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1302.3603 | Ross D. Shachter | Ross D. Shachter, Marvin Mandelbaum | A Measure of Decision Flexibility | Appears in Proceedings of the Twelfth Conference on Uncertainty in
Artificial Intelligence (UAI1996) | null | null | UAI-P-1996-PG-485-491 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We propose a decision-analytical approach to comparing the flexibility of
decision situations from the perspective of a decision-maker who exhibits
constant risk-aversion over a monetary value model. Our approach is simple yet
seems to be consistent with a variety of flexibility concepts, including robust
and adaptive alternatives. We try to compensate within the model for
uncertainty that was not anticipated or not modeled. This approach not only
allows one to compare the flexibility of plans, but also guides the search for
new, more flexible alternatives.
| [
{
"version": "v1",
"created": "Wed, 13 Feb 2013 14:16:35 GMT"
}
] | 1,361,145,600,000 | [
[
"Shachter",
"Ross D.",
""
],
[
"Mandelbaum",
"Marvin",
""
]
] |
1302.3604 | Prakash P. Shenoy | Prakash P. Shenoy | Binary Join Trees | Appears in Proceedings of the Twelfth Conference on Uncertainty in
Artificial Intelligence (UAI1996) | null | null | UAI-P-1996-PG-492-499 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The main goal of this paper is to describe a data structure called binary
join trees that are useful in computing multiple marginals efficiently using
the Shenoy-Shafer architecture. We define binary join trees, describe their
utility, and sketch a procedure for constructing them.
| [
{
"version": "v1",
"created": "Wed, 13 Feb 2013 14:16:41 GMT"
}
] | 1,361,145,600,000 | [
[
"Shenoy",
"Prakash P.",
""
]
] |
1302.3605 | Sampath Srinivas | Sampath Srinivas, Pandurang Nayak | Efficient Enumeration of Instantiations in Bayesian Networks | Appears in Proceedings of the Twelfth Conference on Uncertainty in
Artificial Intelligence (UAI1996) | null | null | UAI-P-1996-PG-500-508 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Over the past several years Bayesian networks have been applied to a wide
variety of problems. A central problem in applying Bayesian networks is that of
finding one or more of the most probable instantiations of a network. In this
paper we develop an efficient algorithm that incrementally enumerates the
instantiations of a Bayesian network in decreasing order of probability. Such
enumeration algorithms are applicable in a variety of applications ranging from
medical expert systems to model-based diagnosis. Fundamentally, our algorithm
is simply performing a lazy enumeration of the sorted list of all
instantiations of the network. This insight leads to a very concise algorithm
statement which is both easily understood and implemented. We show that for
singly connected networks, our algorithm generates the next instantiation in
time polynomial in the size of the network. The algorithm extends to arbitrary
Bayesian networks using standard conditioning techniques. We empirically
evaluate the enumeration algorithm and demonstrate its practicality.
| [
{
"version": "v1",
"created": "Wed, 13 Feb 2013 14:16:47 GMT"
}
] | 1,361,145,600,000 | [
[
"Srinivas",
"Sampath",
""
],
[
"Nayak",
"Pandurang",
""
]
] |
1302.3606 | Milan Studeny | Milan Studeny | On Separation Criterion and Recovery Algorithm for Chain Graphs | Appears in Proceedings of the Twelfth Conference on Uncertainty in
Artificial Intelligence (UAI1996) | null | null | UAI-P-1996-PG-509-516 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Chain graphs give a natural unifying point of view on Markov and Bayesian
networks and enlarge the potential of graphical models for description of
conditional independence structures. In the paper a direct graphical separation
criterion for chain graphs, called c-separation, which generalizes the
d-separation criterion for Bayesian networks is introduced (recalled). It is
equivalent to the classic moralization criterion for chain graphs and complete
in sense that for every chain graph there exists a probability distribution
satisfying exactly conditional independencies derivable from the chain graph by
the c-separation criterion. Every class of Markov equivalent chain graphs can
be uniquely described by a natural representative, called the largest chain
graph. A recovery algorithm, which on basis of the (conditional) dependency
model induced by an unknown chain graph finds the corresponding largest chain
graph, is presented.
| [
{
"version": "v1",
"created": "Wed, 13 Feb 2013 14:16:53 GMT"
}
] | 1,361,145,600,000 | [
[
"Studeny",
"Milan",
""
]
] |
1302.3607 | Choh Man Teng | Choh Man Teng | Possible World Partition Sequences: A Unifying Framework for Uncertain
Reasoning | Appears in Proceedings of the Twelfth Conference on Uncertainty in
Artificial Intelligence (UAI1996) | null | null | UAI-P-1996-PG-517-524 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | When we work with information from multiple sources, the formalism each
employs to handle uncertainty may not be uniform. In order to be able to
combine these knowledge bases of different formats, we need to first establish
a common basis for characterizing and evaluating the different formalisms, and
provide a semantics for the combined mechanism. A common framework can provide
an infrastructure for building an integrated system, and is essential if we are
to understand its behavior. We present a unifying framework based on an ordered
partition of possible worlds called partition sequences, which corresponds to
our intuitive notion of biasing towards certain possible scenarios when we are
uncertain of the actual situation. We show that some of the existing
formalisms, namely, default logic, autoepistemic logic, probabilistic
conditioning and thresholding (generalized conditioning), and possibility
theory can be incorporated into this general framework.
| [
{
"version": "v1",
"created": "Wed, 13 Feb 2013 14:17:00 GMT"
}
] | 1,361,145,600,000 | [
[
"Teng",
"Choh Man",
""
]
] |
1302.3608 | Sylvie Thiebaux | Sylvie Thiebaux, Marie-Odile Cordier, Olivier Jehl, Jean-Paul Krivine | Supply Restoration in Power Distribution Systems - A Case Study in
Integrating Model-Based Diagnosis and Repair Planning | Appears in Proceedings of the Twelfth Conference on Uncertainty in
Artificial Intelligence (UAI1996) | null | null | UAI-P-1996-PG-525-532 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Integrating diagnosis and repair is particularly crucial when gaining
sufficient information to discriminate between several candidate diagnoses
requires carrying out some repair actions. A typical case is supply restoration
in a faulty power distribution system. This problem, which is a major concern
for electricity distributors, features partial observability, and stochastic
repair actions which are more elaborate than simple replacement of components.
This paper analyses the difficulties in applying existing work on integrating
model-based diagnosis and repair and on planning in partially observable
stochastic domains to this real-world problem, and describes the pragmatic
approach we have retained so far.
| [
{
"version": "v1",
"created": "Wed, 13 Feb 2013 14:17:05 GMT"
}
] | 1,361,145,600,000 | [
[
"Thiebaux",
"Sylvie",
""
],
[
"Cordier",
"Marie-Odile",
""
],
[
"Jehl",
"Olivier",
""
],
[
"Krivine",
"Jean-Paul",
""
]
] |
1302.3609 | Robert L. Welch | Robert L. Welch | Real Time Estimation of Bayesian Networks | Appears in Proceedings of the Twelfth Conference on Uncertainty in
Artificial Intelligence (UAI1996) | null | null | UAI-P-1996-PG-533-544 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | For real time evaluation of a Bayesian network when there is not sufficient
time to obtain an exact solution, a guaranteed response time, approximate
solution is required. It is shown that nontraditional methods utilizing
estimators based on an archive of trial solutions and genetic search can
provide an approximate solution that is considerably superior to the
traditional Monte Carlo simulation methods.
| [
{
"version": "v1",
"created": "Wed, 13 Feb 2013 14:17:11 GMT"
}
] | 1,361,145,600,000 | [
[
"Welch",
"Robert L.",
""
]
] |
1302.3610 | Michael S. K. M. Wong | Michael S. K. M. Wong | Testing Implication of Probabilistic Dependencies | Appears in Proceedings of the Twelfth Conference on Uncertainty in
Artificial Intelligence (UAI1996) | null | null | UAI-P-1996-PG-545-553 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Axiomatization has been widely used for testing logical implications. This
paper suggests a non-axiomatic method, the chase, to test if a new dependency
follows from a given set of probabilistic dependencies. Although the chase
computation may require exponential time in some cases, this technique is a
powerful tool for establishing nontrivial theoretical results. More
importantly, this approach provides valuable insight into the intriguing
connection between relational databases and probabilistic reasoning systems.
| [
{
"version": "v1",
"created": "Wed, 13 Feb 2013 14:17:17 GMT"
}
] | 1,361,145,600,000 | [
[
"Wong",
"Michael S. K. M.",
""
]
] |
1302.3611 | Peter R. Wurman | Peter R. Wurman, Michael P. Wellman | Optimal Factory Scheduling using Stochastic Dominance A* | Appears in Proceedings of the Twelfth Conference on Uncertainty in
Artificial Intelligence (UAI1996) | null | null | UAI-P-1996-PG-554-563 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We examine a standard factory scheduling problem with stochastic processing
and setup times, minimizing the expectation of the weighted number of tardy
jobs. Because the costs of operators in the schedule are stochastic and
sequence dependent, standard dynamic programming algorithms such as A* may fail
to find the optimal schedule. The SDA* (Stochastic Dominance A*) algorithm
remedies this difficulty by relaxing the pruning condition. We present an
improved state-space search formulation for these problems and discuss the
conditions under which stochastic scheduling problems can be solved optimally
using SDA*. In empirical testing on randomly generated problems, we found that
in 70%, the expected cost of the optimal stochastic solution is lower than that
of the solution derived using a deterministic approximation, with comparable
search effort.
| [
{
"version": "v1",
"created": "Wed, 13 Feb 2013 14:17:23 GMT"
}
] | 1,361,145,600,000 | [
[
"Wurman",
"Peter R.",
""
],
[
"Wellman",
"Michael P.",
""
]
] |
1302.3612 | Yang Xiang | Yang Xiang, Michael S. K. M. Wong, N. Cercone | Critical Remarks on Single Link Search in Learning Belief Networks | Appears in Proceedings of the Twelfth Conference on Uncertainty in
Artificial Intelligence (UAI1996) | null | null | UAI-P-1996-PG-564-571 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In learning belief networks, the single link lookahead search is widely
adopted to reduce the search space. We show that there exists a class of
probabilistic domain models which displays a special pattern of dependency. We
analyze the behavior of several learning algorithms using different scoring
metrics such as the entropy, conditional independence, minimal description
length and Bayesian metrics. We demonstrate that single link lookahead search
procedures (employed in these algorithms) cannot learn these models correctly.
Thus, when the underlying domain model actually belongs to this class, the use
of a single link search procedure will result in learning of an incorrect
model. This may lead to inference errors when the model is used. Our analysis
suggests that if the prior knowledge about a domain does not rule out the
possible existence of these models, a multi-link lookahead search or other
heuristics should be used for the learning process.
| [
{
"version": "v1",
"created": "Wed, 13 Feb 2013 14:17:29 GMT"
}
] | 1,361,145,600,000 | [
[
"Xiang",
"Yang",
""
],
[
"Wong",
"Michael S. K. M.",
""
],
[
"Cercone",
"N.",
""
]
] |
1302.4381 | Marc Maier | Marc Maier, Katerina Marazopoulou, David Jensen | Reasoning about Independence in Probabilistic Models of Relational Data | 61 pages, substantial revisions to formalisms, theory, and related
work | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We extend the theory of d-separation to cases in which data instances are not
independent and identically distributed. We show that applying the rules of
d-separation directly to the structure of probabilistic models of relational
data inaccurately infers conditional independence. We introduce relational
d-separation, a theory for deriving conditional independence facts from
relational models. We provide a new representation, the abstract ground graph,
that enables a sound, complete, and computationally efficient method for
answering d-separation queries about relational models, and we present
empirical results that demonstrate effectiveness.
| [
{
"version": "v1",
"created": "Mon, 18 Feb 2013 18:33:47 GMT"
},
{
"version": "v2",
"created": "Fri, 10 May 2013 21:20:06 GMT"
},
{
"version": "v3",
"created": "Mon, 6 Jan 2014 18:04:39 GMT"
}
] | 1,389,052,800,000 | [
[
"Maier",
"Marc",
""
],
[
"Marazopoulou",
"Katerina",
""
],
[
"Jensen",
"David",
""
]
] |
1302.4928 | Fahiem Bacchus | Fahiem Bacchus, Adam J. Grove | Graphical Models for Preference and Utility | Appears in Proceedings of the Eleventh Conference on Uncertainty in
Artificial Intelligence (UAI1995) | null | null | UAI-P-1995-PG-3-10 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Probabilistic independence can dramatically simplify the task of eliciting,
representing, and computing with probabilities in large domains. A key
technique in achieving these benefits is the idea of graphical modeling. We
survey existing notions of independence for utility functions in a
multi-attribute space, and suggest that these can be used to achieve similar
advantages. Our new results concern conditional additive independence, which we
show always has a perfect representation as separation in an undirected graph
(a Markov network). Conditional additive independencies entail a particular
functional for the utility function that is analogous to a product
decomposition of a probability function, and confers analogous benefits. This
functional form has been utilized in the Bayesian network and influence diagram
literature, but generally without an explanation in terms of independence. The
functional form yields a decomposition of the utility function that can greatly
speed up expected utility calculations, particularly when the utility graph has
a similar topology to the probabilistic network being used.
| [
{
"version": "v1",
"created": "Wed, 20 Feb 2013 15:18:51 GMT"
}
] | 1,361,404,800,000 | [
[
"Bacchus",
"Fahiem",
""
],
[
"Grove",
"Adam J.",
""
]
] |
1302.4929 | Alexander Balke | Alexander Balke, Judea Pearl | Counterfactuals and Policy Analysis in Structural Models | Appears in Proceedings of the Eleventh Conference on Uncertainty in
Artificial Intelligence (UAI1995) | null | null | UAI-P-1995-PG-11-18 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Evaluation of counterfactual queries (e.g., "If A were true, would C have
been true?") is important to fault diagnosis, planning, determination of
liability, and policy analysis. We present a method of revaluating
counterfactuals when the underlying causal model is represented by structural
models - a nonlinear generalization of the simultaneous equations models
commonly used in econometrics and social sciences. This new method provides a
coherent means for evaluating policies involving the control of variables
which, prior to enacting the policy were influenced by other variables in the
system.
| [
{
"version": "v1",
"created": "Wed, 20 Feb 2013 15:18:56 GMT"
}
] | 1,361,404,800,000 | [
[
"Balke",
"Alexander",
""
],
[
"Pearl",
"Judea",
""
]
] |
1302.4930 | Salem Benferhat | Salem Benferhat, Alessandro Saffiotti, Philippe Smets | Belief Functions and Default Reasoning | Appears in Proceedings of the Eleventh Conference on Uncertainty in
Artificial Intelligence (UAI1995) | null | null | UAI-P-1995-PG-19-26 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We present a new approach to dealing with default information based on the
theory of belief functions. Our semantic structures, inspired by Adams'
epsilon-semantics, are epsilon-belief assignments, where values committed to
focal elements are either close to 0 or close to 1. We define two systems based
on these structures, and relate them to other non-monotonic systems presented
in the literature. We show that our second system correctly addresses the
well-known problems of specificity, irrelevance, blocking of inheritance,
ambiguity, and redundancy.
| [
{
"version": "v1",
"created": "Wed, 20 Feb 2013 15:19:02 GMT"
}
] | 1,361,404,800,000 | [
[
"Benferhat",
"Salem",
""
],
[
"Saffiotti",
"Alessandro",
""
],
[
"Smets",
"Philippe",
""
]
] |
1302.4932 | John S. Breese | John S. Breese, Russ Blake | Automating Computer Bottleneck Detection with Belief Nets | Appears in Proceedings of the Eleventh Conference on Uncertainty in
Artificial Intelligence (UAI1995) | null | null | UAI-P-1995-PG-36-45 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We describe an application of belief networks to the diagnosis of bottlenecks
in computer systems. The technique relies on a high-level functional model of
the interaction between application workloads, the Windows NT operating system,
and system hardware. Given a workload description, the model predicts the
values of observable system counters available from the Windows NT performance
monitoring tool. Uncertainty in workloads, predictions, and counter values are
characterized with Gaussian distributions. During diagnostic inference, we use
observed performance monitor values to find the most probable assignment to the
workload parameters. In this paper we provide some background on automated
bottleneck detection, describe the structure of the system model, and discuss
empirical procedures for model calibration and verification. Part of the
calibration process includes generating a dataset to estimate a multivariate
Gaussian error model. Initial results in diagnosing bottlenecks are presented.
| [
{
"version": "v1",
"created": "Wed, 20 Feb 2013 15:19:11 GMT"
}
] | 1,361,404,800,000 | [
[
"Breese",
"John S.",
""
],
[
"Blake",
"Russ",
""
]
] |
1302.4933 | Wray L. Buntine | Wray L. Buntine | Chain Graphs for Learning | Appears in Proceedings of the Eleventh Conference on Uncertainty in
Artificial Intelligence (UAI1995) | null | null | UAI-P-1995-PG-46-54 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Chain graphs combine directed and undirected graphs and their underlying
mathematics combines properties of the two. This paper gives a simplified
definition of chain graphs based on a hierarchical combination of Bayesian
(directed) and Markov (undirected) networks. Examples of a chain graph are
multivariate feed-forward networks, clustering with conditional interaction
between variables, and forms of Bayes classifiers. Chain graphs are then
extended using the notation of plates so that samples and data analysis
problems can be represented in a graphical model as well. Implications for
learning are discussed in the conclusion.
| [
{
"version": "v1",
"created": "Wed, 20 Feb 2013 15:19:16 GMT"
}
] | 1,361,404,800,000 | [
[
"Buntine",
"Wray L.",
""
]
] |
1302.4934 | Enrique F. Castillo | Enrique F. Castillo, Remco R. Bouckaert, Jose M. Sarabia, Cristina
Solares | Error Estimation in Approximate Bayesian Belief Network Inference | Appears in Proceedings of the Eleventh Conference on Uncertainty in
Artificial Intelligence (UAI1995) | null | null | UAI-P-1995-PG-55-62 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We can perform inference in Bayesian belief networks by enumerating
instantiations with high probability thus approximating the marginals. In this
paper, we present a method for determining the fraction of instantiations that
has to be considered such that the absolute error in the marginals does not
exceed a predefined value. The method is based on extreme value theory.
Essentially, the proposed method uses the reversed generalized Pareto
distribution to model probabilities of instantiations below a given threshold.
Based on this distribution, an estimate of the maximal absolute error if
instantiations with probability smaller than u are disregarded can be made.
| [
{
"version": "v1",
"created": "Wed, 20 Feb 2013 15:19:22 GMT"
}
] | 1,361,404,800,000 | [
[
"Castillo",
"Enrique F.",
""
],
[
"Bouckaert",
"Remco R.",
""
],
[
"Sarabia",
"Jose M.",
""
],
[
"Solares",
"Cristina",
""
]
] |
1302.4935 | Juan Luis Castro | Juan Luis Castro, Jose Manuel Zurita | Generating the Structure of a Fuzzy Rule under Uncertainty | Appears in Proceedings of the Eleventh Conference on Uncertainty in
Artificial Intelligence (UAI1995) | null | null | UAI-P-1995-PG-63-67 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The aim of this paper is to present a method for identifying the structure of
a rule in a fuzzy model. For this purpose, an ATMS shall be used (Zurita 1994).
An algorithm obtaining the identification of the structure will be suggested
(Castro 1995). The minimal structure of the rule (with respect to the number of
variables that must appear in the rule) will be found by this algorithm.
Furthermore, the identification parameters shall be obtained simultaneously.
The proposed method shall be applied for classification to an example. The {em
Iris Plant Database} shall be learnt for all three kinds of plants.
| [
{
"version": "v1",
"created": "Wed, 20 Feb 2013 15:19:27 GMT"
}
] | 1,361,404,800,000 | [
[
"Castro",
"Juan Luis",
""
],
[
"Zurita",
"Jose Manuel",
""
]
] |
1302.4936 | Didier Cayrac | Didier Cayrac, Didier Dubois, Henri Prade | Practical Model-Based Diagnosis with Qualitative Possibilistic
Uncertainty | Appears in Proceedings of the Eleventh Conference on Uncertainty in
Artificial Intelligence (UAI1995) | null | null | UAI-P-1995-PG-68-76 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | An approach to fault isolation that exploits vastly incomplete models is
presented. It relies on separate descriptions of each component behavior,
together with the links between them, which enables focusing of the reasoning
to the relevant part of the system. As normal observations do not need
explanation, the behavior of the components is limited to anomaly propagation.
Diagnostic solutions are disorders (fault modes or abnormal signatures) that
are consistent with the observations, as well as abductive explanations. An
ordinal representation of uncertainty based on possibility theory provides a
simple exception-tolerant description of the component behaviors. We can for
instance distinguish between effects that are more or less certainly present
(or absent) and effects that are more or less certainly present (or absent)
when a given anomaly is present. A realistic example illustrates the benefits
of this approach.
| [
{
"version": "v1",
"created": "Wed, 20 Feb 2013 15:19:32 GMT"
}
] | 1,361,404,800,000 | [
[
"Cayrac",
"Didier",
""
],
[
"Dubois",
"Didier",
""
],
[
"Prade",
"Henri",
""
]
] |
1302.4937 | Tom Chavez | Tom Chavez, Ross D. Shachter | Decision Flexibility | Appears in Proceedings of the Eleventh Conference on Uncertainty in
Artificial Intelligence (UAI1995) | null | null | UAI-P-1995-PG-77-86 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The development of new methods and representations for temporal
decision-making requires a principled basis for characterizing and measuring
the flexibility of decision strategies in the face of uncertainty. Our goal in
this paper is to provide a framework - not a theory - for observing how
decision policies behave in the face of informational perturbations, to gain
clues as to how they might behave in the face of unanticipated, possibly
unarticulated uncertainties. To this end, we find it beneficial to distinguish
between two types of uncertainty: "Small World" and "Large World" uncertainty.
The first type can be resolved by posing an unambiguous question to a
"clairvoyant," and is anchored on some well-defined aspect of a decision frame.
The second type is more troublesome, yet it is often of greater interest when
we address the issue of flexibility; this type of uncertainty can be resolved
only by consulting a "psychic." We next observe that one approach to
flexibility used in the economics literature is already implicitly accounted
for in the Maximum Expected Utility (MEU) principle from decision theory.
Though simple, the observation establishes the context for a more illuminating
notion of flexibility, what we term flexibility with respect to information
revelation. We show how to perform flexibility analysis of a static (i.e.,
single period) decision problem using a simple example, and we observe that the
most flexible alternative thus identified is not necessarily the MEU
alternative. We extend our analysis for a dynamic (i.e., multi-period) model,
and we demonstrate how to calculate the value of flexibility for decision
strategies that allow downstream revision of an upstream commitment decision.
| [
{
"version": "v1",
"created": "Wed, 20 Feb 2013 15:19:37 GMT"
}
] | 1,361,404,800,000 | [
[
"Chavez",
"Tom",
""
],
[
"Shachter",
"Ross D.",
""
]
] |
1302.4938 | David Maxwell Chickering | David Maxwell Chickering | A Transformational Characterization of Equivalent Bayesian Network
Structures | Appears in Proceedings of the Eleventh Conference on Uncertainty in
Artificial Intelligence (UAI1995) | null | null | UAI-P-1995-PG-87-98 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We present a simple characterization of equivalent Bayesian network
structures based on local transformations. The significance of the
characterization is twofold. First, we are able to easily prove several new
invariant properties of theoretical interest for equivalent structures. Second,
we use the characterization to derive an efficient algorithm that identifies
all of the compelled edges in a structure. Compelled edge identification is of
particular importance for learning Bayesian network structures from data
because these edges indicate causal relationships when certain assumptions
hold.
| [
{
"version": "v1",
"created": "Wed, 20 Feb 2013 15:19:42 GMT"
}
] | 1,361,404,800,000 | [
[
"Chickering",
"David Maxwell",
""
]
] |
1302.4939 | Adnan Darwiche | Adnan Darwiche | Conditioning Methods for Exact and Approximate Inference in Causal
Networks | Appears in Proceedings of the Eleventh Conference on Uncertainty in
Artificial Intelligence (UAI1995) | null | null | UAI-P-1995-PG-99-107 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We present two algorithms for exact and approximate inference in causal
networks. The first algorithm, dynamic conditioning, is a refinement of cutset
conditioning that has linear complexity on some networks for which cutset
conditioning is exponential. The second algorithm, B-conditioning, is an
algorithm for approximate inference that allows one to trade-off the quality of
approximations with the computation time. We also present some experimental
results illustrating the properties of the proposed algorithms.
| [
{
"version": "v1",
"created": "Wed, 20 Feb 2013 15:19:47 GMT"
}
] | 1,361,404,800,000 | [
[
"Darwiche",
"Adnan",
""
]
] |
1302.4940 | Luis M. de Campos | Luis M. de Campos, Serafin Moral | Independence Concepts for Convex Sets of Probabilities | Appears in Proceedings of the Eleventh Conference on Uncertainty in
Artificial Intelligence (UAI1995) | null | null | UAI-P-1995-PG-108-115 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this paper we study different concepts of independence for convex sets of
probabilities. There will be two basic ideas for independence. The first is
irrelevance. Two variables are independent when a change on the knowledge about
one variable does not affect the other. The second one is factorization. Two
variables are independent when the joint convex set of probabilities can be
decomposed on the product of marginal convex sets. In the case of the Theory of
Probability, these two starting points give rise to the same definition. In the
case of convex sets of probabilities, the resulting concepts will be strongly
related, but they will not be equivalent. As application of the concept of
independence, we shall consider the problem of building a global convex set
from marginal convex sets of probabilities.
| [
{
"version": "v1",
"created": "Wed, 20 Feb 2013 15:19:52 GMT"
}
] | 1,361,404,800,000 | [
[
"de Campos",
"Luis M.",
""
],
[
"Moral",
"Serafin",
""
]
] |
1302.4941 | Denise L. Draper | Denise L. Draper | Clustering Without (Thinking About) Triangulation | Appears in Proceedings of the Eleventh Conference on Uncertainty in
Artificial Intelligence (UAI1995) | null | null | UAI-P-1995-PG-125-133 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The undirected technique for evaluating belief networks [Jensen, et.al.,
1990, Lauritzen and Spiegelhalter, 1988] requires clustering the nodes in the
network into a junction tree. In the traditional view, the junction tree is
constructed from the cliques of the moralized and triangulated belief network:
triangulation is taken to be the primitive concept, the goal towards which any
clustering algorithm (e.g. node elimination) is directed. In this paper, we
present an alternative conception of clustering, in which clusters and the
junction tree property play the role of primitives: given a graph (not a tree)
of clusters which obey (a modified version of) the junction tree property, we
transform this graph until we have obtained a tree. There are several
advantages to this approach: it is much clearer and easier to understand, which
is important for humans who are constructing belief networks; it admits a wider
range of heuristics which may enable more efficient or superior clustering
algorithms; and it serves as the natural basis for an incremental clustering
scheme, which we describe.
| [
{
"version": "v1",
"created": "Wed, 20 Feb 2013 15:19:57 GMT"
}
] | 1,361,404,800,000 | [
[
"Draper",
"Denise L.",
""
]
] |
1302.4942 | Eric Driver | Eric Driver, Darryl Morrell | Implementation of Continuous Bayesian Networks Using Sums of Weighted
Gaussians | Appears in Proceedings of the Eleventh Conference on Uncertainty in
Artificial Intelligence (UAI1995) | null | null | UAI-P-1995-PG-134-140 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Bayesian networks provide a method of representing conditional independence
between random variables and computing the probability distributions associated
with these random variables. In this paper, we extend Bayesian network
structures to compute probability density functions for continuous random
variables. We make this extension by approximating prior and conditional
densities using sums of weighted Gaussian distributions and then finding the
propagation rules for updating the densities in terms of these weights. We
present a simple example that illustrates the Bayesian network for continuous
variables; this example shows the effect of the network structure and
approximation errors on the computation of densities for variables in the
network.
| [
{
"version": "v1",
"created": "Wed, 20 Feb 2013 15:20:02 GMT"
}
] | 1,361,404,800,000 | [
[
"Driver",
"Eric",
""
],
[
"Morrell",
"Darryl",
""
]
] |
1302.4943 | Marek J. Druzdzel | Marek J. Druzdzel, Linda C. van der Gaag | Elicitation of Probabilities for Belief Networks: Combining Qualitative
and Quantitative Information | Appears in Proceedings of the Eleventh Conference on Uncertainty in
Artificial Intelligence (UAI1995) | null | null | UAI-P-1995-PG-141-148 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Although the usefulness of belief networks for reasoning under uncertainty is
widely accepted, obtaining numerical probabilities that they require is still
perceived a major obstacle. Often not enough statistical data is available to
allow for reliable probability estimation. Available information may not be
directly amenable for encoding in the network. Finally, domain experts may be
reluctant to provide numerical probabilities. In this paper, we propose a
method for elicitation of probabilities from a domain expert that is
non-invasive and accommodates whatever probabilistic information the expert is
willing to state. We express all available information, whether qualitative or
quantitative in nature, in a canonical form consisting of (in) equalities
expressing constraints on the hyperspace of possible joint probability
distributions. We then use this canonical form to derive second-order
probability distributions over the desired probabilities.
| [
{
"version": "v1",
"created": "Wed, 20 Feb 2013 15:20:07 GMT"
}
] | 1,361,404,800,000 | [
[
"Druzdzel",
"Marek J.",
""
],
[
"van der Gaag",
"Linda C.",
""
]
] |
1302.4944 | Didier Dubois | Didier Dubois, Henri Prade | Numerical Representations of Acceptance | Appears in Proceedings of the Eleventh Conference on Uncertainty in
Artificial Intelligence (UAI1995) | null | null | UAI-P-1995-PG-149-156 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Accepting a proposition means that our confidence in this proposition is
strictly greater than the confidence in its negation. This paper investigates
the subclass of uncertainty measures, expressing confidence, that capture the
idea of acceptance, what we call acceptance functions. Due to the monotonicity
property of confidence measures, the acceptance of a proposition entails the
acceptance of any of its logical consequences. In agreement with the idea that
a belief set (in the sense of Gardenfors) must be closed under logical
consequence, it is also required that the separate acceptance o two
propositions entail the acceptance of their conjunction. Necessity (and
possibility) measures agree with this view of acceptance while probability and
belief functions generally do not. General properties of acceptance functions
are estabilished. The motivation behind this work is the investigation of a
setting for belief revision more general than the one proposed by Alchourron,
Gardenfors and Makinson, in connection with the notion of conditioning.
| [
{
"version": "v1",
"created": "Wed, 20 Feb 2013 15:20:13 GMT"
}
] | 1,361,404,800,000 | [
[
"Dubois",
"Didier",
""
],
[
"Prade",
"Henri",
""
]
] |
1302.4945 | Kazuo J. Ezawa | Kazuo J. Ezawa, Til Schuermann | Fraud/Uncollectible Debt Detection Using a Bayesian Network Based
Learning System: A Rare Binary Outcome with Mixed Data Structures | Appears in Proceedings of the Eleventh Conference on Uncertainty in
Artificial Intelligence (UAI1995) | null | null | UAI-P-1995-PG-157-166 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The fraud/uncollectible debt problem in the telecommunications industry
presents two technical challenges: the detection and the treatment of the
account given the detection. In this paper, we focus on the first problem of
detection using Bayesian network models, and we briefly discuss the application
of a normative expert system for the treatment at the end. We apply Bayesian
network models to the problem of fraud/uncollectible debt detection for
telecommunication services. In addition to being quite successful at predicting
rare event outcomes, it is able to handle a mixture of categorical and
continuous data. We present a performance comparison using linear and
non-linear discriminant analysis, classification and regression trees, and
Bayesian network models
| [
{
"version": "v1",
"created": "Wed, 20 Feb 2013 15:20:19 GMT"
}
] | 1,361,404,800,000 | [
[
"Ezawa",
"Kazuo J.",
""
],
[
"Schuermann",
"Til",
""
]
] |
1302.4946 | Helene Fargier | Helene Fargier, Jerome Lang, Roger Martin-Clouaire, Thomas Schiex | A Constraint Satisfaction Approach to Decision under Uncertainty | Appears in Proceedings of the Eleventh Conference on Uncertainty in
Artificial Intelligence (UAI1995) | null | null | UAI-P-1995-PG-167-174 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The Constraint Satisfaction Problem (CSP) framework offers a simple and sound
basis for representing and solving simple decision problems, without
uncertainty. This paper is devoted to an extension of the CSP framework
enabling us to deal with some decisions problems under uncertainty. This
extension relies on a differentiation between the agent-controllable decision
variables and the uncontrollable parameters whose values depend on the
occurrence of uncertain events. The uncertainty on the values of the parameters
is assumed to be given under the form of a probability distribution. Two
algorithms are given, for computing respectively decisions solving the problem
with a maximal probability, and conditional decisions mapping the largest
possible amount of possible cases to actual decisions.
| [
{
"version": "v1",
"created": "Wed, 20 Feb 2013 15:20:24 GMT"
}
] | 1,361,404,800,000 | [
[
"Fargier",
"Helene",
""
],
[
"Lang",
"Jerome",
""
],
[
"Martin-Clouaire",
"Roger",
""
],
[
"Schiex",
"Thomas",
""
]
] |
1302.4947 | Nir Friedman | Nir Friedman, Joseph Y. Halpern | Plausibility Measures: A User's Guide | Appears in Proceedings of the Eleventh Conference on Uncertainty in
Artificial Intelligence (UAI1995) | null | null | UAI-P-1995-PG-175-184 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We examine a new approach to modeling uncertainty based on plausibility
measures, where a plausibility measure just associates with an event its
plausibility, an element is some partially ordered set. This approach is easily
seen to generalize other approaches to modeling uncertainty, such as
probability measures, belief functions, and possibility measures. The lack of
structure in a plausibility measure makes it easy for us to add structure on an
"as needed" basis, letting us examine what is required to ensure that a
plausibility measure has certain properties of interest. This gives us insight
into the essential features of the properties in question, while allowing us to
prove general results that apply to many approaches to reasoning about
uncertainty. Plausibility measures have already proved useful in analyzing
default reasoning. In this paper, we examine their "algebraic properties,"
analogues to the use of + and * in probability theory. An understanding of such
properties will be essential if plausibility measures are to be used in
practice as a representation tool.
| [
{
"version": "v1",
"created": "Wed, 20 Feb 2013 15:20:29 GMT"
}
] | 1,361,404,800,000 | [
[
"Friedman",
"Nir",
""
],
[
"Halpern",
"Joseph Y.",
""
]
] |
1302.4948 | David Galles | David Galles, Judea Pearl | Testing Identifiability of Causal Effects | Appears in Proceedings of the Eleventh Conference on Uncertainty in
Artificial Intelligence (UAI1995) | null | null | UAI-P-1995-PG-185-195 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper concerns the probabilistic evaluation of the effects of actions in
the presence of unmeasured variables. We show that the identification of causal
effect between a singleton variable X and a set of variables Y can be
accomplished systematically, in time polynomial in the number of variables in
the graph. When the causal effect is identifiable, a closed-form expression can
be obtained for the probability that the action will achieve a specified goal,
or a set of goals.
| [
{
"version": "v1",
"created": "Wed, 20 Feb 2013 15:20:35 GMT"
}
] | 1,361,404,800,000 | [
[
"Galles",
"David",
""
],
[
"Pearl",
"Judea",
""
]
] |
1302.4950 | Moises Goldszmidt | Moises Goldszmidt | Fast Belief Update Using Order-of-Magnitude Probabilities | Appears in Proceedings of the Eleventh Conference on Uncertainty in
Artificial Intelligence (UAI1995) | null | null | UAI-P-1995-PG-208-216 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We present an algorithm, called Predict, for updating beliefs in causal
networks quantified with order-of-magnitude probabilities. The algorithm takes
advantage of both the structure and the quantification of the network and
presents a polynomial asymptotic complexity. Predict exhibits a conservative
behavior in that it is always sound but not always complete. We provide
sufficient conditions for completeness and present algorithms for testing these
conditions and for computing a complete set of plausible values. We propose
Predict as an efficient method to estimate probabilistic values and illustrate
its use in conjunction with two known algorithms for probabilistic inference.
Finally, we describe an application of Predict to plan evaluation, present
experimental results, and discuss issues regarding its use with conditional
logics of belief, and in the characterization of irrelevance.
| [
{
"version": "v1",
"created": "Wed, 20 Feb 2013 15:20:47 GMT"
}
] | 1,361,404,800,000 | [
[
"Goldszmidt",
"Moises",
""
]
] |
1302.4951 | Benjamin N. Grosof | Benjamin N. Grosof | Transforming Prioritized Defaults and Specificity into Parallel Defaults | Appears in Proceedings of the Eleventh Conference on Uncertainty in
Artificial Intelligence (UAI1995) | null | null | UAI-P-1995-PG-217-228 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We show how to transform any set of prioritized propositional defaults into
an equivalent set of parallel (i.e., unprioritized) defaults, in
circumscription. We give an algorithm to implement the transform. We show how
to use the transform algorithm as a generator of a whole family of inferencing
algorithms for circumscription. The method is to employ the transform algorithm
as a front end to any inferencing algorithm, e.g., one of the previously
available, that handles the parallel (empty) case of prioritization. Our
algorithms provide not just coverage of a new expressive class, but also
alternatives to previous algorithms for implementing the previously covered
class (?layered?) of prioritization. In particular, we give a new
query-answering algorithm for prioritized cirumscription which is sound and
complete for the full expressive class of unrestricted finite prioritization
partial orders, for propositional defaults (or minimized predicates). By
contrast, previous algorithms required that the prioritization partial order be
layered, i.e., structured similar to the system of rank in the military. Our
algorithm enables, for the first time, the implementation of the most useful
class of prioritization: non-layered prioritization partial orders. Default
inheritance, for example, typically requires non-layered prioritization to
represent specificity adequately. Our algorithm enables not only the
implementation of default inheritance (and specificity) within prioritized
circumscription, but also the extension and combination of default inheritance
with other kinds of prioritized default reasoning, e.g.: with stratified logic
programs with negation-as-failure. Such logic programs are previously known to
be representable equivalently as layered-priority predicate circumscriptions.
Worst-case, the transform increases the number of defaults exponentially. We
discuss how inferencing is practically implementable nevertheless in two kinds
of situations: general expressiveness but small numbers of defaults, or
expressive special cases with larger numbers of defaults. One such expressive
special case is non-?top-heaviness? of the prioritization partial order. In
addition to its direct implementation, the transform can also be exploited
analytically to generate special case algorithms, e.g., a tractable transform
for a class within default inheritance (detailed in another, forthcoming
paper). We discuss other aspects of the significance of the fundamental result.
One can view the transform as reducing n degrees of partially ordered belief
confidence to just 2 degrees of confidence: for-sure and (unprioritized)
default. Ordinary, parallel default reasoning, e.g., in parallel
circumscription or Poole's Theorist, can be viewed in these terms as reducing 2
degrees of confidence to just 1 degree of confidence: that of the non-monotonic
theory's conclusions. The expressive reduction's computational complexity
suggests that prioritization is valuable for its expressive conciseness, just
as defaults are for theirs. For Reiter's Default Logic and Poole's Theorist,
the transform implies how to extend those formalisms so as to equip them with a
concept of prioritization that is exactly equivalent to that in
circumscription. This provides an interesting alternative to Brewka's approach
to equipping them with prioritization-type precedence.
| [
{
"version": "v1",
"created": "Wed, 20 Feb 2013 15:20:52 GMT"
}
] | 1,361,404,800,000 | [
[
"Grosof",
"Benjamin N.",
""
]
] |
1302.4952 | Peter Haddawy | Peter Haddawy, AnHai Doan, Richard Goodwin | Efficient Decision-Theoretic Planning: Techniques and Empirical Analysis | Appears in Proceedings of the Eleventh Conference on Uncertainty in
Artificial Intelligence (UAI1995) | null | null | UAI-P-1995-PG-229-236 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper discusses techniques for performing efficient decision-theoretic
planning. We give an overview of the DRIPS decision-theoretic refinement
planning system, which uses abstraction to efficiently identify optimal plans.
We present techniques for automatically generating search control information,
which can significantly improve the planner's performance. We evaluate the
efficiency of DRIPS both with and without the search control rules on a complex
medical planning problem and compare its performance to that of a
branch-and-bound decision tree algorithm.
| [
{
"version": "v1",
"created": "Wed, 20 Feb 2013 15:20:57 GMT"
}
] | 1,361,404,800,000 | [
[
"Haddawy",
"Peter",
""
],
[
"Doan",
"AnHai",
""
],
[
"Goodwin",
"Richard",
""
]
] |
1302.4953 | Petr Hajek | Petr Hajek, Lluis Godo, Francesc Esteva | Fuzzy Logic and Probability | Appears in Proceedings of the Eleventh Conference on Uncertainty in
Artificial Intelligence (UAI1995) | null | null | UAI-P-1995-PG-237-244 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this paper we deal with a new approach to probabilistic reasoning in a
logical framework. Nearly almost all logics of probability that have been
proposed in the literature are based on classical two-valued logic. After
making clear the differences between fuzzy logic and probability theory, here
we propose a {em fuzzy} logic of probability for which completeness results (in
a probabilistic sense) are provided. The main idea behind this approach is that
probability values of crisp propositions can be understood as truth-values of
some suitable fuzzy propositions associated to the crisp ones. Moreover,
suggestions and examples of how to extend the formalism to cope with
conditional probabilities and with other uncertainty formalisms are also
provided.
| [
{
"version": "v1",
"created": "Wed, 20 Feb 2013 15:21:03 GMT"
}
] | 1,361,404,800,000 | [
[
"Hajek",
"Petr",
""
],
[
"Godo",
"Lluis",
""
],
[
"Esteva",
"Francesc",
""
]
] |
1302.4954 | Steve Hanks | Steve Hanks, David Madigan, Jonathan Gavrin | Probabilistic Temporal Reasoning with Endogenous Change | Appears in Proceedings of the Eleventh Conference on Uncertainty in
Artificial Intelligence (UAI1995) | null | null | UAI-P-1995-PG-245-254 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper presents a probabilistic model for reasoning about the state of a
system as it changes over time, both due to exogenous and endogenous
influences. Our target domain is a class of medical prediction problems that
are neither so urgent as to preclude careful diagnosis nor progress so slowly
as to allow arbitrary testing and treatment options. In these domains there is
typically enough time to gather information about the patient's state and
consider alternative diagnoses and treatments, but the temporal interaction
between the timing of tests, treatments, and the course of the disease must
also be considered. Our approach is to elicit a qualitative structural model of
the patient from a human expert---the model identifies important attributes,
the way in which exogenous changes affect attribute values, and the way in
which the patient's condition changes endogenously. We then elicit
probabilistic information to capture the expert's uncertainty about the effects
of tests and treatments and the nature and timing of endogenous state changes.
This paper describes the model in the context of a problem in treating vehicle
accident trauma, and suggests a method for solving the model based on the
technique of sequential imputation. A complementary goal of this work is to
understand and synthesize a disparate collection of research efforts all using
the name ?probabilistic temporal reasoning.? This paper analyzes related work
and points out essential differences between our proposed model and other
approaches in the literature.
| [
{
"version": "v1",
"created": "Wed, 20 Feb 2013 15:21:08 GMT"
}
] | 1,361,404,800,000 | [
[
"Hanks",
"Steve",
""
],
[
"Madigan",
"David",
""
],
[
"Gavrin",
"Jonathan",
""
]
] |
1302.4955 | David Harmanec | David Harmanec | Toward a Characterization of Uncertainty Measure for the Dempster-Shafer
Theory | Appears in Proceedings of the Eleventh Conference on Uncertainty in
Artificial Intelligence (UAI1995) | null | null | UAI-P-1995-PG-255-261 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This is a working paper summarizing results of an ongoing research project
whose aim is to uniquely characterize the uncertainty measure for the
Dempster-Shafer Theory. A set of intuitive axiomatic requirements is presented,
some of their implications are shown, and the proof is given of the minimality
of recently proposed measure AU among all measures satisfying the proposed
requirements.
| [
{
"version": "v1",
"created": "Wed, 20 Feb 2013 15:21:13 GMT"
}
] | 1,361,404,800,000 | [
[
"Harmanec",
"David",
""
]
] |
1302.4956 | David Heckerman | David Heckerman, Ross D. Shachter | A Definition and Graphical Representation for Causality | Appears in Proceedings of the Eleventh Conference on Uncertainty in
Artificial Intelligence (UAI1995) | null | null | UAI-P-1995-PG-262-273 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We present a precise definition of cause and effect in terms of a fundamental
notion called unresponsiveness. Our definition is based on Savage's (1954)
formulation of decision theory and departs from the traditional view of
causation in that our causal assertions are made relative to a set of
decisions. An important consequence of this departure is that we can reason
about cause locally, not requiring a causal explanation for every dependency.
Such local reasoning can be beneficial because it may not be necessary to
determine whether a particular dependency is causal to make a decision. Also in
this paper, we examine the graphical encoding of causal relationships. We show
that influence diagrams in canonical form are an accurate and efficient
representation of causal relationships. In addition, we establish a
correspondence between canonical form and Pearl's causal theory.
| [
{
"version": "v1",
"created": "Wed, 20 Feb 2013 15:21:18 GMT"
},
{
"version": "v2",
"created": "Sat, 16 May 2015 23:43:57 GMT"
}
] | 1,431,993,600,000 | [
[
"Heckerman",
"David",
""
],
[
"Shachter",
"Ross D.",
""
]
] |
1302.4957 | David Heckerman | David Heckerman, Dan Geiger | Learning Bayesian Networks: A Unification for Discrete and Gaussian
Domains | This version has improved pointers to the literature | null | null | UAI-P-1995-PG-274-284 | cs.AI | http://creativecommons.org/licenses/by/4.0/ | We examine Bayesian methods for learning Bayesian networks from a combination
of prior knowledge and statistical data. In particular, we unify the approaches
we presented at last year's conference for discrete and Gaussian domains. We
derive a general Bayesian scoring metric, appropriate for both domains. We then
use this metric in combination with well-known statistical facts about the
Dirichlet and normal--Wishart distributions to derive our metrics for discrete
and Gaussian domains.
| [
{
"version": "v1",
"created": "Wed, 20 Feb 2013 15:21:23 GMT"
},
{
"version": "v2",
"created": "Thu, 13 May 2021 13:25:36 GMT"
},
{
"version": "v3",
"created": "Tue, 29 Jun 2021 19:41:53 GMT"
}
] | 1,625,097,600,000 | [
[
"Heckerman",
"David",
""
],
[
"Geiger",
"Dan",
""
]
] |
1302.4958 | David Heckerman | David Heckerman | A Bayesian Approach to Learning Causal Networks | Appears in Proceedings of the Eleventh Conference on Uncertainty in
Artificial Intelligence (UAI1995) | null | null | UAI-P-1995-PG-285-295 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Whereas acausal Bayesian networks represent probabilistic independence,
causal Bayesian networks represent causal relationships. In this paper, we
examine Bayesian methods for learning both types of networks. Bayesian methods
for learning acausal networks are fairly well developed. These methods often
employ assumptions to facilitate the construction of priors, including the
assumptions of parameter independence, parameter modularity, and likelihood
equivalence. We show that although these assumptions also can be appropriate
for learning causal networks, we need additional assumptions in order to learn
causal networks. We introduce two sufficient assumptions, called {em mechanism
independence} and {em component independence}. We show that these new
assumptions, when combined with parameter independence, parameter modularity,
and likelihood equivalence, allow us to apply methods for learning acausal
networks to learn causal networks.
| [
{
"version": "v1",
"created": "Wed, 20 Feb 2013 15:21:29 GMT"
},
{
"version": "v2",
"created": "Sat, 16 May 2015 23:38:36 GMT"
}
] | 1,431,993,600,000 | [
[
"Heckerman",
"David",
""
]
] |
1302.4959 | Eric J. Horvitz | Eric J. Horvitz, Matthew Barry | Display of Information for Time-Critical Decision Making | Appears in Proceedings of the Eleventh Conference on Uncertainty in
Artificial Intelligence (UAI1995) | null | null | UAI-P-1995-PG-296-305 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We describe methods for managing the complexity of information displayed to
people responsible for making high-stakes, time-critical decisions. The
techniques provide tools for real-time control of the configuration and
quantity of information displayed to a user, and a methodology for designing
flexible human-computer interfaces for monitoring applications. After defining
a prototypical set of display decision problems, we introduce the expected
value of revealed information (EVRI) and the related measure of expected value
of displayed information (EVDI). We describe how these measures can be used to
enhance computer displays used for monitoring complex systems. We motivate the
presentation by discussing our efforts to employ decision-theoretic control of
displays for a time-critical monitoring application at the NASA Mission Control
Center in Houston.
| [
{
"version": "v1",
"created": "Wed, 20 Feb 2013 15:21:34 GMT"
}
] | 1,361,404,800,000 | [
[
"Horvitz",
"Eric J.",
""
],
[
"Barry",
"Matthew",
""
]
] |
1302.4960 | Eric J. Horvitz | Eric J. Horvitz, Adrian Klein | Reasoning, Metareasoning, and Mathematical Truth: Studies of Theorem
Proving under Limited Resources | Appears in Proceedings of the Eleventh Conference on Uncertainty in
Artificial Intelligence (UAI1995) | null | null | UAI-P-1995-PG-306-314 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In earlier work, we introduced flexible inference and decision-theoretic
metareasoning to address the intractability of normative inference. Here,
rather than pursuing the task of computing beliefs and actions with decision
models composed of distinctions about uncertain events, we examine methods for
inferring beliefs about mathematical truth before an automated theorem prover
completes a proof. We employ a Bayesian analysis to update belief in truth,
given theorem-proving progress, and show how decision-theoretic methods can be
used to determine the value of continuing to deliberate versus taking immediate
action in time-critical situations.
| [
{
"version": "v1",
"created": "Wed, 20 Feb 2013 15:21:39 GMT"
}
] | 1,361,404,800,000 | [
[
"Horvitz",
"Eric J.",
""
],
[
"Klein",
"Adrian",
""
]
] |
1302.4961 | Mark Hulme | Mark Hulme | Improved Sampling for Diagnostic Reasoning in Bayesian Networks | Appears in Proceedings of the Eleventh Conference on Uncertainty in
Artificial Intelligence (UAI1995) | null | null | UAI-P-1995-PG-315-322 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Bayesian networks offer great potential for use in automating large scale
diagnostic reasoning tasks. Gibbs sampling is the main technique used to
perform diagnostic reasoning in large richly interconnected Bayesian networks.
Unfortunately Gibbs sampling can take an excessive time to generate a
representative sample. In this paper we describe and test a number of heuristic
strategies for improving sampling in noisy-or Bayesian networks. The strategies
include Monte Carlo Markov chain sampling techniques other than Gibbs sampling.
Emphasis is put on strategies that can be implemented in distributed systems.
| [
{
"version": "v1",
"created": "Wed, 20 Feb 2013 15:21:44 GMT"
}
] | 1,361,404,800,000 | [
[
"Hulme",
"Mark",
""
]
] |
1302.4962 | Finn Verner Jensen | Finn Verner Jensen | Cautious Propagation in Bayesian Networks | Appears in Proceedings of the Eleventh Conference on Uncertainty in
Artificial Intelligence (UAI1995) | null | null | UAI-P-1995-PG-323-328 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Consider the situation where some evidence e has been entered to a Bayesian
network. When performing conflict analysis, sensitivity analysis, or when
answering questions like "What if the finding on X had been y instead of x?"
you need probabilities P (e'| h), where e' is a subset of e, and h is a
configuration of a (possibly empty) set of variables. Cautious propagation is a
modification of HUGIN propagation into a Shafer-Shenoy-like architecture. It is
less efficient than HUGIN propagation; however, it provides easy access to P
(e'| h) for a great deal of relevant subsets e'.
| [
{
"version": "v1",
"created": "Wed, 20 Feb 2013 15:21:50 GMT"
}
] | 1,361,404,800,000 | [
[
"Jensen",
"Finn Verner",
""
]
] |
1302.4963 | Ali Jenzarli | Ali Jenzarli | Information/Relevance Influence Diagrams | Appears in Proceedings of the Eleventh Conference on Uncertainty in
Artificial Intelligence (UAI1995) | null | null | UAI-P-1995-PG-329-337 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this paper we extend the influence diagram (ID) representation for
decisions under uncertainty. In the standard ID, arrows into a decision node
are only informational; they do not represent constraints on what the decision
maker can do. We can represent such constraints only indirectly, using arrows
to the children of the decision and sometimes adding more variables to the
influence diagram, thus making the ID more complicated. Users of influence
diagrams often want to represent constraints by arrows into decision nodes. We
represent constraints on decisions by allowing relevance arrows into decision
nodes. We call the resulting representation information/relevance influence
diagrams (IRIDs). Information/relevance influence diagrams allow for direct
representation and specification of constrained decisions. We use a combination
of stochastic dynamic programming and Gibbs sampling to solve IRIDs. This
method is especially useful when exact methods for solving IDs fail.
| [
{
"version": "v1",
"created": "Wed, 20 Feb 2013 15:21:55 GMT"
}
] | 1,361,404,800,000 | [
[
"Jenzarli",
"Ali",
""
]
] |
1302.4965 | Keiji Kanazawa | Keiji Kanazawa, Daphne Koller, Stuart Russell | Stochastic Simulation Algorithms for Dynamic Probabilistic Networks | Appears in Proceedings of the Eleventh Conference on Uncertainty in
Artificial Intelligence (UAI1995) | null | null | UAI-P-1995-PG-346-351 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Stochastic simulation algorithms such as likelihood weighting often give
fast, accurate approximations to posterior probabilities in probabilistic
networks, and are the methods of choice for very large networks. Unfortunately,
the special characteristics of dynamic probabilistic networks (DPNs), which are
used to represent stochastic temporal processes, mean that standard simulation
algorithms perform very poorly. In essence, the simulation trials diverge
further and further from reality as the process is observed over time. In this
paper, we present simulation algorithms that use the evidence observed at each
time step to push the set of trials back towards reality. The first algorithm,
"evidence reversal" (ER) restructures each time slice of the DPN so that the
evidence nodes for the slice become ancestors of the state variables. The
second algorithm, called "survival of the fittest" sampling (SOF),
"repopulates" the set of trials at each time step using a stochastic
reproduction rate weighted by the likelihood of the evidence according to each
trial. We compare the performance of each algorithm with likelihood weighting
on the original network, and also investigate the benefits of combining the ER
and SOF methods. The ER/SOF combination appears to maintain bounded error
independent of the number of time steps in the simulation.
| [
{
"version": "v1",
"created": "Wed, 20 Feb 2013 15:22:06 GMT"
}
] | 1,480,118,400,000 | [
[
"Kanazawa",
"Keiji",
""
],
[
"Koller",
"Daphne",
""
],
[
"Russell",
"Stuart",
""
]
] |
1302.4966 | Grigoris I. Karakoulas | Grigoris I. Karakoulas | Probabilistic Exploration in Planning while Learning | Appears in Proceedings of the Eleventh Conference on Uncertainty in
Artificial Intelligence (UAI1995) | null | null | UAI-P-1995-PG-352-361 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Sequential decision tasks with incomplete information are characterized by
the exploration problem; namely the trade-off between further exploration for
learning more about the environment and immediate exploitation of the accrued
information for decision-making. Within artificial intelligence, there has been
an increasing interest in studying planning-while-learning algorithms for these
decision tasks. In this paper we focus on the exploration problem in
reinforcement learning and Q-learning in particular. The existing exploration
strategies for Q-learning are of a heuristic nature and they exhibit limited
scaleability in tasks with large (or infinite) state and action spaces.
Efficient experimentation is needed for resolving uncertainties when possible
plans are compared (i.e. exploration). The experimentation should be sufficient
for selecting with statistical significance a locally optimal plan (i.e.
exploitation). For this purpose, we develop a probabilistic hill-climbing
algorithm that uses a statistical selection procedure to decide how much
exploration is needed for selecting a plan which is, with arbitrarily high
probability, arbitrarily close to a locally optimal one. Due to its generality
the algorithm can be employed for the exploration strategy of robust
Q-learning. An experiment on a relatively complex control task shows that the
proposed exploration strategy performs better than a typical exploration
strategy.
| [
{
"version": "v1",
"created": "Wed, 20 Feb 2013 15:22:12 GMT"
}
] | 1,361,404,800,000 | [
[
"Karakoulas",
"Grigoris I.",
""
]
] |
1302.4967 | Young-Gyun Kim | Young-Gyun Kim, Marco Valtorta | On the Detection of Conflicts in Diagnostic Bayesian Networks Using
Abstraction | Appears in Proceedings of the Eleventh Conference on Uncertainty in
Artificial Intelligence (UAI1995) | null | null | UAI-P-1995-PG-362-367 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | An important issue in the use of expert systems is the so-called brittleness
problem. Expert systems model only a limited part of the world. While the
explicit management of uncertainty in expert systems itigates the brittleness
problem, it is still possible for a system to be used, unwittingly, in ways
that the system is not prepared to address. Such a situation may be detected by
the method of straw models, first presented by Jensen et al. [1990] and later
generalized and justified by Laskey [1991]. We describe an algorithm, which we
have implemented, that takes as input an annotated diagnostic Bayesian network
(the base model) and constructs, without assistance, a bipartite network to be
used as a straw model. We show that in some cases this straw model is better
that the independent straw model of Jensen et al., the only other straw model
for which a construction algorithm has been designed and implemented.
| [
{
"version": "v1",
"created": "Wed, 20 Feb 2013 15:22:17 GMT"
}
] | 1,361,404,800,000 | [
[
"Kim",
"Young-Gyun",
""
],
[
"Valtorta",
"Marco",
""
]
] |
1302.4968 | Uffe Kj{\ae}rulff | Uffe Kj{\ae}rulff | HUGS: Combining Exact Inference and Gibbs Sampling in Junction Trees | Appears in Proceedings of the Eleventh Conference on Uncertainty in
Artificial Intelligence (UAI1995) | null | null | UAI-P-1995-PG-368-375 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Dawid, Kjaerulff and Lauritzen (1994) provided a preliminary description of a
hybrid between Monte-Carlo sampling methods and exact local computations in
junction trees. Utilizing the strengths of both methods, such hybrid inference
methods has the potential of expanding the class of problems which can be
solved under bounded resources as well as solving problems which otherwise
resist exact solutions. The paper provides a detailed description of a
particular instance of such a hybrid scheme; namely, combination of exact
inference and Gibbs sampling in discrete Bayesian networks. We argue that this
combination calls for an extension of the usual message passing scheme of
ordinary junction trees.
| [
{
"version": "v1",
"created": "Wed, 20 Feb 2013 15:22:22 GMT"
}
] | 1,376,265,600,000 | [
[
"Kjærulff",
"Uffe",
""
]
] |
1302.4969 | Alexander V. Kozlov | Alexander V. Kozlov, Jaswinder Pal Singh | Sensitivities: An Alternative to Conditional Probabilities for Bayesian
Belief Networks | Appears in Proceedings of the Eleventh Conference on Uncertainty in
Artificial Intelligence (UAI1995) | null | null | UAI-P-1995-PG-376-385 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We show an alternative way of representing a Bayesian belief network by
sensitivities and probability distributions. This representation is equivalent
to the traditional representation by conditional probabilities, but makes
dependencies between nodes apparent and intuitively easy to understand. We also
propose a QR matrix representation for the sensitivities and/or conditional
probabilities which is more efficient, in both memory requirements and
computational speed, than the traditional representation for computer-based
implementations of probabilistic inference. We use sensitivities to show that
for a certain class of binary networks, the computation time for approximate
probabilistic inference with any positive upper bound on the error of the
result is independent of the size of the network. Finally, as an alternative to
traditional algorithms that use conditional probabilities, we describe an exact
algorithm for probabilistic inference that uses the QR-representation for
sensitivities and updates probability distributions of nodes in a network
according to messages from the neighbors.
| [
{
"version": "v1",
"created": "Wed, 20 Feb 2013 15:22:26 GMT"
}
] | 1,361,404,800,000 | [
[
"Kozlov",
"Alexander V.",
""
],
[
"Singh",
"Jaswinder Pal",
""
]
] |
1302.4970 | Paul J. Krause | Paul J. Krause, John Fox, Philip Judson | Is There a Role for Qualitative Risk Assessment? | Appears in Proceedings of the Eleventh Conference on Uncertainty in
Artificial Intelligence (UAI1995) | null | null | UAI-P-1995-PG-386-393 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Classically, risk is characterized by a point value probability indicating
the likelihood of occurrence of an adverse effect. However, there are domains
where the attainability of objective numerical risk characterizations is
increasingly being questioned. This paper reviews the arguments in favour of
extending classical techniques of risk assessment to incorporate meaningful
qualitative and weak quantitative risk characterizations. A technique in which
linguistic uncertainty terms are defined in terms of patterns of argument is
then proposed. The technique is demonstrated using a prototype computer-based
system for predicting the carcinogenic risk due to novel chemical compounds.
| [
{
"version": "v1",
"created": "Wed, 20 Feb 2013 15:22:31 GMT"
}
] | 1,361,404,800,000 | [
[
"Krause",
"Paul J.",
""
],
[
"Fox",
"John",
""
],
[
"Judson",
"Philip",
""
]
] |
1302.4971 | Michael L. Littman | Michael L. Littman, Thomas L. Dean, Leslie Pack Kaelbling | On the Complexity of Solving Markov Decision Problems | Appears in Proceedings of the Eleventh Conference on Uncertainty in
Artificial Intelligence (UAI1995) | null | null | UAI-P-1995-PG-394-402 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Markov decision problems (MDPs) provide the foundations for a number of
problems of interest to AI researchers studying automated planning and
reinforcement learning. In this paper, we summarize results regarding the
complexity of solving MDPs and the running time of MDP solution algorithms. We
argue that, although MDPs can be solved efficiently in theory, more study is
needed to reveal practical algorithms for solving large problems quickly. To
encourage future research, we sketch some alternative methods of analysis that
rely on the structure of MDPs.
| [
{
"version": "v1",
"created": "Wed, 20 Feb 2013 15:22:36 GMT"
}
] | 1,361,404,800,000 | [
[
"Littman",
"Michael L.",
""
],
[
"Dean",
"Thomas L.",
""
],
[
"Kaelbling",
"Leslie Pack",
""
]
] |
1302.4972 | Christopher Meek | Christopher Meek | Causal Inference and Causal Explanation with Background Knowledge | Appears in Proceedings of the Eleventh Conference on Uncertainty in
Artificial Intelligence (UAI1995) | null | null | UAI-P-1995-PG-403-410 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper presents correct algorithms for answering the following two
questions; (i) Does there exist a causal explanation consistent with a set of
background knowledge which explains all of the observed independence facts in a
sample? (ii) Given that there is such a causal explanation what are the causal
relationships common to every such causal explanation?
| [
{
"version": "v1",
"created": "Wed, 20 Feb 2013 15:22:41 GMT"
}
] | 1,361,404,800,000 | [
[
"Meek",
"Christopher",
""
]
] |
1302.4973 | Christopher Meek | Christopher Meek | Strong Completeness and Faithfulness in Bayesian Networks | Appears in Proceedings of the Eleventh Conference on Uncertainty in
Artificial Intelligence (UAI1995) | null | null | UAI-P-1995-PG-411-418 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | A completeness result for d-separation applied to discrete Bayesian networks
is presented and it is shown that in a strong measure-theoretic sense almost
all discrete distributions for a given network structure are faithful; i.e. the
independence facts true of the distribution are all and only those entailed by
the network structure.
| [
{
"version": "v1",
"created": "Wed, 20 Feb 2013 15:22:46 GMT"
}
] | 1,361,404,800,000 | [
[
"Meek",
"Christopher",
""
]
] |
1302.4974 | Liem Ngo | Liem Ngo, Peter Haddawy, James Helwig | A Theoretical Framework for Context-Sensitive Temporal Probability Model
Construction with Application to Plan Projection | Appears in Proceedings of the Eleventh Conference on Uncertainty in
Artificial Intelligence (UAI1995) | null | null | UAI-P-1995-PG-419-426 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We define a context-sensitive temporal probability logic for representing
classes of discrete-time temporal Bayesian networks. Context constraints allow
inference to be focused on only the relevant portions of the probabilistic
knowledge. We provide a declarative semantics for our language. We present a
Bayesian network construction algorithm whose generated networks give sound and
complete answers to queries. We use related concepts in logic programming to
justify our approach. We have implemented a Bayesian network construction
algorithm for a subset of the theory and demonstrate it's application to the
problem of evaluating the effectiveness of treatments for acute cardiac
conditions.
| [
{
"version": "v1",
"created": "Wed, 20 Feb 2013 15:22:52 GMT"
}
] | 1,361,404,800,000 | [
[
"Ngo",
"Liem",
""
],
[
"Haddawy",
"Peter",
""
],
[
"Helwig",
"James",
""
]
] |
1302.4975 | Simon Parsons | Simon Parsons | Refining Reasoning in Qualitative Probabilistic Networks | Appears in Proceedings of the Eleventh Conference on Uncertainty in
Artificial Intelligence (UAI1995) | null | null | UAI-P-1995-PG-427-434 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In recent years there has been a spate of papers describing systems for
probabilisitic reasoning which do not use numerical probabilities. In some
cases the simple set of values used by these systems make it impossible to
predict how a probability will change or which hypothesis is most likely given
certain evidence. This paper concentrates on such situations, and suggests a
number of ways in which they may be resolved by refining the representation.
| [
{
"version": "v1",
"created": "Wed, 20 Feb 2013 15:22:58 GMT"
}
] | 1,361,404,800,000 | [
[
"Parsons",
"Simon",
""
]
] |
1302.4976 | Judea Pearl | Judea Pearl | On the Testability of Causal Models with Latent and Instrumental
Variables | Appears in Proceedings of the Eleventh Conference on Uncertainty in
Artificial Intelligence (UAI1995) | null | null | UAI-P-1995-PG-435-443 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Certain causal models involving unmeasured variables induce no independence
constraints among the observed variables but imply, nevertheless, inequality
contraints on the observed distribution. This paper derives a general formula
for such instrumental variables, that is, exogenous variables that directly
affect some variables but not all. With the help of this formula, it is
possible to test whether a model involving instrumental variables may account
for the data, or, conversely, whether a given variables can be deemed
instrumental.
| [
{
"version": "v1",
"created": "Wed, 20 Feb 2013 15:23:04 GMT"
}
] | 1,361,404,800,000 | [
[
"Pearl",
"Judea",
""
]
] |
1302.4977 | Judea Pearl | Judea Pearl, James M. Robins | Probabilistic Evaluation of Sequential Plans from Causal Models with
Hidden Variables | Appears in Proceedings of the Eleventh Conference on Uncertainty in
Artificial Intelligence (UAI1995) | null | null | UAI-P-1995-PG-444-453 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The paper concerns the probabilistic evaluation of plans in the presence of
unmeasured variables, each plan consisting of several concurrent or sequential
actions. We establish a graphical criterion for recognizing when the effects of
a given plan can be predicted from passive observations on measured variables
only. When the criterion is satisfied, a closed-form expression is provided for
the probability that the plan will achieve a specified goal.
| [
{
"version": "v1",
"created": "Wed, 20 Feb 2013 15:23:09 GMT"
}
] | 1,361,404,800,000 | [
[
"Pearl",
"Judea",
""
],
[
"Robins",
"James M.",
""
]
] |
1302.4978 | David L Poole | David L. Poole | Exploiting the Rule Structure for Decision Making within the Independent
Choice Logic | Appears in Proceedings of the Eleventh Conference on Uncertainty in
Artificial Intelligence (UAI1995) | null | null | UAI-P-1995-PG-454-463 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper introduces the independent choice logic, and in particular the
"single agent with nature" instance of the independent choice logic, namely
ICLdt. This is a logical framework for decision making uncertainty that extends
both logic programming and stochastic models such as influence diagrams. This
paper shows how the representation of a decision problem within the independent
choice logic can be exploited to cut down the combinatorics of dynamic
programming. One of the main problems with influence diagram evaluation
techniques is the need to optimise a decision for all values of the 'parents'
of a decision variable. In this paper we show how the rule based nature of the
ICLdt can be exploited so that we only make distinctions in the values of the
information available for a decision that will make a difference to utility.
| [
{
"version": "v1",
"created": "Wed, 20 Feb 2013 15:23:14 GMT"
}
] | 1,361,404,800,000 | [
[
"Poole",
"David L.",
""
]
] |
1302.4979 | Gregory M. Provan | Gregory M. Provan | Abstraction in Belief Networks: The Role of Intermediate States in
Diagnostic Reasoning | Appears in Proceedings of the Eleventh Conference on Uncertainty in
Artificial Intelligence (UAI1995) | null | null | UAI-P-1995-PG-464-473 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Bayesian belief networks are bing increasingly used as a knowledge
representation for diagnostic reasoning. One simple method for conducting
diagnostic reasoning is to represent system faults and observations only. In
this paper, we investigate how having intermediate nodes-nodes other than fault
and observation nodes affects the diagnostic performance of a Bayesian belief
network. We conducted a series of experiments on a set of real belief networks
for medical diagnosis in liver and bile disease. We compared the effects on
diagnostic performance of a two-level network consisting just of disease and
finding nodes with that of a network which models intermediate
pathophysiological disease states as well. We provide some theoretical evidence
for differences observed between the abstracted two-level network and the full
network.
| [
{
"version": "v1",
"created": "Wed, 20 Feb 2013 15:23:19 GMT"
}
] | 1,361,404,800,000 | [
[
"Provan",
"Gregory M.",
""
]
] |
1302.4980 | David V. Pynadath | David V. Pynadath, Michael P. Wellman | Accounting for Context in Plan Recognition, with Application to Traffic
Monitoring | Appears in Proceedings of the Eleventh Conference on Uncertainty in
Artificial Intelligence (UAI1995) | null | null | UAI-P-1995-PG-472-481 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Typical approaches to plan recognition start from a representation of an
agent's possible plans, and reason evidentially from observations of the
agent's actions to assess the plausibility of the various candidates. A more
expansive view of the task (consistent with some prior work) accounts for the
context in which the plan was generated, the mental state and planning process
of the agent, and consequences of the agent's actions in the world. We present
a general Bayesian framework encompassing this view, and focus on how context
can be exploited in plan recognition. We demonstrate the approach on a problem
in traffic monitoring, where the objective is to induce the plan of the driver
from observation of vehicle movements. Starting from a model of how the driver
generates plans, we show how the highway context can appropriately influence
the recognizer's interpretation of observed driver behavior.
| [
{
"version": "v1",
"created": "Wed, 20 Feb 2013 15:23:24 GMT"
}
] | 1,361,404,800,000 | [
[
"Pynadath",
"David V.",
""
],
[
"Wellman",
"Michael P.",
""
]
] |
1302.4981 | Prakash P. Shenoy | Prakash P. Shenoy | A New Pruning Method for Solving Decision Trees and Game Trees | Appears in Proceedings of the Eleventh Conference on Uncertainty in
Artificial Intelligence (UAI1995) | null | null | UAI-P-1995-PG-482-490 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The main goal of this paper is to describe a new pruning method for solving
decision trees and game trees. The pruning method for decision trees suggests a
slight variant of decision trees that we call scenario trees. In scenario
trees, we do not need a conditional probability for each edge emanating from a
chance node. Instead, we require a joint probability for each path from the
root node to a leaf node. We compare the pruning method to the traditional
rollback method for decision trees and game trees. For problems that require
Bayesian revision of probabilities, a scenario tree representation with the
pruning method is more efficient than a decision tree representation with the
rollback method. For game trees, the pruning method is more efficient than the
rollback method.
| [
{
"version": "v1",
"created": "Wed, 20 Feb 2013 15:23:29 GMT"
}
] | 1,361,404,800,000 | [
[
"Shenoy",
"Prakash P.",
""
]
] |
1302.4982 | Peter L. Spirtes | Peter L. Spirtes | Directed Cyclic Graphical Representations of Feedback Models | Appears in Proceedings of the Eleventh Conference on Uncertainty in
Artificial Intelligence (UAI1995) | null | null | UAI-P-1995-PG-491-498 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The use of directed acyclic graphs (DAGs) to represent conditional
independence relations among random variables has proved fruitful in a variety
of ways. Recursive structural equation models are one kind of DAG model.
However, non-recursive structural equation models of the kinds used to model
economic processes are naturally represented by directed cyclic graphs with
independent errors, a characterization of conditional independence errors, a
characterization of conditional independence constraints is obtained, and it is
shown that the result generalizes in a natural way to systems in which the
error variables or noises are statistically dependent. For non-linear systems
with independent errors a sufficient condition for conditional independence of
variables in associated distributions is obtained.
| [
{
"version": "v1",
"created": "Wed, 20 Feb 2013 15:23:34 GMT"
}
] | 1,361,404,800,000 | [
[
"Spirtes",
"Peter L.",
""
]
] |
1302.4983 | Peter L. Spirtes | Peter L. Spirtes, Christopher Meek, Thomas S. Richardson | Causal Inference in the Presence of Latent Variables and Selection Bias | Appears in Proceedings of the Eleventh Conference on Uncertainty in
Artificial Intelligence (UAI1995) | null | null | UAI-P-1995-PG-499-506 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We show that there is a general, informative and reliable procedure for
discovering causal relations when, for all the investigator knows, both latent
variables and selection bias may be at work. Given information about
conditional independence and dependence relations between measured variables,
even when latent variables and selection bias may be present, there are
sufficient conditions for reliably concluding that there is a causal path from
one variable to another, and sufficient conditions for reliably concluding when
no such causal path exists.
| [
{
"version": "v1",
"created": "Wed, 20 Feb 2013 15:23:38 GMT"
}
] | 1,361,404,800,000 | [
[
"Spirtes",
"Peter L.",
""
],
[
"Meek",
"Christopher",
""
],
[
"Richardson",
"Thomas S.",
""
]
] |
1302.4984 | Sampath Srinivas | Sampath Srinivas | Modeling Failure Priors and Persistence in Model-Based Diagnosis | Appears in Proceedings of the Eleventh Conference on Uncertainty in
Artificial Intelligence (UAI1995) | null | null | UAI-P-1995-PG-507-514 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Probabilistic model-based diagnosis computes the posterior probabilities of
failure of components from the prior probabilities of component failure and
observations of system behavior. One problem with this method is that such
priors are almost never directly available. One of the reasons is that the
prior probability estimates include an implicit notion of a time interval over
which they are specified -- for example, if the probability of failure of a
component is 0.05, is this over the period of a day or is this over a week? A
second problem facing probabilistic model-based diagnosis is the modeling of
persistence. Say we have an observation about a system at time t_1 and then
another observation at a later time t_2. To compute posterior probabilities
that take into account both the observations, we need some model of how the
state of the system changes from time t_1 to t_2. In this paper, we address
these problems using techniques from Reliability theory. We show how to compute
the failure prior of a component from an empirical measure of its reliability
-- the Mean Time Between Failure (MTBF). We also develop a scheme to model
persistence when handling multiple time tagged observations.
| [
{
"version": "v1",
"created": "Wed, 20 Feb 2013 15:23:43 GMT"
}
] | 1,361,404,800,000 | [
[
"Srinivas",
"Sampath",
""
]
] |
1302.4985 | Sampath Srinivas | Sampath Srinivas | A Polynomial Algorithm for Computing the Optimal Repair Strategy in a
System with Independent Component Failures | Appears in Proceedings of the Eleventh Conference on Uncertainty in
Artificial Intelligence (UAI1995) | null | null | UAI-P-1995-PG-515-522 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The goal of diagnosis is to compute good repair strategies in response to
anomalous system behavior. In a decision theoretic framework, a good repair
strategy has low expected cost. In a general formulation of the problem, the
computation of the optimal (lowest expected cost) repair strategy for a system
with multiple faults is intractable. In this paper, we consider an interesting
and natural restriction on the behavior of the system being diagnosed: (a) the
system exhibits faulty behavior if and only if one or more components is
malfunctioning. (b) The failures of the system components are independent.
Given this restriction on system behavior, we develop a polynomial time
algorithm for computing the optimal repair strategy. We then go on to introduce
a system hierarchy and the notion of inspecting (testing) components before
repair. We develop a linear time algorithm for computing an optimal repair
strategy for the hierarchical system which includes both repair and inspection.
| [
{
"version": "v1",
"created": "Wed, 20 Feb 2013 15:23:48 GMT"
}
] | 1,361,404,800,000 | [
[
"Srinivas",
"Sampath",
""
]
] |
1302.4986 | Sampath Srinivas | Sampath Srinivas, Eric J. Horvitz | Exploiting System Hierarchy to Compute Repair Plans in Probabilistic
Model-based Diagnosis | Appears in Proceedings of the Eleventh Conference on Uncertainty in
Artificial Intelligence (UAI1995) | null | null | UAI-P-1995-PG-523-531 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The goal of model-based diagnosis is to isolate causes of anomalous system
behavior and recommend inexpensive repair actions in response. In general,
precomputing optimal repair policies is intractable. To date, investigators
addressing this problem have explored approximations that either impose
restrictions on the system model (such as a single fault assumption) or compute
an immediate best action with limited lookahead. In this paper, we develop a
formulation of repair in model-based diagnosis and a repair algorithm that
computes optimal sequences of actions. This optimal approach is costly but can
be applied to precompute an optimal repair strategy for compact systems. We
show how we can exploit a hierarchical system specification to make this
approach tractable for large systems. When introducing hierarchy, we also
consider the tradeoff between simply replacing a component and decomposing it
to repair its subcomponents. The hierarchical repair algorithm is suitable for
off-line precomputation of an optimal repair strategy. A modification of the
algorithm takes advantage of an iterative deepening scheme to trade off
inference time and the quality of the computed strategy.
| [
{
"version": "v1",
"created": "Wed, 20 Feb 2013 15:23:54 GMT"
}
] | 1,361,404,800,000 | [
[
"Srinivas",
"Sampath",
""
],
[
"Horvitz",
"Eric J.",
""
]
] |
1302.4987 | Michael P. Wellman | Michael P. Wellman, Matthew Ford, Kenneth Larson | Path Planning under Time-Dependent Uncertainty | Appears in Proceedings of the Eleventh Conference on Uncertainty in
Artificial Intelligence (UAI1995) | null | null | UAI-P-1995-PG-532-539 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Standard algorithms for finding the shortest path in a graph require that the
cost of a path be additive in edge costs, and typically assume that costs are
deterministic. We consider the problem of uncertain edge costs, with potential
probabilistic dependencies among the costs. Although these dependencies violate
the standard dynamic-programming decomposition, we identify a weaker stochastic
consistency condition that justifies a generalized dynamic-programming approach
based on stochastic dominance. We present a revised path-planning algorithm and
prove that it produces optimal paths under time-dependent uncertain costs. We
test the algorithm by applying it to a model of stochastic bus networks, and
present empirical performance results comparing it to some alternatives.
Finally, we consider extensions of these concepts to a more general class of
problems of heuristic search under uncertainty.
| [
{
"version": "v1",
"created": "Wed, 20 Feb 2013 15:24:00 GMT"
}
] | 1,361,404,800,000 | [
[
"Wellman",
"Michael P.",
""
],
[
"Ford",
"Matthew",
""
],
[
"Larson",
"Kenneth",
""
]
] |
1302.4988 | Emil Weydert | Emil Weydert | Defaults and Infinitesimals: Defeasible Inference by Nonarchimedean
Entropy-Maximization | Appears in Proceedings of the Eleventh Conference on Uncertainty in
Artificial Intelligence (UAI1995) | null | null | UAI-P-1995-PG-540-547 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We develop a new semantics for defeasible inference based on extended
probability measures allowed to take infinitesimal values, on the
interpretation of defaults as generalized conditional probability constraints
and on a preferred-model implementation of entropy maximization.
| [
{
"version": "v1",
"created": "Wed, 20 Feb 2013 15:24:05 GMT"
}
] | 1,361,404,800,000 | [
[
"Weydert",
"Emil",
""
]
] |
1302.4989 | Nic Wilson | Nic Wilson | An Order of Magnitude Calculus | Appears in Proceedings of the Eleventh Conference on Uncertainty in
Artificial Intelligence (UAI1995) | null | null | UAI-P-1995-PG-548-555 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper develops a simple calculus for order of magnitude reasoning. A
semantics is given with soundness and completeness results. Order of magnitude
probability functions are easily defined and turn out to be equivalent to kappa
functions, which are slight generalizations of Spohn's Natural Conditional
Functions. The calculus also gives rise to an order of magnitude decision
theory, which can be used to justify an amended version of Pearl's decision
theory for kappa functions, although the latter is weaker and less expressive.
| [
{
"version": "v1",
"created": "Wed, 20 Feb 2013 15:24:11 GMT"
}
] | 1,361,404,800,000 | [
[
"Wilson",
"Nic",
""
]
] |
1302.4990 | Michael S. K. M. Wong | Michael S. K. M. Wong, C. J. Butz, Yang Xiang | A Method for Implementing a Probabilistic Model as a Relational Database | Appears in Proceedings of the Eleventh Conference on Uncertainty in
Artificial Intelligence (UAI1995) | null | null | UAI-P-1995-PG-556-564 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper discusses a method for implementing a probabilistic inference
system based on an extended relational data model. This model provides a
unified approach for a variety of applications such as dynamic programming,
solving sparse linear equations, and constraint propagation. In this framework,
the probability model is represented as a generalized relational database.
Subsequent probabilistic requests can be processed as standard relational
queries. Conventional database management systems can be easily adopted for
implementing such an approximate reasoning system.
| [
{
"version": "v1",
"created": "Wed, 20 Feb 2013 15:24:15 GMT"
}
] | 1,361,404,800,000 | [
[
"Wong",
"Michael S. K. M.",
""
],
[
"Butz",
"C. J.",
""
],
[
"Xiang",
"Yang",
""
]
] |
1302.4991 | Yang Xiang | Yang Xiang | Optimization of Inter-Subnet Belief Updating in Multiply Sectioned
Bayesian Networks | Appears in Proceedings of the Eleventh Conference on Uncertainty in
Artificial Intelligence (UAI1995) | null | null | UAI-P-1995-PG-565-573 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Recent developments show that Multiply Sectioned Bayesian Networks (MSBNs)
can be used for diagnosis of natural systems as well as for model-based
diagnosis of artificial systems. They can be applied to single-agent oriented
reasoning systems as well as multi-agent distributed probabilistic reasoning
systems. Belief propagation between a pair of subnets plays a central role in
maintenance of global consistency in a MSBN. This paper studies the operation
UpdateBelief, presented originally with MSBNs, for inter-subnet propagation. We
analyze how the operation achieves its intended functionality, which provides
hints as for how its efficiency can be improved. We then define two new
versions of UpdateBelief that reduce the computation time for inter-subnet
propagation. One of them is optimal in the sense that the minimum amount of
computation for coordinating multi-linkage belief propagation is required. The
optimization problem is solved through the solution of a graph-theoretic
problem: the minimum weight open tour in a tree.
| [
{
"version": "v1",
"created": "Wed, 20 Feb 2013 15:24:20 GMT"
}
] | 1,361,404,800,000 | [
[
"Xiang",
"Yang",
""
]
] |
1302.4992 | Hong Xu | Hong Xu, Philippe Smets | Generating Explanations for Evidential Reasoning | Appears in Proceedings of the Eleventh Conference on Uncertainty in
Artificial Intelligence (UAI1995) | null | null | UAI-P-1995-PG-574-581 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this paper, we present two methods to provide explanations for reasoning
with belief functions in the valuation-based systems. One approach, inspired by
Strat's method, is based on sensitivity analysis, but its computation is
simpler thus easier to implement than Strat's. The other one is to examine the
impact of evidence on the conclusion based on the measure of the information
content in the evidence. We show the property of additivity for the pieces of
evidence that are conditional independent within the context of the
valuation-based systems. We will give an example to show how these approaches
are applied in an evidential network.
| [
{
"version": "v1",
"created": "Wed, 20 Feb 2013 15:24:26 GMT"
}
] | 1,361,404,800,000 | [
[
"Xu",
"Hong",
""
],
[
"Smets",
"Philippe",
""
]
] |
1302.4993 | Nevin Lianwen Zhang | Nevin Lianwen Zhang | Inference with Causal Independence in the CPSC Network | Appears in Proceedings of the Eleventh Conference on Uncertainty in
Artificial Intelligence (UAI1995) | null | null | UAI-P-1995-PG-582-589 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper reports experiments with the causal independence inference
algorithm proposed by Zhang and Poole (1994b) on the CPSC network created by
Pradhan et al. (1994). It is found that the algorithm is able to answer 420 of
the 422 possible zero-observation queries, 94 of 100 randomly generated
five-observation queries, 87 of 100 randomly generated ten-observation queries,
and 69 of 100 randomly generated twenty-observation queries.
| [
{
"version": "v1",
"created": "Wed, 20 Feb 2013 15:24:30 GMT"
}
] | 1,361,404,800,000 | [
[
"Zhang",
"Nevin Lianwen",
""
]
] |
1302.5417 | Anandaraj | P. Kalaivani, A. Anandaraj, K. Raja | An Ontology Construction Approach for the Domain Of Poultry Science
Using Protege | arXiv admin note: text overlap with arXiv:1302.5215 | International Journal of Information Technology and Management
Sciences / Volume 1, Issue 2, 2011, ISSN:2231-6752 | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The information retrieval systems that are present nowadays are mainly based
on full text matching of keywords or topic based classification. This matching
of keywords often returns a large number of irrelevant information and this
does not meet the users query requirement. In order to solve this problem and
to enhance the search using semantic environment, a technique named ontology is
implemented for the field of poultry in this paper. Ontology is an emerging
technique in the current field of research in semantic environment. This paper
constructs ontology using the tool named Protege version 4.0 and this also
generates Resource Description Framework schema and XML scripts for using
poultry ontology in web.
| [
{
"version": "v1",
"created": "Thu, 21 Feb 2013 09:19:11 GMT"
}
] | 1,361,750,400,000 | [
[
"Kalaivani",
"P.",
""
],
[
"Anandaraj",
"A.",
""
],
[
"Raja",
"K.",
""
]
] |
1302.5824 | Min Chen | B. Duffy, A. Dasgupta, R. Kosara, S. Walton and M. Chen | Measuring Visual Complexity of Cluster-Based Visualizations | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Handling visual complexity is a challenging problem in visualization owing to
the subjectiveness of its definition and the difficulty in devising
generalizable quantitative metrics. In this paper we address this challenge by
measuring the visual complexity of two common forms of cluster-based
visualizations: scatter plots and parallel coordinatess. We conceptualize
visual complexity as a form of visual uncertainty, which is a measure of the
degree of difficulty for humans to interpret a visual representation correctly.
We propose an algorithm for estimating visual complexity for the aforementioned
visualizations using Allen's interval algebra. We first establish a set of
primitive 2-cluster cases in scatter plots and another set for parallel
coordinatess based on symmetric isomorphism. We confirm that both are the
minimal sets and verify the correctness of their members computationally. We
score the uncertainty of each primitive case based on its topological
properties, including the existence of overlapping regions, splitting regions
and meeting points or edges. We compare a few optional scoring schemes against
a set of subjective scores by humans, and identify the one that is the most
consistent with the subjective scores. Finally, we extend the 2-cluster measure
to k-cluster measure as a general purpose estimator of visual complexity for
these two forms of cluster-based visualization.
| [
{
"version": "v1",
"created": "Sat, 23 Feb 2013 16:34:35 GMT"
}
] | 1,361,836,800,000 | [
[
"Duffy",
"B.",
""
],
[
"Dasgupta",
"A.",
""
],
[
"Kosara",
"R.",
""
],
[
"Walton",
"S.",
""
],
[
"Chen",
"M.",
""
]
] |
1302.6214 | Alexander Korobeynikov Vasilyevich | A.V. Korobeynikov, I.I. Islamgaliev | Modification of conceptual clustering algorithm Cobweb for numerical
data using fuzzy membership function | in Russian | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Modification of a conceptual clustering algorithm Cobweb for the purpose of
its application for numerical data is offered. Keywords: clustering, algorithm
Cobweb, numerical data, fuzzy membership function.
| [
{
"version": "v1",
"created": "Mon, 25 Feb 2013 20:15:06 GMT"
}
] | 1,361,836,800,000 | [
[
"Korobeynikov",
"A. V.",
""
],
[
"Islamgaliev",
"I. I.",
""
]
] |
1302.6442 | Alain-J\'er\^ome Foug\`eres | Alain-J\'er\^ome Foug\`eres | A Modelling Approach Based on Fuzzy Agents | 10 pages, 8 figures, 35 references | IJCSI-2012-9-6-4655 | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Modelling of complex systems is mainly based on the decomposition of these
systems in autonomous elements, and the identification and definitio9n of
possible interactions between these elements. For this, the agent-based
approach is a modelling solution often proposed. Complexity can also be due to
external events or internal to systems, whose main characteristics are
uncertainty, imprecision, or whose perception is subjective (i.e. interpreted).
Insofar as fuzzy logic provides a solution for modelling uncertainty, the
concept of fuzzy agent can model both the complexity and uncertainty. This
paper focuses on introducing the concept of fuzzy agent: a classical
architecture of agent is redefined according to a fuzzy perspective. A
pedagogical illustration of fuzzy agentification of a smart watering system is
then proposed.
| [
{
"version": "v1",
"created": "Tue, 26 Feb 2013 14:24:36 GMT"
}
] | 1,361,923,200,000 | [
[
"Fougères",
"Alain-Jérôme",
""
]
] |
1302.6779 | Constantin F. Aliferis | Constantin F. Aliferis, Gregory F. Cooper | An Evaluation of an Algorithm for Inductive Learning of Bayesian Belief
Networks Usin | Appears in Proceedings of the Tenth Conference on Uncertainty in
Artificial Intelligence (UAI1994) | null | null | UAI-P-1994-PG-8-14 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Bayesian learning of belief networks (BLN) is a method for automatically
constructing belief networks (BNs) from data using search and Bayesian scoring
techniques. K2 is a particular instantiation of the method that implements a
greedy search strategy. To evaluate the accuracy of K2, we randomly generated a
number of BNs and for each of those we simulated data sets. K2 was then used to
induce the generating BNs from the simulated data. We examine the performance
of the program, and the factors that influence it. We also present a simple BN
model, developed from our results, which predicts the accuracy of K2, when
given various characteristics of the data set.
| [
{
"version": "v1",
"created": "Wed, 27 Feb 2013 14:13:16 GMT"
}
] | 1,362,009,600,000 | [
[
"Aliferis",
"Constantin F.",
""
],
[
"Cooper",
"Gregory F.",
""
]
] |
1302.6780 | Russ B. Altman | Russ B. Altman, Cheng C. Chen, William B. Poland, Jaswinder Pal Singh | Probabilistic Constraint Satisfaction with Non-Gaussian Noise | Appears in Proceedings of the Tenth Conference on Uncertainty in
Artificial Intelligence (UAI1994) | null | null | UAI-P-1994-PG-15-22 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We have previously reported a Bayesian algorithm for determining the
coordinates of points in three-dimensional space from uncertain constraints.
This method is useful in the determination of biological molecular structure.
It is limited, however, by the requirement that the uncertainty in the
constraints be normally distributed. In this paper, we present an extension of
the original algorithm that allows constraint uncertainty to be represented as
a mixture of Gaussians, and thereby allows arbitrary constraint distributions.
We illustrate the performance of this algorithm on a problem drawn from the
domain of molecular structure determination, in which a multicomponent
constraint representation produces a much more accurate solution than the old
single component mechanism. The new mechanism uses mixture distributions to
decompose the problem into a set of independent problems with unimodal
constraint uncertainty. The results of the unimodal subproblems are
periodically recombined using Bayes' law, to avoid combinatorial explosion. The
new algorithm is particularly suited for parallel implementation.
| [
{
"version": "v1",
"created": "Wed, 27 Feb 2013 14:13:22 GMT"
}
] | 1,362,009,600,000 | [
[
"Altman",
"Russ B.",
""
],
[
"Chen",
"Cheng C.",
""
],
[
"Poland",
"William B.",
""
],
[
"Singh",
"Jaswinder Pal",
""
]
] |
1302.6781 | Derek D. Ayers | Derek D. Ayers | A Bayesian Method Reexamined | Appears in Proceedings of the Tenth Conference on Uncertainty in
Artificial Intelligence (UAI1994) | null | null | UAI-P-1994-PG-23-27 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper examines the "K2" network scoring metric of Cooper and Herskovits.
It shows counterintuitive results from applying this metric to simple networks.
One family of noninformative priors is suggested for assigning equal scores to
equivalent networks.
| [
{
"version": "v1",
"created": "Wed, 27 Feb 2013 14:13:31 GMT"
}
] | 1,362,009,600,000 | [
[
"Ayers",
"Derek D.",
""
]
] |
1302.6782 | Adriano Azevedo-Filho | Adriano Azevedo-Filho, Ross D. Shachter | Laplace's Method Approximations for Probabilistic Inference in Belief
Networks with Continuous Variables | Appears in Proceedings of the Tenth Conference on Uncertainty in
Artificial Intelligence (UAI1994) | null | null | UAI-P-1994-PG-28-36 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Laplace's method, a family of asymptotic methods used to approximate
integrals, is presented as a potential candidate for the tool box of techniques
used for knowledge acquisition and probabilistic inference in belief networks
with continuous variables. This technique approximates posterior moments and
marginal posterior distributions with reasonable accuracy [errors are O(n^-2)
for posterior means] in many interesting cases. The method also seems promising
for computing approximations for Bayes factors for use in the context of model
selection, model uncertainty and mixtures of pdfs. The limitations, regularity
conditions and computational difficulties for the implementation of Laplace's
method are comparable to those associated with the methods of maximum
likelihood and posterior mode analysis.
| [
{
"version": "v1",
"created": "Wed, 27 Feb 2013 14:13:37 GMT"
}
] | 1,362,009,600,000 | [
[
"Azevedo-Filho",
"Adriano",
""
],
[
"Shachter",
"Ross D.",
""
]
] |
1302.6783 | Fahiem Bacchus | Fahiem Bacchus, Adam J. Grove, Joseph Y. Halpern, Daphne Koller | Generating New Beliefs From Old | Appears in Proceedings of the Tenth Conference on Uncertainty in
Artificial Intelligence (UAI1994) | null | null | UAI-P-1994-PG-37-45 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In previous work [BGHK92, BGHK93], we have studied the random-worlds approach
-- a particular (and quite powerful) method for generating degrees of belief
(i.e., subjective probabilities) from a knowledge base consisting of objective
(first-order, statistical, and default) information. But allowing a knowledge
base to contain only objective information is sometimes limiting. We
occasionally wish to include information about degrees of belief in the
knowledge base as well, because there are contexts in which old beliefs
represent important information that should influence new beliefs. In this
paper, we describe three quite general techniques for extending a method that
generates degrees of belief from objective information to one that can make use
of degrees of belief as well. All of our techniques are bloused on well-known
approaches, such as cross-entropy. We discuss general connections between the
techniques and in particular show that, although conceptually and technically
quite different, all of the techniques give the same answer when applied to the
random-worlds method.
| [
{
"version": "v1",
"created": "Wed, 27 Feb 2013 14:13:44 GMT"
}
] | 1,362,009,600,000 | [
[
"Bacchus",
"Fahiem",
""
],
[
"Grove",
"Adam J.",
""
],
[
"Halpern",
"Joseph Y.",
""
],
[
"Koller",
"Daphne",
""
]
] |
1302.6784 | Alexander Balke | Alexander Balke, Judea Pearl | Counterfactual Probabilities: Computational Methods, Bounds and
Applications | Appears in Proceedings of the Tenth Conference on Uncertainty in
Artificial Intelligence (UAI1994) | null | null | UAI-P-1994-PG-46-54 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Evaluation of counterfactual queries (e.g., "If A were true, would C have
been true?") is important to fault diagnosis, planning, and determination of
liability. In this paper we present methods for computing the probabilities of
such queries using the formulation proposed in [Balke and Pearl, 1994], where
the antecedent of the query is interpreted as an external action that forces
the proposition A to be true. When a prior probability is available on the
causal mechanisms governing the domain, counterfactual probabilities can be
evaluated precisely. However, when causal knowledge is specified as conditional
probabilities on the observables, only bounds can computed. This paper develops
techniques for evaluating these bounds, and demonstrates their use in two
applications: (1) the determination of treatment efficacy from studies in which
subjects may choose their own treatment, and (2) the determination of liability
in product-safety litigation.
| [
{
"version": "v1",
"created": "Wed, 27 Feb 2013 14:13:50 GMT"
}
] | 1,362,009,600,000 | [
[
"Balke",
"Alexander",
""
],
[
"Pearl",
"Judea",
""
]
] |
1302.6786 | Ildar Z. Batyrshin | Ildar Z. Batyrshin | Modus Ponens Generating Function in the Class of ^-valuations of
Plausibility | Appears in Proceedings of the Tenth Conference on Uncertainty in
Artificial Intelligence (UAI1994) | null | null | UAI-P-1994-PG-55-59 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We discuss the problem of construction of inference procedures which can
manipulate with uncertainties measured in ordinal scales and fulfill to the
property of strict monotonicity of conclusion. The class of A-valuations of
plausibility is considered where operations based only on information about
linear ordering of plausibility values are used. In this class the modus ponens
generating function fulfiling to the property of strict monotonicity of
conclusions is introduced.
| [
{
"version": "v1",
"created": "Wed, 27 Feb 2013 14:13:56 GMT"
}
] | 1,362,009,600,000 | [
[
"Batyrshin",
"Ildar Z.",
""
]
] |
1302.6788 | Philippe Besnard | Philippe Besnard, Jerome Lang | Possibility and Necessity Functions over Non-classical Logics | Appears in Proceedings of the Tenth Conference on Uncertainty in
Artificial Intelligence (UAI1994) | null | null | UAI-P-1994-PG-69-76 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We propose an integration of possibility theory into non-classical logics. We
obtain many formal results that generalize the case where possibility and
necessity functions are based on classical logic. We show how useful such an
approach is by applying it to reasoning under uncertain and inconsistent
information.
| [
{
"version": "v1",
"created": "Wed, 27 Feb 2013 14:14:08 GMT"
}
] | 1,362,009,600,000 | [
[
"Besnard",
"Philippe",
""
],
[
"Lang",
"Jerome",
""
]
] |
1302.6789 | Raj Bhatnagar | Raj Bhatnagar | Exploratory Model Building | Appears in Proceedings of the Tenth Conference on Uncertainty in
Artificial Intelligence (UAI1994) | null | null | UAI-P-1994-PG-77-85 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Some instances of creative thinking require an agent to build and test
hypothetical theories. Such a reasoner needs to explore the space of not only
those situations that have occurred in the past, but also those that are
rationally conceivable. In this paper we present a formalism for exploring the
space of conceivable situation-models for those domains in which the knowledge
is primarily probabilistic in nature. The formalism seeks to construct
consistent, minimal, and desirable situation-descriptions by selecting suitable
domain-attributes and dependency relationships from the available domain
knowledge.
| [
{
"version": "v1",
"created": "Wed, 27 Feb 2013 14:14:14 GMT"
}
] | 1,362,009,600,000 | [
[
"Bhatnagar",
"Raj",
""
]
] |
1302.6791 | Jim S. Blythe | Jim S. Blythe | Planning with External Events | Appears in Proceedings of the Tenth Conference on Uncertainty in
Artificial Intelligence (UAI1994) | null | null | UAI-P-1994-PG-94-101 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | I describe a planning methodology for domains with uncertainty in the form of
external events that are not completely predictable. The events are represented
by enabling conditions and probabilities of occurrence. The planner is
goal-directed and backward chaining, but the subgoals are suggested by
analyzing the probability of success of the partial plan rather than being
simply the open conditions of the operators in the plan. The partial plan is
represented as a Bayesian belief net to compute its probability of success.
Since calculating the probability of success of a plan can be very expensive I
introduce two other techniques for computing it, one that uses Monte Carlo
simulation to estimate it and one based on a Markov chain representation that
uses knowledge about the dependencies between the predicates describing the
domain.
| [
{
"version": "v1",
"created": "Wed, 27 Feb 2013 14:14:26 GMT"
}
] | 1,362,009,600,000 | [
[
"Blythe",
"Jim S.",
""
]
] |
1302.6792 | Remco R. Bouckaert | Remco R. Bouckaert | Properties of Bayesian Belief Network Learning Algorithms | Appears in Proceedings of the Tenth Conference on Uncertainty in
Artificial Intelligence (UAI1994) | null | null | UAI-P-1994-PG-102-109 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Bayesian belief network learning algorithms have three basic components: a
measure of a network structure and a database, a search heuristic that chooses
network structures to be considered, and a method of estimating the probability
tables from the database. This paper contributes to all these three topics. The
behavior of the Bayesian measure of Cooper and Herskovits and a minimum
description length (MDL) measure are compared with respect to their properties
for both limiting size and finite size databases. It is shown that the MDL
measure has more desirable properties than the Bayesian measure when a
distribution is to be learned. It is shown that selecting belief networks with
certain minimallity properties is NP-hard. This result justifies the use of
search heuristics instead of exact algorithms for choosing network structures
to be considered. In some cases, a collection of belief networks can be
represented by a single belief network which leads to a new kind of probability
table estimation called smoothing. We argue that smoothing can be efficiently
implemented by incorporating it in the search heuristic. Experimental results
suggest that for learning probabilities of belief networks smoothing is
helpful.
| [
{
"version": "v1",
"created": "Wed, 27 Feb 2013 14:14:31 GMT"
}
] | 1,362,009,600,000 | [
[
"Bouckaert",
"Remco R.",
""
]
] |
1302.6793 | Remco R. Bouckaert | Remco R. Bouckaert | A Stratified Simulation Scheme for Inference in Bayesian Belief Networks | Appears in Proceedings of the Tenth Conference on Uncertainty in
Artificial Intelligence (UAI1994) | null | null | UAI-P-1994-PG-110-118 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Simulation schemes for probabilistic inference in Bayesian belief networks
offer many advantages over exact algorithms; for example, these schemes have a
linear and thus predictable runtime while exact algorithms have exponential
runtime. Experiments have shown that likelihood weighting is one of the most
promising simulation schemes. In this paper, we present a new simulation scheme
that generates samples more evenly spread in the sample space than the
likelihood weighting scheme. We show both theoretically and experimentally that
the stratified scheme outperforms likelihood weighting in average runtime and
error in estimates of beliefs.
| [
{
"version": "v1",
"created": "Wed, 27 Feb 2013 14:14:37 GMT"
}
] | 1,362,009,600,000 | [
[
"Bouckaert",
"Remco R.",
""
]
] |
1302.6794 | Tom Chavez | Tom Chavez, Max Henrion | Efficient Estimation of the Value of Information in Monte Carlo Models | Appears in Proceedings of the Tenth Conference on Uncertainty in
Artificial Intelligence (UAI1994) | null | null | UAI-P-1994-PG-119-127 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The expected value of information (EVI) is the most powerful measure of
sensitivity to uncertainty in a decision model: it measures the potential of
information to improve the decision, and hence measures the expected value of
outcome. Standard methods for computing EVI use discrete variables and are
computationally intractable for models that contain more than a few variables.
Monte Carlo simulation provides the basis for more tractable evaluation of
large predictive models with continuous and discrete variables, but so far
computation of EVI in a Monte Carlo setting also has appeared impractical. We
introduce an approximate approach based on pre-posterior analysis for
estimating EVI in Monte Carlo models. Our method uses a linear approximation to
the value function and multiple linear regression to estimate the linear model
from the samples. The approach is efficient and practical for extremely large
models. It allows easy estimation of EVI for perfect or partial information on
individual variables or on combinations of variables. We illustrate its
implementation within Demos (a decision modeling system), and its application
to a large model for crisis transportation planning.
| [
{
"version": "v1",
"created": "Wed, 27 Feb 2013 14:14:43 GMT"
}
] | 1,362,009,600,000 | [
[
"Chavez",
"Tom",
""
],
[
"Henrion",
"Max",
""
]
] |
1302.6795 | Bruce D'Ambrosio | Bruce D'Ambrosio | Symbolic Probabilitistic Inference in Large BN2O Networks | Appears in Proceedings of the Tenth Conference on Uncertainty in
Artificial Intelligence (UAI1994) | null | null | UAI-P-1994-PG-128-135 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | A BN2O network is a two level belief net in which the parent interactions are
modeled using the noisy-or interaction model. In this paper we discuss
application of the SPI local expression language to efficient inference in
large BN2O networks. In particular, we show that there is significant
structure, which can be exploited to improve over the Quickscore result. We
further describe how symbolic techniques can provide information which can
significantly reduce the computation required for computing all cause posterior
marginals. Finally, we present a novel approximation technique with preliminary
experimental results.
| [
{
"version": "v1",
"created": "Wed, 27 Feb 2013 14:14:49 GMT"
}
] | 1,362,009,600,000 | [
[
"D'Ambrosio",
"Bruce",
""
]
] |
1302.6796 | Adnan Darwiche | Adnan Darwiche, Moises Goldszmidt | Action Networks: A Framework for Reasoning about Actions and Change
under Uncertainty | Appears in Proceedings of the Tenth Conference on Uncertainty in
Artificial Intelligence (UAI1994) | null | null | UAI-P-1994-PG-136-144 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This work proposes action networks as a semantically well-founded framework
for reasoning about actions and change under uncertainty. Action networks add
two primitives to probabilistic causal networks: controllable variables and
persistent variables. Controllable variables allow the representation of
actions as directly setting the value of specific events in the domain, subject
to preconditions. Persistent variables provide a canonical model of persistence
according to which both the state of a variable and the causal mechanism
dictating its value persist over time unless intervened upon by an action (or
its consequences). Action networks also allow different methods for quantifying
the uncertainty in causal relationships, which go beyond traditional
probabilistic quantification. This paper describes both recent results and work
in progress.
| [
{
"version": "v1",
"created": "Wed, 27 Feb 2013 14:14:55 GMT"
}
] | 1,362,009,600,000 | [
[
"Darwiche",
"Adnan",
""
],
[
"Goldszmidt",
"Moises",
""
]
] |
1302.6797 | Adnan Darwiche | Adnan Darwiche, Moises Goldszmidt | On the Relation between Kappa Calculus and Probabilistic Reasoning | Appears in Proceedings of the Tenth Conference on Uncertainty in
Artificial Intelligence (UAI1994) | null | null | UAI-P-1994-PG-145-153 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We study the connection between kappa calculus and probabilistic reasoning in
diagnosis applications. Specifically, we abstract a probabilistic belief
network for diagnosing faults into a kappa network and compare the ordering of
faults computed using both methods. We show that, at least for the example
examined, the ordering of faults coincide as long as all the causal relations
in the original probabilistic network are taken into account. We also provide a
formal analysis of some network structures where the two methods will differ.
Both kappa rankings and infinitesimal probabilities have been used extensively
to study default reasoning and belief revision. But little has been done on
utilizing their connection as outlined above. This is partly because the
relation between kappa and probability calculi assumes that probabilities are
arbitrarily close to one (or zero). The experiments in this paper investigate
this relation when this assumption is not satisfied. The reported results have
important implications on the use of kappa rankings to enhance the knowledge
engineering of uncertainty models.
| [
{
"version": "v1",
"created": "Wed, 27 Feb 2013 14:15:01 GMT"
}
] | 1,362,009,600,000 | [
[
"Darwiche",
"Adnan",
""
],
[
"Goldszmidt",
"Moises",
""
]
] |
1302.6798 | Ron Davidson | Ron Davidson, Michael R. Fehling | A Structured, Probabilistic Representation of Action | Appears in Proceedings of the Tenth Conference on Uncertainty in
Artificial Intelligence (UAI1994) | null | null | UAI-P-1994-PG-154-161 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | When agents devise plans for execution in the real world, they face two
important forms of uncertainty: they can never have complete knowledge about
the state of the world, and they do not have complete control, as the effects
of their actions are uncertain. While most classical planning methods avoid
explicit uncertainty reasoning, we believe that uncertainty should be
explicitly represented and reasoned about. We develop a probabilistic
representation for states and actions, based on belief networks. We define
conditional belief nets (CBNs) to capture the probabilistic dependency of the
effects of an action upon the state of the world. We also use a CBN to
represent the intrinsic relationships among entities in the environment, which
persist from state to state. We present a simple projection algorithm to
construct the belief network of the state succeeding an action, using the
environment CBN model to infer indirect effects. We discuss how the qualitative
aspects of belief networks and CBNs make them appropriate for the various
stages of the problem solving process, from model construction to the design of
planning algorithms.
| [
{
"version": "v1",
"created": "Wed, 27 Feb 2013 14:15:07 GMT"
}
] | 1,362,009,600,000 | [
[
"Davidson",
"Ron",
""
],
[
"Fehling",
"Michael R.",
""
]
] |
1302.6799 | Richard Dearden | Richard Dearden, Craig Boutilier | Integrating Planning and Execution in Stochastic Domains | Appears in Proceedings of the Tenth Conference on Uncertainty in
Artificial Intelligence (UAI1994) | null | null | UAI-P-1994-PG-162-169 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We investigate planning in time-critical domains represented as Markov
Decision Processes, showing that search based techniques can be a very powerful
method for finding close to optimal plans. To reduce the computational cost of
planning in these domains, we execute actions as we construct the plan, and
sacrifice optimality by searching to a fixed depth and using a heuristic
function to estimate the value of states. Although this paper concentrates on
the search algorithm, we also discuss ways of constructing heuristic functions
suitable for this approach. Our results show that by interleaving search and
execution, close to optimal policies can be found without the computational
requirements of other approaches.
| [
{
"version": "v1",
"created": "Wed, 27 Feb 2013 14:15:13 GMT"
}
] | 1,362,009,600,000 | [
[
"Dearden",
"Richard",
""
],
[
"Boutilier",
"Craig",
""
]
] |
1302.6800 | Denise L. Draper | Denise L. Draper, Steve Hanks | Localized Partial Evaluation of Belief Networks | Appears in Proceedings of the Tenth Conference on Uncertainty in
Artificial Intelligence (UAI1994) | null | null | UAI-P-1994-PG-170-177 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Most algorithms for propagating evidence through belief networks have been
exact and exhaustive: they produce an exact (point-valued) marginal probability
for every node in the network. Often, however, an application will not need
information about every n ode in the network nor will it need exact
probabilities. We present the localized partial evaluation (LPE) propagation
algorithm, which computes interval bounds on the marginal probability of a
specified query node by examining a subset of the nodes in the entire network.
Conceptually, LPE ignores parts of the network that are "too far away" from the
queried node to have much impact on its value. LPE has the "anytime" property
of being able to produce better solutions (tighter intervals) given more time
to consider more of the network.
| [
{
"version": "v1",
"created": "Wed, 27 Feb 2013 14:15:20 GMT"
}
] | 1,362,009,600,000 | [
[
"Draper",
"Denise L.",
""
],
[
"Hanks",
"Steve",
""
]
] |
1302.6801 | Denise L. Draper | Denise L. Draper, Steve Hanks, Daniel Weld | A Probabilistic Model of Action for Least-Commitment Planning with
Information Gather | Appears in Proceedings of the Tenth Conference on Uncertainty in
Artificial Intelligence (UAI1994) | null | null | UAI-P-1994-PG-178-186 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | AI planning algorithms have addressed the problem of generating sequences of
operators that achieve some input goal, usually assuming that the planning
agent has perfect control over and information about the world. Relaxing these
assumptions requires an extension to the action representation that allows
reasoning both about the changes an action makes and the information it
provides. This paper presents an action representation that extends the
deterministic STRIPS model, allowing actions to have both causal and
informational effects, both of which can be context dependent and noisy. We
also demonstrate how a standard least-commitment planning algorithm can be
extended to include informational actions and contingent execution.
| [
{
"version": "v1",
"created": "Wed, 27 Feb 2013 14:15:26 GMT"
}
] | 1,362,009,600,000 | [
[
"Draper",
"Denise L.",
""
],
[
"Hanks",
"Steve",
""
],
[
"Weld",
"Daniel",
""
]
] |
1302.6802 | Marek J. Druzdzel | Marek J. Druzdzel | Some Properties of Joint Probability Distributions | Appears in Proceedings of the Tenth Conference on Uncertainty in
Artificial Intelligence (UAI1994) | null | null | UAI-P-1994-PG-187-194 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Several Artificial Intelligence schemes for reasoning under uncertainty
explore either explicitly or implicitly asymmetries among probabilities of
various states of their uncertain domain models. Even though the correct
working of these schemes is practically contingent upon the existence of a
small number of probable states, no formal justification has been proposed of
why this should be the case. This paper attempts to fill this apparent gap by
studying asymmetries among probabilities of various states of uncertain models.
By rewriting the joint probability distribution over a model's variables into a
product of individual variables' prior and conditional probability
distributions, and applying central limit theorem to this product, we can
demonstrate that the probabilities of individual states of the model can be
expected to be drawn from highly skewed, log-normal distributions. With
sufficient asymmetry in individual prior and conditional probability
distributions, a small fraction of states can be expected to cover a large
portion of the total probability space with the remaining states having
practically negligible probability. Theoretical discussion is supplemented by
simulation results and an illustrative real-world example.
| [
{
"version": "v1",
"created": "Wed, 27 Feb 2013 14:15:32 GMT"
}
] | 1,362,009,600,000 | [
[
"Druzdzel",
"Marek J.",
""
]
] |
1302.6803 | Didier Dubois | Didier Dubois, Luis Farinas del Cerro, Andreas Herzig, Henri Prade | An Ordinal View of Independence with Application to Plausible Reasoning | Appears in Proceedings of the Tenth Conference on Uncertainty in
Artificial Intelligence (UAI1994) | null | null | UAI-P-1994-PG-195-203 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | An ordinal view of independence is studied in the framework of possibility
theory. We investigate three possible definitions of dependence, of increasing
strength. One of them is the counterpart to the multiplication law in
probability theory, and the two others are based on the notion of conditional
possibility. These two have enough expressive power to support the whole
possibility theory, and a complete axiomatization is provided for the strongest
one. Moreover we show that weak independence is well-suited to the problems of
belief change and plausible reasoning, especially to address the problem of
blocking of property inheritance in exception-tolerant taxonomic reasoning.
| [
{
"version": "v1",
"created": "Wed, 27 Feb 2013 14:15:38 GMT"
}
] | 1,362,009,600,000 | [
[
"Dubois",
"Didier",
""
],
[
"del Cerro",
"Luis Farinas",
""
],
[
"Herzig",
"Andreas",
""
],
[
"Prade",
"Henri",
""
]
] |
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