Unnamed: 0 int64 0 41k | title stringlengths 4 274 | category stringlengths 5 18 | summary stringlengths 22 3.66k | theme stringclasses 8
values |
|---|---|---|---|---|
19,102 | Optimal Decomposition of Belief Networks | cs.AI | In this paper, optimum decomposition of belief networks is discussed. Some
methods of decomposition are examined and a new method - the method of Minimum
Total Number of States (MTNS) - is proposed. The problem of optimum belief
network decomposition under our framework, as under all the other frameworks,
is shown to b... | computer science |
19,103 | Pruning Bayesian Networks for Efficient Computation | cs.AI | This paper analyzes the circumstances under which Bayesian networks can be
pruned in order to reduce computational complexity without altering the
computation for variables of interest. Given a problem instance which consists
of a query and evidence for a set of nodes in the network, it is possible to
delete portions o... | computer science |
19,104 | On Heuristics for Finding Loop Cutsets in Multiply-Connected Belief
Networks | cs.AI | We introduce a new heuristic algorithm for the problem of finding minimum
size loop cutsets in multiply connected belief networks. We compare this
algorithm to that proposed in [Suemmondt and Cooper, 1988]. We provide lower
bounds on the performance of these algorithms with respect to one another and
with respect to op... | computer science |
19,105 | A Combination of Cutset Conditioning with Clique-Tree Propagation in the
Pathfinder System | cs.AI | Cutset conditioning and clique-tree propagation are two popular methods for
performing exact probabilistic inference in Bayesian belief networks. Cutset
conditioning is based on decomposition of a subset of network nodes, whereas
clique-tree propagation depends on aggregation of nodes. We describe a means to
combine cu... | computer science |
19,106 | Possibility as Similarity: the Semantics of Fuzzy Logic | cs.AI | This paper addresses fundamental issues on the nature of the concepts and
structures of fuzzy logic, focusing, in particular, on the conceptual and
functional differences that exist between probabilistic and possibilistic
approaches. A semantic model provides the basic framework to define
possibilistic structures and c... | computer science |
19,107 | Integrating Case-Based and Rule-Based Reasoning: the Possibilistic
Connection | cs.AI | Rule based reasoning (RBR) and case based reasoning (CBR) have emerged as two
important and complementary reasoning methodologies in artificial intelligence
(Al). For problem solving in complex, real world situations, it is useful to
integrate RBR and CBR. This paper presents an approach to achieve a compact and
seamle... | computer science |
19,108 | Credibility Discounting in the Theory of Approximate Reasoning | cs.AI | We are concerned with the problem of introducing credibility type information
into reasoning systems. The concept of credibility allows us to discount
information provided by agents. An important characteristic of this kind of
procedure is that a complete lack of credibility rather than resulting in the
negation of the... | computer science |
19,109 | Updating with Belief Functions, Ordinal Conditioning Functions and
Possibility Measures | cs.AI | This paper discusses how a measure of uncertainty representing a state of
knowledge can be updated when a new information, which may be pervaded with
uncertainty, becomes available. This problem is considered in various
framework, namely: Shafer's evidence theory, Zadeh's possibility theory,
Spohn's theory of epistemic... | computer science |
19,110 | The Transferable Belief Model and Other Interpretations of
Dempster-Shafer's Model | cs.AI | Dempster-Shafer's model aims at quantifying degrees of belief But there are
so many interpretations of Dempster-Shafer's theory in the literature that it
seems useful to present the various contenders in order to clarify their
respective positions. We shall successively consider the classical probability
model, the upp... | computer science |
19,111 | Valuation-Based Systems for Discrete Optimization | cs.AI | This paper describes valuation-based systems for representing and solving
discrete optimization problems. In valuation-based systems, we represent
information in an optimization problem using variables, sample spaces of
variables, a set of values, and functions that map sample spaces of sets of
variables to the set of ... | computer science |
19,112 | Computational Aspects of the Mobius Transform | cs.AI | In this paper we associate with every (directed) graph G a transformation
called the Mobius transformation of the graph G. The Mobius transformation of
the graph (O) is of major significance for Dempster-Shafer theory of evidence.
However, because it is computationally very heavy, the Mobius transformation
together wit... | computer science |
19,113 | Using Dempster-Shafer Theory in Knowledge Representation | cs.AI | In this paper, we suggest marrying Dempster-Shafer (DS) theory with Knowledge
Representation (KR). Born out of this marriage is the definition of
"Dempster-Shafer Belief Bases", abstract data types representing uncertain
knowledge that use DS theory for representing strength of belief about our
knowledge, and the lingu... | computer science |
19,114 | A Hierarchical Approach to Designing Approximate Reasoning-Based
Controllers for Dynamic Physical Systems | cs.AI | This paper presents a new technique for the design of approximate reasoning
based controllers for dynamic physical systems with interacting goals. In this
approach, goals are achieved based on a hierarchy defined by a control
knowledge base and remain highly interactive during the execution of the
control task. The app... | computer science |
19,115 | Evidence Combination and Reasoning and Its Application to Real-World
Problem-Solving | cs.AI | In this paper a new mathematical procedure is presented for combining
different pieces of evidence which are represented in the interval form to
reflect our knowledge about the truth of a hypothesis. Evidences may be
correlated to each other (dependent evidences) or conflicting in supports
(conflicting evidences). Firs... | computer science |
19,116 | On Some Equivalence Relations between Incidence Calculus and
Dempster-Shafer Theory of Evidence | cs.AI | Incidence Calculus and Dempster-Shafer Theory of Evidence are both theories
to describe agents' degrees of belief in propositions, thus being appropriate
to represent uncertainty in reasoning systems. This paper presents a
straightforward equivalence proof between some special cases of these theories. | computer science |
19,117 | Using Belief Functions for Uncertainty Management and Knowledge
Acquisition: An Expert Application | cs.AI | This paper describes recent work on an ongoing project in medical diagnosis
at the University of Guelph. A domain on which experts are not very good at
pinpointing a single disease outcome is explored. On-line medical data is
available over a relatively short period of time. Belief Functions
(Dempster-Shafer theory) ar... | computer science |
19,118 | An Architecture for Probabilistic Concept-Based Information Retrieval | cs.AI | While concept-based methods for information retrieval can provide improved
performance over more conventional techniques, they require large amounts of
effort to acquire the concepts and their qualitative and quantitative
relationships. This paper discusses an architecture for probabilistic
concept-based information re... | computer science |
19,119 | Amplitude-Based Approach to Evidence Accumulation | cs.AI | We point out the need to use probability amplitudes rather than probabilities
to model evidence accumulation in decision processes involving real physical
sensors. Optical information processing systems are given as typical examples
of systems that naturally gather evidence in this manner. We derive a new,
amplitude-ba... | computer science |
19,120 | A Probabilistic Reasoning Environment | cs.AI | A framework is presented for a computational theory of probabilistic
argument. The Probabilistic Reasoning Environment encodes knowledge at three
levels. At the deepest level are a set of schemata encoding the system's domain
knowledge. This knowledge is used to build a set of second-level arguments,
which are structur... | computer science |
19,121 | On Non-monotonic Conditional Reasoning | cs.AI | This note is concerned with a formal analysis of the problem of non-monotonic
reasoning in intelligent systems, especially when the uncertainty is taken into
account in a quantitative way. A firm connection between logic and probability
is established by introducing conditioning notions by means of formal
structures th... | computer science |
19,122 | Decisions with Limited Observations over a Finite Product Space: the
Klir Effect | cs.AI | Probability estimation by maximum entropy reconstruction of an initial
relative frequency estimate from its projection onto a hypergraph model of the
approximate conditional independence relations exhibited by it is investigated.
The results of this study suggest that use of this estimation technique may
improve the qu... | computer science |
19,123 | Fine-Grained Decision-Theoretic Search Control | cs.AI | Decision-theoretic control of search has previously used as its basic unit.
of computation the generation and evaluation of a complete set of successors.
Although this simplifies analysis, it results in some lost opportunities for
pruning and satisficing. This paper therefore extends the analysis of the value
of comput... | computer science |
19,124 | Rules, Belief Functions and Default Logic | cs.AI | This paper describes a natural framework for rules, based on belief
functions, which includes a repre- sentation of numerical rules, default rules
and rules allowing and rules not allowing contraposition. In particular it
justifies the use of the Dempster-Shafer Theory for representing a particular
class of rules, Beli... | computer science |
19,125 | Combination of Evidence Using the Principle of Minimum Information Gain | cs.AI | One of the most important aspects in any treatment of uncertain information
is the rule of combination for updating the degrees of uncertainty. The theory
of belief functions uses the Dempster rule to combine two belief functions
defined by independent bodies of evidence. However, with limited dependency
information ab... | computer science |
19,126 | Probabilistic Evaluation of Candidates and Symptom Clustering for
Multidisorder Diagnosis | cs.AI | This paper derives a formula for computing the conditional probability of a
set of candidates, where a candidate is a set of disorders that explain a given
set of positive findings. Such candidate sets are produced by a recent method
for multidisorder diagnosis called symptom clustering. A symptom clustering
represents... | computer science |
19,127 | Extending Term Subsumption systems for Uncertainty Management | cs.AI | A major difficulty in developing and maintaining very large knowledge bases
originates from the variety of forms in which knowledge is made available to
the KB builder. The objective of this research is to bring together two
complementary knowledge representation schemes: term subsumption languages,
which represent and... | computer science |
19,128 | Refinement and Coarsening of Bayesian Networks | cs.AI | In almost all situation assessment problems, it is useful to dynamically
contract and expand the states under consideration as assessment proceeds.
Contraction is most often used to combine similar events or low probability
events together in order to reduce computation. Expansion is most often used to
make distinction... | computer science |
19,129 | Second Order Probabilities for Uncertain and Conflicting Evidence | cs.AI | In this paper the elicitation of probabilities from human experts is
considered as a measurement process, which may be disturbed by random
'measurement noise'. Using Bayesian concepts a second order probability
distribution is derived reflecting the uncertainty of the input probabilities.
The algorithm is based on an a... | computer science |
19,130 | Computing Probability Intervals Under Independency Constraints | cs.AI | Many AI researchers argue that probability theory is only capable of dealing
with uncertainty in situations where a full specification of a joint
probability distribution is available, and conclude that it is not suitable for
application in knowledge-based systems. Probability intervals, however,
constitute a means for... | computer science |
19,131 | An Empirical Analysis of Likelihood-Weighting Simulation on a Large,
Multiply-Connected Belief Network | cs.AI | We analyzed the convergence properties of likelihood- weighting algorithms on
a two-level, multiply connected, belief-network representation of the QMR
knowledge base of internal medicine. Specifically, on two difficult diagnostic
cases, we examined the effects of Markov blanket scoring, importance sampling,
demonstrat... | computer science |
19,132 | Towards a Normative Theory of Scientific Evidence | cs.AI | A scientific reasoning system makes decisions using objective evidence in the
form of independent experimental trials, propositional axioms, and constraints
on the probabilities of events. As a first step towards this goal, we propose a
system that derives probability intervals from objective evidence in those
forms. O... | computer science |
19,133 | A Model for Non-Monotonic Reasoning Using Dempster's Rule | cs.AI | Considerable attention has been given to the problem of non-monotonic
reasoning in a belief function framework. Earlier work (M. Ginsberg) proposed
solutions introducing meta-rules which recognized conditional independencies in
a probabilistic sense. More recently an e-calculus formulation of default
reasoning (J. Pear... | computer science |
19,134 | Default Reasoning and the Transferable Belief Model | cs.AI | Inappropriate use of Dempster's rule of combination has led some authors to
reject the Dempster-Shafer model, arguing that it leads to supposedly
unacceptable conclusions when defaults are involved. A most classic example is
about the penguin Tweety. This paper will successively present: the origin of
the miss-manageme... | computer science |
19,135 | Separable and transitive graphoids | cs.AI | We examine three probabilistic formulations of the sentence a and b are
totally unrelated with respect to a given set of variables U. First, two
variables a and b are totally independent if they are independent given any
value of any subset of the variables in U. Second, two variables are totally
uncoupled if U can be ... | computer science |
19,136 | Analysis in HUGIN of Data Conflict | cs.AI | After a brief introduction to causal probabilistic networks and the HUGIN
approach, the problem of conflicting data is discussed. A measure of conflict
is defined, and it is used in the medical diagnostic system MUNIN. Finally, it
is discussed how to distinguish between conflicting data and a rare case. | computer science |
19,137 | Computing Datalog Rewritings beyond Horn Ontologies | cs.AI | Rewriting-based approaches for answering queries over an OWL 2 DL ontology
have so far been developed mainly for Horn fragments of OWL 2 DL. In this
paper, we study the possibilities of answering queries over non-Horn ontologies
using datalog rewritings. We prove that this is impossible in general even for
very simple ... | computer science |
19,138 | Lp : A Logic for Statistical Information | cs.AI | This extended abstract presents a logic, called Lp, that is capable of
representing and reasoning with a wide variety of both qualitative and
quantitative statistical information. The advantage of this logical formalism
is that it offers a declarative representation of statistical knowledge;
knowledge represented in th... | computer science |
19,139 | Map Learning with Indistinguishable Locations | cs.AI | Nearly all spatial reasoning problems involve uncertainty of one sort or
another. Uncertainty arises due to the inaccuracies of sensors used in
measuring distances and angles. We refer to this as directional uncertainty.
Uncertainty also arises in combining spatial information when one location is
mistakenly identified... | computer science |
19,140 | Temporal Reasoning with Probabilities | cs.AI | 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 ... | computer science |
19,141 | Now that I Have a Good Theory of Uncertainty, What Else Do I Need? | cs.AI | 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 Classificat... | computer science |
19,142 | Uncertainty and Incompleteness | cs.AI | 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 uncerta... | computer science |
19,143 | BaRT: A Bayesian Reasoning Tool for Knowledge Based Systems | cs.AI | 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 ... | computer science |
19,144 | Plan Recognition in Stories and in Life | cs.AI | 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-... | computer science |
19,145 | An Empirical Evaluation of a Randomized Algorithm for Probabilistic
Inference | cs.AI | 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 network... | computer science |
19,146 | Decision Making "Biases" and Support for Assumption-Based Higher-Order
Reasoning | cs.AI | 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... | computer science |
19,147 | Automated Reasoning Using Possibilistic Logic: Semantics, Belief
Revision and Variable Certainty Weights | cs.AI | 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 w... | computer science |
19,148 | How Much More Probable is "Much More Probable"? Verbal Expressions for
Probability Updates | cs.AI | 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 probabil... | computer science |
19,149 | Positive and Negative Explanations of Uncertain Reasoning in the
Framework of Possibility Theory | cs.AI | 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 ... | computer science |
19,150 | Interval Influence Diagrams | cs.AI | 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 bou... | computer science |
19,151 | Weighing and Integrating Evidence for Stochastic Simulation in Bayesian
Networks | cs.AI | 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 algor... | computer science |
19,152 | d-Separation: From Theorems to Algorithms | cs.AI | 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 ne... | computer science |
19,153 | The Effects of Perfect and Sample Information on Fuzzy Utilities in
Decision-Making | cs.AI | 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 ran... | computer science |
19,154 | Deciding Consistency of Databases Containing Defeasible and Strict
Information | cs.AI | 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 ... | computer science |
19,155 | The Relationship between Knowledge, Belief and Certainty | cs.AI | 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 h... | computer science |
19,156 | Heuristic Search as Evidential Reasoning | cs.AI | 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 ca... | computer science |
19,157 | The Compilation of Decision Models | cs.AI | 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 co... | computer science |
19,158 | A Tractable Inference Algorithm for Diagnosing Multiple Diseases | cs.AI | 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 a... | computer science |
19,159 | Bounded Conditioning: Flexible Inference for Decisions under Scarce
Resources | cs.AI | 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 approa... | computer science |
19,160 | Hierarchical Evidence Accumulation in the Pseiki System and Experiments
in Model-Driven Mobile Robot Navigation | cs.AI | 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 ... | computer science |
19,161 | A Decision-Theoretic Model for Using Scientific Data | cs.AI | 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 fram... | computer science |
19,162 | When Should a Decision Maker Ignore the Advice of a Decision Aid? | cs.AI | This paper argues that the principal difference between decision aids and
most other types of information systems is the greater reliance of decision
aids on fallible algorithms--algorithms that sometimes generate incorrect
advice. It is shown that interactive problem solving with a decision aid that
is based on a fall... | computer science |
19,163 | Inference Policies | cs.AI | It is suggested that an AI inference system should reflect an inference
policy that is tailored to the domain of problems to which it is applied -- and
furthermore that an inference policy need not conform to any general theory of
rational inference or induction. We note, for instance, that Bayesian reasoning
about the... | computer science |
19,164 | Defeasible Decisions: What the Proposal is and isn't | cs.AI | In two recent papers, I have proposed a description of decision analysis that
differs from the Bayesian picture painted by Savage, Jeffrey and other classic
authors. Response to this view has been either overly enthusiastic or unduly
pessimistic. In this paper I try to place the idea in its proper place, which
must be ... | computer science |
19,165 | Experiments Using Belief Functions and Weights of Evidence incorporating
Statistical Data and Expert Opinions | cs.AI | This paper presents some ideas and results of using uncertainty management
methods in the presence of data in preference to other statistical and machine
learning methods. A medical domain is used as a test-bed with data available
from a large hospital database system which collects symptom and outcome
information abou... | computer science |
19,166 | Shootout-89: A Comparative Evaluation of Knowledge-based Systems that
Forecast Severe Weather | cs.AI | During the summer of 1989, the Forecast Systems Laboratory of the National
Oceanic and Atmospheric Administration sponsored an evaluation of artificial
intelligence-based systems that forecast severe convective storms. The
evaluation experiment, called Shootout-89, took place in Boulder, and focussed
on storms over the... | computer science |
19,167 | Conditioning on Disjunctive Knowledge: Defaults and Probabilities | cs.AI | Many writers have observed that default logics appear to contain the "lottery
paradox" of probability theory. This arises when a default "proof by
contradiction" lets us conclude that a typical X is not a Y where Y is an
unusual subclass of X. We show that there is a similar problem with default
"proof by cases" and co... | computer science |
19,168 | Maximum Uncertainty Procedures for Interval-Valued Probability
Distributions | cs.AI | Measures of uncertainty and divergence are introduced for interval-valued
probability distributions and are shown to have desirable mathematical
properties. A maximum uncertainty inference procedure for marginal interval
distributions is presented. A technique for reconstruction of interval
distributions from projectio... | computer science |
19,169 | A Logical Interpretation of Dempster-Shafer Theory, with Application to
Visual Recognition | cs.AI | We formulate Dempster Shafer Belief functions in terms of Propositional Logic
using the implicit notion of provability underlying Dempster Shafer Theory.
Given a set of propositional clauses, assigning weights to certain
propositional literals enables the Belief functions to be explicitly computed
using Network Reliabi... | computer science |
19,170 | Strategies for Generating Micro Explanations for Bayesian Belief
Networks | cs.AI | Bayesian Belief Networks have been largely overlooked by Expert Systems
practitioners on the grounds that they do not correspond to the human inference
mechanism. In this paper, we introduce an explanation mechanism designed to
generate intuitive yet probabilistically sound explanations of inferences drawn
by a Bayesia... | computer science |
19,171 | Evidence Absorption and Propagation through Evidence Reversals | cs.AI | The arc reversal/node reduction approach to probabilistic inference is
extended to include the case of instantiated evidence by an operation called
"evidence reversal." This not only provides a technique for computing posterior
joint distributions on general belief networks, but also provides insight into
the methods o... | computer science |
19,172 | Simulation Approaches to General Probabilistic Inference on Belief
Networks | cs.AI | A number of algorithms have been developed to solve probabilistic inference
problems on belief networks. These algorithms can be divided into two main
groups: exact techniques which exploit the conditional independence revealed
when the graph structure is relatively sparse, and probabilistic sampling
techniques which e... | computer science |
19,173 | Decision under Uncertainty | cs.AI | We derive axiomatically the probability function that should be used to make
decisions given any form of underlying uncertainty. | computer science |
19,174 | Freedom: A Measure of Second-order Uncertainty for Intervalic
Probability Schemes | cs.AI | This paper discusses a new measure that is adaptable to certain intervalic
probability frameworks, possibility theory, and belief theory. As such, it has
the potential for wide use in knowledge engineering, expert systems, and
related problems in the human sciences. This measure (denoted here by F) has
been introduced ... | computer science |
19,175 | Assessment, Criticism and Improvement of Imprecise Subjective
Probabilities for a Medical Expert System | cs.AI | Three paediatric cardiologists assessed nearly 1000 imprecise subjective
conditional probabilities for a simple belief network representing congenital
heart disease, and the quality of the assessments has been measured using
prospective data on 200 babies. Quality has been assessed by a Brier scoring
rule, which decomp... | computer science |
19,176 | Automated Construction of Sparse Bayesian Networks from Unstructured
Probabilistic Models and Domain Information | cs.AI | An algorithm for automated construction of a sparse Bayesian network given an
unstructured probabilistic model and causal domain information from an expert
has been developed and implemented. The goal is to obtain a network that
explicitly reveals as much information regarding conditional independence as
possible. The ... | computer science |
19,177 | Making Decisions with Belief Functions | cs.AI | A primary motivation for reasoning under uncertainty is to derive decisions
in the face of inconclusive evidence. However, Shafer's theory of belief
functions, which explicitly represents the underconstrained nature of many
reasoning problems, lacks a formal procedure for making decisions. Clearly,
when sufficient info... | computer science |
19,178 | Efficient Parallel Estimation for Markov Random Fields | cs.AI | We present a new, deterministic, distributed MAP estimation algorithm for
Markov Random Fields called Local Highest Confidence First (Local HCF). The
algorithm has been applied to segmentation problems in computer vision and its
performance compared with stochastic algorithms. The experiments show that
Local HCF finds ... | computer science |
19,179 | Comparing Expert Systems Built Using Different Uncertain Inference
Systems | cs.AI | This study compares the inherent intuitiveness or usability of the most
prominent methods for managing uncertainty in expert systems, including those
of EMYCIN, PROSPECTOR, Dempster-Shafer theory, fuzzy set theory, simplified
probability theory (assuming marginal independence), and linear regression
using probability e... | computer science |
19,180 | Directed Cycles in Belief Networks | cs.AI | The most difficult task in probabilistic reasoning may be handling directed
cycles in belief networks. To the best knowledge of this author, there is no
serious discussion of this problem at all in the literature of probabilistic
reasoning so far. | computer science |
19,181 | Can Uncertainty Management be Realized in a Finite Totally Ordered
Probability Algebra? | cs.AI | In this paper, the feasibility of using finite totally ordered probability
models under Alelinnas's Theory of Probabilistic Logic [Aleliunas, 1988] is
investigated. The general form of the probability algebra of these models is
derived and the number of possible algebras with given size is deduced. Based
on this analys... | computer science |
19,182 | Normalization and the Representation of Nonmonotonic Knowledge in the
Theory of Evidence | cs.AI | We discuss the Dempster-Shafer theory of evidence. We introduce a concept of
monotonicity which is related to the diminution of the range between belief and
plausibility. We show that the accumulation of knowledge in this framework
exhibits a nonmonotonic property. We show how the belief structure can be used
to repres... | computer science |
19,183 | Probability Aggregates in Probability Answer Set Programming | cs.AI | Probability answer set programming is a declarative programming that has been
shown effective for representing and reasoning about a variety of probability
reasoning tasks. However, the lack of probability aggregates, e.g. {\em
expected values}, in the language of disjunctive hybrid probability logic
programs (DHPP) di... | computer science |
19,184 | Model-based Bayesian Reinforcement Learning for Dialogue Management | cs.AI | Reinforcement learning methods are increasingly used to optimise dialogue
policies from experience. Most current techniques are model-free: they directly
estimate the utility of various actions, without explicit model of the
interaction dynamics. In this paper, we investigate an alternative strategy
grounded in model-b... | computer science |
19,185 | Fuzzy Aggregates in Fuzzy Answer Set Programming | cs.AI | Fuzzy answer set programming is a declarative framework for representing and
reasoning about knowledge in fuzzy environments. However, the unavailability of
fuzzy aggregates in disjunctive fuzzy logic programs, DFLP, with fuzzy answer
set semantics prohibits the natural and concise representation of many
interesting pr... | computer science |
19,186 | The structure of Bayes nets for vision recognition | cs.AI | This paper is part of a study whose goal is to show the effciency of using
Bayes networks to carry out model based vision calculations. [Binford et al.
1987] Recognition proceeds by drawing up a network model from the object's
geometric and functional description that predicts the appearance of an object.
Then this net... | computer science |
19,187 | Summary of A New Normative Theory of Probabilistic Logic | cs.AI | By probabilistic logic I mean a normative theory of belief that explains how
a body of evidence affects one's degree of belief in a possible hypothesis. A
new axiomatization of such a theory is presented which avoids a finite
additivity axiom, yet which retains many useful inference rules. Many of the
examples of this ... | computer science |
19,188 | Probability Distributions Over Possible Worlds | cs.AI | In Probabilistic Logic Nilsson uses the device of a probability distribution
over a set of possible worlds to assign probabilities to the sentences of a
logical language. In his paper Nilsson concentrated on inference and associated
computational issues. This paper, on the other hand, examines the probabilistic
semanti... | computer science |
19,189 | Hierarchical Evidence and Belief Functions | cs.AI | Dempster/Shafer (D/S) theory has been advocated as a way of representing
incompleteness of evidence in a system's knowledge base. Methods now exist for
propagating beliefs through chains of inference. This paper discusses how rules
with attached beliefs, a common representation for knowledge in automated
reasoning syst... | computer science |
19,190 | Decision-Theoretic Control of Problem Solving: Principles and
Architecture | cs.AI | 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 probl... | computer science |
19,191 | Induction and Uncertainty Management Techniques Applied to Veterinary
Medical Diagnosis | cs.AI | This paper discusses a project undertaken between the Departments of
Computing Science, Statistics, and the College of Veterinary Medicine to design
a medical diagnostic system. On-line medical data has been collected in the
hospital database system for several years. A number of induction methods are
being used to ext... | computer science |
19,192 | KNET: Integrating Hypermedia and Bayesian Modeling | cs.AI | KNET is a general-purpose shell for constructing expert systems based on
belief networks and decision networks. Such networks serve as graphical
representations for decision models, in which the knowledge engineer must
define clearly the alternatives, states, preferences, and relationships that
constitute a decision ba... | computer science |
19,193 | A Method for Using Belief Networks as Influence Diagrams | cs.AI | This paper demonstrates a method for using belief-network algorithms to solve
influence diagram problems. In particular, both exact and approximation
belief-network algorithms may be applied to solve influence-diagram problems.
More generally, knowing the relationship between belief-network and
influence-diagram proble... | computer science |
19,194 | Process, Structure, and Modularity in Reasoning with Uncertainty | cs.AI | Computational mechanisms for uncertainty management must support interactive
and incremental problem formulation, inference, hypothesis testing, and
decision making. However, most current uncertainty inference systems
concentrate primarily on inference, and provide no support for the larger
issues. We present a computa... | computer science |
19,195 | Probabilistic Causal Reasoning | cs.AI | Predicting the future is an important component of decision making. In most
situations, however, there is not enough information to make accurate
predictions. In this paper, we develop a theory of causal reasoning for
predictive inference under uncertainty. We emphasize a common type of
prediction that involves reasoni... | computer science |
19,196 | Modeling uncertain and vague knowledge in possibility and evidence
theories | cs.AI | This paper advocates the usefulness of new theories of uncertainty for the
purpose of modeling some facets of uncertain knowledge, especially vagueness,
in AI. It can be viewed as a partial reply to Cheeseman's (among others)
defense of probability. | computer science |
19,197 | A Temporal Logic for Uncertain Events and An Outline of A Possible
Implementation in An Extension of PROLOG | cs.AI | There is uncertainty associated with the occurrence of many events in real
life. In this paper we develop a temporal logic to deal with such uncertain
events and outline a possible implementation in an extension of PROLOG. Events
are represented as fuzzy sets with the membership function giving the
possibility of occur... | computer science |
19,198 | Uncertainty Management for Fuzzy Decision Support Systems | cs.AI | A new approach for uncertainty management for fuzzy, rule based decision
support systems is proposed: The domain expert's knowledge is expressed by a
set of rules that frequently refer to vague and uncertain propositions. The
certainty of propositions is represented using intervals [a, b] expressing that
the propositio... | computer science |
19,199 | Probability as a Modal Operator | cs.AI | This paper argues for a modal view of probability. The syntax and semantics
of one particularly strong probability logic are discussed and some examples of
the use of the logic are provided. We show that it is both natural and useful
to think of probability as a modal operator. Contrary to popular belief in AI,
a proba... | computer science |
19,200 | Truth Maintenance Under Uncertainty | cs.AI | This paper addresses the problem of resolving errors under uncertainty in a
rule-based system. A new approach has been developed that reformulates this
problem as a neural-network learning problem. The strength and the fundamental
limitations of this approach are explored and discussed. The main result is
that neural h... | computer science |
19,201 | Bayesian Assessment of a Connectionist Model for Fault Detection | cs.AI | A previous paper [2] showed how to generate a linear discriminant network
(LDN) that computes likely faults for a noisy fault detection problem by using
a modification of the perceptron learning algorithm called the pocket
algorithm. Here we compare the performance of this connectionist model with
performance of the op... | computer science |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.