in foreign hostile
environments such as might be experienced by NOE helicopter or similar mission
craft. An innovative extension of the evidential reasoning methodology, termed
the Sum-and-Lattice-Points Method, has been developed. The research and
development effort presented in this paper consists of a formal mathematical
development of the Sum-and-Lattice-Points Method, its formulation and
representation in a parallel environment, prototype software development of the
method within an expert system, and initial testing of the system within the
confines of the vehicle guidance system.
Reasoning About Beliefs and Actions Under Computational Resource Constraints
Authors: Eric J. Horvitz
Date: 2013-03-27 00:00:00

Although many investigators affirm a desire to build reasoning systems that
behave consistently with the axiomatic basis defined by probability theory and
utility theory, limited resources for engineering and computation can make a
complete normative analysis impossible. We attempt to move discussion beyond
the debate over the scope of problems that can be handled effectively to cases
where it is clear that there are insufficient computational resources to
perform an analysis deemed as complete. Under these conditions, we stress the
importance of considering the expected costs and benefits of applying
alternative approximation procedures and heuristics for computation and
knowledge acquisition. We discuss how knowledge about the structure of user
utility can be used to control value tradeoffs for tailoring inference to
alternative contexts. We address the notion of real-time rationality, focusing
on the application of knowledge about the expected timewise-refinement
abilities of reasoning strategies to balance the benefits of additional
computation with the costs of acting with a partial result. We discuss the
benefits of applying decision theory to control the solution of difficult
problems given limitations and uncertainty in reasoning resources.
Efficient Inference on Generalized Fault Diagrams
Authors: Ross D. Shachter, Leonard Bertrand
Date: 2013-03-27 00:00:00

The generalized fault diagram, a data structure for failure analysis based on
the influence diagram, is defined. Unlike the fault tree, this structure allows
for dependence among the basic events and replicated logical elements. A
heuristic procedure is developed for efficient processing of these structures.
Estimation Procedures for Robust Sensor Control
Authors: Greg Hager, Max Mintz
Date: 2013-03-27 00:00:00

Many robotic sensor estimation problems can characterized in terms of
nonlinear measurement systems. These systems are contaminated with noise and
may be underdetermined from a single observation. In order to get reliable
estimation results, the system must choose views which result in an
overdetermined system. This is the sensor control problem. Accurate and
reliable sensor control requires an estimation procedure which yields both
estimates and measures of its own performance. In the case of nonlinear
measurement systems, computationally simple closed-form estimation solutions
may not exist. However, approximation techniques provide viable alternatives.
In this paper, we evaluate three estimation techniques: the extended Kalman
filter, a discrete Bayes approximation, and an iterative Bayes approximation.
We present mathematical results and simulation statistics illustrating
operating conditions where the extended Kalman filter is inappropriate for
sensor control, and discuss issues in the use of the discrete Bayes
approximation.
Explanation of Probabilistic Inference for Decision Support Systems
Authors: Christopher Elsaesser
Date: 2013-03-27 00:00:00

An automated explanation facility for Bayesian conditioning aimed at
improving user acceptance of probability-based decision support systems has
been developed. The domain-independent facility is based on an information
processing perspective on reasoning about conditional evidence that accounts
both for biased and normative inferences. Experimental results indicate that
the facility is both acceptable to naive users and effective in improving
understanding.
Combining Symbolic and Numeric Approaches to Uncertainty Management
Authors: Bruce D'Ambrosio
Date: 2013-03-27 00:00:00
Tasks: Management

A complete approach to reasoning under uncertainty requires support for
incremental and interactive formulation and revision of, as well as reasoning
with, models of the problem domain capable of representing our uncertainty. We
present a hybrid reasoning scheme which combines symbolic and numeric methods
for uncertainty management to provide efficient and effective support for each
of these tasks. The hybrid is based on symbolic techniques adapted from
Assumption-based Truth Maintenance systems (ATMS), combined with numeric
methods adapted from the Dempster/Shafer theory of evidence, as extended in
Baldwin's Support Logic Programming system. The hybridization is achieved by
viewing an ATMS as a symbolic algebra system for uncertainty calculations. This
technique has several major advantages over conventional methods for performing
inference with numeric certainty estimates in addition to the ability to
dynamically determine hypothesis spaces, including improved management of
dependent and partially independent evidence, faster run-time evaluation of
propositional certainties, the ability to query the certainty value of a
proposition from multiple perspectives, and the ability to incrementally extend
or revise domain models.
An Algorithm for Computing Probabilistic Propositions
Authors: Gregory F. Cooper
Date: 2013-03-27 00:00:00

A method for computing probabilistic propositions is presented. It assumes
the availability of a single external routine for computing the probability of
one instantiated variable, given a conjunction of other instantiated variables.
In particular, the method allows belief network algorithms to calculate general
probabilistic propositions over nodes in the network. Although in the worst
case the time complexity of the method is exponential in the size of a query,
it is polynomial in the size of a number of common types of queries.
Steps Towards Programs that Manage Uncertainty
Authors: Paul Cohen
Date: 2013-03-27 00:00:00
Tasks: Diagnostic

Reasoning under uncertainty in Al hats come to mean assessing the credibility
of hypotheses inferred from evidence. But techniques for assessing credibility
do not tell a problem solver what to do when it is uncertain. This is the focus
of our current research. We have developed a medical expert system called MUM,
for Managing Uncertainty in Medicine, that plans diagnostic sequences of
questions, tests, and treatments. This paper describes the kinds of problems
that MUM was designed to solve and gives a brief description of its
architecture. More recently, we have built an empty version of MUM called MU,
and used it to reimplement MUM and a small diagnostic system for plant
pathology. The latter part of the paper describes the features of MU that make
it appropriate for building expert systems that manage uncertainty.
Compiling Fuzzy Logic Control Rules to Hardware Implementations
Authors: Stephen Chiu, Masaki Togai
Date: 2013-03-27 00:00:00
Tasks: Decision Making

A major aspect of human reasoning involves the use of approximations.
Particularly in situations where the decision-making process is under stringent
time constraints, decisions are based largely on approximate, qualitative
assessments of the situations. Our work is concerned with the application of
approximate reasoning to real-time control. Because of the stringent processing
speed requirements in such applications, hardware implementations of fuzzy
logic inferencing are being pursued. We describe a programming environment for
translating fuzzy control rules into hardware realizations. Two methods of
hardware realizations are possible. The First is based on a special purpose
chip for fuzzy inferencing. The second is based on a simple memory chip. The
ability to directly translate a set of decision rules into hardware
implementations is expected to make fuzzy control an increasingly practical
approach to the control of complex systems.
Integrating Logical and Probabilistic Reasoning for Decision Making
Authors: John S. Breese, Edison Tse
Date: 2013-03-27 00:00:00
Tasks: Decision Making

We describe a representation and a set of inference methods that combine
logic programming techniques with probabilistic network representations for
uncertainty (influence diagrams). The techniques emphasize the dynamic
construction and solution of probabilistic and decision-theoretic models for
complex and uncertain domains. Given a query, a logical proof is produced if
possible; if not, an influence diagram based on the query and the knowledge of
the decision domain is produced and subsequently solved. A uniform declarative,
first-order, knowledge representation is combined with a set of integrated
inference procedures for logical, probabilistic, and decision-theoretic
reasoning.
Implementing a Bayesian Scheme for Revising Belief Commitments
Authors: Lashon B. Booker, Naveen Hota, Gavin Hemphill
Date: 2013-03-27 00:00:00
Tasks: General Classification

Our previous work on classifying complex ship images [1,2] has evolved into
an effort to develop software tools for building and solving generic
classification problems. Managing the uncertainty associated with feature data
and other evidence is an important issue in this endeavor. Bayesian techniques
for managing uncertainty [7,12,13] have proven to be useful for managing
several of the belief maintenance requirements of classification problem
solving. One such requirement is the need to give qualitative explanations of
what is believed. Pearl [11] addresses this need by computing what he calls a
belief commitment-the most probable instantiation of all hypothesis variables
given the evidence available. Before belief commitments can be computed, the
straightforward implementation of Pearl's procedure involves finding an
analytical solution to some often difficult optimization problems. We describe
an efficient implementation of this procedure using tensor products that solves
these problems enumeratively and avoids the need for case by case analysis. The
procedure is thereby made more practical to use in the general case.
Evidential Reasoning in Image Understanding
Authors: Minchuan Zhang, Su-shing Chen
Date: 2013-03-27 00:00:00
Tasks: Classification, General Classification

In this paper, we present some results of evidential reasoning in
understanding multispectral images of remote sensing systems. The
Dempster-Shafer approach of combination of evidences is pursued to yield
contextual classification results, which are compared with previous results of
the Bayesian context free classification, contextual classifications of dynamic
programming and stochastic relaxation approaches.
The Role of Tuning Uncertain Inference Systems
Authors: Ben P. Wise, Bruce M. Perrin, David S. Vaughan, Robert M. Yadrick
Date: 2013-03-27 00:00:00

This study examined the effects of "tuning" the parameters of the incremental
function of MYCIN, the independent function of PROSPECTOR, a probability model
that assumes independence, and a simple additive linear equation. me parameters
of each of these models were optimized to provide solutions which most nearly
approximated those from a full probability model for a large set of simple
networks. Surprisingly, MYCIN, PROSPECTOR, and the linear equation performed
equivalently; the independence model was clearly more accurate on the networks
studied.