126 research outputs found
Automating Computer Bottleneck Detection with Belief Nets
We describe an application of belief networks to the diagnosis of bottlenecks
in computer systems. The technique relies on a high-level functional model of
the interaction between application workloads, the Windows NT operating system,
and system hardware. Given a workload description, the model predicts the
values of observable system counters available from the Windows NT performance
monitoring tool. Uncertainty in workloads, predictions, and counter values are
characterized with Gaussian distributions. During diagnostic inference, we use
observed performance monitor values to find the most probable assignment to the
workload parameters. In this paper we provide some background on automated
bottleneck detection, describe the structure of the system model, and discuss
empirical procedures for model calibration and verification. Part of the
calibration process includes generating a dataset to estimate a multivariate
Gaussian error model. Initial results in diagnosing bottlenecks are presented.Comment: Appears in Proceedings of the Eleventh Conference on Uncertainty in
Artificial Intelligence (UAI1995
IDEAL: A Software Package for Analysis of Influence Diagrams
IDEAL (Influence Diagram Evaluation and Analysis in Lisp) is a software
environment for creation and evaluation of belief networks and influence
diagrams. IDEAL is primarily a research tool and provides an implementation of
many of the latest developments in belief network and influence diagram
evaluation in a unified framework. This paper describes IDEAL and some lessons
learned during its development.Comment: Appears in Proceedings of the Sixth Conference on Uncertainty in
Artificial Intelligence (UAI1990
Integrating Logical and Probabilistic Reasoning for 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.Comment: Appears in Proceedings of the Third Conference on Uncertainty in
Artificial Intelligence (UAI1987
A New Look at Causal Independence
Heckerman (1993) defined causal independence in terms of a set of temporal
conditional independence statements. These statements formalized certain types
of causal interaction where (1) the effect is independent of the order that
causes are introduced and (2) the impact of a single cause on the effect does
not depend on what other causes have previously been applied. In this paper, we
introduce an equivalent a temporal characterization of causal independence
based on a functional representation of the relationship between causes and the
effect. In this representation, the interaction between causes and effect can
be written as a nested decomposition of functions. Causal independence can be
exploited by representing this decomposition in the belief network, resulting
in representations that are more efficient for inference than general causal
models. We present empirical results showing the benefits of a
causal-independence representation for belief-network inference.Comment: Appears in Proceedings of the Tenth Conference on Uncertainty in
Artificial Intelligence (UAI1994
Decision-Theoretic Control of Problem Solving: Principles and Architecture
This paper presents an approach to the design of autonomous, real-time
systems operating in uncertain environments. We address issues of problem
solving and reflective control of reasoning under uncertainty in terms of two
fundamental elements: l) a set of decision-theoretic models for selecting among
alternative problem-solving methods and 2) a general computational architecture
for resource-bounded problem solving. The decisiontheoretic models provide a
set of principles for choosing among alternative problem-solving methods based
on their relative costs and benefits, where benefits are characterized in terms
of the value of information provided by the output of a reasoning activity. The
output may be an estimate of some uncertain quantity or a recommendation for
action. The computational architecture, called Schemer-ll, provides for
interleaving of and communication among various problem-solving subsystems.
These subsystems provide alternative approaches to information gathering,
belief refinement, solution construction, and solution execution. In
particular, the architecture provides a mechanism for interrupting the
subsystems in response to critical events. We provide a decision theoretic
account for scheduling problem-solving elements and for critical-event-driven
interruption of activities in an architecture such as Schemer-II.Comment: Appears in Proceedings of the Fourth Conference on Uncertainty in
Artificial Intelligence (UAI1988
Decision Making with Interval Influence Diagrams
In previous work (Fertig and Breese, 1989; Fertig and Breese, 1990) we
defined a mechanism for performing probabilistic reasoning in influence
diagrams using interval rather than point-valued probabilities. In this paper
we extend these procedures to incorporate decision nodes and interval-valued
value functions in the diagram. We derive the procedures for chance node
removal (calculating expected value) and decision node removal (optimization)
in influence diagrams where lower bounds on probabilities are stored at each
chance node and interval bounds are stored on the value function associated
with the diagram's value node. The output of the algorithm are a set of
admissible alternatives for each decision variable and a set of bounds on
expected value based on the imprecision in the input. The procedure can be
viewed as an approximation to a full e-dimensional sensitivity analysis where n
are the number of imprecise probability distributions in the input. We show the
transformations are optimal and sound. The performance of the algorithm on an
influence diagrams is investigated and compared to an exact algorithm.Comment: Appears in Proceedings of the Sixth Conference on Uncertainty in
Artificial Intelligence (UAI1990
Ideal Reformulation of Belief Networks
The intelligent reformulation or restructuring of a belief network can
greatly increase the efficiency of inference. However, time expended for
reformulation is not available for performing inference. Thus, under time
pressure, there is a tradeoff between the time dedicated to reformulating the
network and the time applied to the implementation of a solution. We
investigate this partition of resources into time applied to reformulation and
time used for inference. We shall describe first general principles for
computing the ideal partition of resources under uncertainty. These principles
have applicability to a wide variety of problems that can be divided into
interdependent phases of problem solving. After, we shall present results of
our empirical study of the problem of determining the ideal amount of time to
devote to searching for clusters in belief networks. In this work, we acquired
and made use of probability distributions that characterize (1) the performance
of alternative heuristic search methods for reformulating a network instance
into a set of cliques, and (2) the time for executing inference procedures on
various belief networks. Given a preference model describing the value of a
solution as a function of the delay required for its computation, the system
selects an ideal time to devote to reformulation.Comment: Appears in Proceedings of the Sixth Conference on Uncertainty in
Artificial Intelligence (UAI1990
Interval Influence Diagrams
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.Comment: Appears in Proceedings of the Fifth Conference on Uncertainty in
Artificial Intelligence (UAI1989
Empirical Analysis of Predictive Algorithms for Collaborative Filtering
Collaborative filtering or recommender systems use a database about user
preferences to predict additional topics or products a new user might like. In
this paper we describe several algorithms designed for this task, including
techniques based on correlation coefficients, vector-based similarity
calculations, and statistical Bayesian methods. We compare the predictive
accuracy of the various methods in a set of representative problem domains. We
use two basic classes of evaluation metrics. The first characterizes accuracy
over a set of individual predictions in terms of average absolute deviation.
The second estimates the utility of a ranked list of suggested items. This
metric uses an estimate of the probability that a user will see a
recommendation in an ordered list. Experiments were run for datasets associated
with 3 application areas, 4 experimental protocols, and the 2 evaluation
metrics for the various algorithms. Results indicate that for a wide range of
conditions, Bayesian networks with decision trees at each node and correlation
methods outperform Bayesian-clustering and vector-similarity methods. Between
correlation and Bayesian networks, the preferred method depends on the nature
of the dataset, nature of the application (ranked versus one-by-one
presentation), and the availability of votes with which to make predictions.
Other considerations include the size of database, speed of predictions, and
learning time.Comment: Appears in Proceedings of the Fourteenth Conference on Uncertainty in
Artificial Intelligence (UAI1998
The Compilation of Decision Models
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.Comment: Appears in Proceedings of the Fifth Conference on Uncertainty in
Artificial Intelligence (UAI1989
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