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19,002 | On Quantified Linguistic Approximation | cs.AI | Most fuzzy systems including fuzzy decision support and fuzzy control systems
provide out-puts in the form of fuzzy sets that represent the inferred
conclusions. Linguistic interpretation of such outputs often involves the use
of linguistic approximation that assigns a linguistic label to a fuzzy set
based on the prede... | computer science |
19,003 | Choosing Among Interpretations of Probability | cs.AI | There is available an ever-increasing variety of procedures for managing
uncertainty. These methods are discussed in the literature of artificial
intelligence, as well as in the literature of philosophy of science. Heretofore
these methods have been evaluated by intuition, discussion, and the general
philosophical meth... | computer science |
19,004 | My Brain is Full: When More Memory Helps | cs.AI | We consider the problem of finding good finite-horizon policies for POMDPs
under the expected reward metric. The policies considered are {em free
finite-memory policies with limited memory}; a policy is a mapping from the
space of observation-memory pairs to the space of action-memeory pairs (the
policy updates the mem... | computer science |
19,005 | Lazy Evaluation of Symmetric Bayesian Decision Problems | cs.AI | Solving symmetric Bayesian decision problems is a computationally intensive
task to perform regardless of the algorithm used. In this paper we propose a
method for improving the efficiency of algorithms for solving Bayesian decision
problems. The method is based on the principle of lazy evaluation - a principle
recentl... | computer science |
19,006 | Representing and Combining Partially Specified CPTs | cs.AI | This paper extends previous work with network fragments and
situation-specific network construction. We formally define the asymmetry
network, an alternative representation for a conditional probability table. We
also present an object-oriented representation for partially specified
asymmetry networks. We show that the... | computer science |
19,007 | On the Complexity of Policy Iteration | cs.AI | Decision-making problems in uncertain or stochastic domains are often
formulated as Markov decision processes (MDPs). Policy iteration (PI) is a
popular algorithm for searching over policy-space, the size of which is
exponential in the number of states. We are interested in bounds on the
complexity of PI that do not de... | computer science |
19,008 | Approximate Planning for Factored POMDPs using Belief State
Simplification | cs.AI | We are interested in the problem of planning for factored POMDPs. Building on
the recent results of Kearns, Mansour and Ng, we provide a planning algorithm
for factored POMDPs that exploits the accuracy-efficiency tradeoff in the
belief state simplification introduced by Boyen and Koller. | computer science |
19,009 | Solving POMDPs by Searching the Space of Finite Policies | cs.AI | Solving partially observable Markov decision processes (POMDPs) is highly
intractable in general, at least in part because the optimal policy may be
infinitely large. In this paper, we explore the problem of finding the optimal
policy from a restricted set of policies, represented as finite state automata
of a given si... | computer science |
19,010 | Welldefined Decision Scenarios | cs.AI | Influence diagrams serve as a powerful tool for modelling symmetric decision
problems. When solving an influence diagram we determine a set of strategies
for the decisions involved. A strategy for a decision variable is in principle
a function over its past. However, some of the past may be irrelevant for the
decision,... | computer science |
19,011 | Graphical Representations of Consensus Belief | cs.AI | Graphical models based on conditional independence support concise encodings
of the subjective belief of a single agent. A natural question is whether the
consensus belief of a group of agents can be represented with equal parsimony.
We prove, under relatively mild assumptions, that even if everyone agrees on a
common ... | computer science |
19,012 | SPOOK: A System for Probabilistic Object-Oriented Knowledge
Representation | cs.AI | In previous work, we pointed out the limitations of standard Bayesian
networks as a modeling framework for large, complex domains. We proposed a new,
richly structured modeling language, {em Object-oriented Bayesian Netorks},
that we argued would be able to deal with such domains. However, it turns out
that OOBNs are n... | computer science |
19,013 | Bayesian Networks for Dependability Analysis: an Application to Digital
Control Reliability | cs.AI | Bayesian Networks (BN) provide robust probabilistic methods of reasoning
under uncertainty, but despite their formal grounds are strictly based on the
notion of conditional dependence, not much attention has been paid so far to
their use in dependability analysis. The aim of this paper is to propose BN as
a suitable to... | computer science |
19,014 | Enhancing QPNs for Trade-off Resolution | cs.AI | Qualitative probabilistic networks have been introduced as qualitative
abstractions of Bayesian belief networks. One of the major drawbacks of these
qualitative networks is their coarse level of detail, which may lead to
unresolved trade-offs during inference. We present an enhanced formalism for
qualitative networks w... | computer science |
19,015 | A Possibilistic Model for Qualitative Sequential Decision Problems under
Uncertainty in Partially Observable Environments | cs.AI | In this article we propose a qualitative (ordinal) counterpart for the
Partially Observable Markov Decision Processes model (POMDP) in which the
uncertainty, as well as the preferences of the agent, are modeled by
possibility distributions. This qualitative counterpart of the POMDP model
relies on a possibilistic theor... | computer science |
19,016 | Efficient Value of Information Computation | cs.AI | One of the most useful sensitivity analysis techniques of decision analysis
is the computation of value of information (or clairvoyance), the difference in
value obtained by changing the decisions by which some of the uncertainties are
observed. In this paper, some simple but powerful extensions to previous
algorithms ... | computer science |
19,017 | Learning Hidden Markov Models with Geometrical Constraints | cs.AI | Hidden Markov models (HMMs) and partially observable Markov decision
processes (POMDPs) form a useful tool for modeling dynamical systems. They are
particularly useful for representing environments such as road networks and
office buildings, which are typical for robot navigation and planning. The work
presented here i... | computer science |
19,018 | Practical Uses of Belief Functions | cs.AI | We present examples where the use of belief functions provided sound and
elegant solutions to real life problems. These are essentially characterized by
?missing' information. The examples deal with 1) discriminant analysis using a
learning set where classes are only partially known; 2) an information
retrieval systems... | computer science |
19,019 | Multiplicative Factorization of Noisy-Max | cs.AI | The noisy-or and its generalization noisy-max have been utilized to reduce
the complexity of knowledge acquisition. In this paper, we present a new
representation of noisy-max that allows for efficient inference in general
Bayesian networks. Empirical studies show that our method is capable of
computing queries in well... | computer science |
19,020 | Mixture Approximations to Bayesian Networks | cs.AI | Structure and parameters in a Bayesian network uniquely specify the
probability distribution of the modeled domain. The locality of both structure
and probabilistic information are the great benefits of Bayesian networks and
require the modeler to only specify local information. On the other hand this
locality of infor... | computer science |
19,021 | How to Elicit Many Probabilities | cs.AI | In building Bayesian belief networks, the elicitation of all probabilities
required can be a major obstacle. We learned the extent of this often-cited
observation in the construction of the probabilistic part of a complex
influence diagram in the field of cancer treatment. Based upon our negative
experiences with exist... | computer science |
19,022 | Probabilistic Belief Change: Expansion, Conditioning and Constraining | cs.AI | The AGM theory of belief revision has become an important paradigm for
investigating rational belief changes. Unfortunately, researchers working in
this paradigm have restricted much of their attention to rather simple
representations of belief states, namely logically closed sets of propositional
sentences. In our opi... | computer science |
19,023 | Contextual Weak Independence in Bayesian Networks | cs.AI | It is well-known that the notion of (strong) conditional independence (CI) is
too restrictive to capture independencies that only hold in certain contexts.
This kind of contextual independency, called context-strong independence (CSI),
can be used to facilitate the acquisition, representation, and inference of
probabil... | computer science |
19,024 | Inference in Multiply Sectioned Bayesian Networks with Extended
Shafer-Shenoy and Lazy Propagation | cs.AI | As Bayesian networks are applied to larger and more complex problem domains,
search for flexible modeling and more efficient inference methods is an ongoing
effort. Multiply sectioned Bayesian networks (MSBNs) extend the HUGIN inference
for Bayesian networks into a coherent framework for flexible modeling and
distribut... | computer science |
19,025 | Time-Critical Dynamic Decision Making | cs.AI | Recent interests in dynamic decision modeling have led to the development of
several representation and inference methods. These methods however, have
limited application under time critical conditions where a trade-off between
model quality and computational tractability is essential. This paper presents
an approach t... | computer science |
19,026 | A Method for Speeding Up Value Iteration in Partially Observable Markov
Decision Processes | cs.AI | We present a technique for speeding up the convergence of value iteration for
partially observable Markov decisions processes (POMDPs). The underlying idea
is similar to that behind modified policy iteration for fully observable Markov
decision processes (MDPs). The technique can be easily incorporated into any
existin... | computer science |
19,027 | Approximation of Classification and Measures of Uncertainty in Rough Set
on Two Universal Sets | cs.AI | The notion of rough set captures indiscernibility of elements in a set. But,
in many real life situations, an information system establishes the relation
between different universes. This gave the extension of rough set on single
universal set to rough set on two universal sets. In this paper, we introduce
approximatio... | computer science |
19,028 | On the Semantics and Automated Deduction for PLFC, a Logic of
Possibilistic Uncertainty and Fuzziness | cs.AI | Possibilistic logic is a well-known graded logic of uncertainty suitable to
reason under incomplete information and partially inconsistent knowledge, which
is built upon classical first order logic. There exists for Possibilistic logic
a proof procedure based on a refutation complete resolution-style calculus.
Recently... | computer science |
19,029 | On the Acceptability of Arguments in Preference-Based Argumentation | cs.AI | Argumentation is a promising model for reasoning with uncertain knowledge.
The key concept of acceptability enables to differentiate arguments and
counterarguments: The certainty of a proposition can then be evaluated through
the most acceptable arguments for that proposition. In this paper, we
investigate different co... | computer science |
19,030 | Merging Uncertain Knowledge Bases in a Possibilistic Logic Framework | cs.AI | This paper addresses the problem of merging uncertain information in the
framework of possibilistic logic. It presents several syntactic combination
rules to merge possibilistic knowledge bases, provided by different sources,
into a new possibilistic knowledge base. These combination rules are first
described at the me... | computer science |
19,031 | A Hybrid Algorithm to Compute Marginal and Joint Beliefs in Bayesian
Networks and Its Complexity | cs.AI | There exist two general forms of exact algorithms for updating probabilities
in Bayesian Networks. The first approach involves using a structure, usually a
clique tree, and performing local message based calculation to extract the
belief in each variable. The second general class of algorithm involves the use
of non-se... | computer science |
19,032 | Structured Reachability Analysis for Markov Decision Processes | cs.AI | Recent research in decision theoretic planning has focussed on making the
solution of Markov decision processes (MDPs) more feasible. We develop a family
of algorithms for structured reachability analysis of MDPs that are suitable
when an initial state (or set of states) is known. Using compact, structured
representati... | computer science |
19,033 | Tractable Inference for Complex Stochastic Processes | cs.AI | The monitoring and control of any dynamic system depends crucially on the
ability to reason about its current status and its future trajectory. In the
case of a stochastic system, these tasks typically involve the use of a belief
state- a probability distribution over the state of the process at a given
point in time. ... | computer science |
19,034 | Dealing with Uncertainty in Situation Assessment: towards a Symbolic
Approach | cs.AI | The situation assessment problem is considered, in terms of object,
condition, activity, and plan recognition, based on data coming from the
real-word {em via} various sensors. It is shown that uncertainty issues are
linked both to the models and to the matching algorithm. Three different types
of uncertainties are ide... | computer science |
19,035 | Marginalizing in Undirected Graph and Hypergraph Models | cs.AI | Given an undirected graph G or hypergraph X model for a given set of
variables V, we introduce two marginalization operators for obtaining the
undirected graph GA or hypergraph HA associated with a given subset A c V such
that the marginal distribution of A factorizes according to GA or HA,
respectively. Finally, we il... | computer science |
19,036 | Utility Elicitation as a Classification Problem | cs.AI | We investigate the application of classification techniques to utility
elicitation. In a decision problem, two sets of parameters must generally be
elicited: the probabilities and the utilities. While the prior and conditional
probabilities in the model do not change from user to user, the utility models
do. Thus it is... | computer science |
19,037 | Irrelevance and Independence Relations in Quasi-Bayesian Networks | cs.AI | This paper analyzes irrelevance and independence relations in graphical
models associated with convex sets of probability distributions (called
Quasi-Bayesian networks). The basic question in Quasi-Bayesian networks is, How
can irrelevance/independence relations in Quasi-Bayesian networks be detected,
enforced and expl... | computer science |
19,038 | Dynamic Jointrees | cs.AI | It is well known that one can ignore parts of a belief network when computing
answers to certain probabilistic queries. It is also well known that the
ignorable parts (if any) depend on the specific query of interest and,
therefore, may change as the query changes. Algorithms based on jointrees,
however, do not seem to... | computer science |
19,039 | On the Semi-Markov Equivalence of Causal Models | cs.AI | The variability of structure in a finite Markov equivalence class of causally
sufficient models represented by directed acyclic graphs has been fully
characterized. Without causal sufficiency, an infinite semi-Markov equivalence
class of models has only been characterized by the fact that each model in the
equivalence ... | computer science |
19,040 | Comparative Uncertainty, Belief Functions and Accepted Beliefs | cs.AI | This paper relates comparative belief structures and a general view of belief
management in the setting of deductively closed logical representations of
accepted beliefs. We show that the range of compatibility between the classical
deductive closure and uncertain reasoning covers precisely the nonmonotonic
'preferenti... | computer science |
19,041 | Qualitative Decision Theory with Sugeno Integrals | cs.AI | This paper presents an axiomatic framework for qualitative decision under
uncertainty in a finite setting. The corresponding utility is expressed by a
sup-min expression, called Sugeno (or fuzzy) integral. Technically speaking,
Sugeno integral is a median, which is indeed a qualitative counterpart to the
averaging oper... | computer science |
19,042 | Towards Case-Based Preference Elicitation: Similarity Measures on
Preference Structures | cs.AI | While decision theory provides an appealing normative framework for
representing rich preference structures, eliciting utility or value functions
typically incurs a large cost. For many applications involving interactive
systems this overhead precludes the use of formal decision-theoretic models of
preference. Instead ... | computer science |
19,043 | Solving POMDPs by Searching in Policy Space | cs.AI | Most algorithms for solving POMDPs iteratively improve a value function that
implicitly represents a policy and are said to search in value function space.
This paper presents an approach to solving POMDPs that represents a policy
explicitly as a finite-state controller and iteratively improves the controller
by search... | computer science |
19,044 | Hierarchical Solution of Markov Decision Processes using Macro-actions | cs.AI | We investigate the use of temporally abstract actions, or macro-actions, in
the solution of Markov decision processes. Unlike current models that combine
both primitive actions and macro-actions and leave the state space unchanged,
we propose a hierarchical model (using an abstract MDP) that works with
macro-actions on... | computer science |
19,045 | Evaluating Las Vegas Algorithms - Pitfalls and Remedies | cs.AI | Stochastic search algorithms are among the most sucessful approaches for
solving hard combinatorial problems. A large class of stochastic search
approaches can be cast into the framework of Las Vegas Algorithms (LVAs). As
the run-time behavior of LVAs is characterized by random variables, the
detailed knowledge of run-... | computer science |
19,046 | An Anytime Algorithm for Decision Making under Uncertainty | cs.AI | We present an anytime algorithm which computes policies for decision problems
represented as multi-stage influence diagrams. Our algorithm constructs
policies incrementally, starting from a policy which makes no use of the
available information. The incremental process constructs policies which
includes more of the inf... | computer science |
19,047 | Any Time Probabilistic Reasoning for Sensor Validation | cs.AI | For many real time applications, it is important to validate the information
received from the sensors before entering higher levels of reasoning. This
paper presents an any time probabilistic algorithm for validating the
information provided by sensors. The system consists of two Bayesian network
models. The first one... | computer science |
19,048 | Measure Selection: Notions of Rationality and Representation
Independence | cs.AI | We take another look at the general problem of selecting a preferred
probability measure among those that comply with some given constraints. The
dominant role that entropy maximization has obtained in this context is
questioned by arguing that the minimum information principle on which it is
based could be supplanted ... | computer science |
19,049 | Exact Inference of Hidden Structure from Sample Data in Noisy-OR
Networks | cs.AI | In the literature on graphical models, there has been increased attention
paid to the problems of learning hidden structure (see Heckerman [H96] for
survey) and causal mechanisms from sample data [H96, P88, S93, P95, F98]. In
most settings we should expect the former to be difficult, and the latter
potentially impossib... | computer science |
19,050 | A Comparison of Lauritzen-Spiegelhalter, Hugin, and Shenoy-Shafer
Architectures for Computing Marginals of Probability Distributions | cs.AI | In the last decade, several architectures have been proposed for exact
computation of marginals using local computation. In this paper, we compare
three architectures - Lauritzen-Spiegelhalter, Hugin, and Shenoy-Shafer - from
the perspective of graphical structure for message propagation, message-passing
scheme, comput... | computer science |
19,051 | Incremental Tradeoff Resolution in Qualitative Probabilistic Networks | cs.AI | Qualitative probabilistic reasoning in a Bayesian network often reveals
tradeoffs: relationships that are ambiguous due to competing qualitative
influences. We present two techniques that combine qualitative and numeric
probabilistic reasoning to resolve such tradeoffs, inferring the qualitative
relationship between no... | computer science |
19,052 | Using Qualitative Relationships for Bounding Probability Distributions | cs.AI | We exploit qualitative probabilistic relationships among variables for
computing bounds of conditional probability distributions of interest in
Bayesian networks. Using the signs of qualitative relationships, we can
implement abstraction operations that are guaranteed to bound the distributions
of interest in the desir... | computer science |
19,053 | Magic Inference Rules for Probabilistic Deduction under Taxonomic
Knowledge | cs.AI | We present locally complete inference rules for probabilistic deduction from
taxonomic and probabilistic knowledge-bases over conjunctive events. Crucially,
in contrast to similar inference rules in the literature, our inference rules
are locally complete for conjunctive events and under additional taxonomic
knowledge.... | computer science |
19,054 | Lazy Propagation in Junction Trees | cs.AI | The efficiency of algorithms using secondary structures for probabilistic
inference in Bayesian networks can be improved by exploiting independence
relations induced by evidence and the direction of the links in the original
network. In this paper we present an algorithm that on-line exploits
independence relations ind... | computer science |
19,055 | Constructing Situation Specific Belief Networks | cs.AI | This paper describes a process for constructing situation-specific belief
networks from a knowledge base of network fragments. A situation-specific
network is a minimal query complete network constructed from a knowledge base
in response to a query for the probability distribution on a set of target
variables given evi... | computer science |
19,056 | From Likelihood to Plausibility | cs.AI | Several authors have explained that the likelihood ratio measures the
strength of the evidence represented by observations in statistical problems.
This idea works fine when the goal is to evaluate the strength of the available
evidence for a simple hypothesis versus another simple hypothesis. However, the
applicabilit... | computer science |
19,057 | Resolving Conflicting Arguments under Uncertainties | cs.AI | Distributed knowledge based applications in open domain rely on common sense
information which is bound to be uncertain and incomplete. To draw the useful
conclusions from ambiguous data, one must address uncertainties and conflicts
incurred in a holistic view. No integrated frameworks are viable without an
in-depth an... | computer science |
19,058 | Flexible Decomposition Algorithms for Weakly Coupled Markov Decision
Problems | cs.AI | This paper presents two new approaches to decomposing and solving large
Markov decision problems (MDPs), a partial decoupling method and a complete
decoupling method. In these approaches, a large, stochastic decision problem is
divided into smaller pieces. The first approach builds a cache of policies for
each part of ... | computer science |
19,059 | Logarithmic Time Parallel Bayesian Inference | cs.AI | I present a parallel algorithm for exact probabilistic inference in Bayesian
networks. For polytree networks with n variables, the worst-case time
complexity is O(log n) on a CREW PRAM (concurrent-read, exclusive-write
parallel random-access machine) with n processors, for any constant number of
evidence variables. For... | computer science |
19,060 | Learning From What You Don't Observe | cs.AI | The process of diagnosis involves learning about the state of a system from
various observations of symptoms or findings about the system. Sophisticated
Bayesian (and other) algorithms have been developed to revise and maintain
beliefs about the system as observations are made. Nonetheless, diagnostic
models have tende... | computer science |
19,061 | Context-Specific Approximation in Probabilistic Inference | cs.AI | There is evidence that the numbers in probabilistic inference don't really
matter. This paper considers the idea that we can make a probabilistic model
simpler by making fewer distinctions. Unfortunately, the level of a Bayesian
network seems too coarse; it is unlikely that a parent will make little
difference for all ... | computer science |
19,062 | Empirical Evaluation of Approximation Algorithms for Probabilistic
Decoding | cs.AI | It was recently shown that the problem of decoding messages transmitted
through a noisy channel can be formulated as a belief updating task over a
probabilistic network [McEliece]. Moreover, it was observed that iterative
application of the (linear time) Pearl's belief propagation algorithm designed
for polytrees outpe... | computer science |
19,063 | Decision Theoretic Foundations of Graphical Model Selection | cs.AI | This paper describes a decision theoretic formulation of learning the
graphical structure of a Bayesian Belief Network from data. This framework
subsumes the standard Bayesian approach of choosing the model with the largest
posterior probability as the solution of a decision problem with a 0-1 loss
function and allows ... | computer science |
19,064 | Bayes-Ball: The Rational Pastime (for Determining Irrelevance and
Requisite Information in Belief Networks and Influence Diagrams) | cs.AI | One of the benefits of belief networks and influence diagrams is that so much
knowledge is captured in the graphical structure. In particular, statements of
conditional irrelevance (or independence) can be verified in time linear in the
size of the graph. To resolve a particular inference query or decision problem,
onl... | computer science |
19,065 | Bayesian Networks from the Point of View of Chain Graphs | cs.AI | AThe paper gives a few arguments in favour of the use of chain graphs for
description of probabilistic conditional independence structures. Every
Bayesian network model can be equivalently introduced by means of a
factorization formula with respect to a chain graph which is Markov equivalent
to the Bayesian network. A ... | computer science |
19,066 | Probabilistic Inference in Influence Diagrams | cs.AI | This paper is about reducing influence diagram (ID) evaluation into Bayesian
network (BN) inference problems. Such reduction is interesting because it
enables one to readily use one's favorite BN inference algorithm to efficiently
evaluate IDs. Two such reduction methods have been proposed previously (Cooper
1988, Shac... | computer science |
19,067 | Planning with Partially Observable Markov Decision Processes: Advances
in Exact Solution Method | cs.AI | There is much interest in using partially observable Markov decision
processes (POMDPs) as a formal model for planning in stochastic domains. This
paper is concerned with finding optimal policies for POMDPs. We propose several
improvements to incremental pruning, presently the most efficient exact
algorithm for solving... | computer science |
19,068 | Flexible and Approximate Computation through State-Space Reduction | cs.AI | In the real world, insufficient information, limited computation resources,
and complex problem structures often force an autonomous agent to make a
decision in time less than that required to solve the problem at hand
completely. Flexible and approximate computations are two approaches to
decision making under limited... | computer science |
19,069 | Phase Transition and Network Structure in Realistic SAT Problems | cs.AI | A fundamental question in Computer Science is understanding when a specific
class of problems go from being computationally easy to hard. Because of its
generality and applications, the problem of Boolean Satisfiability (aka SAT) is
often used as a vehicle for investigating this question. A signal result from
these stu... | computer science |
19,070 | Disjunctive Logic Programs versus Normal Logic Programs | cs.AI | This paper focuses on the expressive power of disjunctive and normal logic
programs under the stable model semantics over finite, infinite, or arbitrary
structures. A translation from disjunctive logic programs into normal logic
programs is proposed and then proved to be sound over infinite structures. The
equivalence ... | computer science |
19,071 | IFP-Intuitionistic fuzzy soft set theory and its applications | cs.AI | In this work, we present definition of intuitionistic fuzzy parameterized
(IFP) intuitionistic fuzzy soft set and its operations. Then we define
IFP-aggregation operator to form IFP-intuitionistic fuzzy soft-decision-making
method which allows constructing more efficient decision processes. | computer science |
19,072 | Duality in STRIPS planning | cs.AI | We describe a duality mapping between STRIPS planning tasks. By exchanging
the initial and goal conditions, taking their respective complements, and
swapping for every action its precondition and delete list, one obtains for
every STRIPS task its dual version, which has a solution if and only if the
original does. This... | computer science |
19,073 | Exploiting Functional Dependencies in Qualitative Probabilistic
Reasoning | cs.AI | Functional dependencies restrict the potential interactions among variables
connected in a probabilistic network. This restriction can be exploited in
qualitative probabilistic reasoning by introducing deterministic variables and
modifying the inference rules to produce stronger conclusions in the presence
of functiona... | computer science |
19,074 | Qualitative Propagation and Scenario-based Explanation of Probabilistic
Reasoning | cs.AI | Comprehensible explanations of probabilistic reasoning are a prerequisite for
wider acceptance of Bayesian methods in expert systems and decision support
systems. A study of human reasoning under uncertainty suggests two different
strategies for explaining probabilistic reasoning: The first, qualitative
belief propagat... | computer science |
19,075 | Managing Uncertainty in Rule Based Cognitive Models | cs.AI | An experiment replicated and extended recent findings on psychologically
realistic ways of modeling propagation of uncertainty in rule based reasoning.
Within a single production rule, the antecedent evidence can be summarized by
taking the maximum of disjunctively connected antecedents and the minimum of
conjunctively... | computer science |
19,076 | Context-Dependent Similarity | cs.AI | Attribute weighting and differential weighting, two major mechanisms for
computing context-dependent similarity or dissimilarity measures are studied
and compared. A dissimilarity measure based on subset size in the context is
proposed and its metrization and application are given. It is also shown that
while all attri... | computer science |
19,077 | Similarity Networks for the Construction of Multiple-Faults Belief
Networks | cs.AI | A similarity network is a tool for constructing belief networks for the
diagnosis of a single fault. In this paper, we examine modifications to the
similarity-network representation that facilitate the construction of belief
networks for the diagnosis of multiple coexisting faults. | computer science |
19,078 | Integrating Probabilistic, Taxonomic and Causal Knowledge in Abductive
Diagnosis | cs.AI | We propose an abductive diagnosis theory that integrates probabilistic,
causal and taxonomic knowledge. Probabilistic knowledge allows us to select the
most likely explanation; causal knowledge allows us to make reasonable
independence assumptions; taxonomic knowledge allows causation to be modeled at
different levels ... | computer science |
19,079 | What is an Optimal Diagnosis? | cs.AI | Within diagnostic reasoning there have been a number of proposed definitions
of a diagnosis, and thus of the most likely diagnosis, including most probable
posterior hypothesis, most probable interpretation, most probable covering
hypothesis, etc. Most of these approaches assume that the most likely diagnosis
must be c... | computer science |
19,080 | Kutato: An Entropy-Driven System for Construction of Probabilistic
Expert Systems from Databases | cs.AI | Kutato is a system that takes as input a database of cases and produces a
belief network that captures many of the dependence relations represented by
those data. This system incorporates a module for determining the entropy of a
belief network and a module for constructing belief networks based on entropy
calculations... | computer science |
19,081 | Ideal Reformulation of Belief Networks | cs.AI | The intelligent reformulation or restructuring of a belief network can
greatly increase the efficiency of inference. However, time expended for
reformulation is not available for performing inference. Thus, under time
pressure, there is a tradeoff between the time dedicated to reformulating the
network and the time app... | computer science |
19,082 | Computationally-Optimal Real-Resource Strategies | cs.AI | This paper focuses on managing the cost of deliberation before action. In
many problems, the overall quality of the solution reflects costs incurred and
resources consumed in deliberation as well as the cost and benefit of
execution, when both the resource consumption in deliberation phase, and the
costs in deliberatio... | computer science |
19,083 | Problem Formulation as the Reduction of a Decision Model | cs.AI | In this paper, we extend the QMRDT probabilistic model for the domain of
internal medicine to include decisions about treatments. In addition, we
describe how we can use the comprehensive decision model to construct a simpler
decision model for a specific patient. In so doing, we transform the task of
problem formulati... | computer science |
19,084 | Dynamic Construction of Belief Networks | cs.AI | We describe a method for incrementally constructing belief networks. We have
developed a network-construction language similar to a forward-chaining
language using data dependencies, but with additional features for specifying
distributions. Using this language, we can define parameterized classes of
probabilistic mode... | computer science |
19,085 | A New Algorithm for Finding MAP Assignments to Belief Networks | cs.AI | We present a new algorithm for finding maximum a-posterior) (MAP) assignments
of values to belief networks. The belief network is compiled into a network
consisting only of nodes with boolean (i.e. only 0 or 1) conditional
probabilities. The MAP assignment is then found using a best-first search on
the resulting networ... | computer science |
19,086 | Reducing Uncertainty in Navigation and Exploration | cs.AI | A significant problem in designing mobile robot control systems involves
coping with the uncertainty that arises in moving about in an unknown or
partially unknown environment and relying on noisy or ambiguous sensor data to
acquire knowledge about that environment. We describe a control system that
chooses what activi... | computer science |
19,087 | Ergo: A Graphical Environment for Constructing Bayesian | cs.AI | We describe an environment that considerably simplifies the process of
generating Bayesian belief networks. The system has been implemented on readily
available, inexpensive hardware, and provides clarity and high performance. We
present an introduction to Bayesian belief networks, discuss algorithms for
inference with... | computer science |
19,088 | Decision Making with Interval Influence Diagrams | cs.AI | In previous work (Fertig and Breese, 1989; Fertig and Breese, 1990) we
defined a mechanism for performing probabilistic reasoning in influence
diagrams using interval rather than point-valued probabilities. In this paper
we extend these procedures to incorporate decision nodes and interval-valued
value functions in the... | computer science |
19,089 | A Randomized Approximation Algorithm of Logic Sampling | cs.AI | In recent years, researchers in decision analysis and artificial intelligence
(AI) have used Bayesian belief networks to build models of expert opinion.
Using standard methods drawn from the theory of computational complexity,
workers in the field have shown that the problem of exact probabilistic
inference on belief n... | computer science |
19,090 | Time, Chance, and Action | cs.AI | To operate intelligently in the world, an agent must reason about its
actions. The consequences of an action are a function of both the state of the
world and the action itself. Many aspects of the world are inherently
stochastic, so a representation for reasoning about actions must be able to
express chances of world ... | computer science |
19,091 | A Dynamic Approach to Probabilistic Inference | cs.AI | In this paper we present a framework for dynamically constructing Bayesian
networks. We introduce the notion of a background knowledge base of schemata,
which is a collection of parameterized conditional probability statements.
These schemata explicitly separate the general knowledge of properties an
individual may hav... | computer science |
19,092 | Approximations in Bayesian Belief Universe for Knowledge Based Systems | cs.AI | When expert systems based on causal probabilistic networks (CPNs) reach a
certain size and complexity, the "combinatorial explosion monster" tends to be
present. We propose an approximation scheme that identifies rarely occurring
cases and excludes these from being processed as ordinary cases in a CPN-based
expert syst... | computer science |
19,093 | Robust Inference Policies | cs.AI | A series of monte carlo studies were performed to assess the extent to which
different inference procedures robustly output reasonable belief values in the
context of increasing levels of judgmental imprecision. It was found that, when
compared to an equal-weights linear model, the Bayesian procedures are more
likely t... | computer science |
19,094 | Minimum Error Tree Decomposition | cs.AI | This paper describes a generalization of previous methods for constructing
tree-structured belief network with hidden variables. The major new feature of
the described method is the ability to produce a tree decomposition even when
there are errors in the correlation data among the input variables. This is an
important... | computer science |
19,095 | A Polynomial Time Algorithm for Finding Bayesian Probabilities from
Marginal Constraints | cs.AI | A method of calculating probability values from a system of marginal
constraints is presented. Previous systems for finding the probability of a
single attribute have either made an independence assumption concerning the
evidence or have required, in the worst case, time exponential in the number of
attributes of the s... | computer science |
19,096 | Computation of Variances in Causal Networks | cs.AI | The causal (belief) network is a well-known graphical structure for
representing independencies in a joint probability distribution. The exact
methods and the approximation methods, which perform probabilistic inference in
causal networks, often treat the conditional probabilities which are stored in
the network as cer... | computer science |
19,097 | A Sensitivity Analysis of Pathfinder | cs.AI | Knowledge elicitation is one of the major bottlenecks in expert system
design. Systems based on Bayes nets require two types of information--network
structure and parameters (or probabilities). Both must be elicited from the
domain expert. In general, parameters have greater opacity than structure, and
more time is spe... | computer science |
19,098 | IDEAL: A Software Package for Analysis of Influence Diagrams | cs.AI | IDEAL (Influence Diagram Evaluation and Analysis in Lisp) is a software
environment for creation and evaluation of belief networks and influence
diagrams. IDEAL is primarily a research tool and provides an implementation of
many of the latest developments in belief network and influence diagram
evaluation in a unified ... | computer science |
19,099 | On the Equivalence of Causal Models | cs.AI | Scientists often use directed acyclic graphs (days) to model the qualitative
structure of causal theories, allowing the parameters to be estimated from
observational data. Two causal models are equivalent if there is no experiment
which could distinguish one from the other. A canonical representation for
causal models ... | computer science |
19,100 | Application of Confidence Intervals to the Autonomous Acquisition of
High-level Spatial Knowledge | cs.AI | Objects in the world usually appear in context, participating in spatial
relationships and interactions that are predictable and expected. Knowledge of
these contexts can be used in the task of using a mobile camera to search for a
specified object in a room. We call this the object search task. This paper is
concerned... | computer science |
19,101 | Directed Reduction Algorithms and Decomposable Graphs | cs.AI | In recent years, there have been intense research efforts to develop
efficient methods for probabilistic inference in probabilistic influence
diagrams or belief networks. Many people have concluded that the best methods
are those based on undirected graph structures, and that those methods are
inherently superior to th... | computer science |
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