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18,902 | A Calculus for Causal Relevance | cs.AI | This paper presents a sound and completecalculus for causal relevance, based
onPearl's functional models semantics.The calculus consists of axioms and
rulesof inference for reasoning about causalrelevance relationships.We extend
the set of known axioms for causalrelevance with three new axioms, andintroduce
two new rul... | computer science |
18,903 | UCP-Networks: A Directed Graphical Representation of Conditional
Utilities | cs.AI | We propose a new directed graphical representation of utility functions,
called UCP-networks, that combines aspects of two existing graphical models:
generalized additive models and CP-networks. The network decomposes a utility
function into a number of additive factors, with the directionality of the arcs
reflecting c... | computer science |
18,904 | Confidence Inference in Bayesian Networks | cs.AI | We present two sampling algorithms for probabilistic confidence inference in
Bayesian networks. These two algorithms (we call them AIS-BN-mu and
AIS-BN-sigma algorithms) guarantee that estimates of posterior probabilities
are with a given probability within a desired precision bound. Our algorithms
are based on recent ... | computer science |
18,905 | Linearity Properties of Bayes Nets with Binary Variables | cs.AI | It is "well known" that in linear models: (1) testable constraints on the
marginal distribution of observed variables distinguish certain cases in which
an unobserved cause jointly influences several observed variables; (2) the
technique of "instrumental variables" sometimes permits an estimation of the
influence of on... | computer science |
18,906 | Hybrid Processing of Beliefs and Constraints | cs.AI | This paper explores algorithms for processing probabilistic and deterministic
information when the former is represented as a belief network and the latter
as a set of boolean clauses. The motivating tasks are 1. evaluating beliefs
networks having a large number of deterministic relationships and2. evaluating
probabili... | computer science |
18,907 | A Comparison of Axiomatic Approaches to Qualitative Decision Making
Using Possibility Theory | cs.AI | In this paper we analyze two recent axiomatic approaches proposed by Dubois
et al and by Giang and Shenoy to qualitative decision making where uncertainty
is described by possibility theory. Both axiomtizations are inspired by von
Neumann and Morgenstern's system of axioms for the case of probability theory.
We show th... | computer science |
18,908 | Enumerating Markov Equivalence Classes of Acyclic Digraph Models | cs.AI | Graphical Markov models determined by acyclic digraphs (ADGs), also called
directed acyclic graphs (DAGs), are widely studied in statistics, computer
science (as Bayesian networks), operations research (as influence diagrams),
and many related fields. Because different ADGs may determine the same Markov
equivalence cla... | computer science |
18,909 | Similarity Measures on Preference Structures, Part II: Utility Functions | cs.AI | In previous work cite{Ha98:Towards} we presented a case-based approach to
eliciting and reasoning with preferences. A key issue in this approach is the
definition of similarity between user preferences. We introduced the
probabilistic distance as a measure of similarity on user preferences, and
provided an algorithm to... | computer science |
18,910 | Causes and Explanations: A Structural-Model Approach --- Part 1: Causes | cs.AI | We propose a new definition of actual causes, using structural equations to
model counterfactuals.We show that the definitions yield a plausible and
elegant account ofcausation that handles well examples which have caused
problems forother definitions and resolves major difficulties in the
traditionalaccount. In a comp... | computer science |
18,911 | A Bayesian Approach to Tackling Hard Computational Problems | cs.AI | We are developing a general framework for using learned Bayesian models for
decision-theoretic control of search and reasoningalgorithms. We illustrate the
approach on the specific task of controlling both general and domain-specific
solvers on a hard class of structured constraint satisfaction problems. A
successful s... | computer science |
18,912 | On characterizing Inclusion of Bayesian Networks | cs.AI | Every directed acyclic graph (DAG) over a finite non-empty set of variables
(= nodes) N induces an independence model over N, which is a list of
conditional independence statements over N.The inclusion problem is how to
characterize (in graphical terms) whether all independence statements in the
model induced by a DAG ... | computer science |
18,913 | Plausible reasoning from spatial observations | cs.AI | This article deals with plausible reasoning from incomplete knowledge about
large-scale spatial properties. The availableinformation, consisting of a set
of pointwise observations,is extrapolated to neighbour points. We make use of
belief functions to represent the influence of the knowledge at a given point
to another... | computer science |
18,914 | Hypothesis Management in Situation-Specific Network Construction | cs.AI | This paper considers the problem of knowledge-based model construction in the
presence of uncertainty about the association of domain entities to random
variables. Multi-entity Bayesian networks (MEBNs) are defined as a
representation for knowledge in domains characterized by uncertainty in the
number of relevant entit... | computer science |
18,915 | Inference in Hybrid Networks: Theoretical Limits and Practical
Algorithms | cs.AI | An important subclass of hybrid Bayesian networks are those that represent
Conditional Linear Gaussian (CLG) distributions --- a distribution with a
multivariate Gaussian component for each instantiation of the discrete
variables. In this paper we explore the problem of inference in CLGs. We show
that inference in CLGs... | computer science |
18,916 | Exact Inference in Networks with Discrete Children of Continuous Parents | cs.AI | Many real life domains contain a mixture of discrete and continuous variables
and can be modeled as hybrid Bayesian Networks. Animportant subclass of hybrid
BNs are conditional linear Gaussian (CLG) networks, where the conditional
distribution of the continuous variables given an assignment to the discrete
variables is... | computer science |
18,917 | Probabilistic Logic Programming under Inheritance with Overriding | cs.AI | We present probabilistic logic programming under inheritance with overriding.
This approach is based on new notions of entailment for reasoning with
conditional constraints, which are obtained from the classical notion of
logical entailment by adding the principle of inheritance with overriding. This
is done by using r... | computer science |
18,918 | Solving Influence Diagrams using HUGIN, Shafer-Shenoy and Lazy
Propagation | cs.AI | In this paper we compare three different architectures for the evaluation of
influence diagrams: HUGIN, Shafer-Shenoy, and Lazy Evaluation architecture. The
computational complexity of the architectures are compared on the LImited
Memory Influence Diagram (LIMID): a diagram where only the requiste information
for the c... | computer science |
18,919 | Aggregating Learned Probabilistic Beliefs | cs.AI | We consider the task of aggregating beliefs of severalexperts. We assume that
these beliefs are represented as probabilitydistributions. We argue that the
evaluation of any aggregationtechnique depends on the semantic context of this
task. We propose aframework, in which we assume that nature generates samples
from a`t... | computer science |
18,920 | Recognition Networks for Approximate Inference in BN20 Networks | cs.AI | We propose using recognition networks for approximate inference inBayesian
networks (BNs). A recognition network is a multilayerperception (MLP) trained
to predict posterior marginals given observedevidence in a particular BN. The
input to the MLP is a vector of thestates of the evidential nodes. The activity
of an out... | computer science |
18,921 | The Factored Frontier Algorithm for Approximate Inference in DBNs | cs.AI | The Factored Frontier (FF) algorithm is a simple approximate
inferencealgorithm for Dynamic Bayesian Networks (DBNs). It is very similar
tothe fully factorized version of the Boyen-Koller (BK) algorithm, butinstead
of doing an exact update at every step followed bymarginalisation (projection),
it always works with fact... | computer science |
18,922 | A Case Study in Knowledge Discovery and Elicitation in an Intelligent
Tutoring Application | cs.AI | Most successful Bayesian network (BN) applications to datehave been built
through knowledge elicitation from experts.This is difficult and time
consuming, which has lead to recentinterest in automated methods for learning
BNs from data. We present a case study in the construction of a BN in
anintelligent tutoring appli... | computer science |
18,923 | Approximating MAP using Local Search | cs.AI | MAP is the problem of finding a most probable instantiation of a set of
variables in a Bayesian network, given evidence. Unlike computing marginals,
posteriors, and MPE (a special case of MAP), the time and space complexity of
MAP is not only exponential in the network treewidth, but also in a larger
parameter known as... | computer science |
18,924 | Sufficiency, Separability and Temporal Probabilistic Models | cs.AI | Suppose we are given the conditional probability of one variable given some
other variables.Normally the full joint distribution over the conditioning
variablesis required to determine the probability of the conditioned
variable.Under what circumstances are the marginal distributions over the
conditioning variables suf... | computer science |
18,925 | Toward General Analysis of Recursive Probability Models | cs.AI | There is increasing interest within the research community in the design and
use of recursive probability models. Although there still remains concern about
computational complexity costs and the fact that computing exact solutions can
be intractable for many nonrecursive models and impossible in the general case
for r... | computer science |
18,926 | Vector-space Analysis of Belief-state Approximation for POMDPs | cs.AI | We propose a new approach to value-directed belief state approximation for
POMDPs. The value-directed model allows one to choose approximation methods for
belief state monitoring that have a small impact on decision quality. Using a
vector space analysis of the problem, we devise two new search procedures for
selecting... | computer science |
18,927 | Value-Directed Sampling Methods for POMDPs | cs.AI | We consider the problem of approximate belief-state monitoring using particle
filtering for the purposes of implementing a policy for a partially-observable
Markov decision process (POMDP). While particle filtering has become a
widely-used tool in AI for monitoring dynamical systems, rather scant attention
has been pai... | computer science |
18,928 | Decision-Theoretic Planning with Concurrent Temporally Extended Actions | cs.AI | We investigate a model for planning under uncertainty with temporallyextended
actions, where multiple actions can be taken concurrently at each decision
epoch. Our model is based on the options framework, and combines it with
factored state space models,where the set of options can be partitioned into
classes that affe... | computer science |
18,929 | A Tractable POMDP for a Class of Sequencing Problems | cs.AI | We consider a partially observable Markov decision problem (POMDP) that
models a class of sequencing problems. Although POMDPs are typically
intractable, our formulation admits tractable solution. Instead of maintaining
a value function over a high-dimensional set of belief states, we reduce the
state space to one of s... | computer science |
18,930 | Causal Discovery from Changes | cs.AI | We propose a new method of discovering causal structures, based on the
detection of local, spontaneous changes in the underlying data-generating
model. We analyze the classes of structures that are equivalent relative to a
stream of distributions produced by local changes, and devise algorithms that
output graphical re... | computer science |
18,931 | Bayesian Error-Bars for Belief Net Inference | cs.AI | A Bayesian Belief Network (BN) is a model of a joint distribution over a
setof n variables, with a DAG structure to represent the immediate
dependenciesbetween the variables, and a set of parameters (aka CPTables) to
represent thelocal conditional probabilities of a node, given each assignment
to itsparents. In many si... | computer science |
18,932 | Analysing Sensitivity Data from Probabilistic Networks | cs.AI | With the advance of efficient analytical methods for sensitivity analysis
ofprobabilistic networks, the interest in the sensitivities revealed by
real-life networks is rekindled. As the amount of data resulting from a
sensitivity analysis of even a moderately-sized network is alreadyoverwhelming,
methods for extracting... | computer science |
18,933 | Planning and Acting under Uncertainty: A New Model for Spoken Dialogue
Systems | cs.AI | Uncertainty plays a central role in spoken dialogue systems. Some stochastic
models like Markov decision process (MDP) are used to model the dialogue
manager. But the partially observable system state and user intention hinder
the natural representation of the dialogue state. MDP-based system degrades
fast when uncerta... | computer science |
18,934 | Crowd Labeling: a survey | cs.AI | Recently, there has been a burst in the number of research projects on human
computation via crowdsourcing. Multiple choice (or labeling) questions could be
referred to as a common type of problem which is solved by this approach. As an
application, crowd labeling is applied to find true labels for large machine
learni... | computer science |
18,935 | A Complete Calculus for Possibilistic Logic Programming with Fuzzy
Propositional Variables | cs.AI | In this paper we present a propositional logic programming language for
reasoning under possibilistic uncertainty and representing vague knowledge.
Formulas are represented by pairs (A, c), where A is a many-valued proposition
and c is value in the unit interval [0,1] which denotes a lower bound on the
belief on A in t... | computer science |
18,936 | Perfect Tree-Like Markovian Distributions | cs.AI | We show that if a strictly positive joint probability distribution for a set
of binary random variables factors according to a tree, then vertex separation
represents all and only the independence relations enclosed in the
distribution. The same result is shown to hold also for multivariate strictly
positive normal dis... | computer science |
18,937 | A Principled Analysis of Merging Operations in Possibilistic Logic | cs.AI | Possibilistic logic offers a qualitative framework for representing pieces of
information associated with levels of uncertainty of priority. The fusion of
multiple sources information is discussed in this setting. Different classes of
merging operators are considered including conjunctive, disjunctive,
reinforcement, a... | computer science |
18,938 | The Complexity of Decentralized Control of Markov Decision Processes | cs.AI | Planning for distributed agents with partial state information is considered
from a decision- theoretic perspective. We describe generalizations of both the
MDP and POMDP models that allow for decentralized control. For even a small
number of agents, the finite-horizon problems corresponding to both of our
models are c... | computer science |
18,939 | Approximately Optimal Monitoring of Plan Preconditions | cs.AI | Monitoring plan preconditions can allow for replanning when a precondition
fails, generally far in advance of the point in the plan where the precondition
is relevant. However, monitoring is generally costly, and some precondition
failures have a very small impact on plan quality. We formulate a model for
optimal preco... | computer science |
18,940 | Computational Investigation of Low-Discrepancy Sequences in Simulation
Algorithms for Bayesian Networks | cs.AI | Monte Carlo sampling has become a major vehicle for approximate inference in
Bayesian networks. In this paper, we investigate a family of related simulation
approaches, known collectively as quasi-Monte Carlo methods based on
deterministic low-discrepancy sequences. We first outline several theoretical
aspects of deter... | computer science |
18,941 | A Decision Theoretic Approach to Targeted Advertising | cs.AI | A simple advertising strategy that can be used to help increase sales of a
product is to mail out special offers to selected potential customers. Because
there is a cost associated with sending each offer, the optimal mailing
strategy depends on both the benefit obtained from a purchase and how the offer
affects the bu... | computer science |
18,942 | A Bayesian Method for Causal Modeling and Discovery Under Selection | cs.AI | This paper describes a Bayesian method for learning causal networks using
samples that were selected in a non-random manner from a population of
interest. Examples of data obtained by non-random sampling include convenience
samples and case-control data in which a fixed number of samples with and
without some condition... | computer science |
18,943 | Separation Properties of Sets of Probability Measures | cs.AI | This paper analyzes independence concepts for sets of probability measures
associated with directed acyclic graphs. The paper shows that epistemic
independence and the standard Markov condition violate desirable separation
properties. The adoption of a contraction condition leads to d-separation but
still fails to guar... | computer science |
18,944 | Stochastic Logic Programs: Sampling, Inference and Applications | cs.AI | Algorithms for exact and approximate inference in stochastic logic programs
(SLPs) are presented, based respectively, on variable elimination and
importance sampling. We then show how SLPs can be used to represent prior
distributions for machine learning, using (i) logic programs and (ii) Bayes net
structures as exampl... | computer science |
18,945 | A Differential Approach to Inference in Bayesian Networks | cs.AI | We present a new approach for inference in Bayesian networks, which is mainly
based on partial differentiation. According to this approach, one compiles a
Bayesian network into a multivariate polynomial and then computes the partial
derivatives of this polynomial with respect to each variable. We show that once
such de... | computer science |
18,946 | Any-Space Probabilistic Inference | cs.AI | We have recently introduced an any-space algorithm for exact inference in
Bayesian networks, called Recursive Conditioning, RC, which allows one to trade
space with time at increments of X-bytes, where X is the number of bytes needed
to cache a floating point number. In this paper, we present three key
extensions of RC... | computer science |
18,947 | Likelihood Computations Using Value Abstractions | cs.AI | In this paper, we use evidence-specific value abstraction for speeding
Bayesian networks inference. This is done by grouping variable values and
treating the combined values as a single entity. As we show, such abstractions
can exploit regularities in conditional probability distributions and also the
specific values o... | computer science |
18,948 | A Qualitative Linear Utility Theory for Spohn's Theory of Epistemic
Beliefs | cs.AI | In this paper, we formulate a qualitative "linear" utility theory for
lotteries in which uncertainty is expressed qualitatively using a Spohnian
disbelief function. We argue that a rational decision maker facing an uncertain
decision problem in which the uncertainty is expressed qualitatively should
behave so as to max... | computer science |
18,949 | Building a Stochastic Dynamic Model of Application Use | cs.AI | Many intelligent user interfaces employ application and user models to
determine the user's preferences, goals and likely future actions. Such models
require application analysis, adaptation and expansion. Building and
maintaining such models adds a substantial amount of time and labour to the
application development c... | computer science |
18,950 | Maximum Entropy and the Glasses You Are Looking Through | cs.AI | We give an interpretation of the Maximum Entropy (MaxEnt) Principle in
game-theoretic terms. Based on this interpretation, we make a formal
distinction between different ways of {em applying/} Maximum Entropy
distributions. MaxEnt has frequently been criticized on the grounds that it
leads to highly representation depe... | computer science |
18,951 | Probabilistic Arc Consistency: A Connection between Constraint Reasoning
and Probabilistic Reasoning | cs.AI | We document a connection between constraint reasoning and probabilistic
reasoning. We present an algorithm, called {em probabilistic arc consistency},
which is both a generalization of a well known algorithm for arc consistency
used in constraint reasoning, and a specialization of the belief updating
algorithm for sing... | computer science |
18,952 | Marginalization in Composed Probabilistic Models | cs.AI | Composition of low-dimensional distributions, whose foundations were laid in
the papaer published in the Proceeding of UAI'97 (Jirousek 1997), appeared to
be an alternative apparatus to describe multidimensional probabilistic models.
In contrast to Graphical Markov Models, which define multidomensinoal
distributions in... | computer science |
18,953 | Making Sensitivity Analysis Computationally Efficient | cs.AI | To investigate the robustness of the output probabilities of a Bayesian
network, a sensitivity analysis can be performed. A one-way sensitivity
analysis establishes, for each of the probability parameters of a network, a
function expressing a posterior marginal probability of interest in terms of
the parameter. Current... | computer science |
18,954 | Policy Iteration for Factored MDPs | cs.AI | Many large MDPs can be represented compactly using a dynamic Bayesian
network. Although the structure of the value function does not retain the
structure of the process, recent work has shown that value functions in
factored MDPs can often be approximated well using a decomposed value function:
a linear combination of ... | computer science |
18,955 | Causal Mechanism-based Model Construction | cs.AI | We propose a framework for building graphical causal model that is based on
the concept of causal mechanisms. Causal models are intuitive for human users
and, more importantly, support the prediction of the effect of manipulation. We
describe an implementation of the proposed framework as an interactive model
construct... | computer science |
18,956 | Credal Networks under Maximum Entropy | cs.AI | We apply the principle of maximum entropy to select a unique joint
probability distribution from the set of all joint probability distributions
specified by a credal network. In detail, we start by showing that the unique
joint distribution of a Bayesian tree coincides with the maximum entropy model
of its conditional ... | computer science |
18,957 | Risk Agoras: Dialectical Argumentation for Scientific Reasoning | cs.AI | We propose a formal framework for intelligent systems which can reason about
scientific domains, in particular about the carcinogenicity of chemicals, and
we study its properties. Our framework is grounded in a philosophy of
scientific enquiry and discourse, and uses a model of dialectical
argumentation. The formalism ... | computer science |
18,958 | Probabilistic Models for Agents' Beliefs and Decisions | cs.AI | Many applications of intelligent systems require reasoning about the mental
states of agents in the domain. We may want to reason about an agent's beliefs,
including beliefs about other agents; we may also want to reason about an
agent's preferences, and how his beliefs and preferences relate to his
behavior. We define... | computer science |
18,959 | Representing and Solving Asymmetric Bayesian Decision Problems | cs.AI | This paper deals with the representation and solution of asymmetric Bayesian
decision problems. We present a formal framework, termed asymmetric influence
diagrams, that is based on the influence diagram and allows an efficient
representation of asymmetric decision problems. As opposed to existing
frameworks, the asymm... | computer science |
18,960 | Using ROBDDs for Inference in Bayesian Networks with Troubleshooting as
an Example | cs.AI | When using Bayesian networks for modelling the behavior of man-made
machinery, it usually happens that a large part of the model is deterministic.
For such Bayesian networks deterministic part of the model can be represented
as a Boolean function, and a central part of belief updating reduces to the
task of calculating... | computer science |
18,961 | Evaluating Influence Diagrams using LIMIDs | cs.AI | We present a new approach to the solution of decision problems formulated as
influence diagrams. The approach converts the influence diagram into a simpler
structure, the LImited Memory Influence Diagram (LIMID), where only the
requisite information for the computation of optimal policies is depicted.
Because the requi... | computer science |
18,962 | Conversation as Action Under Uncertainty | cs.AI | Conversations abound with uncetainties of various kinds. Treating
conversation as inference and decision making under uncertainty, we propose a
task independent, multimodal architecture for supporting robust continuous
spoken dialog called Quartet. We introduce four interdependent levels of
analysis, and describe repre... | computer science |
18,963 | Value-Directed Belief State Approximation for POMDPs | cs.AI | We consider the problem belief-state monitoring for the purposes of
implementing a policy for a partially-observable Markov decision process
(POMDP), specifically how one might approximate the belief state. Other schemes
for belief-state approximation (e.g., based on minimixing a measures such as
KL-diveregence between... | computer science |
18,964 | Probabilistic State-Dependent Grammars for Plan Recognition | cs.AI | Techniques for plan recognition under uncertainty require a stochastic model
of the plan-generation process. We introduce Probabilistic State-Dependent
Grammars (PSDGs) to represent an agent's plan-generation process. The PSDG
language model extends probabilistic context-free grammars (PCFGs) by allowing
production pro... | computer science |
18,965 | Pivotal Pruning of Trade-offs in QPNs | cs.AI | Qualitative probabilistic networks have been designed for probabilistic
reasoning in a qualitative way. Due to their coarse level of representation
detail, qualitative probabilistic networks do not provide for resolving
trade-offs and typically yield ambiguous results upon inference. We present an
algorithm for computi... | computer science |
18,966 | A Knowledge Acquisition Tool for Bayesian-Network Troubleshooters | cs.AI | This paper describes a domain-specific knowledge acquisition tool for
intelligent automated troubleshooters based on Bayesian networks. No Bayesian
network knowledge is required to use the tool, and troubleshooting information
can be specified as natural and intuitive as possible. Probabilities can be
specified in the ... | computer science |
18,967 | On the Use of Skeletons when Learning in Bayesian Networks | cs.AI | In this paper, we present a heuristic operator which aims at simultaneously
optimizing the orientations of all the edges in an intermediate Bayesian
network structure during the search process. This is done by alternating
between the space of directed acyclic graphs (DAGs) and the space of skeletons.
The found orientat... | computer science |
18,968 | Probabilities of Causation: Bounds and Identification | cs.AI | This paper deals with the problem of estimating the probability that one
event was a cause of another in a given scenario. Using structural-semantical
definitions of the probabilities of necessary or sufficient causation (or
both), we show how to optimally bound these quantities from data obtained in
experimental and o... | computer science |
18,969 | Conditional Independence and Markov Properties in Possibility Theory | cs.AI | Conditional independence and Markov properties are powerful tools allowing
expression of multidimensional probability distributions by means of
low-dimensional ones. As multidimensional possibilistic models have been
studied for several years, the demand for analogous tools in possibility theory
seems to be quite natur... | computer science |
18,970 | Exploiting Qualitative Knowledge in the Learning of Conditional
Probabilities of Bayesian Networks | cs.AI | Algorithms for learning the conditional probabilities of Bayesian networks
with hidden variables typically operate within a high-dimensional search space
and yield only locally optimal solutions. One way of limiting the search space
and avoiding local optima is to impose qualitative constraints that are based
on backgr... | computer science |
18,971 | View-based propagation of decomposable constraints | cs.AI | Constraints that may be obtained by composition from simpler constraints are
present, in some way or another, in almost every constraint program. The
decomposition of such constraints is a standard technique for obtaining an
adequate propagation algorithm from a combination of propagators designed for
simpler constrain... | computer science |
18,972 | User Interface Tools for Navigation in Conditional Probability Tables
and Elicitation of Probabilities in Bayesian Networks | cs.AI | Elicitation of probabilities is one of the most laborious tasks in building
decision-theoretic models, and one that has so far received only moderate
attention in decision-theoretic systems. We propose a set of user interface
tools for graphical probabilistic models, focusing on two aspects of
probability elicitation: ... | computer science |
18,973 | Proceedings of the Twenty-Eighth Conference on Uncertainty in Artificial
Intelligence (2012) | cs.AI | This is the Proceedings of the Twenty-Eighth Conference on Uncertainty in
Artificial Intelligence, which was held on Catalina Island, CA August 14-18
2012. | computer science |
18,974 | Proceedings of the Nineteenth Conference on Uncertainty in Artificial
Intelligence (2003) | cs.AI | This is the Proceedings of the Nineteenth Conference on Uncertainty in
Artificial Intelligence, which was held in Acapulco, Mexico, August 7-10 2003 | computer science |
18,975 | Proceedings of the Seventeenth Conference on Uncertainty in Artificial
Intelligence (2001) | cs.AI | This is the Proceedings of the Seventeenth Conference on Uncertainty in
Artificial Intelligence, which was held in Seattle, WA, August 2-5 2001 | computer science |
18,976 | Proceedings of the Eighteenth Conference on Uncertainty in Artificial
Intelligence (2002) | cs.AI | This is the Proceedings of the Eighteenth Conference on Uncertainty in
Artificial Intelligence, which was held in Alberta, Canada, August 1-4 2002 | computer science |
18,977 | English Sentence Recognition using Artificial Neural Network through
Mouse-based Gestures | cs.AI | Handwriting is one of the most important means of daily communication.
Although the problem of handwriting recognition has been considered for more
than 60 years there are still many open issues, especially in the task of
unconstrained handwritten sentence recognition. This paper focuses on the
automatic system that re... | computer science |
18,978 | Knowledge Base Approach for 3D Objects Detection in Point Clouds Using
3D Processing and Specialists Knowledge | cs.AI | This paper presents a knowledge-based detection of objects approach using the
OWL ontology language, the Semantic Web Rule Language, and 3D processing
built-ins aiming at combining geometrical analysis of 3D point clouds and
specialist's knowledge. Here, we share our experience regarding the creation of
3D semantic fac... | computer science |
18,979 | From 9-IM Topological Operators to Qualitative Spatial Relations using
3D Selective Nef Complexes and Logic Rules for bodies | cs.AI | This paper presents a method to compute automatically topological relations
using SWRL rules. The calculation of these rules is based on the definition of
a Selective Nef Complexes Nef Polyhedra structure generated from standard
Polyhedron. The Selective Nef Complexes is a data model providing a set of
binary Boolean o... | computer science |
18,980 | Computer Poker Research at LIACC | cs.AI | Computer Poker's unique characteristics present a well-suited challenge for
research in artificial intelligence. For that reason, and due to the Poker's
market increase in popularity in Portugal since 2008, several members of LIACC
have researched in this field. Several works were published as papers and
master theses ... | computer science |
18,981 | A Framework for Intelligent Medical Diagnosis using Rough Set with
Formal Concept Analysis | cs.AI | Medical diagnosis process vary in the degree to which they attempt to deal
with different complicating aspects of diagnosis such as relative importance of
symptoms, varied symptom pattern and the relation between diseases them selves.
Based on decision theory, in the past many mathematical models such as crisp
set, pro... | computer science |
18,982 | Developing Parallel Dependency Graph In Improving Game Balancing | cs.AI | The dependency graph is a data architecture that models all the dependencies
between the different types of assets in the game. It depicts the
dependency-based relationships between the assets of a game. For example, a
player must construct an arsenal before he can build weapons. It is vital that
the dependency graph o... | computer science |
18,983 | Subjective Reality and Strong Artificial Intelligence | cs.AI | The main prospective aim of modern research related to Artificial
Intelligence is the creation of technical systems that implement the idea of
Strong Intelligence. According our point of view the path to the development of
such systems comes through the research in the field related to perceptions.
Here we formulate th... | computer science |
18,984 | A Temporal Bayesian Network for Diagnosis and Prediction | cs.AI | Diagnosis and prediction in some domains, like medical and industrial
diagnosis, require a representation that combines uncertainty management and
temporal reasoning. Based on the fact that in many cases there are few state
changes in the temporal range of interest, we propose a novel representation
called Temporal Nod... | computer science |
18,985 | Possibilistic logic bases and possibilistic graphs | cs.AI | Possibilistic logic bases and possibilistic graphs are two different
frameworks of interest for representing knowledge. The former stratifies the
pieces of knowledge (expressed by logical formulas) according to their level of
certainty, while the latter exhibits relationships between variables. The two
types of represe... | computer science |
18,986 | Artificial Decision Making Under Uncertainty in Intelligent Buildings | cs.AI | Our hypothesis is that by equipping certain agents in a multi-agent system
controlling an intelligent building with automated decision support, two
important factors will be increased. The first is energy saving in the
building. The second is customer value---how the people in the building
experience the effects of the... | computer science |
18,987 | Reasoning With Conditional Ceteris Paribus Preference Statem | cs.AI | In many domains it is desirable to assess the preferences of users in a
qualitative rather than quantitative way. Such representations of qualitative
preference orderings form an importnat component of automated decision tools.
We propose a graphical representation of preferences that reflects conditional
dependence an... | computer science |
18,988 | Causal Discovery from a Mixture of Experimental and Observational Data | cs.AI | This paper describes a Bayesian method for combining an arbitrary mixture of
observational and experimental data in order to learn causal Bayesian networks.
Observational data are passively observed. Experimental data, such as that
produced by randomized controlled trials, result from the experimenter
manipulating one ... | computer science |
18,989 | Loglinear models for first-order probabilistic reasoning | cs.AI | Recent work on loglinear models in probabilistic constraint logic programming
is applied to first-order probabilistic reasoning. Probabilities are defined
directly on the proofs of atomic formulae, and by marginalisation on the atomic
formulae themselves. We use Stochastic Logic Programs (SLPs) composed of
labelled and... | computer science |
18,990 | A Hybrid Anytime Algorithm for the Constructiion of Causal Models From
Sparse Data | cs.AI | We present a hybrid constraint-based/Bayesian algorithm for learning causal
networks in the presence of sparse data. The algorithm searches the space of
equivalence classes of models (essential graphs) using a heuristic based on
conventional constraint-based techniques. Each essential graph is then
converted into a dir... | computer science |
18,991 | Hybrid Probabilistic Programs: Algorithms and Complexity | cs.AI | Hybrid Probabilistic Programs (HPPs) are logic programs that allow the
programmer to explicitly encode his knowledge of the dependencies between
events being described in the program. In this paper, we classify HPPs into
three classes called HPP_1,HPP_2 and HPP_r,r>= 3. For these classes, we provide
three types of resu... | computer science |
18,992 | Assessing the value of a candidate. Comparing belief function and
possibility theories | cs.AI | The problem of assessing the value of a candidate is viewed here as a
multiple combination problem. On the one hand a candidate can be evaluated
according to different criteria, and on the other hand several experts are
supposed to assess the value of candidates according to each criterion.
Criteria are not equally imp... | computer science |
18,993 | Qualitative Models for Decision Under Uncertainty without the
Commensurability Assumption | cs.AI | This paper investigates a purely qualitative version of Savage's theory for
decision making under uncertainty. Until now, most representation theorems for
preference over acts rely on a numerical representation of utility and
uncertainty where utility and uncertainty are commensurate. Disrupting the
tradition, we relax... | computer science |
18,994 | On Transformations between Probability and Spohnian Disbelief Functions | cs.AI | In this paper, we analyze the relationship between probability and Spohn's
theory for representation of uncertain beliefs. Using the intuitive idea that
the more probable a proposition is, the more believable it is, we study
transformations from probability to Sphonian disbelief and vice-versa. The
transformations desc... | computer science |
18,995 | A New Model of Plan Recognition | cs.AI | We present a new abductive, probabilistic theory of plan recognition. This
model differs from previous plan recognition theories in being centered around
a model of plan execution: most previous methods have been based on plans as
formal objects or on rules describing the recognition process. We show that our
new model... | computer science |
18,996 | A Hybrid Approach to Reasoning with Partially Elicited Preference Models | cs.AI | Classical Decision Theory provides a normative framework for representing and
reasoning about complex preferences. Straightforward application of this theory
to automate decision making is difficult due to high elicitation cost. In
response to this problem, researchers have recently developed a number of
qualitative, l... | computer science |
18,997 | Faithful Approximations of Belief Functions | cs.AI | A conceptual foundation for approximation of belief functions is proposed and
investigated. It is based on the requirements of consistency and closeness. An
optimal approximation is studied. Unfortunately, the computation of the optimal
approximation turns out to be intractable. Hence, various heuristic methods are
pro... | computer science |
18,998 | SPUDD: Stochastic Planning using Decision Diagrams | cs.AI | Markov decisions processes (MDPs) are becoming increasing popular as models
of decision theoretic planning. While traditional dynamic programming methods
perform well for problems with small state spaces, structured methods are
needed for large problems. We propose and examine a value iteration algorithm
for MDPs that ... | computer science |
18,999 | Estimating the Value of Computation in Flexible Information Refinement | cs.AI | We outline a method to estimate the value of computation for a flexible
algorithm using empirical data. To determine a reasonable trade-off between
cost and value, we build an empirical model of the value obtained through
computation, and apply this model to estimate the value of computation for
quite different problem... | computer science |
19,000 | Mini-Bucket Heuristics for Improved Search | cs.AI | The paper is a second in a series of two papers evaluating the power of a new
scheme that generates search heuristics mechanically. The heuristics are
extracted from an approximation scheme called mini-bucket elimination that was
recently introduced. The first paper introduced the idea and evaluated it
within Branch-an... | computer science |
19,001 | A General Algorithm for Approximate Inference and its Application to
Hybrid Bayes Nets | cs.AI | The clique tree algorithm is the standard method for doing inference in
Bayesian networks. It works by manipulating clique potentials - distributions
over the variables in a clique. While this approach works well for many
networks, it is limited by the need to maintain an exact representation of the
clique potentials. ... | computer science |
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