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19,902 | Model Based Framework for Estimating Mutation Rate of Hepatitis C Virus
in Egypt | cs.AI | Hepatitis C virus (HCV) is a widely spread disease all over the world. HCV
has very high mutation rate that makes it resistant to antibodies. Modeling HCV
to identify the virus mutation process is essential to its detection and
predicting its evolution. This paper presents a model based framework for
estimating mutatio... | computer science |
19,903 | RES - a Relative Method for Evidential Reasoning | cs.AI | In this paper we describe a novel method for evidential reasoning [1]. It
involves modelling the process of evidential reasoning in three steps, namely,
evidence structure construction, evidence accumulation, and decision making.
The proposed method, called RES, is novel in that evidence strength is
associated with an ... | computer science |
19,904 | Optimizing Causal Orderings for Generating DAGs from Data | cs.AI | An algorithm for generating the structure of a directed acyclic graph from
data using the notion of causal input lists is presented. The algorithm
manipulates the ordering of the variables with operations which very much
resemble arc reversal. Operations are only applied if the DAG after the
operation represents at lea... | computer science |
19,905 | Modal Logics for Qualitative Possibility and Beliefs | cs.AI | Possibilistic logic has been proposed as a numerical formalism for reasoning
with uncertainty. There has been interest in developing qualitative accounts of
possibility, as well as an explanation of the relationship between possibility
and modal logics. We present two modal logics that can be used to represent and
reas... | computer science |
19,906 | Structural Controllability and Observability in Influence Diagrams | cs.AI | Influence diagram is a graphical representation of belief networks with
uncertainty. This article studies the structural properties of a probabilistic
model in an influence diagram. In particular, structural controllability
theorems and structural observability theorems are developed and algorithms are
formulated. Cont... | computer science |
19,907 | Lattice-Based Graded Logic: a Multimodal Approach | cs.AI | Experts do not always feel very, comfortable when they have to give precise
numerical estimations of certainty degrees. In this paper we present a
qualitative approach which allows for attaching partially ordered symbolic
grades to logical formulas. Uncertain information is expressed by means of
parameterized modal ope... | computer science |
19,908 | Dynamic Network Models for Forecasting | cs.AI | We have developed a probabilistic forecasting methodology through a synthesis
of belief network models and classical time-series analysis. We present the
dynamic network model (DNM) and describe methods for constructing, refining,
and performing inference with this representation of temporal probabilistic
knowledge. Th... | computer science |
19,909 | Reformulating Inference Problems Through Selective Conditioning | cs.AI | We describe how we selectively reformulate portions of a belief network that
pose difficulties for solution with a stochastic-simulation algorithm. With
employ the selective conditioning approach to target specific nodes in a belief
network for decomposition, based on the contribution the nodes make to the
tractability... | computer science |
19,910 | Entropy and Belief Networks | cs.AI | The product expansion of conditional probabilities for belief nets is not
maximum entropy. This appears to deny a desirable kind of assurance for the
model. However, a kind of guarantee that is almost as strong as maximum entropy
can be derived. Surprisingly, a variant model also exhibits the guarantee, and
for many ca... | computer science |
19,911 | Parallelizing Probabilistic Inference: Some Early Explorations | cs.AI | We report on an experimental investigation into opportunities for parallelism
in beliefnet inference. Specifically, we report on a study performed of the
available parallelism, on hypercube style machines, of a set of randomly
generated belief nets, using factoring (SPI) style inference algorithms. Our
results indicate... | computer science |
19,912 | Objection-Based Causal Networks | cs.AI | This paper introduces the notion of objection-based causal networks which
resemble probabilistic causal networks except that they are quantified using
objections. An objection is a logical sentence and denotes a condition under
which a, causal dependency does not exist. Objection-based causal networks
enjoy almost all ... | computer science |
19,913 | A Symbolic Approach to Reasoning with Linguistic Quantifiers | cs.AI | This paper investigates the possibility of performing automated reasoning in
probabilistic logic when probabilities are expressed by means of linguistic
quantifiers. Each linguistic term is expressed as a prescribed interval of
proportions. Then instead of propagating numbers, qualitative terms are
propagated in accord... | computer science |
19,914 | Possibilistic Assumption based Truth Maintenance System, Validation in a
Data Fusion Application | cs.AI | Data fusion allows the elaboration and the evaluation of a situation
synthesized from low level informations provided by different kinds of sensors.
The fusion of the collected data will result in fewer and higher level
informations more easily assessed by a human operator and that will assist him
effectively in his de... | computer science |
19,915 | Knowledge Integration for Conditional Probability Assessments | cs.AI | In the probabilistic approach to uncertainty management the input knowledge
is usually represented by means of some probability distributions. In this
paper we assume that the input knowledge is given by two discrete conditional
probability distributions, represented by two stochastic matrices P and Q. The
consistency ... | computer science |
19,916 | Integrating Model Construction and Evaluation | cs.AI | To date, most probabilistic reasoning systems have relied on a fixed belief
network constructed at design time. The network is used by an application
program as a representation of (in)dependencies in the domain. Probabilistic
inference algorithms operate over the network to answer queries. Recognizing
the inflexibilit... | computer science |
19,917 | Reasoning With Qualitative Probabilities Can Be Tractable | cs.AI | We recently described a formalism for reasoning with if-then rules that re
expressed with different levels of firmness [18]. The formalism interprets
these rules as extreme conditional probability statements, specifying orders of
magnitude of disbelief, which impose constraints over possible rankings of
worlds. It was ... | computer science |
19,918 | A computational scheme for Reasoning in Dynamic Probabilistic Networks | cs.AI | A computational scheme for reasoning about dynamic systems using (causal)
probabilistic networks is presented. The scheme is based on the framework of
Lauritzen and Spiegelhalter (1988), and may be viewed as a generalization of
the inference methods of classical time-series analysis in the sense that it
allows descript... | computer science |
19,919 | The Dynamic of Belief in the Transferable Belief Model and
Specialization-Generalization Matrices | cs.AI | The fundamental updating process in the transferable belief model is related
to the concept of specialization and can be described by a specialization
matrix. The degree of belief in the truth of a proposition is a degree of
justified support. The Principle of Minimal Commitment implies that one should
never give more ... | computer science |
19,920 | A Note on the Measure of Discord | cs.AI | A new entropy-like measure as well as a new measure of total uncertainty
pertaining to the Dempster-Shafer theory are introduced. It is argued that
these measures are better justified than any of the previously proposed
candidates. | computer science |
19,921 | Semantics for Probabilistic Inference | cs.AI | A number of writers(Joseph Halpern and Fahiem Bacchus among them) have
offered semantics for formal languages in which inferences concerning
probabilities can be made. Our concern is different. This paper provides a
formalization of nonmonotonic inferences in which the conclusion is supported
only to a certain degree. ... | computer science |
19,922 | Some Problems for Convex Bayesians | cs.AI | We discuss problems for convex Bayesian decision making and uncertainty
representation. These include the inability to accommodate various natural and
useful constraints and the possibility of an analog of the classical Dutch Book
being made against an agent behaving in accordance with convex Bayesian
prescriptions. A ... | computer science |
19,923 | Bayesian Meta-Reasoning: Determining Model Adequacy from Within a Small
World | cs.AI | This paper presents a Bayesian framework for assessing the adequacy of a
model without the necessity of explicitly enumerating a specific alternate
model. A test statistic is developed for tracking the performance of the model
across repeated problem instances. Asymptotic methods are used to derive an
approximate distr... | computer science |
19,924 | The Bounded Bayesian | cs.AI | The ideal Bayesian agent reasons from a global probability model, but real
agents are restricted to simplified models which they know to be adequate only
in restricted circumstances. Very little formal theory has been developed to
help fallibly rational agents manage the process of constructing and revising
small world... | computer science |
19,925 | Representing Context-Sensitive Knowledge in a Network Formalism: A
Preliminary Report | cs.AI | Automated decision making is often complicated by the complexity of the
knowledge involved. Much of this complexity arises from the context sensitive
variations of the underlying phenomena. We propose a framework for representing
descriptive, context-sensitive knowledge. Our approach attempts to integrate
categorical a... | computer science |
19,926 | A Probabilistic Network of Predicates | cs.AI | Bayesian networks are directed acyclic graphs representing independence
relationships among a set of random variables. A random variable can be
regarded as a set of exhaustive and mutually exclusive propositions. We argue
that there are several drawbacks resulting from the propositional nature and
acyclic structure of ... | computer science |
19,927 | Representing Heuristic Knowledge in D-S Theory | cs.AI | The Dempster-Shafer theory of evidence has been used intensively to deal with
uncertainty in knowledge-based systems. However the representation of uncertain
relationships between evidence and hypothesis groups (heuristic knowledge) is
still a major research problem. This paper presents an approach to representing
such... | computer science |
19,928 | The Topological Fusion of Bayes Nets | cs.AI | Bayes nets are relatively recent innovations. As a result, most of their
theoretical development has focused on the simplest class of single-author
models. The introduction of more sophisticated multiple-author settings raises
a variety of interesting questions. One such question involves the nature of
compromise and c... | computer science |
19,929 | Calculating Uncertainty Intervals From Conditional Convex Sets of
Probabilities | cs.AI | In Moral, Campos (1991) and Cano, Moral, Verdegay-Lopez (1991) a new method
of conditioning convex sets of probabilities has been proposed. The result of
it is a convex set of non-necessarily normalized probability distributions. The
normalizing factor of each probability distribution is interpreted as the
possibility ... | computer science |
19,930 | Sensor Validation Using Dynamic Belief Networks | cs.AI | The trajectory of a robot is monitored in a restricted dynamic environment
using light beam sensor data. We have a Dynamic Belief Network (DBN), based on
a discrete model of the domain, which provides discrete monitoring analogous to
conventional quantitative filter techniques. Sensor observations are added to
the basi... | computer science |
19,931 | aHUGIN: A System Creating Adaptive Causal Probabilistic Networks | cs.AI | The paper describes aHUGIN, a tool for creating adaptive systems. aHUGIN is
an extension of the HUGIN shell, and is based on the methods reported by
Spiegelhalter and Lauritzen (1990a). The adaptive systems resulting from aHUGIN
are able to adjust the C011ditional probabilities in the model. A short
analysis of the ada... | computer science |
19,932 | MESA: Maximum Entropy by Simulated Annealing | cs.AI | Probabilistic reasoning systems combine different probabilistic rules and
probabilistic facts to arrive at the desired probability values of
consequences. In this paper we describe the MESA-algorithm (Maximum Entropy by
Simulated Annealing) that derives a joint distribution of variables or
propositions. It takes into a... | computer science |
19,933 | Decision Methods for Adaptive Task-Sharing in Associate Systems | cs.AI | This paper describes some results of research on associate systems:
knowledge-based systems that flexibly and adaptively support their human users
in carrying out complex, time-dependent problem-solving tasks under
uncertainty. Based on principles derived from decision theory and decision
analysis, a problem-solving ap... | computer science |
19,934 | Modeling Uncertain Temporal Evolutions in Model-Based Diagnosis | cs.AI | Although the notion of diagnostic problem has been extensively investigated
in the context of static systems, in most practical applications the behavior
of the modeled system is significantly variable during time. The goal of the
paper is to propose a novel approach to the modeling of uncertainty about
temporal evolut... | computer science |
19,935 | Guess-And-Verify Heuristics for Reducing Uncertainties in Expert
Classification Systems | cs.AI | An expert classification system having statistical information about the
prior probabilities of the different classes should be able to use this
knowledge to reduce the amount of additional information that it must collect,
e.g., through questions, in order to make a correct classification. This paper
examines how best... | computer science |
19,936 | R&D Analyst: An Interactive Approach to Normative Decision System Model
Construction | cs.AI | This paper describes the architecture of R&D Analyst, a commercial
intelligent decision system for evaluating corporate research and development
projects and portfolios. In analyzing projects, R&D Analyst interactively
guides a user in constructing an influence diagram model for an individual
research project. The syst... | computer science |
19,937 | Possibilistic Constraint Satisfaction Problems or "How to handle soft
constraints?" | cs.AI | Many AI synthesis problems such as planning or scheduling may be modelized as
constraint satisfaction problems (CSP). A CSP is typically defined as the
problem of finding any consistent labeling for a fixed set of variables
satisfying all given constraints between these variables. However, for many
real tasks such as j... | computer science |
19,938 | Decision Making Using Probabilistic Inference Methods | cs.AI | The analysis of decision making under uncertainty is closely related to the
analysis of probabilistic inference. Indeed, much of the research into
efficient methods for probabilistic inference in expert systems has been
motivated by the fundamental normative arguments of decision theory. In this
paper we show how the d... | computer science |
19,939 | Conditional Independence in Uncertainty Theories | cs.AI | This paper introduces the notions of independence and conditional
independence in valuation-based systems (VBS). VBS is an axiomatic framework
capable of representing many different uncertainty calculi. We define
independence and conditional independence in terms of factorization of the
joint valuation. The definitions... | computer science |
19,940 | The Nature of the Unnormalized Beliefs Encountered in the Transferable
Belief Model | cs.AI | Within the transferable belief model, positive basic belief masses can be
allocated to the empty set, leading to unnormalized belief functions. The
nature of these unnormalized beliefs is analyzed. | computer science |
19,941 | Intuitions about Ordered Beliefs Leading to Probabilistic Models | cs.AI | The general use of subjective probabilities to model belief has been
justified using many axiomatic schemes. For example, ?consistent betting
behavior' arguments are well-known. To those not already convinced of the
unique fitness and generality of probability models, such justifications are
often unconvincing. The pre... | computer science |
19,942 | Expressing Relational and Temporal Knowledge in Visual Probabilistic
Networks | cs.AI | Bayesian networks have been used extensively in diagnostic tasks such as
medicine, where they represent the dependency relations between a set of
symptoms and a set of diseases. A criticism of this type of knowledge
representation is that it is restricted to this kind of task, and that it
cannot cope with the knowledge... | computer science |
19,943 | A Fuzzy Logic Approach to Target Tracking | cs.AI | This paper discusses a target tracking problem in which no dynamic
mathematical model is explicitly assumed. A nonlinear filter based on the fuzzy
If-then rules is developed. A comparison with a Kalman filter is made, and
empirical results show that the performance of the fuzzy filter is better.
Intensive simulations s... | computer science |
19,944 | Towards Precision of Probabilistic Bounds Propagation | cs.AI | The DUCK-calculus presented here is a recent approach to cope with
probabilistic uncertainty in a sound and efficient way. Uncertain rules with
bounds for probabilities and explicit conditional independences can be
maintained incrementally. The basic inference mechanism relies on local bounds
propagation, implementable... | computer science |
19,945 | An Algorithm for Deciding if a Set of Observed Independencies Has a
Causal Explanation | cs.AI | In a previous paper [Pearl and Verma, 1991] we presented an algorithm for
extracting causal influences from independence information, where a causal
influence was defined as the existence of a directed arc in all minimal causal
models consistent with the data. In this paper we address the question of
deciding whether t... | computer science |
19,946 | Generalizing Jeffrey Conditionalization | cs.AI | Jeffrey's rule has been generalized by Wagner to the case in which new
evidence bounds the possible revisions of a prior probability below by a
Dempsterian lower probability. Classical probability kinematics arises within
this generalization as the special case in which the evidentiary focal elements
of the bounding lo... | computer science |
19,947 | Interval Structure: A Framework for Representing Uncertain Information | cs.AI | In this paper, a unified framework for representing uncertain information
based on the notion of an interval structure is proposed. It is shown that the
lower and upper approximations of the rough-set model, the lower and upper
bounds of incidence calculus, and the belief and plausibility functions all
obey the axioms ... | computer science |
19,948 | Exploring Localization in Bayesian Networks for Large Expert Systems | cs.AI | Current Bayesian net representations do not consider structure in the domain
and include all variables in a homogeneous network. At any time, a human
reasoner in a large domain may direct his attention to only one of a number of
natural subdomains, i.e., there is ?localization' of queries and evidence. In
such a case, ... | computer science |
19,949 | A Decision Calculus for Belief Functions in Valuation-Based Systems | cs.AI | Valuation-based system (VBS) provides a general framework for representing
knowledge and drawing inferences under uncertainty. Recent studies have shown
that the semantics of VBS can represent and solve Bayesian decision problems
(Shenoy, 1991a). The purpose of this paper is to propose a decision calculus
for Dempster-... | computer science |
19,950 | Sidestepping the Triangulation Problem in Bayesian Net Computations | cs.AI | This paper presents a new approach for computing posterior probabilities in
Bayesian nets, which sidesteps the triangulation problem. The current state of
art is the clique tree propagation approach. When the underlying graph of a
Bayesian net is triangulated, this approach arranges its cliques into a tree
and computes... | computer science |
19,951 | Viterbi training in PRISM | cs.AI | VT (Viterbi training), or hard EM, is an efficient way of parameter learning
for probabilistic models with hidden variables. Given an observation $y$, it
searches for a state of hidden variables $x$ that maximizes $p(x,y \mid
\theta)$ by coordinate ascent on parameters $\theta$ and $x$. In this paper we
introduce VT to... | computer science |
19,952 | "Conditional Inter-Causally Independent" Node Distributions, a Property
of "Noisy-Or" Models | cs.AI | This paper examines the interdependence generated between two parent nodes
with a common instantiated child node, such as two hypotheses sharing common
evidence. The relation so generated has been termed "intercausal." It is shown
by construction that inter-causal independence is possible for binary
distributions at on... | computer science |
19,953 | Combining Multiple-Valued Logics in Modular Expert Systems | cs.AI | The way experts manage uncertainty usually changes depending on the task they
are performing. This fact has lead us to consider the problem of communicating
modules (task implementations) in a large and structured knowledge based system
when modules have different uncertainty calculi. In this paper, the analysis of
the... | computer science |
19,954 | Constraint Propagation with Imprecise Conditional Probabilities | cs.AI | An approach to reasoning with default rules where the proportion of
exceptions, or more generally the probability of encountering an exception, can
be at least roughly assessed is presented. It is based on local uncertainty
propagation rules which provide the best bracketing of a conditional
probability of interest fro... | computer science |
19,955 | Some Properties of Plausible Reasoning | cs.AI | This paper presents a plausible reasoning system to illustrate some broad
issues in knowledge representation: dualities between different reasoning
forms, the difficulty of unifying complementary reasoning styles, and the
approximate nature of plausible reasoning. These issues have a common
underlying theme: there shou... | computer science |
19,956 | Theory Refinement on Bayesian Networks | cs.AI | Theory refinement is the task of updating a domain theory in the light of new
cases, to be done automatically or with some expert assistance. The problem of
theory refinement under uncertainty is reviewed here in the context of Bayesian
statistics, a theory of belief revision. The problem is reduced to an
incremental l... | computer science |
19,957 | Combination of Upper and Lower Probabilities | cs.AI | In this paper, we consider several types of information and methods of
combination associated with incomplete probabilistic systems. We discriminate
between 'a priori' and evidential information. The former one is a description
of the whole population, the latest is a restriction based on observations for
a particular ... | computer science |
19,958 | A Probabilistic Analysis of Marker-Passing Techniques for
Plan-Recognition | cs.AI | Useless paths are a chronic problem for marker-passing techniques. We use a
probabilistic analysis to justify a method for quickly identifying and
rejecting useless paths. Using the same analysis, we identify key conditions
and assumptions necessary for marker-passing to perform well. | computer science |
19,959 | Symbolic Probabilistic Inference with Continuous Variables | cs.AI | Research on Symbolic Probabilistic Inference (SPI) [2, 3] has provided an
algorithm for resolving general queries in Bayesian networks. SPI applies the
concept of dependency directed backward search to probabilistic inference, and
is incremental with respect to both queries and observations. Unlike
traditional Bayesian... | computer science |
19,960 | Symbolic Probabilistic Inference with Evidence Potential | cs.AI | Recent research on the Symbolic Probabilistic Inference (SPI) algorithm[2]
has focused attention on the importance of resolving general queries in
Bayesian networks. SPI applies the concept of dependency-directed backward
search to probabilistic inference, and is incremental with respect to both
queries and observation... | computer science |
19,961 | A Bayesian Method for Constructing Bayesian Belief Networks from
Databases | cs.AI | This paper presents a Bayesian method for constructing Bayesian belief
networks from a database of cases. Potential applications include
computer-assisted hypothesis testing, automated scientific discovery, and
automated construction of probabilistic expert systems. Results are presented
of a preliminary evaluation of ... | computer science |
19,962 | Local Expression Languages for Probabilistic Dependence: a Preliminary
Report | cs.AI | We present a generalization of the local expression language used in the
Symbolic Probabilistic Inference (SPI) approach to inference in belief nets
[1l, [8]. The local expression language in SPI is the language in which the
dependence of a node on its antecedents is described. The original language
represented the dep... | computer science |
19,963 | Symbolic Decision Theory and Autonomous Systems | cs.AI | The ability to reason under uncertainty and with incomplete information is a
fundamental requirement of decision support technology. In this paper we argue
that the concentration on theoretical techniques for the evaluation and
selection of decision options has distracted attention from many of the wider
issues in deci... | computer science |
19,964 | A Reason Maintenace System Dealing with Vague Data | cs.AI | A reason maintenance system which extends an ATMS through Mukaidono's fuzzy
logic is described. It supports a problem solver in situations affected by
incomplete information and vague data, by allowing nonmonotonic inferences and
the revision of previous conclusions when contradictions are detected. | computer science |
19,965 | Advances in Probabilistic Reasoning | cs.AI | This paper discuses multiple Bayesian networks representation paradigms for
encoding asymmetric independence assertions. We offer three contributions: (1)
an inference mechanism that makes explicit use of asymmetric independence to
speed up computations, (2) a simplified definition of similarity networks and
extensions... | computer science |
19,966 | Probability Estimation in Face of Irrelevant Information | cs.AI | In this paper, we consider one aspect of the problem of applying decision
theory to the design of agents that learn how to make decisions under
uncertainty. This aspect concerns how an agent can estimate probabilities for
the possible states of the world, given that it only makes limited observations
before committing ... | computer science |
19,967 | An Approximate Nonmyopic Computation for Value of Information | cs.AI | Value-of-information analyses provide a straightforward means for selecting
the best next observation to make, and for determining whether it is better to
gather additional information or to act immediately. Determining the next best
test to perform, given a state of uncertainty about the world, requires a
consideratio... | computer science |
19,968 | Search-based Methods to Bound Diagnostic Probabilities in Very Large
Belief Nets | cs.AI | Since exact probabilistic inference is intractable in general for large
multiply connected belief nets, approximate methods are required. A promising
approach is to use heuristic search among hypotheses (instantiations of the
network) to find the most probable ones, as in the TopN algorithm. Search is
based on the rela... | computer science |
19,969 | Time-Dependent Utility and Action Under Uncertainty | cs.AI | We discuss representing and reasoning with knowledge about the time-dependent
utility of an agent's actions. Time-dependent utility plays a crucial role in
the interaction between computation and action under bounded resources. We
present a semantics for time-dependent utility and describe the use of
time-dependent inf... | computer science |
19,970 | Non-monotonic Reasoning and the Reversibility of Belief Change | cs.AI | Traditional approaches to non-monotonic reasoning fail to satisfy a number of
plausible axioms for belief revision and suffer from conceptual difficulties as
well. Recent work on ranked preferential models (RPMs) promises to overcome
some of these difficulties. Here we show that RPMs are not adequate to handle
iterated... | computer science |
19,971 | Belief and Surprise - A Belief-Function Formulation | cs.AI | We motivate and describe a theory of belief in this paper. This theory is
developed with the following view of human belief in mind. Consider the belief
that an event E will occur (or has occurred or is occurring). An agent either
entertains this belief or does not entertain this belief (i.e., there is no
"grade" in en... | computer science |
19,972 | Evidential Reasoning in a Categorial Perspective: Conjunction and
Disjunction of Belief Functions | cs.AI | The categorial approach to evidential reasoning can be seen as a combination
of the probability kinematics approach of Richard Jeffrey (1965) and the
maximum (cross-) entropy inference approach of E. T. Jaynes (1957). As a
consequence of that viewpoint, it is well known that category theory provides
natural definitions... | computer science |
19,973 | Reasoning with Mass Distributions | cs.AI | The concept of movable evidence masses that flow from supersets to subsets as
specified by experts represents a suitable framework for reasoning under
uncertainty. The mass flow is controlled by specialization matrices. New
evidence is integrated into the frame of discernment by conditioning or
revision (Dempster's rul... | computer science |
19,974 | Conflict and Surprise: Heuristics for Model Revision | cs.AI | Any probabilistic model of a problem is based on assumptions which, if
violated, invalidate the model. Users of probability based decision aids need
to be alerted when cases arise that are not covered by the aid's model.
Diagnosis of model failure is also necessary to control dynamic model
construction and revision. Th... | computer science |
19,975 | Reasoning under Uncertainty: Some Monte Carlo Results | cs.AI | A series of monte carlo studies were performed to compare the behavior of
some alternative procedures for reasoning under uncertainty. The behavior of
several Bayesian, linear model and default reasoning procedures were examined
in the context of increasing levels of calibration error. The most interesting
result is th... | computer science |
19,976 | Representation Requirements for Supporting Decision Model Formulation | cs.AI | This paper outlines a methodology for analyzing the representational support
for knowledge-based decision-modeling in a broad domain. A relevant set of
inference patterns and knowledge types are identified. By comparing the
analysis results to existing representations, some insights are gained into a
design approach fo... | computer science |
19,977 | A Language for Planning with Statistics | cs.AI | When a planner must decide whether it has enough evidence to make a decision
based on probability, it faces the sample size problem. Current planners using
probabilities need not deal with this problem because they do not generate
their probabilities from observations. This paper presents an event based
language in whi... | computer science |
19,978 | A Modification to Evidential Probability | cs.AI | Selecting the right reference class and the right interval when faced with
conflicting candidates and no possibility of establishing subset style
dominance has been a problem for Kyburg's Evidential Probability system.
Various methods have been proposed by Loui and Kyburg to solve this problem in
a way that is both int... | computer science |
19,979 | Investigation of Variances in Belief Networks | cs.AI | The belief network is a well-known graphical structure for representing
independences in a joint probability distribution. The methods, which perform
probabilistic inference in belief networks, often treat the conditional
probabilities which are stored in the network as certain values. However, if
one takes either a su... | computer science |
19,980 | A Sensitivity Analysis of Pathfinder: A Follow-up Study | cs.AI | At last year?s Uncertainty in AI Conference, we reported the results of a
sensitivity analysis study of Pathfinder. Our findings were quite
unexpected-slight variations to Pathfinder?s parameters appeared to lead to
substantial degradations in system performance. A careful look at our first
analysis, together with the ... | computer science |
19,981 | Non-monotonic Negation in Probabilistic Deductive Databases | cs.AI | In this paper we study the uses and the semantics of non-monotonic negation
in probabilistic deductive data bases. Based on the stable semantics for
classical logic programming, we introduce the notion of stable formula,
functions. We show that stable formula, functions are minimal fixpoints of
operators associated wit... | computer science |
19,982 | Management of Uncertainty in the Multi-Level Monitoring and Diagnosis of
the Time of Flight Scintillation Array | cs.AI | We present a general architecture for the monitoring and diagnosis of large
scale sensor-based systems with real time diagnostic constraints. This
architecture is multileveled, combining a single monitoring level based on
statistical methods with two model based diagnostic levels. At each level,
sources of uncertainty ... | computer science |
19,983 | Integrating Probabilistic Rules into Neural Networks: A Stochastic EM
Learning Algorithm | cs.AI | The EM-algorithm is a general procedure to get maximum likelihood estimates
if part of the observations on the variables of a network are missing. In this
paper a stochastic version of the algorithm is adapted to probabilistic neural
networks describing the associative dependency of variables. These networks
have a pro... | computer science |
19,984 | Representing Bayesian Networks within Probabilistic Horn Abduction | cs.AI | This paper presents a simple framework for Horn clause abduction, with
probabilities associated with hypotheses. It is shown how this representation
can represent any probabilistic knowledge representable in a Bayesian belief
network. The main contributions are in finding a relationship between logical
and probabilisti... | computer science |
19,985 | Dynamic Network Updating Techniques For Diagnostic Reasoning | cs.AI | A new probabilistic network construction system, DYNASTY, is proposed for
diagnostic reasoning given variables whose probabilities change over time.
Diagnostic reasoning is formulated as a sequential stochastic process, and is
modeled using influence diagrams. Given a set O of observations, DYNASTY
creates an influence... | computer science |
19,986 | High Level Path Planning with Uncertainty | cs.AI | For high level path planning, environments are usually modeled as distance
graphs, and path planning problems are reduced to computing the shortest path
in distance graphs. One major drawback of this modeling is the inability to
model uncertainties, which are often encountered in practice. In this paper, a
new tool, ca... | computer science |
19,987 | Formal Model of Uncertainty for Possibilistic Rules | cs.AI | Given a universe of discourse X-a domain of possible outcomes-an experiment
may consist of selecting one of its elements, subject to the operation of
chance, or of observing the elements, subject to imprecision. A priori
uncertainty about the actual result of the experiment may be quantified,
representing either the li... | computer science |
19,988 | Deliberation and its Role in the Formation of Intentions | cs.AI | Deliberation plays an important role in the design of rational agents
embedded in the real-world. In particular, deliberation leads to the formation
of intentions, i.e., plans of action that the agent is committed to achieving.
In this paper, we present a branching time possible-worlds model for
representing and reason... | computer science |
19,989 | Handling Uncertainty during Plan Recognition in Task-Oriented
Consultation Systems | cs.AI | During interactions with human consultants, people are used to providing
partial and/or inaccurate information, and still be understood and assisted. We
attempt to emulate this capability of human consultants; in computer
consultation systems. In this paper, we present a mechanism for handling
uncertainty in plan recog... | computer science |
19,990 | Truth as Utility: A Conceptual Synthesis | cs.AI | This paper introduces conceptual relations that synthesize utilitarian and
logical concepts, extending the logics of preference of Rescher. We define
first, in the context of a possible worlds model, constraint-dependent measures
that quantify the relative quality of alternative solutions of reasoning
problems or the r... | computer science |
19,991 | Pulcinella: A General Tool for Propagating Uncertainty in Valuation
Networks | cs.AI | We present PULCinella and its use in comparing uncertainty theories.
PULCinella is a general tool for Propagating Uncertainty based on the Local
Computation technique of Shafer and Shenoy. It may be specialized to different
uncertainty theories: at the moment, Pulcinella can propagate probabilities,
belief functions, B... | computer science |
19,992 | Structuring Bodies of Evidence | cs.AI | In this article we present two ways of structuring bodies of evidence, which
allow us to reduce the complexity of the operations usually performed in the
framework of evidence theory. The first structure just partitions the focal
elements in a body of evidence by their cardinality. With this structure we are
able to re... | computer science |
19,993 | On the Generation of Alternative Explanations with Implications for
Belief Revision | cs.AI | In general, the best explanation for a given observation makes no promises on
how good it is with respect to other alternative explanations. A major
deficiency of message-passing schemes for belief revision in Bayesian networks
is their inability to generate alternatives beyond the second best. In this
paper, we presen... | computer science |
19,994 | Completing Knowledge by Competing Hierarchies | cs.AI | A control strategy for expert systems is presented which is based on Shafer's
Belief theory and the combination rule of Dempster. In contrast to well known
strategies it is not sequentially and hypotheses-driven, but parallel and self
organizing, determined by the concept of information gain. The information
gain, calc... | computer science |
19,995 | A Graph-Based Inference Method for Conditional Independence | cs.AI | The graphoid axioms for conditional independence, originally described by
Dawid [1979], are fundamental to probabilistic reasoning [Pearl, 19881. Such
axioms provide a mechanism for manipulating conditional independence assertions
without resorting to their numerical definition. This paper explores a
representation for... | computer science |
19,996 | A Fusion Algorithm for Solving Bayesian Decision Problems | cs.AI | This paper proposes a new method for solving Bayesian decision problems. The
method consists of representing a Bayesian decision problem as a
valuation-based system and applying a fusion algorithm for solving it. The
fusion algorithm is a hybrid of local computational methods for computation of
marginals of joint proba... | computer science |
19,997 | Algorithms for Irrelevance-Based Partial MAPs | cs.AI | Irrelevance-based partial MAPs are useful constructs for domain-independent
explanation using belief networks. We look at two definitions for such partial
MAPs, and prove important properties that are useful in designing algorithms
for computing them effectively. We make use of these properties in modifying
our standar... | computer science |
19,998 | About Updating | cs.AI | Survey of several forms of updating, with a practical illustrative example.
We study several updating (conditioning) schemes that emerge naturally from a
common scenarion to provide some insights into their meaning. Updating is a
subtle operation and there is no single method, no single 'good' rule. The
choice of the a... | computer science |
19,999 | Compressed Constraints in Probabilistic Logic and Their Revision | cs.AI | In probabilistic logic entailments, even moderate size problems can yield
linear constraint systems with so many variables that exact methods are
impractical. This difficulty can be remedied in many cases of interest by
introducing a three valued logic (true, false, and "don't care"). The
three-valued approach allows t... | computer science |
20,000 | Balancing bike sharing systems (BBSS): instance generation from the
CitiBike NYC data | cs.AI | Bike sharing systems are a very popular means to provide bikes to citizens in
a simple and cheap way. The idea is to install bike stations at various points
in the city, from which a registered user can easily loan a bike by removing it
from a specialized rack. After the ride, the user may return the bike at any
statio... | computer science |
20,001 | Dependence space of matroids and its application to attribute reduction | cs.AI | Attribute reduction is a basic issue in knowledge representation and data
mining. Rough sets provide a theoretical foundation for the issue. Matroids
generalized from matrices have been widely used in many fields, particularly
greedy algorithm design, which plays an important role in attribute reduction.
Therefore, it ... | computer science |
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