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19,202 | On the Logic of Causal Models | cs.AI | This paper explores the role of Directed Acyclic Graphs (DAGs) as a
representation of conditional independence relationships. We show that DAGs
offer polynomially sound and complete inference mechanisms for inferring
conditional independence relationships from a given causal set of such
relationships. As a consequence,... | computer science |
19,203 | The Optimality of Satisficing Solutions | cs.AI | This paper addresses a prevailing assumption in single-agent heuristic search
theory- that problem-solving algorithms should guarantee shortest-path
solutions, which are typically called optimal. Optimality implies a metric for
judging solution quality, where the optimal solution is the solution with the
highest qualit... | computer science |
19,204 | An Empirical Comparison of Three Inference Methods | cs.AI | In this paper, an empirical evaluation of three inference methods for
uncertain reasoning is presented in the context of Pathfinder, a large expert
system for the diagnosis of lymph-node pathology. The inference procedures
evaluated are (1) Bayes' theorem, assuming evidence is conditionally
independent given each hypot... | computer science |
19,205 | Parallel Belief Revision | cs.AI | This paper describes a formal system of belief revision developed by Wolfgang
Spohn and shows that this system has a parallel implementation that can be
derived from an influence diagram in a manner similar to that in which Bayesian
networks are derived. The proof rests upon completeness results for an
axiomatization o... | computer science |
19,206 | Stochastic Sensitivity Analysis Using Fuzzy Influence Diagrams | cs.AI | The practice of stochastic sensitivity analysis described in the decision
analysis literature is a testimonial to the need for considering deviations
from precise point estimates of uncertainty. We propose the use of Bayesian
fuzzy probabilities within an influence diagram computational scheme for
performing sensitivit... | computer science |
19,207 | A Representation of Uncertainty to Aid Insight into Decision Models | cs.AI | Many real world models can be characterized as weak, meaning that there is
significant uncertainty in both the data input and inferences. This lack of
determinism makes it especially difficult for users of computer decision aids
to understand and have confidence in the models. This paper presents a
representation for u... | computer science |
19,208 | Rational Nonmonotonic Reasoning | cs.AI | Nonmonotonic reasoning is a pattern of reasoning that allows an agent to make
and retract (tentative) conclusions from inconclusive evidence. This paper
gives a possible-worlds interpretation of the nonmonotonic reasoning problem
based on standard decision theory and the emerging probability logic. The
system's central... | computer science |
19,209 | A Comparison of Decision Analysis and Expert Rules for Sequential
Diagnosis | cs.AI | There has long been debate about the relative merits of decision theoretic
methods and heuristic rule-based approaches for reasoning under uncertainty. We
report an experimental comparison of the performance of the two approaches to
troubleshooting, specifically to test selection for fault diagnosis. We use as
experime... | computer science |
19,210 | Probabilistic Inference and Probabilistic Reasoning | cs.AI | Uncertainty enters into human reasoning and inference in at least two ways.
It is reasonable to suppose that there will be roles for these distinct uses of
uncertainty also in automated reasoning. | computer science |
19,211 | Probabilistic and Non-Monotonic Inference | cs.AI | (l) I have enough evidence to render the sentence S probable. (la) So,
relative to what I know, it is rational of me to believe S. (2) Now that I have
more evidence, S may no longer be probable. (2a) So now, relative to what I
know, it is not rational of me to believe S. These seem a perfectly ordinary,
common sense, p... | computer science |
19,212 | Epistemological Relevance and Statistical Knowledge | cs.AI | For many years, at least since McCarthy and Hayes (1969), writers have
lamented, and attempted to compensate for, the alleged fact that we often do
not have adequate statistical knowledge for governing the uncertainty of
belief, for making uncertain inferences, and the like. It is hardly ever
spelled out what "adequate... | computer science |
19,213 | Evidential Reasoning in a Network Usage Prediction Testbed | cs.AI | This paper reports on empirical work aimed at comparing evidential reasoning
techniques. While there is prima facie evidence for some conclusions, this i6
work in progress; the present focus is methodology, with the goal that
subsequent results be meaningful. The domain is a network of UNIX* cycle
servers, and the task... | computer science |
19,214 | Justifying the Principle of Interval Constraints | cs.AI | When knowledge is obtained from a database, it is only possible to deduce
confidence intervals for probability values. With confidence intervals
replacing point values, the results in the set covering model include interval
constraints for the probabilities of mutually exclusive and exhaustive
explanations. The Princip... | computer science |
19,215 | Probabilistic Semantics and Defaults | cs.AI | There is much interest in providing probabilistic semantics for defaults but
most approaches seem to suffer from one of two problems: either they require
numbers, a problem defaults were intended to avoid, or they generate peculiar
side effects. Rather than provide semantics for defaults, we address the
problem default... | computer science |
19,216 | Decision Making with Linear Constraints on Probabilities | cs.AI | Techniques for decision making with knowledge of linear constraints on
condition probabilities are examined. These constraints arise naturally in many
situations: upper and lower condition probabilities are known; an ordering
among the probabilities is determined; marginal probabilities or bounds on such
probabilities ... | computer science |
19,217 | Maintenance in Probabilistic Knowledge-Based Systems | cs.AI | Recent developments using directed acyclical graphs (i.e., influence diagrams
and Bayesian networks) for knowledge representation have lessened the problems
of using probability in knowledge-based systems (KBS). Most current research
involves the efficient propagation of new evidence, but little has been done
concernin... | computer science |
19,218 | A Linear Approximation Method for Probabilistic Inference | cs.AI | An approximation method is presented for probabilistic inference with
continuous random variables. These problems can arise in many practical
problems, in particular where there are "second order" probabilities. The
approximation, based on the Gaussian influence diagram, iterates over linear
approximations to the infer... | computer science |
19,219 | An Axiomatic Framework for Bayesian and Belief-function Propagation | cs.AI | In this paper, we describe an abstract framework and axioms under which exact
local computation of marginals is possible. The primitive objects of the
framework are variables and valuations. The primitive operators of the
framework are combination and marginalization. These operate on valuations. We
state three axioms ... | computer science |
19,220 | A General Non-Probabilistic Theory of Inductive Reasoning | cs.AI | Probability theory, epistemically interpreted, provides an excellent, if not
the best available account of inductive reasoning. This is so because there are
general and definite rules for the change of subjective probabilities through
information or experience; induction and belief change are one and same topic,
after ... | computer science |
19,221 | Generating Decision Structures and Causal Explanations for Decision
Making | cs.AI | This paper examines two related problems that are central to developing an
autonomous decision-making agent, such as a robot. Both problems require
generating structured representafions from a database of unstructured
declarative knowledge that includes many facts and rules that are irrelevant in
the problem context. T... | computer science |
19,222 | Updating Probabilities in Multiply-Connected Belief Networks | cs.AI | This paper focuses on probability updates in multiply-connected belief
networks. Pearl has designed the method of conditioning, which enables us to
apply his algorithm for belief updates in singly-connected networks to
multiply-connected belief networks by selecting a loop-cutset for the network
and instantiating these... | computer science |
19,223 | Handling uncertainty in a system for text-symbol context analysis | cs.AI | In pattern analysis, information regarding an object can often be drawn from
its surroundings. This paper presents a method for handling uncertainty when
using context of symbols and texts for analyzing technical drawings. The method
is based on Dempster-Shafer theory and possibility theory. | computer science |
19,224 | Causal Networks: Semantics and Expressiveness | cs.AI | Dependency knowledge of the form "x is independent of y once z is known"
invariably obeys the four graphoid axioms, examples include probabilistic and
database dependencies. Often, such knowledge can be represented efficiently
with graphical structures such as undirected graphs and directed acyclic graphs
(DAGs). In th... | computer science |
19,225 | MCE Reasoning in Recursive Causal Networks | cs.AI | A probabilistic method of reasoning under uncertainty is proposed based on
the principle of Minimum Cross Entropy (MCE) and concept of Recursive Causal
Model (RCM). The dependency and correlations among the variables are described
in a special language BNDL (Belief Networks Description Language). Beliefs are
propagated... | computer science |
19,226 | Nonmonotonic Reasoning via Possibility Theory | cs.AI | We introduce the operation of possibility qualification and show how. this
modal-like operator can be used to represent "typical" or default knowledge in
a theory of nonmonotonic reasoning. We investigate the representational power
of this approach by looking at a number of prototypical problems from the
nonmonotonic r... | computer science |
19,227 | Generalizing the Dempster-Shafer Theory to Fuzzy Sets | cs.AI | With the desire to apply the Dempster-Shafer theory to complex real world
problems where the evidential strength is often imprecise and vague, several
attempts have been made to generalize the theory. However, the important
concept in the D-S theory that the belief and plausibility functions are lower
and upper probabi... | computer science |
19,228 | Logical Fuzzy Optimization | cs.AI | We present a logical framework to represent and reason about fuzzy
optimization problems based on fuzzy answer set optimization programming. This
is accomplished by allowing fuzzy optimization aggregates, e.g., minimum and
maximum in the language of fuzzy answer set optimization programming to allow
minimization or max... | computer science |
19,229 | Modèle flou d'expression des préférences basé sur les CP-Nets | cs.AI | This article addresses the problem of expressing preferences in flexible
queries while basing on a combination of the fuzzy logic theory and Conditional
Preference Networks or CP-Nets. | computer science |
19,230 | Symmetry-Aware Marginal Density Estimation | cs.AI | The Rao-Blackwell theorem is utilized to analyze and improve the scalability
of inference in large probabilistic models that exhibit symmetries. A novel
marginal density estimator is introduced and shown both analytically and
empirically to outperform standard estimators by several orders of magnitude.
The developed th... | computer science |
19,231 | Is Shafer General Bayes? | cs.AI | This paper examines the relationship between Shafer's belief functions and
convex sets of probability distributions. Kyburg's (1986) result showed that
belief function models form a subset of the class of closed convex probability
distributions. This paper emphasizes the importance of Kyburg's result by
looking at simp... | computer science |
19,232 | Modifiable Combining Functions | cs.AI | Modifiable combining functions are a synthesis of two common approaches to
combining evidence. They offer many of the advantages of these approaches and
avoid some disadvantages. Because they facilitate the acquisition,
representation, explanation, and modification of knowledge about combinations
of evidence, they are ... | computer science |
19,233 | Dempster-Shafer vs. Probabilistic Logic | cs.AI | The combination of evidence in Dempster-Shafer theory is compared with the
combination of evidence in probabilistic logic. Sufficient conditions are
stated for these two methods to agree. It is then shown that these conditions
are minimal in the sense that disagreement can occur when any one of them is
removed. An exam... | computer science |
19,234 | Higher Order Probabilities | cs.AI | A number of writers have supposed that for the full specification of belief,
higher order probabilities are required. Some have even supposed that there may
be an unending sequence of higher order probabilities of probabilities of
probabilities.... In the present paper we show that higher order probabilities
can always... | computer science |
19,235 | Belief in Belief Functions: An Examination of Shafer's Canonical
Examples | cs.AI | In the canonical examples underlying Shafer-Dempster theory, beliefs over the
hypotheses of interest are derived from a probability model for a set of
auxiliary hypotheses. Beliefs are derived via a compatibility relation
connecting the auxiliary hypotheses to subsets of the primary hypotheses. A
belief function differ... | computer science |
19,236 | Do We Need Higher-Order Probabilities and, If So, What Do They Mean? | cs.AI | The apparent failure of individual probabilistic expressions to distinguish
uncertainty about truths from uncertainty about probabilistic assessments have
prompted researchers to seek formalisms where the two types of uncertainties
are given notational distinction. This paper demonstrates that the desired
distinction i... | computer science |
19,237 | Bayesian Prediction for Artificial Intelligence | cs.AI | This paper shows that the common method used for making predictions under
uncertainty in A1 and science is in error. This method is to use currently
available data to select the best model from a given class of models-this
process is called abduction-and then to use this model to make predictions
about future data. The... | computer science |
19,238 | Can Evidence Be Combined in the Dempster-Shafer Theory | cs.AI | Dempster's rule of combination has been the most controversial part of the
Dempster-Shafer (D-S) theory. In particular, Zadeh has reached a conjecture on
the noncombinability of evidence from a relational model of the D-S theory. In
this paper, we will describe another relational model where D-S masses are
represented ... | computer science |
19,239 | An Interesting Uncertainty-Based Combinatoric Problem in Spare Parts
Forecasting: The FRED System | cs.AI | The domain of spare parts forecasting is examined, and is found to present
unique uncertainty based problems in the architectural design of a
knowledge-based system. A mixture of different uncertainty paradigms is
required for the solution, with an intriguing combinatoric problem arising from
an uncertain choice of inf... | computer science |
19,240 | Bayesian Inference in Model-Based Machine Vision | cs.AI | This is a preliminary version of visual interpretation integrating multiple
sensors in SUCCESSOR, an intelligent, model-based vision system. We pursue a
thorough integration of hierarchical Bayesian inference with comprehensive
physical representation of objects and their relations in a system for
reasoning with geomet... | computer science |
19,241 | Using the Dempster-Shafer Scheme in a Diagnostic Expert System Shell | cs.AI | This paper discusses an expert system shell that integrates rule-based
reasoning and the Dempster-Shafer evidence combination scheme. Domain knowledge
is stored as rules with associated belief functions. The reasoning component
uses a combination of forward and backward inferencing mechanisms to allow
interaction with ... | computer science |
19,242 | Stochastic Simulation of Bayesian Belief Networks | cs.AI | This paper examines Bayesian belief network inference using simulation as a
method for computing the posterior probabilities of network variables.
Specifically, it examines the use of a method described by Henrion, called
logic sampling, and a method described by Pearl, called stochastic simulation.
We first review the... | computer science |
19,243 | Temporal Reasoning About Uncertain Worlds | cs.AI | We present a program that manages a database of temporally scoped beliefs.
The basic functionality of the system includes maintaining a network of
constraints among time points, supporting a variety of fetches, mediating the
application of causal rules, monitoring intervals of time for the addition of
new facts, and ma... | computer science |
19,244 | A Perspective on Confidence and Its Use in Focusing Attention During
Knowledge Acquisition | cs.AI | We present a representation of partial confidence in belief and preference
that is consistent with the tenets of decision-theory. The fundamental insight
underlying the representation is that if a person is not completely confident
in a probability or utility assessment, additional modeling of the assessment
may improv... | computer science |
19,245 | Practical Issues in Constructing a Bayes' Belief Network | cs.AI | Bayes belief networks and influence diagrams are tools for constructing
coherent probabilistic representations of uncertain knowledge. The process of
constructing such a network to represent an expert's knowledge is used to
illustrate a variety of techniques which can facilitate the process of
structuring and quantifyi... | computer science |
19,246 | NAIVE: A Method for Representing Uncertainty and Temporal Relationships
in an Automated Reasoner | cs.AI | This paper describes NAIVE, a low-level knowledge representation language and
inferencing process. NAIVE has been designed for reasoning about
nondeterministic dynamic systems like those found in medicine. Knowledge is
represented in a graph structure consisting of nodes, which correspond to the
variables describing th... | computer science |
19,247 | Objective Probability | cs.AI | A distinction is sometimes made between "statistical" and "subjective"
probabilities. This is based on a distinction between "unique" events and
"repeatable" events. We argue that this distinction is untenable, since all
events are "unique" and all events belong to "kinds", and offer a conception of
probability for A1 ... | computer science |
19,248 | Coefficients of Relations for Probabilistic Reasoning | cs.AI | Definitions and notations with historical references are given for some
numerical coefficients commonly used to quantify relations among collections of
objects for the purpose of expressing approximate knowledge and probabilistic
reasoning. | computer science |
19,249 | Satisfaction of Assumptions is a Weak Predictor of Performance | cs.AI | This paper demonstrates a methodology for examining the accuracy of uncertain
inference systems (UIS), after their parameters have been optimized, and does
so for several common UIS's. This methodology may be used to test the accuracy
when either the prior assumptions or updating formulae are not exactly
satisfied. Sur... | computer science |
19,250 | Structuring Causal Tree Models with Continuous Variables | cs.AI | This paper considers the problem of invoking auxiliary, unobservable
variables to facilitate the structuring of causal tree models for a given set
of continuous variables. Paralleling the treatment of bi-valued variables in
[Pearl 1986], we show that if a collection of coupled variables are governed by
a joint normal d... | computer science |
19,251 | Implementing Evidential Reasoning in Expert Systems | cs.AI | The Dempster-Shafer theory has been extended recently for its application to
expert systems. However, implementing the extended D-S reasoning model in
rule-based systems greatly complicates the task of generating informative
explanations. By implementing GERTIS, a prototype system for diagnosing
rheumatoid arthritis, w... | computer science |
19,252 | Decision Tree Induction Systems: A Bayesian Analysis | cs.AI | Decision tree induction systems are being used for knowledge acquisition in
noisy domains. This paper develops a subjective Bayesian interpretation of the
task tackled by these systems and the heuristic methods they use. It is argued
that decision tree systems implicitly incorporate a prior belief that the
simpler (in ... | computer science |
19,253 | The Automatic Training of Rule Bases that Use Numerical Uncertainty
Representations | cs.AI | The use of numerical uncertainty representations allows better modeling of
some aspects of human evidential reasoning. It also makes knowledge acquisition
and system development, test, and modification more difficult. We propose that
where possible, the assignment and/or refinement of rule weights should be
performed a... | computer science |
19,254 | The Inductive Logic of Information Systems | cs.AI | An inductive logic can be formulated in which the elements are not
propositions or probability distributions, but information systems. The logic
is complete for information systems with binary hypotheses, i.e., it applies to
all such systems. It is not complete for information systems with more than two
hypotheses, but... | computer science |
19,255 | Automated Generation of Connectionist Expert Systems for Problems
Involving Noise and Redundancy | cs.AI | When creating an expert system, the most difficult and expensive task is
constructing a knowledge base. This is particularly true if the problem
involves noisy data and redundant measurements. This paper shows how to modify
the MACIE process for generating connectionist expert systems from training
examples so that it ... | computer science |
19,256 | The Recovery of Causal Poly-Trees from Statistical Data | cs.AI | Poly-trees are singly connected causal networks in which variables may arise
from multiple causes. This paper develops a method of recovering ply-trees from
empirically measured probability distributions of pairs of variables. The
method guarantees that, if the measured distributions are generated by a causal
process s... | computer science |
19,257 | A Heuristic Bayesian Approach to Knowledge Acquisition: Application to
Analysis of Tissue-Type Plasminogen Activator | cs.AI | This paper describes a heuristic Bayesian method for computing probability
distributions from experimental data, based upon the multivariate normal form
of the influence diagram. An example illustrates its use in medical technology
assessment. This approach facilitates the integration of results from different
studies,... | computer science |
19,258 | Theory-Based Inductive Learning: An Integration of Symbolic and
Quantitative Methods | cs.AI | The objective of this paper is to propose a method that will generate a
causal explanation of observed events in an uncertain world and then make
decisions based on that explanation. Feedback can cause the explanation and
decisions to be modified. I call the method Theory-Based Inductive Learning
(T-BIL). T-BIL integra... | computer science |
19,259 | Using T-Norm Based Uncertainty Calculi in a Naval Situation Assessment
Application | cs.AI | RUM (Reasoning with Uncertainty Module), is an integrated software tool based
on a KEE, a frame system implemented in an object oriented language. RUM's
architecture is composed of three layers: representation, inference, and
control. The representation layer is based on frame-like data structures that
capture the unce... | computer science |
19,260 | A Study of Associative Evidential Reasoning | cs.AI | Evidential reasoning is cast as the problem of simplifying the
evidence-hypothesis relation and constructing combination formulas that possess
certain testable properties. Important classes of evidence as identifiers,
annihilators, and idempotents and their roles in determining binary operations
on intervals of reals a... | computer science |
19,261 | A Measure-Free Approach to Conditioning | cs.AI | In an earlier paper, a new theory of measurefree "conditional" objects was
presented. In this paper, emphasis is placed upon the motivation of the theory.
The central part of this motivation is established through an example involving
a knowledge-based system. In order to evaluate combination of evidence for this
syste... | computer science |
19,262 | Convergent Deduction for Probabilistic Logic | cs.AI | This paper discusses the semantics and proof theory of Nilsson's
probabilistic logic, outlining both the benefits of its well-defined model
theory and the drawbacks of its proof theory. Within Nilsson's semantic
framework, we derive a set of inference rules which are provably sound. The
resulting proof system, in contr... | computer science |
19,263 | A Knowledge Engineer's Comparison of Three Evidence Aggregation Methods | cs.AI | The comparisons of uncertainty calculi from the last two Uncertainty
Workshops have all used theoretical probabilistic accuracy as the sole metric.
While mathematical correctness is important, there are other factors which
should be considered when developing reasoning systems. These other factors
include, among other ... | computer science |
19,264 | Towards Solving the Multiple Extension Problem: Combining Defaults and
Probabilities | cs.AI | The multiple extension problem arises frequently in diagnostic and default
inference. That is, we can often use any of a number of sets of defaults or
possible hypotheses to explain observations or make Predictions. In default
inference, some extensions seem to be simply wrong and we use qualitative
techniques to weed ... | computer science |
19,265 | Problem Structure and Evidential Reasoning | cs.AI | In our previous series of studies to investigate the role of evidential
reasoning in the RUBRIC system for full-text document retrieval (Tong et al.,
1985; Tong and Shapiro, 1985; Tong and Appelbaum, 1987), we identified the
important role that problem structure plays in the overall performance of the
system. In this p... | computer science |
19,266 | The Role of Calculi in Uncertain Inference Systems | cs.AI | Much of the controversy about methods for automated decision making has
focused on specific calculi for combining beliefs or propagating uncertainty.
We broaden the debate by (1) exploring the constellation of secondary tasks
surrounding any primary decision problem, and (2) identifying knowledge
engineering concerns t... | computer science |
19,267 | The Role of Tuning Uncertain Inference Systems | cs.AI | 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
appro... | computer science |
19,268 | Implementing a Bayesian Scheme for Revising Belief Commitments | cs.AI | 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 unce... | computer science |
19,269 | Integrating Logical and Probabilistic Reasoning for Decision Making | cs.AI | 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 d... | computer science |
19,270 | Compiling Fuzzy Logic Control Rules to Hardware Implementations | cs.AI | 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 reasoni... | computer science |
19,271 | Steps Towards Programs that Manage Uncertainty | cs.AI | 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 ... | computer science |
19,272 | An Algorithm for Computing Probabilistic Propositions | cs.AI | 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
probab... | computer science |
19,273 | Combining Symbolic and Numeric Approaches to Uncertainty Management | cs.AI | 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 uncertain... | computer science |
19,274 | Explanation of Probabilistic Inference for Decision Support Systems | cs.AI | 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 nor... | computer science |
19,275 | Efficient Inference on Generalized Fault Diagrams | cs.AI | 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. | computer science |
19,276 | Reasoning About Beliefs and Actions Under Computational Resource
Constraints | cs.AI | 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 ... | computer science |
19,277 | Advantages and a Limitation of Using LEG Nets in a Real-TIme Problem | cs.AI | After experimenting with a number of non-probabilistic methods for dealing
with uncertainty many researchers reaffirm a preference for probability methods
[1] [2], although this remains controversial. The importance of being able to
form decisions from incomplete data in diagnostic problems has highlighted
probabilisti... | computer science |
19,278 | Logical Fuzzy Preferences | cs.AI | We present a unified logical framework for representing and reasoning about
both quantitative and qualitative preferences in fuzzy answer set programming,
called fuzzy answer set optimization programs. The proposed framework is vital
to allow defining quantitative preferences over the possible outcomes of
qualitative p... | computer science |
19,279 | Nested Aggregates in Answer Sets: An Application to a Priori
Optimization | cs.AI | We allow representing and reasoning in the presence of nested multiple
aggregates over multiple variables and nested multiple aggregates over
functions involving multiple variables in answer sets, precisely, in answer set
optimization programming and in answer set programming. We show the
applicability of the answer se... | computer science |
19,280 | Knowledge Engineering Within A Generalized Bayesian Framework | cs.AI | 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 p... | computer science |
19,281 | Taxonomy, Structure, and Implementation of Evidential Reasoning | cs.AI | 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 reasonin... | computer science |
19,282 | Probabilistic Reasoning About Ship Images | cs.AI | 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 se... | computer science |
19,283 | Towards The Inductive Acquisition of Temporal Knowledge | cs.AI | 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... | computer science |
19,284 | Some Extensions of Probabilistic Logic | cs.AI | In [12], Nilsson proposed the probabilistic logic in which the truth values
of logical propositions are probability values between 0 and 1. It is
applicable to any logical system for which the consistency of a finite set of
propositions can be established. The probabilistic inference scheme reduces to
the ordinary logi... | computer science |
19,285 | Predicting The Performance of Minimax and Product in Game-Tree | cs.AI | 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 performa... | computer science |
19,286 | Reasoning With Uncertain Knowledge | cs.AI | 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, summari... | computer science |
19,287 | Models vs. Inductive Inference for Dealing With Probabilistic Knowledge | cs.AI | 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 t... | computer science |
19,288 | Towards a General-Purpose Belief Maintenance System | cs.AI | 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 syst... | computer science |
19,289 | Planning, Scheduling, and Uncertainty in the Sequence of Future Events | cs.AI | 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 b... | computer science |
19,290 | Deriving And Combining Continuous Possibility Functions in the Framework
of Evidential Reasoning | cs.AI | 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 simpl... | computer science |
19,291 | Non-Monotonicity in Probabilistic Reasoning | cs.AI | 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 fo... | computer science |
19,292 | Flexible Interpretations: A Computational Model for Dynamic Uncertainty
Assessment | cs.AI | 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... | computer science |
19,293 | The Myth of Modularity in Rule-Based Systems | cs.AI | 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... | computer science |
19,294 | An Axiomatic Framework for Belief Updates | cs.AI | 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 ... | computer science |
19,295 | Evidence as Opinions of Experts | cs.AI | 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 combinat... | computer science |
19,296 | Decision Under Uncertainty in Diagnosis | cs.AI | 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 sp... | computer science |
19,297 | Knowledge and Uncertainty | cs.AI | 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 philosoph... | computer science |
19,298 | An Application of Non-Monotonic Probabilistic Reasoning to Air Force
Threat Correlation | cs.AI | 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 exam... | computer science |
19,299 | Bayesian Inference for Radar Imagery Based Surveillance | cs.AI | 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 th... | computer science |
19,300 | Computing Reference Classes | cs.AI | 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 implementat... | computer science |
19,301 | An Uncertainty Management Calculus for Ordering Searches in Distributed
Dynamic Databases | cs.AI | 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 s... | computer science |
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