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19,802 | Decision Making for Symbolic Probability | cs.AI | This paper proposes a decision theory for a symbolic generalization of
probability theory (SP). Darwiche and Ginsberg [2,3] proposed SP to relax the
requirement of using numbers for uncertainty while preserving desirable
patterns of Bayesian reasoning. SP represents uncertainty by symbolic supports
that are ordered par... | computer science |
19,803 | Metrics for Finite Markov Decision Processes | cs.AI | We present metrics for measuring the similarity of states in a finite Markov
decision process (MDP). The formulation of our metrics is based on the notion
of bisimulation for MDPs, with an aim towards solving discounted infinite
horizon reinforcement learning tasks. Such metrics can be used to aggregate
states, as well... | computer science |
19,804 | Dynamic Programming for Structured Continuous Markov Decision Problems | cs.AI | We describe an approach for exploiting structure in Markov Decision Processes
with continuous state variables. At each step of the dynamic programming, the
state space is dynamically partitioned into regions where the value function is
the same throughout the region. We first describe the algorithm for piecewise
consta... | computer science |
19,805 | Region-Based Incremental Pruning for POMDPs | cs.AI | We present a major improvement to the incremental pruning algorithm for
solving partially observable Markov decision processes. Our technique targets
the cross-sum step of the dynamic programming (DP) update, a key source of
complexity in POMDP algorithms. Instead of reasoning about the whole belief
space when pruning ... | computer science |
19,806 | A Unified framework for order-of-magnitude confidence relations | cs.AI | The aim of this work is to provide a unified framework for ordinal
representations of uncertainty lying at the crosswords between possibility and
probability theories. Such confidence relations between events are commonly
found in monotonic reasoning, inconsistency management, or qualitative decision
theory. They start... | computer science |
19,807 | Mixtures of Deterministic-Probabilistic Networks and their AND/OR Search
Space | cs.AI | The paper introduces mixed networks, a new framework for expressing and
reasoning with probabilistic and deterministic information. The framework
combines belief networks with constraint networks, defining the semantics and
graphical representation. We also introduce the AND/OR search space for
graphical models, and de... | computer science |
19,808 | Stable Independance and Complexity of Representation | cs.AI | The representation of independence relations generally builds upon the
well-known semigraphoid axioms of independence. Recently, a representation has
been proposed that captures a set of dominant statements of an independence
relation from which any other statement can be generated by means of the
axioms; the cardinali... | computer science |
19,809 | Propositional and Relational Bayesian Networks Associated with Imprecise
and Qualitative Probabilistic Assesments | cs.AI | This paper investigates a representation language with flexibility inspired
by probabilistic logic and compactness inspired by relational Bayesian
networks. The goal is to handle propositional and first-order constructs
together with precise, imprecise, indeterminate and qualitative probabilistic
assessments. The paper... | computer science |
19,810 | A Logic Programming Framework for Possibilistic Argumentation with Vague
Knowledge | cs.AI | Defeasible argumentation frameworks have evolved to become a sound setting to
formalize commonsense, qualitative reasoning from incomplete and potentially
inconsistent knowledge. Defeasible Logic Programming (DeLP) is a defeasible
argumentation formalism based on an extension of logic programming. Although
DeLP has bee... | computer science |
19,811 | Sensitivity Analysis in Bayesian Networks: From Single to Multiple
Parameters | cs.AI | Previous work on sensitivity analysis in Bayesian networks has focused on
single parameters, where the goal is to understand the sensitivity of queries
to single parameter changes, and to identify single parameter changes that
would enforce a certain query constraint. In this paper, we expand the work to
multiple param... | computer science |
19,812 | Compact Value-Function Representations for Qualitative Preferences | cs.AI | We consider the challenge of preference elicitation in systems that help
users discover the most desirable item(s) within a given database. Past work on
preference elicitation focused on structured models that provide a factored
representation of users' preferences. Such models require less information to
construct and... | computer science |
19,813 | Using arguments for making decisions: A possibilistic logic approach | cs.AI | Humans currently use arguments for explaining choices which are already made,
or for evaluating potential choices. Each potential choice has usually pros and
cons of various strengths. In spite of the usefulness of arguments in a
decision making process, there have been few formal proposals handling this
idea if we exc... | computer science |
19,814 | Case-Factor Diagrams for Structured Probabilistic Modeling | cs.AI | We introduce a probabilistic formalism subsuming Markov random fields of
bounded tree width and probabilistic context free grammars. Our models are
based on a representation of Boolean formulas that we call case-factor diagrams
(CFDs). CFDs are similar to binary decision diagrams (BDDs) but are concise for
circuits of ... | computer science |
19,815 | Convolutional Factor Graphs as Probabilistic Models | cs.AI | Based on a recent development in the area of error control coding, we
introduce the notion of convolutional factor graphs (CFGs) as a new class of
probabilistic graphical models. In this context, the conventional factor graphs
are referred to as multiplicative factor graphs (MFGs). This paper shows that
CFGs are natura... | computer science |
19,816 | An Empirical Evaluation of Possible Variations of Lazy Propagation | cs.AI | As real-world Bayesian networks continue to grow larger and more complex, it
is important to investigate the possibilities for improving the performance of
existing algorithms of probabilistic inference. Motivated by examples, we
investigate the dependency of the performance of Lazy propagation on the
message computati... | computer science |
19,817 | Solving Factored MDPs with Continuous and Discrete Variables | cs.AI | Although many real-world stochastic planning problems are more naturally
formulated by hybrid models with both discrete and continuous variables,
current state-of-the-art methods cannot adequately address these problems. We
present the first framework that can exploit problem structure for modeling and
solving hybrid p... | computer science |
19,818 | Annealed MAP | cs.AI | Maximum a Posteriori assignment (MAP) is the problem of finding the most
probable instantiation of a set of variables given the partial evidence on the
other variables in a Bayesian network. MAP has been shown to be a NP-hard
problem [22], even for constrained networks, such as polytrees [18]. Hence,
previous approache... | computer science |
19,819 | Monotonicity in Bayesian Networks | cs.AI | For many real-life Bayesian networks, common knowledge dictates that the
output established for the main variable of interest increases with higher
values for the observable variables. We define two concepts of monotonicity to
capture this type of knowledge. We say that a network is isotone in
distribution if the proba... | computer science |
19,820 | Heuristic Search Value Iteration for POMDPs | cs.AI | We present a novel POMDP planning algorithm called heuristic search value
iteration (HSVI).HSVI is an anytime algorithm that returns a policy and a
provable bound on its regret with respect to the optimal policy. HSVI gets its
power by combining two well-known techniques: attention-focusing search
heuristics and piecew... | computer science |
19,821 | A New Characterization of Probabilities in Bayesian Networks | cs.AI | We characterize probabilities in Bayesian networks in terms of algebraic
expressions called quasi-probabilities. These are arrived at by casting
Bayesian networks as noisy AND-OR-NOT networks, and viewing the subnetworks
that lead to a node as arguments for or against a node. Quasi-probabilities are
in a sense the "nat... | computer science |
19,822 | Evidence-invariant Sensitivity Bounds | cs.AI | The sensitivities revealed by a sensitivity analysis of a probabilistic
network typically depend on the entered evidence. For a real-life network
therefore, the analysis is performed a number of times, with different
evidence. Although efficient algorithms for sensitivity analysis exist, a
complete analysis is often in... | computer science |
19,823 | On Modeling Profiles instead of Values | cs.AI | We consider the problem of estimating the distribution underlying an observed
sample of data. Instead of maximum likelihood, which maximizes the probability
of the ob served values, we propose a different estimate, the high-profile
distribution, which maximizes the probability of the observed profile the
number of symb... | computer science |
19,824 | Learning Diagnostic Policies from Examples by Systematic Search | cs.AI | A diagnostic policy specifies what test to perform next, based on the results
of previous tests, and when to stop and make a diagnosis. Cost-sensitive
diagnostic policies perform tradeoffs between (a) the cost of tests and (b) the
cost of misdiagnoses. An optimal diagnostic policy minimizes the expected total
cost. We ... | computer science |
19,825 | Hybrid Influence Diagrams Using Mixtures of Truncated Exponentials | cs.AI | Mixtures of truncated exponentials (MTE) potentials are an alternative to
discretization for representing continuous chance variables in influence
diagrams. Also, MTE potentials can be used to approximate utility functions.
This paper introduces MTE influence diagrams, which can represent decision
problems without rest... | computer science |
19,826 | Towards Understanding Triangle Construction Problems | cs.AI | Straightedge and compass construction problems are one of the oldest and most
challenging problems in elementary mathematics. The central challenge, for a
human or for a computer program, in solving construction problems is a huge
search space. In this paper we analyze one family of triangle construction
problems, aimi... | computer science |
19,827 | The Arcade Learning Environment: An Evaluation Platform for General
Agents | cs.AI | In this article we introduce the Arcade Learning Environment (ALE): both a
challenge problem and a platform and methodology for evaluating the development
of general, domain-independent AI technology. ALE provides an interface to
hundreds of Atari 2600 game environments, each one different, interesting, and
designed to... | computer science |
19,828 | Exploring the rationality of some syntactic merging operators (extended
version) | cs.AI | Most merging operators are defined by semantics methods which have very high
computational complexity. In order to have operators with a lower computational
complexity, some merging operators defined in a syntactical way have be
proposed. In this work we define some syntactical merging operators and
exploring its ratio... | computer science |
19,829 | Stator flux optimization on direct torque control with fuzzy logic | cs.AI | The Direct Torque Control (DTC) is well known as an effective control
technique for high performance drives in a wide variety of industrial
applications and conventional DTC technique uses two constant reference value:
torque and stator flux. In this paper, fuzzy logic based stator flux
optimization technique for DTC d... | computer science |
19,830 | Selecting Computations: Theory and Applications | cs.AI | Sequential decision problems are often approximately solvable by simulating
possible future action sequences. {\em Metalevel} decision procedures have been
developed for selecting {\em which} action sequences to simulate, based on
estimating the expected improvement in decision quality that would result from
any partic... | computer science |
19,831 | Redundant Sudoku Rules | cs.AI | The rules of Sudoku are often specified using twenty seven
\texttt{all\_different} constraints, referred to as the {\em big} \mrules.
Using graphical proofs and exploratory logic programming, the following main
and new result is obtained: many subsets of six of these big \mrules are
redundant (i.e., they are entailed b... | computer science |
19,832 | Earthquake Scenario Reduction by Symmetry Reasoning | cs.AI | A recently identified problem is that of finding an optimal investment plan
for a transportation network, given that a disaster such as an earthquake may
destroy links in the network. The aim is to strengthen key links to preserve
the expected network connectivity. A network based on the Istanbul highway
system has thi... | computer science |
19,833 | Model-Lite Case-Based Planning | cs.AI | There is increasing awareness in the planning community that depending on
complete models impedes the applicability of planning technology in many real
world domains where the burden of specifying complete domain models is too
high. In this paper, we consider a novel solution for this challenge that
combines generative... | computer science |
19,834 | Causality in Bayesian Belief Networks | cs.AI | We address the problem of causal interpretation of the graphical structure of
Bayesian belief networks (BBNs). We review the concept of causality explicated
in the domain of structural equations models and show that it is applicable to
BBNs. In this view, which we call mechanism-based, causality is defined within
model... | computer science |
19,835 | From Conditional Oughts to Qualitative Decision Theory | cs.AI | The primary theme of this investigation is a decision theoretic account of
conditional ought statements (e.g., "You ought to do A, if C") that rectifies
glaring deficiencies in classical deontic logic. The resulting account forms a
sound basis for qualitative decision theory, thus providing a framework for
qualitative ... | computer science |
19,836 | A Probabilistic Algorithm for Calculating Structure: Borrowing from
Simulated Annealing | cs.AI | We have developed a general Bayesian algorithm for determining the
coordinates of points in a three-dimensional space. The algorithm takes as
input a set of probabilistic constraints on the coordinates of the points, and
an a priori distribution for each point location. The output is a
maximum-likelihood estimate of th... | computer science |
19,837 | A Study of Scaling Issues in Bayesian Belief Networks for Ship
Classification | cs.AI | The problems associated with scaling involve active and challenging research
topics in the area of artificial intelligence. The purpose is to solve real
world problems by means of AI technologies, in cases where the complexity of
representation of the real world problem is potentially combinatorial. In this
paper, we p... | computer science |
19,838 | Tradeoffs in Constructing and Evaluating Temporal Influence Diagrams | cs.AI | This paper addresses the tradeoffs which need to be considered in reasoning
using probabilistic network representations, such as Influence Diagrams (IDs).
In particular, we examine the tradeoffs entailed in using Temporal Influence
Diagrams (TIDs) which adequately capture the temporal evolution of a dynamic
system with... | computer science |
19,839 | End-User Construction of Influence Diagrams for Bayesian Statistics | cs.AI | Influence diagrams are ideal knowledge representations for Bayesian
statistical models. However, these diagrams are difficult for end users to
interpret and to manipulate. We present a user-based architecture that enables
end users to create and to manipulate the knowledge representation. We use the
problem of physicia... | computer science |
19,840 | Forecasting Sleep Apnea with Dynamic Network Models | cs.AI | Dynamic network models (DNMs) are belief networks for temporal reasoning. The
DNM methodology combines techniques from time series analysis and probabilistic
reasoning to provide (1) a knowledge representation that integrates
noncontemporaneous and contemporaneous dependencies and (2) methods for
iteratively refining t... | computer science |
19,841 | Normative Engineering Risk Management Systems | cs.AI | This paper describes a normative system design that incorporates diagnosis,
dynamic evolution, decision making, and information gathering. A single
influence diagram demonstrates the design's coherence, yet each activity is
more effectively modeled and evaluated separately. Application to offshore oil
platforms illustr... | computer science |
19,842 | Diagnosis of Multiple Faults: A Sensitivity Analysis | cs.AI | We compare the diagnostic accuracy of three diagnostic inference models: the
simple Bayes model, the multimembership Bayes model, which is isomorphic to the
parallel combination function in the certainty-factor model, and a model that
incorporates the noisy OR-gate interaction. The comparison is done on 20
clinicopatho... | computer science |
19,843 | Additive Belief-Network Models | cs.AI | The inherent intractability of probabilistic inference has hindered the
application of belief networks to large domains. Noisy OR-gates [30] and
probabilistic similarity networks [18, 17] escape the complexity of inference
by restricting model expressiveness. Recent work in the application of
belief-network models to t... | computer science |
19,844 | Parameter Adjustment in Bayes Networks. The generalized noisy OR-gate | cs.AI | Spiegelhalter and Lauritzen [15] studied sequential learning in Bayesian
networks and proposed three models for the representation of conditional
probabilities. A forth model, shown here, assumes that the parameter
distribution is given by a product of Gaussian functions and updates them from
the _ and _r messages of e... | computer science |
19,845 | A fuzzy relation-based extension of Reggia's relational model for
diagnosis handling uncertain and incomplete information | cs.AI | Relational models for diagnosis are based on a direct description of the
association between disorders and manifestations. This type of model has been
specially used and developed by Reggia and his co-workers in the late eighties
as a basic starting point for approaching diagnosis problems. The paper
proposes a new rel... | computer science |
19,846 | Dialectic Reasoning with Inconsistent Information | cs.AI | From an inconsistent database non-trivial arguments may be constructed both
for a proposition, and for the contrary of that proposition. Therefore,
inconsistency in a logical database causes uncertainty about which conclusions
to accept. This kind of uncertainty is called logical uncertainty. We define a
concept of "ac... | computer science |
19,847 | Causal Independence for Knowledge Acquisition and Inference | cs.AI | I introduce a temporal belief-network representation of causal independence
that a knowledge engineer can use to elicit probabilistic models. Like the
current, atemporal belief-network representation of causal independence, the
new representation makes knowledge acquisition tractable. Unlike the atemproal
representatio... | computer science |
19,848 | Utility-Based Abstraction and Categorization | cs.AI | We take a utility-based approach to categorization. We construct
generalizations about events and actions by considering losses associated with
failing to distinguish among detailed distinctions in a decision model. The
utility-based methods transform detailed states of the world into more abstract
categories comprised... | computer science |
19,849 | Sensitivity Analysis for Probability Assessments in Bayesian Networks | cs.AI | When eliciting probability models from experts, knowledge engineers may
compare the results of the model with expert judgment on test scenarios, then
adjust model parameters to bring the behavior of the model more in line with
the expert's intuition. This paper presents a methodology for analytic
computation of sensiti... | computer science |
19,850 | Causal Modeling | cs.AI | Causal Models are like Dependency Graphs and Belief Nets in that they provide
a structure and a set of assumptions from which a joint distribution can, in
principle, be computed. Unlike Dependency Graphs, Causal Models are models of
hierarchical and/or parallel processes, rather than models of distributions
(partially)... | computer science |
19,851 | Some Complexity Considerations in the Combination of Belief Networks | cs.AI | One topic that is likely to attract an increasing amount of attention within
the Knowledge-base systems research community is the coordination of
information provided by multiple experts. We envision a situation in which
several experts independently encode information as belief networks. A
potential user must then coo... | computer science |
19,852 | Deriving a Minimal I-map of a Belief Network Relative to a Target
Ordering of its Nodes | cs.AI | This paper identifies and solves a new optimization problem: Given a belief
network (BN) and a target ordering on its variables, how can we efficiently
derive its minimal I-map whose arcs are consistent with the target ordering? We
present three solutions to this problem, all of which lead to directed acyclic
graphs ba... | computer science |
19,853 | Probabilistic Conceptual Network: A Belief Representation Scheme for
Utility-Based Categorization | cs.AI | Probabilistic conceptual network is a knowledge representation scheme
designed for reasoning about concepts and categorical abstractions in
utility-based categorization. The scheme combines the formalisms of abstraction
and inheritance hierarchies from artificial intelligence, and probabilistic
networks from decision a... | computer science |
19,854 | Reasoning about the Value of Decision-Model Refinement: Methods and
Application | cs.AI | We investigate the value of extending the completeness of a decision model
along different dimensions of refinement. Specifically, we analyze the expected
value of quantitative, conceptual, and structural refinement of decision
models. We illustrate the key dimensions of refinement with examples. The
analyses of value ... | computer science |
19,855 | Mixtures of Gaussians and Minimum Relative Entropy Techniques for
Modeling Continuous Uncertainties | cs.AI | Problems of probabilistic inference and decision making under uncertainty
commonly involve continuous random variables. Often these are discretized to a
few points, to simplify assessments and computations. An alternative
approximation is to fit analytically tractable continuous probability
distributions. This approach... | computer science |
19,856 | Valuation Networks and Conditional Independence | cs.AI | Valuation networks have been proposed as graphical representations of
valuation-based systems (VBSs). The VBS framework is able to capture many
uncertainty calculi including probability theory, Dempster-Shafer's
belief-function theory, Spohn's epistemic belief theory, and Zadeh's
possibility theory. In this paper, we s... | computer science |
19,857 | Relevant Explanations: Allowing Disjunctive Assignments | cs.AI | Relevance-based explanation is a scheme in which partial assignments to
Bayesian belief network variables are explanations (abductive conclusions). We
allow variables to remain unassigned in explanations as long as they are
irrelevant to the explanation, where irrelevance is defined in terms of
statistical independence... | computer science |
19,858 | A Generalization of the Noisy-Or Model | cs.AI | The Noisy-Or model is convenient for describing a class of uncertain
relationships in Bayesian networks [Pearl 1988]. Pearl describes the Noisy-Or
model for Boolean variables. Here we generalize the model to nary input and
output variables and to arbitrary functions other than the Boolean OR function.
This generalizati... | computer science |
19,859 | Using First-Order Probability Logic for the Construction of Bayesian
Networks | cs.AI | We present a mechanism for constructing graphical models, specifically
Bayesian networks, from a knowledge base of general probabilistic information.
The unique feature of our approach is that it uses a powerful first-order
probabilistic logic for expressing the general knowledge base. This logic
allows for the represe... | computer science |
19,860 | Representing and Reasoning With Probabilistic Knowledge: A Bayesian
Approach | cs.AI | PAGODA (Probabilistic Autonomous Goal-Directed Agent) is a model for
autonomous learning in probabilistic domains [desJardins, 1992] that
incorporates innovative techniques for using the agent's existing knowledge to
guide and constrain the learning process and for representing, reasoning with,
and learning probabilist... | computer science |
19,861 | Graph-Grammar Assistance for Automated Generation of Influence Diagrams | cs.AI | One of the most difficult aspects of modeling complex dilemmas in
decision-analytic terms is composing a diagram of relevance relations from a
set of domain concepts. Decision models in domains such as medicine, however,
exhibit certain prototypical patterns that can guide the modeling process.
Medical concepts can be ... | computer science |
19,862 | Using Causal Information and Local Measures to Learn Bayesian Networks | cs.AI | In previous work we developed a method of learning Bayesian Network models
from raw data. This method relies on the well known minimal description length
(MDL) principle. The MDL principle is particularly well suited to this task as
it allows us to tradeoff, in a principled way, the accuracy of the learned
network agai... | computer science |
19,863 | Minimal Assumption Distribution Propagation in Belief Networks | cs.AI | As belief networks are used to model increasingly complex situations, the
need to automatically construct them from large databases will become
paramount. This paper concentrates on solving a part of the belief network
induction problem: that of learning the quantitative structure (the conditional
probabilities), given... | computer science |
19,864 | An Algorithm for the Construction of Bayesian Network Structures from
Data | cs.AI | Previous algorithms for the construction of Bayesian belief network
structures from data have been either highly dependent on conditional
independence (CI) tests, or have required an ordering on the nodes to be
supplied by the user. We present an algorithm that integrates these two
approaches - CI tests are used to gen... | computer science |
19,865 | A Construction of Bayesian Networks from Databases Based on an MDL
Principle | cs.AI | This paper addresses learning stochastic rules especially on an
inter-attribute relation based on a Minimum Description Length (MDL) principle
with a finite number of examples, assuming an application to the design of
intelligent relational database systems. The stochastic rule in this paper
consists of a model giving ... | computer science |
19,866 | Knowledge-Based Decision Model Construction for Hierarchical Diagnosis:
A Preliminary Report | cs.AI | Numerous methods for probabilistic reasoning in large, complex belief or
decision networks are currently being developed. There has been little research
on automating the dynamic, incremental construction of decision models. A
uniform value-driven method of decision model construction is proposed for the
hierarchical c... | computer science |
19,867 | A Synthesis of Logical and Probabilistic Reasoning for Program
Understanding and Debugging | cs.AI | We describe the integration of logical and uncertain reasoning methods to
identify the likely source and location of software problems. To date, software
engineers have had few tools for identifying the sources of error in complex
software packages. We describe a method for diagnosing software problems
through combinin... | computer science |
19,868 | An Implementation of a Method for Computing the Uncertainty in Inferred
Probabilities in Belief Networks | cs.AI | In recent years the belief network has been used increasingly to model
systems in Al that must perform uncertain inference. The development of
efficient algorithms for probabilistic inference in belief networks has been a
focus of much research in AI. Efficient algorithms for certain classes of
belief networks have bee... | computer science |
19,869 | Incremental Probabilistic Inference | cs.AI | Propositional representation services such as truth maintenance systems offer
powerful support for incremental, interleaved, problem-model construction and
evaluation. Probabilistic inference systems, in contrast, have lagged behind in
supporting this incrementality typically demanded by problem solvers. The
problem, w... | computer science |
19,870 | Deliberation Scheduling for Time-Critical Sequential Decision Making | cs.AI | We describe a method for time-critical decision making involving sequential
tasks and stochastic processes. The method employs several iterative refinement
routines for solving different aspects of the decision making problem. This
paper concentrates on the meta-level control problem of deliberation
scheduling, allocat... | computer science |
19,871 | Intercausal Reasoning with Uninstantiated Ancestor Nodes | cs.AI | Intercausal reasoning is a common inference pattern involving probabilistic
dependence of causes of an observed common effect. The sign of this dependence
is captured by a qualitative property called product synergy. The current
definition of product synergy is insufficient for intercausal reasoning where
there are add... | computer science |
19,872 | Inference Algorithms for Similarity Networks | cs.AI | We examine two types of similarity networks each based on a distinct notion
of relevance. For both types of similarity networks we present an efficient
inference algorithm that works under the assumption that every event has a
nonzero probability of occurrence. Another inference algorithm is developed for
type 1 simila... | computer science |
19,873 | Two Procedures for Compiling Influence Diagrams | cs.AI | Two algorithms are presented for "compiling" influence diagrams into a set of
simple decision rules. These decision rules define simple-to-execute, complete,
consistent, and near-optimal decision procedures. These compilation algorithms
can be used to derive decision procedures for human teams solving time
constrained ... | computer science |
19,874 | An efficient approach for finding the MPE in belief networks | cs.AI | Given a belief network with evidence, the task of finding the I most probable
explanations (MPE) in the belief network is that of identifying and ordering
the I most probable instantiations of the non-evidence nodes of the belief
network. Although many approaches have been proposed for solving this problem,
most work o... | computer science |
19,875 | A Method for Planning Given Uncertain and Incomplete Information | cs.AI | This paper describes ongoing research into planning in an uncertain
environment. In particular, it introduces U-Plan, a planning system that
constructs quantitatively ranked plans given an incomplete description of the
state of the world. U-Plan uses a DempsterShafer interval to characterise
uncertain and incomplete in... | computer science |
19,876 | The use of conflicts in searching Bayesian networks | cs.AI | This paper discusses how conflicts (as used by the consistency-based
diagnosis community) can be adapted to be used in a search-based algorithm for
computing prior and posterior probabilities in discrete Bayesian Networks. This
is an "anytime" algorithm, that at any stage can estimate the probabilities and
give an erro... | computer science |
19,877 | GALGO: A Genetic ALGOrithm Decision Support Tool for Complex Uncertain
Systems Modeled with Bayesian Belief Networks | cs.AI | Bayesian belief networks can be used to represent and to reason about complex
systems with uncertain, incomplete and conflicting information. Belief networks
are graphs encoding and quantifying probabilistic dependence and conditional
independence among variables. One type of reasoning of interest in diagnosis is
calle... | computer science |
19,878 | Using Tree-Decomposable Structures to Approximate Belief Networks | cs.AI | Tree structures have been shown to provide an efficient framework for
propagating beliefs [Pearl,1986]. This paper studies the problem of finding an
optimal approximating tree. The star decomposition scheme for sets of three
binary variables [Lazarsfeld,1966; Pearl,1986] is shown to enhance the class of
probability dis... | computer science |
19,879 | Using Potential Influence Diagrams for Probabilistic Inference and
Decision Making | cs.AI | The potential influence diagram is a generalization of the standard
"conditional" influence diagram, a directed network representation for
probabilistic inference and decision analysis [Ndilikilikesha, 1991]. It allows
efficient inference calculations corresponding exactly to those on undirected
graphs. In this paper, ... | computer science |
19,880 | Deciding Morality of Graphs is NP-complete | cs.AI | In order to find a causal explanation for data presented in the form of
covariance and concentration matrices it is necessary to decide if the graph
formed by such associations is a projection of a directed acyclic graph (dag).
We show that the general problem of deciding whether such a dag exists is
NP-complete. | computer science |
19,881 | Incremental computation of the value of perfect information in
stepwise-decomposable influence diagrams | cs.AI | To determine the value of perfect information in an influence diagram, one
needs first to modify the diagram to reflect the change in information
availability, and then to compute the optimal expected values of both the
original diagram and the modified diagram. The value of perfect information is
the difference betwee... | computer science |
19,882 | Argumentative inference in uncertain and inconsistent knowledge bases | cs.AI | This paper presents and discusses several methods for reasoning from
inconsistent knowledge bases. A so-called argumentative-consequence relation
taking into account the existence of consistent arguments in favor of a
conclusion and the absence of consistent arguments in favor of its contrary, is
particularly investiga... | computer science |
19,883 | Argument Calculus and Networks | cs.AI | A major reason behind the success of probability calculus is that it
possesses a number of valuable tools, which are based on the notion of
probabilistic independence. In this paper, I identify a notion of logical
independence that makes some of these tools available to a class of
propositional databases, called argume... | computer science |
19,884 | Argumentation as a General Framework for Uncertain Reasoning | cs.AI | Argumentation is the process of constructing arguments about propositions,
and the assignment of statements of confidence to those propositions based on
the nature and relative strength of their supporting arguments. The process is
modelled as a labelled deductive system, in which propositions are doubly
labelled with ... | computer science |
19,885 | On reasoning in networks with qualitative uncertainty | cs.AI | In this paper some initial work towards a new approach to qualitative
reasoning under uncertainty is presented. This method is not only applicable to
qualitative probabilistic reasoning, as is the case with other methods, but
also allows the qualitative propagation within networks of values based upon
possibility theor... | computer science |
19,886 | Qualitative Measures of Ambiguity | cs.AI | This paper introduces a qualitative measure of ambiguity and analyses its
relationship with other measures of uncertainty. Probability measures relative
likelihoods, while ambiguity measures vagueness surrounding those judgments.
Ambiguity is an important representation of uncertain knowledge. It deals with
a different... | computer science |
19,887 | A Bayesian Variant of Shafer's Commonalities For Modelling Unforeseen
Events | cs.AI | Shafer's theory of belief and the Bayesian theory of probability are two
alternative and mutually inconsistent approaches toward modelling uncertainty
in artificial intelligence. To help reduce the conflict between these two
approaches, this paper reexamines expected utility theory-from which Bayesian
probability theor... | computer science |
19,888 | The Probability of a Possibility: Adding Uncertainty to Default Rules | cs.AI | We present a semantics for adding uncertainty to conditional logics for
default reasoning and belief revision. We are able to treat conditional
sentences as statements of conditional probability, and express rules for
revision such as "If A were believed, then B would be believed to degree p."
This method of revision e... | computer science |
19,889 | Possibilistic decreasing persistence | cs.AI | A key issue in the handling of temporal data is the treatment of persistence;
in most approaches it consists in inferring defeasible confusions by
extrapolating from the actual knowledge of the history of the world; we propose
here a gradual modelling of persistence, following the idea that persistence is
decreasing (t... | computer science |
19,890 | Discounting and Combination Operations in Evidential Reasoning | cs.AI | Evidential reasoning is now a leading topic in Artificial Intelligence.
Evidence is represented by a variety of evidential functions. Evidential
reasoning is carried out by certain kinds of fundamental operation on these
functions. This paper discusses two of the basic operations on evidential
functions, the discount o... | computer science |
19,891 | Probabilistic Assumption-Based Reasoning | cs.AI | The classical propositional assumption-based model is extended to incorporate
probabilities for the assumptions. Then it is placed into the framework of
evidence theory. Several authors like Laskey, Lehner (1989) and Provan (1990)
already proposed a similar point of view, but the first paper is not as much
concerned wi... | computer science |
19,892 | Partially Specified Belief Functions | cs.AI | This paper presents a procedure to determine a complete belief function from
the known values of belief for some of the subsets of the frame of discerment.
The method is based on the principle of minimum commitment and a new principle
called the focusing principle. This additional principle is based on the idea
that be... | computer science |
19,893 | Jeffrey's rule of conditioning generalized to belief functions | cs.AI | Jeffrey's rule of conditioning has been proposed in order to revise a
probability measure by another probability function. We generalize it within
the framework of the models based on belief functions. We show that several
forms of Jeffrey's conditionings can be defined that correspond to the
geometrical rule of condit... | computer science |
19,894 | Inference with Possibilistic Evidence | cs.AI | In this paper, the concept of possibilistic evidence which is a possibility
distribution as well as a body of evidence is proposed over an infinite
universe of discourse. The inference with possibilistic evidence is
investigated based on a unified inference framework maintaining both the
compatibility of concepts and t... | computer science |
19,895 | Constructing Lower Probabilities | cs.AI | An elaboration of Dempster's method of constructing belief functions suggests
a broadly applicable strategy for constructing lower probabilities under a
variety of evidentiary constraints. | computer science |
19,896 | Belief Revision in Probability Theory | cs.AI | In a probability-based reasoning system, Bayes' theorem and its variations
are often used to revise the system's beliefs. However, if the explicit
conditions and the implicit conditions of probability assignments `me properly
distinguished, it follows that Bayes' theorem is not a generally applicable
revision rule. Upo... | computer science |
19,897 | The Assumptions Behind Dempster's Rule | cs.AI | This paper examines the concept of a combination rule for belief functions.
It is shown that two fairly simple and apparently reasonable assumptions
determine Dempster's rule, giving a new justification for it. | computer science |
19,898 | A Belief-Function Based Decision Support System | cs.AI | In this paper, we present a decision support system based on belief functions
and the pignistic transformation. The system is an integration of an evidential
system for belief function propagation and a valuation-based system for
Bayesian decision analysis. The two subsystems are connected through the
pignistic transfo... | computer science |
19,899 | Computing as compression: the SP theory of intelligence | cs.AI | This paper provides an overview of the SP theory of intelligence and its
central idea that artificial intelligence, mainstream computing, and much of
human perception and cognition, may be understood as information compression.
The background and origins of the SP theory are described, and the main
elements of the th... | computer science |
19,900 | Generating extrema approximation of analytically incomputable functions
through usage of parallel computer aided genetic algorithms | cs.AI | This paper presents capabilities of using genetic algorithms to find
approximations of function extrema, which cannot be found using analytic ways.
To enhance effectiveness of calculations, algorithm has been parallelized using
OpenMP library. We gained much increase in speed on platforms using
multithreaded processors... | computer science |
19,901 | Discovering Semantic Spatial and Spatio-Temporal Outliers from Moving
Object Trajectories | cs.AI | Several algorithms have been proposed for discovering patterns from
trajectories of moving objects, but only a few have concentrated on outlier
detection. Existing approaches, in general, discover spatial outliers, and do
not provide any further analysis of the patterns. In this paper we introduce
semantic spatial and ... | computer science |
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