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1301.7398 | Anders L. Madsen | Anders L. Madsen, Finn Verner Jensen | Lazy Propagation in Junction Trees | Appears in Proceedings of the Fourteenth Conference on Uncertainty in
Artificial Intelligence (UAI1998) | null | null | UAI-P-1998-PG-362-369 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The efficiency of algorithms using secondary structures for probabilistic
inference in Bayesian networks can be improved by exploiting independence
relations induced by evidence and the direction of the links in the original
network. In this paper we present an algorithm that on-line exploits
independence relations induced by evidence and the direction of the links in
the original network to reduce both time and space costs. Instead of
multiplying the conditional probability distributions for the various cliques,
we determine on-line which potentials to multiply when a message is to be
produced. The performance improvement of the algorithm is emphasized through
empirical evaluations involving large real world Bayesian networks, and we
compare the method with the HUGIN and Shafer-Shenoy inference algorithms.
| [
{
"version": "v1",
"created": "Wed, 30 Jan 2013 15:05:39 GMT"
}
] | 1,359,676,800,000 | [
[
"Madsen",
"Anders L.",
""
],
[
"Jensen",
"Finn Verner",
""
]
] |
1301.7399 | Suzanne M. Mahoney | Suzanne M. Mahoney, Kathryn Blackmond Laskey | Constructing Situation Specific Belief Networks | Appears in Proceedings of the Fourteenth Conference on Uncertainty in
Artificial Intelligence (UAI1998) | null | null | UAI-P-1998-PG-370-378 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper describes a process for constructing situation-specific belief
networks from a knowledge base of network fragments. A situation-specific
network is a minimal query complete network constructed from a knowledge base
in response to a query for the probability distribution on a set of target
variables given evidence and context variables. We present definitions of query
completeness and situation-specific networks. We describe conditions on the
knowledge base that guarantee query completeness. The relationship of our work
to earlier work on KBMC is also discussed.
| [
{
"version": "v1",
"created": "Wed, 30 Jan 2013 15:05:44 GMT"
}
] | 1,359,676,800,000 | [
[
"Mahoney",
"Suzanne M.",
""
],
[
"Laskey",
"Kathryn Blackmond",
""
]
] |
1301.7402 | Paul-Andre Monney | Paul-Andre Monney | From Likelihood to Plausibility | Appears in Proceedings of the Fourteenth Conference on Uncertainty in
Artificial Intelligence (UAI1998) | null | null | UAI-P-1998-PG-396-403 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Several authors have explained that the likelihood ratio measures the
strength of the evidence represented by observations in statistical problems.
This idea works fine when the goal is to evaluate the strength of the available
evidence for a simple hypothesis versus another simple hypothesis. However, the
applicability of this idea is limited to simple hypotheses because the
likelihood function is primarily defined on points (simple hypotheses) of the
parameter space. In this paper we define a general weight of evidence that is
applicable to both simple and composite hypotheses. It is based on the
Dempster-Shafer concept of plausibility and is shown to be a generalization of
the likelihood ratio. Functional models are of a fundamental importance for the
general weight of evidence proposed in this paper. The relevant concepts and
ideas are explained by means of a familiar urn problem and the general analysis
of a real-world medical problem is presented.
| [
{
"version": "v1",
"created": "Wed, 30 Jan 2013 15:06:00 GMT"
}
] | 1,359,676,800,000 | [
[
"Monney",
"Paul-Andre",
""
]
] |
1301.7404 | Benson Hin Kwong Ng | Benson Hin Kwong Ng, Kam-Fai Wong, Boon-Toh Low | Resolving Conflicting Arguments under Uncertainties | Appears in Proceedings of the Fourteenth Conference on Uncertainty in
Artificial Intelligence (UAI1998) | null | null | UAI-P-1998-PG-414-421 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Distributed knowledge based applications in open domain rely on common sense
information which is bound to be uncertain and incomplete. To draw the useful
conclusions from ambiguous data, one must address uncertainties and conflicts
incurred in a holistic view. No integrated frameworks are viable without an
in-depth analysis of conflicts incurred by uncertainties. In this paper, we
give such an analysis and based on the result, propose an integrated framework.
Our framework extends definite argumentation theory to model uncertainty. It
supports three views over conflicting and uncertain knowledge. Thus, knowledge
engineers can draw different conclusions depending on the application context
(i.e. view). We also give an illustrative example on strategical decision
support to show the practical usefulness of our framework.
| [
{
"version": "v1",
"created": "Wed, 30 Jan 2013 15:06:09 GMT"
}
] | 1,359,676,800,000 | [
[
"Ng",
"Benson Hin Kwong",
""
],
[
"Wong",
"Kam-Fai",
""
],
[
"Low",
"Boon-Toh",
""
]
] |
1301.7405 | Ron Parr | Ron Parr | Flexible Decomposition Algorithms for Weakly Coupled Markov Decision
Problems | Appears in Proceedings of the Fourteenth Conference on Uncertainty in
Artificial Intelligence (UAI1998) | null | null | UAI-P-1998-PG-422-430 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper presents two new approaches to decomposing and solving large
Markov decision problems (MDPs), a partial decoupling method and a complete
decoupling method. In these approaches, a large, stochastic decision problem is
divided into smaller pieces. The first approach builds a cache of policies for
each part of the problem independently, and then combines the pieces in a
separate, light-weight step. A second approach also divides the problem into
smaller pieces, but information is communicated between the different problem
pieces, allowing intelligent decisions to be made about which piece requires
the most attention. Both approaches can be used to find optimal policies or
approximately optimal policies with provable bounds. These algorithms also
provide a framework for the efficient transfer of knowledge across problems
that share similar structure.
| [
{
"version": "v1",
"created": "Wed, 30 Jan 2013 15:06:15 GMT"
}
] | 1,359,676,800,000 | [
[
"Parr",
"Ron",
""
]
] |
1301.7406 | David M Pennock | David M. Pennock | Logarithmic Time Parallel Bayesian Inference | Appears in Proceedings of the Fourteenth Conference on Uncertainty in
Artificial Intelligence (UAI1998) | null | null | UAI-P-1998-PG-431-438 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | I present a parallel algorithm for exact probabilistic inference in Bayesian
networks. For polytree networks with n variables, the worst-case time
complexity is O(log n) on a CREW PRAM (concurrent-read, exclusive-write
parallel random-access machine) with n processors, for any constant number of
evidence variables. For arbitrary networks, the time complexity is O(r^{3w}*log
n) for n processors, or O(w*log n) for r^{3w}*n processors, where r is the
maximum range of any variable, and w is the induced width (the maximum clique
size), after moralizing and triangulating the network.
| [
{
"version": "v1",
"created": "Wed, 30 Jan 2013 15:06:20 GMT"
}
] | 1,359,676,800,000 | [
[
"Pennock",
"David M.",
""
]
] |
1301.7407 | Mark Alan Peot | Mark Alan Peot, Ross D. Shachter | Learning From What You Don't Observe | Appears in Proceedings of the Fourteenth Conference on Uncertainty in
Artificial Intelligence (UAI1998) | null | null | UAI-P-1998-PG-439-446 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The process of diagnosis involves learning about the state of a system from
various observations of symptoms or findings about the system. Sophisticated
Bayesian (and other) algorithms have been developed to revise and maintain
beliefs about the system as observations are made. Nonetheless, diagnostic
models have tended to ignore some common sense reasoning exploited by human
diagnosticians; In particular, one can learn from which observations have not
been made, in the spirit of conversational implicature. There are two concepts
that we describe to extract information from the observations not made. First,
some symptoms, if present, are more likely to be reported before others.
Second, most human diagnosticians and expert systems are economical in their
data-gathering, searching first where they are more likely to find symptoms
present. Thus, there is a desirable bias toward reporting symptoms that are
present. We develop a simple model for these concepts that can significantly
improve diagnostic inference.
| [
{
"version": "v1",
"created": "Wed, 30 Jan 2013 15:06:25 GMT"
}
] | 1,359,676,800,000 | [
[
"Peot",
"Mark Alan",
""
],
[
"Shachter",
"Ross D.",
""
]
] |
1301.7408 | David L Poole | David L. Poole | Context-Specific Approximation in Probabilistic Inference | Appears in Proceedings of the Fourteenth Conference on Uncertainty in
Artificial Intelligence (UAI1998) | null | null | UAI-P-1998-PG-447-454 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | There is evidence that the numbers in probabilistic inference don't really
matter. This paper considers the idea that we can make a probabilistic model
simpler by making fewer distinctions. Unfortunately, the level of a Bayesian
network seems too coarse; it is unlikely that a parent will make little
difference for all values of the other parents. In this paper we consider an
approximation scheme where distinctions can be ignored in some contexts, but
not in other contexts. We elaborate on a notion of a parent context that allows
a structured context-specific decomposition of a probability distribution and
the associated probabilistic inference scheme called probabilistic partial
evaluation (Poole 1997). This paper shows a way to simplify a probabilistic
model by ignoring distinctions which have similar probabilities, a method to
exploit the simpler model, a bound on the resulting errors, and some
preliminary empirical results on simple networks.
| [
{
"version": "v1",
"created": "Wed, 30 Jan 2013 15:06:30 GMT"
}
] | 1,359,676,800,000 | [
[
"Poole",
"David L.",
""
]
] |
1301.7409 | Irina Rish | Irina Rish, Kalev Kask, Rina Dechter | Empirical Evaluation of Approximation Algorithms for Probabilistic
Decoding | Appears in Proceedings of the Fourteenth Conference on Uncertainty in
Artificial Intelligence (UAI1998) | null | null | UAI-P-1998-PG-455-463 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | It was recently shown that the problem of decoding messages transmitted
through a noisy channel can be formulated as a belief updating task over a
probabilistic network [McEliece]. Moreover, it was observed that iterative
application of the (linear time) Pearl's belief propagation algorithm designed
for polytrees outperformed state of the art decoding algorithms, even though
the corresponding networks may have many cycles. This paper demonstrates
empirically that an approximation algorithm approx-mpe for solving the most
probable explanation (MPE) problem, developed within the recently proposed
mini-bucket elimination framework [Dechter96], outperforms iterative belief
propagation on classes of coding networks that have bounded induced width. Our
experiments suggest that approximate MPE decoders can be good competitors to
the approximate belief updating decoders.
| [
{
"version": "v1",
"created": "Wed, 30 Jan 2013 15:06:34 GMT"
}
] | 1,359,676,800,000 | [
[
"Rish",
"Irina",
""
],
[
"Kask",
"Kalev",
""
],
[
"Dechter",
"Rina",
""
]
] |
1301.7410 | Paola Sebastiani | Paola Sebastiani, Marco Ramoni | Decision Theoretic Foundations of Graphical Model Selection | Appears in Proceedings of the Fourteenth Conference on Uncertainty in
Artificial Intelligence (UAI1998) | null | null | UAI-P-1998-PG-464-471 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper describes a decision theoretic formulation of learning the
graphical structure of a Bayesian Belief Network from data. This framework
subsumes the standard Bayesian approach of choosing the model with the largest
posterior probability as the solution of a decision problem with a 0-1 loss
function and allows the use of more general loss functions able to trade-off
the complexity of the selected model and the error of choosing an
oversimplified model. A new class of loss functions, called disintegrable, is
introduced, to allow the decision problem to match the decomposability of the
graphical model. With this class of loss functions, the optimal solution to the
decision problem can be found using an efficient bottom-up search strategy.
| [
{
"version": "v1",
"created": "Wed, 30 Jan 2013 15:06:39 GMT"
}
] | 1,359,676,800,000 | [
[
"Sebastiani",
"Paola",
""
],
[
"Ramoni",
"Marco",
""
]
] |
1301.7412 | Ross D. Shachter | Ross D. Shachter | Bayes-Ball: The Rational Pastime (for Determining Irrelevance and
Requisite Information in Belief Networks and Influence Diagrams) | Appears in Proceedings of the Fourteenth Conference on Uncertainty in
Artificial Intelligence (UAI1998) | null | null | UAI-P-1998-PG-480-487 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | One of the benefits of belief networks and influence diagrams is that so much
knowledge is captured in the graphical structure. In particular, statements of
conditional irrelevance (or independence) can be verified in time linear in the
size of the graph. To resolve a particular inference query or decision problem,
only some of the possible states and probability distributions must be
specified, the "requisite information."
This paper presents a new, simple, and efficient "Bayes-ball" algorithm which
is well-suited to both new students of belief networks and state of the art
implementations. The Bayes-ball algorithm determines irrelevant sets and
requisite information more efficiently than existing methods, and is linear in
the size of the graph for belief networks and influence diagrams.
| [
{
"version": "v1",
"created": "Wed, 30 Jan 2013 15:06:48 GMT"
}
] | 1,359,676,800,000 | [
[
"Shachter",
"Ross D.",
""
]
] |
1301.7414 | Milan Studeny | Milan Studeny | Bayesian Networks from the Point of View of Chain Graphs | Appears in Proceedings of the Fourteenth Conference on Uncertainty in
Artificial Intelligence (UAI1998) | null | null | UAI-P-1998-PG-496-503 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | AThe paper gives a few arguments in favour of the use of chain graphs for
description of probabilistic conditional independence structures. Every
Bayesian network model can be equivalently introduced by means of a
factorization formula with respect to a chain graph which is Markov equivalent
to the Bayesian network. A graphical characterization of such graphs is given.
The class of equivalent graphs can be represented by a distinguished graph
which is called the largest chain graph. The factorization formula with respect
to the largest chain graph is a basis of a proposal of how to represent the
corresponding (discrete) probability distribution in a computer (i.e.
parametrize it). This way does not depend on the choice of a particular
Bayesian network from the class of equivalent networks and seems to be the most
efficient way from the point of view of memory demands. A separation criterion
for reading independency statements from a chain graph is formulated in a
simpler way. It resembles the well-known d-separation criterion for Bayesian
networks and can be implemented locally.
| [
{
"version": "v1",
"created": "Wed, 30 Jan 2013 15:06:56 GMT"
}
] | 1,359,676,800,000 | [
[
"Studeny",
"Milan",
""
]
] |
1301.7416 | Nevin Lianwen Zhang | Nevin Lianwen Zhang | Probabilistic Inference in Influence Diagrams | Appears in Proceedings of the Fourteenth Conference on Uncertainty in
Artificial Intelligence (UAI1998) | null | null | UAI-P-1998-PG-514-522 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper is about reducing influence diagram (ID) evaluation into Bayesian
network (BN) inference problems. Such reduction is interesting because it
enables one to readily use one's favorite BN inference algorithm to efficiently
evaluate IDs. Two such reduction methods have been proposed previously (Cooper
1988, Shachter and Peot 1992). This paper proposes a new method. The BN
inference problems induced by the mew method are much easier to solve than
those induced by the two previous methods.
| [
{
"version": "v1",
"created": "Wed, 30 Jan 2013 15:07:07 GMT"
}
] | 1,359,676,800,000 | [
[
"Zhang",
"Nevin Lianwen",
""
]
] |
1301.7417 | Nevin Lianwen Zhang | Nevin Lianwen Zhang, Stephen S. Lee | Planning with Partially Observable Markov Decision Processes: Advances
in Exact Solution Method | Appears in Proceedings of the Fourteenth Conference on Uncertainty in
Artificial Intelligence (UAI1998) | null | null | UAI-P-1998-PG-523-530 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | There is much interest in using partially observable Markov decision
processes (POMDPs) as a formal model for planning in stochastic domains. This
paper is concerned with finding optimal policies for POMDPs. We propose several
improvements to incremental pruning, presently the most efficient exact
algorithm for solving POMDPs.
| [
{
"version": "v1",
"created": "Wed, 30 Jan 2013 15:07:12 GMT"
}
] | 1,359,676,800,000 | [
[
"Zhang",
"Nevin Lianwen",
""
],
[
"Lee",
"Stephen S.",
""
]
] |
1301.7418 | Weixiong Zhang | Weixiong Zhang | Flexible and Approximate Computation through State-Space Reduction | Appears in Proceedings of the Fourteenth Conference on Uncertainty in
Artificial Intelligence (UAI1998) | null | null | UAI-P-1998-PG-531-538 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In the real world, insufficient information, limited computation resources,
and complex problem structures often force an autonomous agent to make a
decision in time less than that required to solve the problem at hand
completely. Flexible and approximate computations are two approaches to
decision making under limited computation resources. Flexible computation helps
an agent to flexibly allocate limited computation resources so that the overall
system utility is maximized. Approximate computation enables an agent to find
the best satisfactory solution within a deadline. In this paper, we present two
state-space reduction methods for flexible and approximate computation:
quantitative reduction to deal with inaccurate heuristic information, and
structural reduction to handle complex problem structures. These two methods
can be applied successively to continuously improve solution quality if more
computation is available. Our results show that these reduction methods are
effective and efficient, finding better solutions with less computation than
some existing well-known methods.
| [
{
"version": "v1",
"created": "Wed, 30 Jan 2013 15:07:19 GMT"
}
] | 1,359,676,800,000 | [
[
"Zhang",
"Weixiong",
""
]
] |
1302.0216 | Dimiter Dobrev | Dimiter Dobrev | Comparison between the two definitions of AI | added four new sections | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Two different definitions of the Artificial Intelligence concept have been
proposed in papers [1] and [2]. The first definition is informal. It says that
any program that is cleverer than a human being, is acknowledged as Artificial
Intelligence. The second definition is formal because it avoids reference to
the concept of human being. The readers of papers [1] and [2] might be left
with the impression that both definitions are equivalent and the definition in
[2] is simply a formal version of that in [1]. This paper will compare both
definitions of Artificial Intelligence and, hopefully, will bring a better
understanding of the concept.
| [
{
"version": "v1",
"created": "Thu, 31 Jan 2013 15:15:40 GMT"
},
{
"version": "v2",
"created": "Thu, 22 Aug 2013 22:56:04 GMT"
}
] | 1,377,475,200,000 | [
[
"Dobrev",
"Dimiter",
""
]
] |
1302.0334 | Daniel Buehrer | Daniel Buehrer and Chee-Hwa Lee | Class Algebra for Ontology Reasoning | pp.2-13 | Proc. of TOOLS Asia 99 (Technology of Object-Oriented Languages
and Systems, 1999 International Conference), IEEE Press | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Class algebra provides a natural framework for sharing of ISA hierarchies
between users that may be unaware of each other's definitions. This permits
data from relational databases, object-oriented databases, and tagged XML
documents to be unioned into one distributed ontology, sharable by all users
without the need for prior negotiation or the development of a "standard"
ontology for each field. Moreover, class algebra produces a functional
correspondence between a class's class algebraic definition (i.e. its "intent")
and the set of all instances which satisfy the expression (i.e. its "extent").
The framework thus provides assistance in quickly locating examples and
counterexamples of various definitions. This kind of information is very
valuable when developing models of the real world, and serves as an invaluable
tool assisting in the proof of theorems concerning these class algebra
expressions. Finally, the relative frequencies of objects in the ISA hierarchy
can produce a useful Boolean algebra of probabilities. The probabilities can be
used by traditional information-theoretic classification methodologies to
obtain optimal ways of classifying objects in the database.
| [
{
"version": "v1",
"created": "Sat, 2 Feb 2013 02:18:00 GMT"
}
] | 1,360,022,400,000 | [
[
"Buehrer",
"Daniel",
""
],
[
"Lee",
"Chee-Hwa",
""
]
] |
1302.1155 | Kurt Ammon | Kurt Ammon | An Effective Procedure for Computing "Uncomputable" Functions | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We give an effective procedure that produces a natural number in its output
from any natural number in its input, that is, it computes a total function.
The elementary operations of the procedure are Turing-computable. The procedure
has a second input which can contain the Goedel number of any Turing-computable
total function whose range is a subset of the set of the Goedel numbers of all
Turing-computable total functions. We prove that the second input cannot be set
to the Goedel number of any Turing-computable function that computes the output
from any natural number in its first input. In this sense, there is no Turing
program that computes the output from its first input. The procedure is used to
define creative procedures which compute functions that are not
Turing-computable. We argue that creative procedures model an aspect of
reasoning that cannot be modeled by Turing machines.
| [
{
"version": "v1",
"created": "Tue, 5 Feb 2013 19:11:59 GMT"
}
] | 1,360,108,800,000 | [
[
"Ammon",
"Kurt",
""
]
] |
1302.1334 | Yuriy Parzhin | Yuri Parzhin | Principles of modal and vector theory of formal intelligence systems | 34 pages, 8 figures | null | null | null | cs.AI | http://creativecommons.org/licenses/by/3.0/ | The paper considers the class of information systems capable of solving
heuristic problems on basis of formal theory that was termed modal and vector
theory of formal intelligent systems (FIS). The paper justifies the
construction of FIS resolution algorithm, defines the main features of these
systems and proves theorems that underlie the theory. The principle of
representation diversity of FIS construction is formulated. The paper deals
with the main principles of constructing and functioning formal intelligent
system (FIS) on basis of FIS modal and vector theory. The following phenomena
are considered: modular architecture of FIS presentation sub-system, algorithms
of data processing at every step of the stage of creating presentations.
Besides the paper suggests the structure of neural elements, i.e. zone
detectors and processors that are the basis for FIS construction.
| [
{
"version": "v1",
"created": "Wed, 6 Feb 2013 12:16:33 GMT"
}
] | 1,360,195,200,000 | [
[
"Parzhin",
"Yuri",
""
]
] |
1302.1520 | Ami Berler | Ami Berler, Solomon Eyal Shimony | Bayes Networks for Sonar Sensor Fusion | Appears in Proceedings of the Thirteenth Conference on Uncertainty in
Artificial Intelligence (UAI1997) | null | null | UAI-P-1997-PG-14-21 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Wide-angle sonar mapping of the environment by mobile robot is nontrivial due
to several sources of uncertainty: dropouts due to "specular" reflections,
obstacle location uncertainty due to the wide beam, and distance measurement
error. Earlier papers address the latter problems, but dropouts remain a
problem in many environments. We present an approach that lifts the
overoptimistic independence assumption used in earlier work, and use Bayes nets
to represent the dependencies between objects of the model. Objects of the
model consist of readings, and of regions in which "quasi location invariance"
of the (possible) obstacles exists, with respect to the readings. Simulation
supports the method's feasibility. The model is readily extensible to allow for
prior distributions, as well as other types of sensing operations.
| [
{
"version": "v1",
"created": "Wed, 6 Feb 2013 15:53:39 GMT"
}
] | 1,360,281,600,000 | [
[
"Berler",
"Ami",
""
],
[
"Shimony",
"Solomon Eyal",
""
]
] |
1302.1521 | John Bigham | John Bigham | Exploiting Uncertain and Temporal Information in Correlation | Appears in Proceedings of the Thirteenth Conference on Uncertainty in
Artificial Intelligence (UAI1997) | null | null | UAI-P-1997-PG-22-29 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | A modelling language is described which is suitable for the correlation of
information when the underlying functional model of the system is incomplete or
uncertain and the temporal dependencies are imprecise. An efficient and
incremental implementation is outlined which depends on cost functions
satisfying certain criteria. Possibilistic logic and probability theory (as it
is used in the applications targetted) satisfy these criteria.
| [
{
"version": "v1",
"created": "Wed, 6 Feb 2013 15:53:45 GMT"
}
] | 1,360,281,600,000 | [
[
"Bigham",
"John",
""
]
] |
1302.1522 | Craig Boutilier | Craig Boutilier | Correlated Action Effects in Decision Theoretic Regression | Appears in Proceedings of the Thirteenth Conference on Uncertainty in
Artificial Intelligence (UAI1997) | null | null | UAI-P-1997-PG-30-37 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Much recent research in decision theoretic planning has adopted Markov
decision processes (MDPs) as the model of choice, and has attempted to make
their solution more tractable by exploiting problem structure. One particular
algorithm, structured policy construction achieves this by means of a decision
theoretic analog of goal regression using action descriptions based on Bayesian
networks with tree-structured conditional probability tables. The algorithm as
presented is not able to deal with actions with correlated effects. We describe
a new decision theoretic regression operator that corrects this weakness. While
conceptually straightforward, this extension requires a somewhat more
complicated technical approach.
| [
{
"version": "v1",
"created": "Wed, 6 Feb 2013 15:53:51 GMT"
}
] | 1,360,281,600,000 | [
[
"Boutilier",
"Craig",
""
]
] |
1302.1523 | Alex G. Buchner | Alex G. Buchner, Werner Dubitzky, Alfons Schuster, Philippe Lopes,
Peter G. O'Donoghue, John G. Hughes, David A. Bell, Kenny Adamson, John A.
White, John M.C.C. Anderson, Maurice D. Mulvenna | Corporate Evidential Decision Making in Performance Prediction Domains | Appears in Proceedings of the Thirteenth Conference on Uncertainty in
Artificial Intelligence (UAI1997) | null | null | UAI-P-1997-PG-38-45 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Performance prediction or forecasting sporting outcomes involves a great deal
of insight into the particular area one is dealing with, and a considerable
amount of intuition about the factors that bear on such outcomes and
performances. The mathematical Theory of Evidence offers representation
formalisms which grant experts a high degree of freedom when expressing their
subjective beliefs in the context of decision-making situations like
performance prediction. Furthermore, this reasoning framework incorporates a
powerful mechanism to systematically pool the decisions made by individual
subject matter experts. The idea behind such a combination of knowledge is to
improve the competence (quality) of the overall decision-making process. This
paper reports on a performance prediction experiment carried out during the
European Football Championship in 1996. Relying on the knowledge of four
predictors, Evidence Theory was used to forecast the final scores of all 31
matches. The results of this empirical study are very encouraging.
| [
{
"version": "v1",
"created": "Wed, 6 Feb 2013 15:53:56 GMT"
}
] | 1,360,281,600,000 | [
[
"Buchner",
"Alex G.",
""
],
[
"Dubitzky",
"Werner",
""
],
[
"Schuster",
"Alfons",
""
],
[
"Lopes",
"Philippe",
""
],
[
"O'Donoghue",
"Peter G.",
""
],
[
"Hughes",
"John G.",
""
],
[
"Bell",
"David A.",
""
],
[
"Adamson",
"Kenny",
""
],
[
"White",
"John A.",
""
],
[
"Anderson",
"John M. C. C.",
""
],
[
"Mulvenna",
"Maurice D.",
""
]
] |
1302.1524 | Luis M. de Campos | Luis M. de Campos, Juan F. Huete | Algorithms for Learning Decomposable Models and Chordal Graphs | Appears in Proceedings of the Thirteenth Conference on Uncertainty in
Artificial Intelligence (UAI1997) | null | null | UAI-P-1997-PG-46-53 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Decomposable dependency models and their graphical counterparts, i.e.,
chordal graphs, possess a number of interesting and useful properties. On the
basis of two characterizations of decomposable models in terms of independence
relationships, we develop an exact algorithm for recovering the chordal
graphical representation of any given decomposable model. We also propose an
algorithm for learning chordal approximations of dependency models isomorphic
to general undirected graphs.
| [
{
"version": "v1",
"created": "Wed, 6 Feb 2013 15:54:02 GMT"
}
] | 1,360,281,600,000 | [
[
"de Campos",
"Luis M.",
""
],
[
"Huete",
"Juan F.",
""
]
] |
1302.1525 | Anthony R. Cassandra | Anthony R. Cassandra, Michael L. Littman, Nevin Lianwen Zhang | Incremental Pruning: A Simple, Fast, Exact Method for Partially
Observable Markov Decision Processes | Appears in Proceedings of the Thirteenth Conference on Uncertainty in
Artificial Intelligence (UAI1997) | null | null | UAI-P-1997-PG-54-61 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Most exact algorithms for general partially observable Markov decision
processes (POMDPs) use a form of dynamic programming in which a
piecewise-linear and convex representation of one value function is transformed
into another. We examine variations of the "incremental pruning" method for
solving this problem and compare them to earlier algorithms from theoretical
and empirical perspectives. We find that incremental pruning is presently the
most efficient exact method for solving POMDPs.
| [
{
"version": "v1",
"created": "Wed, 6 Feb 2013 15:54:07 GMT"
}
] | 1,360,281,600,000 | [
[
"Cassandra",
"Anthony R.",
""
],
[
"Littman",
"Michael L.",
""
],
[
"Zhang",
"Nevin Lianwen",
""
]
] |
1302.1526 | Urszula Chajewska | Urszula Chajewska, Joseph Y. Halpern | Defining Explanation in Probabilistic Systems | Appears in Proceedings of the Thirteenth Conference on Uncertainty in
Artificial Intelligence (UAI1997) | null | null | UAI-P-1997-PG-62-71 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | As probabilistic systems gain popularity and are coming into wider use, the
need for a mechanism that explains the system's findings and recommendations
becomes more critical. The system will also need a mechanism for ordering
competing explanations. We examine two representative approaches to explanation
in the literature - one due to G\"ardenfors and one due to Pearl - and show
that both suffer from significant problems. We propose an approach to defining
a notion of "better explanation" that combines some of the features of both
together with more recent work by Pearl and others on causality.
| [
{
"version": "v1",
"created": "Wed, 6 Feb 2013 15:54:13 GMT"
}
] | 1,360,281,600,000 | [
[
"Chajewska",
"Urszula",
""
],
[
"Halpern",
"Joseph Y.",
""
]
] |
1302.1527 | Adrian Y. W. Cheuk | Adrian Y. W. Cheuk, Craig Boutilier | Structured Arc Reversal and Simulation of Dynamic Probabilistic Networks | Appears in Proceedings of the Thirteenth Conference on Uncertainty in
Artificial Intelligence (UAI1997) | null | null | UAI-P-1997-PG-72-79 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We present an algorithm for arc reversal in Bayesian networks with
tree-structured conditional probability tables, and consider some of its
advantages, especially for the simulation of dynamic probabilistic networks. In
particular, the method allows one to produce CPTs for nodes involved in the
reversal that exploit regularities in the conditional distributions. We argue
that this approach alleviates some of the overhead associated with arc
reversal, plays an important role in evidence integration and can be used to
restrict sampling of variables in DPNs. We also provide an algorithm that
detects the dynamic irrelevance of state variables in forward simulation. This
algorithm exploits the structured CPTs in a reversed network to determine, in a
time-independent fashion, the conditions under which a variable does or does
not need to be sampled.
| [
{
"version": "v1",
"created": "Wed, 6 Feb 2013 15:54:19 GMT"
}
] | 1,360,281,600,000 | [
[
"Cheuk",
"Adrian Y. W.",
""
],
[
"Boutilier",
"Craig",
""
]
] |
1302.1531 | Fabio Gagliardi Cozman | Fabio Gagliardi Cozman | Robustness Analysis of Bayesian Networks with Local Convex Sets of
Distributions | Appears in Proceedings of the Thirteenth Conference on Uncertainty in
Artificial Intelligence (UAI1997) | null | null | UAI-P-1997-PG-108-115 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Robust Bayesian inference is the calculation of posterior probability bounds
given perturbations in a probabilistic model. This paper focuses on
perturbations that can be expressed locally in Bayesian networks through convex
sets of distributions. Two approaches for combination of local models are
considered. The first approach takes the largest set of joint distributions
that is compatible with the local sets of distributions; we show how to reduce
this type of robust inference to a linear programming problem. The second
approach takes the convex hull of joint distributions generated from the local
sets of distributions; we demonstrate how to apply interior-point optimization
methods to generate posterior bounds and how to generate approximations that
are guaranteed to converge to correct posterior bounds. We also discuss
calculation of bounds for expected utilities and variances, and global
perturbation models.
| [
{
"version": "v1",
"created": "Wed, 6 Feb 2013 15:54:41 GMT"
}
] | 1,360,281,600,000 | [
[
"Cozman",
"Fabio Gagliardi",
""
]
] |
1302.1532 | Adnan Darwiche | Adnan Darwiche, Gregory M. Provan | A Standard Approach for Optimizing Belief Network Inference using Query
DAGs | Appears in Proceedings of the Thirteenth Conference on Uncertainty in
Artificial Intelligence (UAI1997) | null | null | UAI-P-1997-PG-116-123 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper proposes a novel, algorithm-independent approach to optimizing
belief network inference. rather than designing optimizations on an algorithm
by algorithm basis, we argue that one should use an unoptimized algorithm to
generate a Q-DAG, a compiled graphical representation of the belief network,
and then optimize the Q-DAG and its evaluator instead. We present a set of
Q-DAG optimizations that supplant optimizations designed for traditional
inference algorithms, including zero compression, network pruning and caching.
We show that our Q-DAG optimizations require time linear in the Q-DAG size, and
significantly simplify the process of designing algorithms for optimizing
belief network inference.
| [
{
"version": "v1",
"created": "Wed, 6 Feb 2013 15:54:47 GMT"
}
] | 1,360,281,600,000 | [
[
"Darwiche",
"Adnan",
""
],
[
"Provan",
"Gregory M.",
""
]
] |
1302.1533 | Thomas L. Dean | Thomas L. Dean, Robert Givan, Sonia Leach | Model Reduction Techniques for Computing Approximately Optimal Solutions
for Markov Decision Processes | Appears in Proceedings of the Thirteenth Conference on Uncertainty in
Artificial Intelligence (UAI1997) | null | null | UAI-P-1997-PG-124-131 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We present a method for solving implicit (factored) Markov decision processes
(MDPs) with very large state spaces. We introduce a property of state space
partitions which we call epsilon-homogeneity. Intuitively, an
epsilon-homogeneous partition groups together states that behave approximately
the same under all or some subset of policies. Borrowing from recent work on
model minimization in computer-aided software verification, we present an
algorithm that takes a factored representation of an MDP and an 0<=epsilon<=1
and computes a factored epsilon-homogeneous partition of the state space. This
partition defines a family of related MDPs - those MDPs with state space equal
to the blocks of the partition, and transition probabilities "approximately"
like those of any (original MDP) state in the source block. To formally study
such families of MDPs, we introduce the new notion of a "bounded parameter MDP"
(BMDP), which is a family of (traditional) MDPs defined by specifying upper and
lower bounds on the transition probabilities and rewards. We describe
algorithms that operate on BMDPs to find policies that are approximately
optimal with respect to the original MDP. In combination, our method for
reducing a large implicit MDP to a possibly much smaller BMDP using an
epsilon-homogeneous partition, and our methods for selecting actions in BMDPs
constitute a new approach for analyzing large implicit MDPs. Among its
advantages, this new approach provides insight into existing algorithms to
solving implicit MDPs, provides useful connections to work in automata theory
and model minimization, and suggests methods, which involve varying epsilon, to
trade time and space (specifically in terms of the size of the corresponding
state space) for solution quality.
| [
{
"version": "v1",
"created": "Wed, 6 Feb 2013 15:54:52 GMT"
}
] | 1,360,281,600,000 | [
[
"Dean",
"Thomas L.",
""
],
[
"Givan",
"Robert",
""
],
[
"Leach",
"Sonia",
""
]
] |
1302.1534 | Rina Dechter | Rina Dechter, Irina Rish | A Scheme for Approximating Probabilistic Inference | Appears in Proceedings of the Thirteenth Conference on Uncertainty in
Artificial Intelligence (UAI1997) | null | null | UAI-P-1997-PG-132-141 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper describes a class of probabilistic approximation algorithms based
on bucket elimination which offer adjustable levels of accuracy and efficiency.
We analyze the approximation for several tasks: finding the most probable
explanation, belief updating and finding the maximum a posteriori hypothesis.
We identify regions of completeness and provide preliminary empirical
evaluation on randomly generated networks.
| [
{
"version": "v1",
"created": "Wed, 6 Feb 2013 15:54:58 GMT"
}
] | 1,360,281,600,000 | [
[
"Dechter",
"Rina",
""
],
[
"Rish",
"Irina",
""
]
] |
1302.1535 | Soren L. Dittmer | Soren L. Dittmer, Finn Verner Jensen | Myopic Value of Information in Influence Diagrams | Appears in Proceedings of the Thirteenth Conference on Uncertainty in
Artificial Intelligence (UAI1997) | null | null | UAI-P-1997-PG-142-149 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We present a method for calculation of myopic value of information in
influence diagrams (Howard & Matheson, 1981) based on the strong junction tree
framework (Jensen, Jensen & Dittmer, 1994). The difference in instantiation
order in the influence diagrams is reflected in the corresponding junction
trees by the order in which the chance nodes are marginalized. This order of
marginalization can be changed by table expansion and in effect the same
junction tree with expanded tables may be used for calculating the expected
utility for scenarios with different instantiation order. We also compare our
method to the classic method of modeling different instantiation orders in the
same influence diagram.
| [
{
"version": "v1",
"created": "Wed, 6 Feb 2013 15:55:04 GMT"
}
] | 1,360,281,600,000 | [
[
"Dittmer",
"Soren L.",
""
],
[
"Jensen",
"Finn Verner",
""
]
] |
1302.1536 | Jens Doerpmund | Jens Doerpmund | Limitations of Skeptical Default Reasoning | Appears in Proceedings of the Thirteenth Conference on Uncertainty in
Artificial Intelligence (UAI1997) | null | null | UAI-P-1997-PG-150-156 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Poole has shown that nonmonotonic logics do not handle the lottery paradox
correctly. In this paper we will show that Pollock's theory of defeasible
reasoning fails for the same reason: defeasible reasoning is incompatible with
the skeptical notion of derivability.
| [
{
"version": "v1",
"created": "Wed, 6 Feb 2013 15:55:09 GMT"
}
] | 1,360,281,600,000 | [
[
"Doerpmund",
"Jens",
""
]
] |
1302.1537 | Didier Dubois | Didier Dubois, Helene Fargier, Henri Prade | Decision-making Under Ordinal Preferences and Comparative Uncertainty | Appears in Proceedings of the Thirteenth Conference on Uncertainty in
Artificial Intelligence (UAI1997) | null | null | UAI-P-1997-PG-157-164 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper investigates the problem of finding a preference relation on a set
of acts from the knowledge of an ordering on events (subsets of states of the
world) describing the decision-maker (DM)s uncertainty and an ordering of
consequences of acts, describing the DMs preferences. However, contrary to
classical approaches to decision theory, we try to do it without resorting to
any numerical representation of utility nor uncertainty, and without even using
any qualitative scale on which both uncertainty and preference could be mapped.
It is shown that although many axioms of Savage theory can be preserved and
despite the intuitive appeal of the method for constructing a preference over
acts, the approach is inconsistent with a probabilistic representation of
uncertainty, but leads to the kind of uncertainty theory encountered in
non-monotonic reasoning (especially preferential and rational inference),
closely related to possibility theory. Moreover the method turns out to be
either very little decisive or to lead to very risky decisions, although its
basic principles look sound. This paper raises the question of the very
possibility of purely symbolic approaches to Savage-like decision-making under
uncertainty and obtains preliminary negative results.
| [
{
"version": "v1",
"created": "Wed, 6 Feb 2013 15:55:15 GMT"
}
] | 1,360,281,600,000 | [
[
"Dubois",
"Didier",
""
],
[
"Fargier",
"Helene",
""
],
[
"Prade",
"Henri",
""
]
] |
1302.1540 | Judy Goldsmith | Judy Goldsmith, Michael L. Littman, Martin Mundhenk | The Complexity of Plan Existence and Evaluation in Probabilistic Domains | Appears in Proceedings of the Thirteenth Conference on Uncertainty in
Artificial Intelligence (UAI1997) | null | null | UAI-P-1997-PG-182-189 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We examine the computational complexity of testing and finding small plans in
probabilistic planning domains with succinct representations. We find that many
problems of interest are complete for a variety of complexity classes: NP,
co-NP, PP, NP^PP, co-NP^PP, and PSPACE. Of these, the probabilistic classes PP
and NP^PP are likely to be of special interest in the field of uncertainty in
artificial intelligence and are deserving of additional study. These results
suggest a fruitful direction of future algorithmic development.
| [
{
"version": "v1",
"created": "Wed, 6 Feb 2013 15:55:32 GMT"
}
] | 1,360,281,600,000 | [
[
"Goldsmith",
"Judy",
""
],
[
"Littman",
"Michael L.",
""
],
[
"Mundhenk",
"Martin",
""
]
] |
1302.1541 | Carla P. Gomes | Carla P. Gomes, Bart Selman | Algorithm Portfolio Design: Theory vs. Practice | Appears in Proceedings of the Thirteenth Conference on Uncertainty in
Artificial Intelligence (UAI1997) | null | null | UAI-P-1997-PG-190-197 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Stochastic algorithms are among the best for solving computationally hard
search and reasoning problems. The runtime of such procedures is characterized
by a random variable. Different algorithms give rise to different probability
distributions. One can take advantage of such differences by combining several
algorithms into a portfolio, and running them in parallel or interleaving them
on a single processor. We provide a detailed evaluation of the portfolio
approach on distributions of hard combinatorial search problems. We show under
what conditions the protfolio approach can have a dramatic computational
advantage over the best traditional methods.
| [
{
"version": "v1",
"created": "Wed, 6 Feb 2013 15:55:37 GMT"
}
] | 1,360,281,600,000 | [
[
"Gomes",
"Carla P.",
""
],
[
"Selman",
"Bart",
""
]
] |
1302.1543 | Adam J. Grove | Adam J. Grove, Joseph Y. Halpern | Probability Update: Conditioning vs. Cross-Entropy | Appears in Proceedings of the Thirteenth Conference on Uncertainty in
Artificial Intelligence (UAI1997) | null | null | UAI-P-1997-PG-208-214 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Conditioning is the generally agreed-upon method for updating probability
distributions when one learns that an event is certainly true. But it has been
argued that we need other rules, in particular the rule of cross-entropy
minimization, to handle updates that involve uncertain information. In this
paper we re-examine such a case: van Fraassen's Judy Benjamin problem, which in
essence asks how one might update given the value of a conditional probability.
We argue that -- contrary to the suggestions in the literature -- it is
possible to use simple conditionalization in this case, and thereby obtain
answers that agree fully with intuition. This contrasts with proposals such as
cross-entropy, which are easier to apply but can give unsatisfactory answers.
Based on the lessons from this example, we speculate on some general
philosophical issues concerning probability update.
| [
{
"version": "v1",
"created": "Wed, 6 Feb 2013 15:55:49 GMT"
}
] | 1,360,281,600,000 | [
[
"Grove",
"Adam J.",
""
],
[
"Halpern",
"Joseph Y.",
""
]
] |
1302.1546 | Luis D. Hernandez | Luis D. Hernandez, Serafin Moral | Inference with Idempotent Valuations | Appears in Proceedings of the Thirteenth Conference on Uncertainty in
Artificial Intelligence (UAI1997) | null | null | UAI-P-1997-PG-229-237 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Valuation based systems verifying an idempotent property are studied. A
partial order is defined between the valuations giving them a lattice
structure. Then, two different strategies are introduced to represent
valuations: as infimum of the most informative valuations or as supremum of the
least informative ones. It is studied how to carry out computations with both
representations in an efficient way. The particular cases of finite sets and
convex polytopes are considered.
| [
{
"version": "v1",
"created": "Wed, 6 Feb 2013 15:56:12 GMT"
}
] | 1,360,281,600,000 | [
[
"Hernandez",
"Luis D.",
""
],
[
"Moral",
"Serafin",
""
]
] |
1302.1548 | Eric J. Horvitz | Eric J. Horvitz, Adam Seiver | Time-Critical Reasoning: Representations and Application | Appears in Proceedings of the Thirteenth Conference on Uncertainty in
Artificial Intelligence (UAI1997) | null | null | UAI-P-1997-PG-250-257 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We review the problem of time-critical action and discuss a reformulation
that shifts knowledge acquisition from the assessment of complex temporal
probabilistic dependencies to the direct assessment of time-dependent utilities
over key outcomes of interest. We dwell on a class of decision problems
characterized by the centrality of diagnosing and reacting in a timely manner
to pathological processes. We motivate key ideas in the context of trauma-care
triage and transportation decisions.
| [
{
"version": "v1",
"created": "Wed, 6 Feb 2013 15:56:24 GMT"
}
] | 1,360,281,600,000 | [
[
"Horvitz",
"Eric J.",
""
],
[
"Seiver",
"Adam",
""
]
] |
1302.1550 | Manfred Jaeger | Manfred Jaeger | Relational Bayesian Networks | Appears in Proceedings of the Thirteenth Conference on Uncertainty in
Artificial Intelligence (UAI1997) | null | null | UAI-P-1997-PG-266-273 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | A new method is developed to represent probabilistic relations on multiple
random events. Where previously knowledge bases containing probabilistic rules
were used for this purpose, here a probability distribution over the relations
is directly represented by a Bayesian network. By using a powerful way of
specifying conditional probability distributions in these networks, the
resulting formalism is more expressive than the previous ones. Particularly, it
provides for constraints on equalities of events, and it allows to define
complex, nested combination functions.
| [
{
"version": "v1",
"created": "Wed, 6 Feb 2013 15:57:05 GMT"
}
] | 1,360,281,600,000 | [
[
"Jaeger",
"Manfred",
""
]
] |
1302.1551 | Radim Jirousek | Radim Jirousek | Composition of Probability Measures on Finite Spaces | Appears in Proceedings of the Thirteenth Conference on Uncertainty in
Artificial Intelligence (UAI1997) | null | null | UAI-P-1997-PG-274-281 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Decomposable models and Bayesian networks can be defined as sequences of
oligo-dimensional probability measures connected with operators of composition.
The preliminary results suggest that the probabilistic models allowing for
effective computational procedures are represented by sequences possessing a
special property; we shall call them perfect sequences. The paper lays down the
elementary foundation necessary for further study of iterative application of
operators of composition. We believe to develop a technique describing several
graph models in a unifying way. We are convinced that practically all
theoretical results and procedures connected with decomposable models and
Bayesian networks can be translated into the terminology introduced in this
paper. For example, complexity of computational procedures in these models is
closely dependent on possibility to change the ordering of oligo-dimensional
measures defining the model. Therefore, in this paper, lot of attention is paid
to possibility to change ordering of the operators of composition.
| [
{
"version": "v1",
"created": "Wed, 6 Feb 2013 15:57:13 GMT"
}
] | 1,360,281,600,000 | [
[
"Jirousek",
"Radim",
""
]
] |
1302.1553 | Uffe Kj{\ae}rulff | Uffe Kj{\ae}rulff | Nested Junction Trees | Appears in Proceedings of the Thirteenth Conference on Uncertainty in
Artificial Intelligence (UAI1997) | null | null | UAI-P-1997-PG-294-301 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The efficiency of inference in both the Hugin and, most notably, the
Shafer-Shenoy architectures can be improved by exploiting the independence
relations induced by the incoming messages of a clique. That is, the message to
be sent from a clique can be computed via a factorization of the clique
potential in the form of a junction tree. In this paper we show that by
exploiting such nested junction trees in the computation of messages both space
and time costs of the conventional propagation methods may be reduced. The
paper presents a structured way of exploiting the nested junction trees
technique to achieve such reductions. The usefulness of the method is
emphasized through a thorough empirical evaluation involving ten large
real-world Bayesian networks and the Hugin inference algorithm.
| [
{
"version": "v1",
"created": "Wed, 6 Feb 2013 15:57:28 GMT"
}
] | 1,360,281,600,000 | [
[
"Kjærulff",
"Uffe",
""
]
] |
1302.1554 | Daphne Koller | Daphne Koller, Avi Pfeffer | Object-Oriented Bayesian Networks | Appears in Proceedings of the Thirteenth Conference on Uncertainty in
Artificial Intelligence (UAI1997) | null | null | UAI-P-1997-PG-302-313 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Bayesian networks provide a modeling language and associated inference
algorithm for stochastic domains. They have been successfully applied in a
variety of medium-scale applications. However, when faced with a large complex
domain, the task of modeling using Bayesian networks begins to resemble the
task of programming using logical circuits. In this paper, we describe an
object-oriented Bayesian network (OOBN) language, which allows complex domains
to be described in terms of inter-related objects. We use a Bayesian network
fragment to describe the probabilistic relations between the attributes of an
object. These attributes can themselves be objects, providing a natural
framework for encoding part-of hierarchies. Classes are used to provide a
reusable probabilistic model which can be applied to multiple similar objects.
Classes also support inheritance of model fragments from a class to a subclass,
allowing the common aspects of related classes to be defined only once. Our
language has clear declarative semantics: an OOBN can be interpreted as a
stochastic functional program, so that it uniquely specifies a probabilistic
model. We provide an inference algorithm for OOBNs, and show that much of the
structural information encoded by an OOBN--particularly the encapsulation of
variables within an object and the reuse of model fragments in different
contexts--can also be used to speed up the inference process.
| [
{
"version": "v1",
"created": "Wed, 6 Feb 2013 15:57:36 GMT"
}
] | 1,360,281,600,000 | [
[
"Koller",
"Daphne",
""
],
[
"Pfeffer",
"Avi",
""
]
] |
1302.1555 | Alexander V. Kozlov | Alexander V. Kozlov, Daphne Koller | Nonuniform Dynamic Discretization in Hybrid Networks | Appears in Proceedings of the Thirteenth Conference on Uncertainty in
Artificial Intelligence (UAI1997) | null | null | UAI-P-1997-PG-314-325 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We consider probabilistic inference in general hybrid networks, which include
continuous and discrete variables in an arbitrary topology. We reexamine the
question of variable discretization in a hybrid network aiming at minimizing
the information loss induced by the discretization. We show that a nonuniform
partition across all variables as opposed to uniform partition of each variable
separately reduces the size of the data structures needed to represent a
continuous function. We also provide a simple but efficient procedure for
nonuniform partition. To represent a nonuniform discretization in the computer
memory, we introduce a new data structure, which we call a Binary Split
Partition (BSP) tree. We show that BSP trees can be an exponential factor
smaller than the data structures in the standard uniform discretization in
multiple dimensions and show how the BSP trees can be used in the standard join
tree algorithm. We show that the accuracy of the inference process can be
significantly improved by adjusting discretization with evidence. We construct
an iterative anytime algorithm that gradually improves the quality of the
discretization and the accuracy of the answer on a query. We provide empirical
evidence that the algorithm converges.
| [
{
"version": "v1",
"created": "Wed, 6 Feb 2013 15:57:46 GMT"
}
] | 1,360,281,600,000 | [
[
"Kozlov",
"Alexander V.",
""
],
[
"Koller",
"Daphne",
""
]
] |
1302.1556 | Henry E. Kyburg Jr. | Henry E. Kyburg Jr | Probabilistic Acceptance | Appears in Proceedings of the Thirteenth Conference on Uncertainty in
Artificial Intelligence (UAI1997) | null | null | UAI-P-1997-PG-326-333 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The idea of fully accepting statements when the evidence has rendered them
probable enough faces a number of difficulties. We leave the interpretation of
probability largely open, but attempt to suggest a contextual approach to full
belief. We show that the difficulties of probabilistic acceptance are not as
severe as they are sometimes painted, and that though there are oddities
associated with probabilistic acceptance they are in some instances less
awkward than the difficulties associated with other nonmonotonic formalisms. We
show that the structure at which we arrive provides a natural home for
statistical inference.
| [
{
"version": "v1",
"created": "Wed, 6 Feb 2013 15:57:52 GMT"
}
] | 1,360,281,600,000 | [
[
"Kyburg",
"Henry E.",
"Jr"
]
] |
1302.1557 | Kathryn Blackmond Laskey | Kathryn Blackmond Laskey, Suzanne M. Mahoney | Network Fragments: Representing Knowledge for Constructing Probabilistic
Models | Appears in Proceedings of the Thirteenth Conference on Uncertainty in
Artificial Intelligence (UAI1997) | null | null | UAI-P-1997-PG-334-341 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In most current applications of belief networks, domain knowledge is
represented by a single belief network that applies to all problem instances in
the domain. In more complex domains, problem-specific models must be
constructed from a knowledge base encoding probabilistic relationships in the
domain. Most work in knowledge-based model construction takes the rule as the
basic unit of knowledge. We present a knowledge representation framework that
permits the knowledge base designer to specify knowledge in larger semantically
meaningful units which we call network fragments. Our framework provides for
representation of asymmetric independence and canonical intercausal
interaction. We discuss the combination of network fragments to form
problem-specific models to reason about particular problem instances. The
framework is illustrated using examples from the domain of military situation
awareness.
| [
{
"version": "v1",
"created": "Wed, 6 Feb 2013 15:57:59 GMT"
}
] | 1,360,281,600,000 | [
[
"Laskey",
"Kathryn Blackmond",
""
],
[
"Mahoney",
"Suzanne M.",
""
]
] |
1302.1558 | Yan Lin | Yan Lin, Marek J. Druzdzel | Computational Advantages of Relevance Reasoning in Bayesian Belief
Networks | Appears in Proceedings of the Thirteenth Conference on Uncertainty in
Artificial Intelligence (UAI1997) | null | null | UAI-P-1997-PG-342-350 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper introduces a computational framework for reasoning in Bayesian
belief networks that derives significant advantages from focused inference and
relevance reasoning. This framework is based on d -separation and other simple
and computationally efficient techniques for pruning irrelevant parts of a
network. Our main contribution is a technique that we call relevance-based
decomposition. Relevance-based decomposition approaches belief updating in
large networks by focusing on their parts and decomposing them into partially
overlapping subnetworks. This makes reasoning in some intractable networks
possible and, in addition, often results in significant speedup, as the total
time taken to update all subnetworks is in practice often considerably less
than the time taken to update the network as a whole. We report results of
empirical tests that demonstrate practical significance of our approach.
| [
{
"version": "v1",
"created": "Wed, 6 Feb 2013 15:58:06 GMT"
}
] | 1,360,281,600,000 | [
[
"Lin",
"Yan",
""
],
[
"Druzdzel",
"Marek J.",
""
]
] |
1302.1560 | Todd Michael Mansell | Todd Michael Mansell | A Target Classification Decision Aid | Appears in Proceedings of the Thirteenth Conference on Uncertainty in
Artificial Intelligence (UAI1997) | null | null | UAI-P-1997-PG-358-365 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | A submarine's sonar team is responsible for detecting, localising and
classifying targets using information provided by the platform's sensor suite.
The information used to make these assessments is typically uncertain and/or
incomplete and is likely to require a measure of confidence in its reliability.
Moreover, improvements in sensor and communication technology are resulting in
increased amounts of on-platform and off-platform information available for
evaluation. This proliferation of imprecise information increases the risk of
overwhelming the operator. To assist the task of localisation and
classification a concept demonstration decision aid (Horizon), based on
evidential reasoning, has been developed. Horizon is an information fusion
software package for representing and fusing imprecise information about the
state of the world, expressed across suitable frames of reference. The Horizon
software is currently at prototype stage.
| [
{
"version": "v1",
"created": "Wed, 6 Feb 2013 15:58:18 GMT"
}
] | 1,360,281,600,000 | [
[
"Mansell",
"Todd Michael",
""
]
] |
1302.1562 | Paul-Andre Monney | Paul-Andre Monney | Support and Plausibility Degrees in Generalized Functional Models | Appears in Proceedings of the Thirteenth Conference on Uncertainty in
Artificial Intelligence (UAI1997) | null | null | UAI-P-1997-PG-376-383 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | By discussing several examples, the theory of generalized functional models
is shown to be very natural for modeling some situations of reasoning under
uncertainty. A generalized functional model is a pair (f, P) where f is a
function describing the interactions between a parameter variable, an
observation variable and a random source, and P is a probability distribution
for the random source. Unlike traditional functional models, generalized
functional models do not require that there is only one value of the parameter
variable that is compatible with an observation and a realization of the random
source. As a consequence, the results of the analysis of a generalized
functional model are not expressed in terms of probability distributions but
rather by support and plausibility functions. The analysis of a generalized
functional model is very logical and is inspired from ideas already put forward
by R.A. Fisher in his theory of fiducial probability.
| [
{
"version": "v1",
"created": "Wed, 6 Feb 2013 15:58:30 GMT"
}
] | 1,360,281,600,000 | [
[
"Monney",
"Paul-Andre",
""
]
] |
1302.1563 | Scott B. Morris | Scott B. Morris, Doug Cork, Richard E. Neapolitan | The Cognitive Processing of Causal Knowledge | Appears in Proceedings of the Thirteenth Conference on Uncertainty in
Artificial Intelligence (UAI1997) | null | null | UAI-P-1997-PG-384-391 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | There is a brief description of the probabilistic causal graph model for
representing, reasoning with, and learning causal structure using Bayesian
networks. It is then argued that this model is closely related to how humans
reason with and learn causal structure. It is shown that studies in psychology
on discounting (reasoning concerning how the presence of one cause of an effect
makes another cause less probable) support the hypothesis that humans reach the
same judgments as algorithms for doing inference in Bayesian networks. Next, it
is shown how studies by Piaget indicate that humans learn causal structure by
observing the same independencies and dependencies as those used by certain
algorithms for learning the structure of a Bayesian network. Based on this
indication, a subjective definition of causality is forwarded. Finally, methods
for further testing the accuracy of these claims are discussed.
| [
{
"version": "v1",
"created": "Wed, 6 Feb 2013 15:58:35 GMT"
}
] | 1,360,281,600,000 | [
[
"Morris",
"Scott B.",
""
],
[
"Cork",
"Doug",
""
],
[
"Neapolitan",
"Richard E.",
""
]
] |
1302.1567 | Solomon Eyal Shimony | Solomon Eyal Shimony, Carmel Domshlak, Eugene Santos Jr | Cost-Sharing in Bayesian Knowledge Bases | Appears in Proceedings of the Thirteenth Conference on Uncertainty in
Artificial Intelligence (UAI1997) | null | null | UAI-P-1997-PG-421-428 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Bayesian knowledge bases (BKBs) are a generalization of Bayes networks and
weighted proof graphs (WAODAGs), that allow cycles in the causal graph.
Reasoning in BKBs requires finding the most probable inferences consistent with
the evidence. The cost-sharing heuristic for finding least-cost explanations in
WAODAGs was presented and shown to be effective by Charniak and Husain.
However, the cycles in BKBs would make the definition of cost-sharing cyclic as
well, if applied directly to BKBs. By treating the defining equations of
cost-sharing as a system of equations, one can properly define an admissible
cost-sharing heuristic for BKBs. Empirical evaluation shows that cost-sharing
improves performance significantly when applied to BKBs.
| [
{
"version": "v1",
"created": "Wed, 6 Feb 2013 15:58:57 GMT"
}
] | 1,360,281,600,000 | [
[
"Shimony",
"Solomon Eyal",
""
],
[
"Domshlak",
"Carmel",
""
],
[
"Santos",
"Eugene",
"Jr"
]
] |
1302.1569 | Choh Man Teng | Choh Man Teng | Sequential Thresholds: Context Sensitive Default Extensions | Appears in Proceedings of the Thirteenth Conference on Uncertainty in
Artificial Intelligence (UAI1997) | null | null | UAI-P-1997-PG-437-444 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Default logic encounters some conceptual difficulties in representing common
sense reasoning tasks. We argue that we should not try to formulate modular
default rules that are presumed to work in all or most circumstances. We need
to take into account the importance of the context which is continuously
evolving during the reasoning process. Sequential thresholding is a
quantitative counterpart of default logic which makes explicit the role context
plays in the construction of a non-monotonic extension. We present a semantic
characterization of generic non-monotonic reasoning, as well as the
instantiations pertaining to default logic and sequential thresholding. This
provides a link between the two mechanisms as well as a way to integrate the
two that can be beneficial to both.
| [
{
"version": "v1",
"created": "Wed, 6 Feb 2013 15:59:08 GMT"
}
] | 1,360,281,600,000 | [
[
"Teng",
"Choh Man",
""
]
] |
1302.1570 | Moshe Tennenholtz | Moshe Tennenholtz | On Stable Multi-Agent Behavior in Face of Uncertainty | Appears in Proceedings of the Thirteenth Conference on Uncertainty in
Artificial Intelligence (UAI1997) | null | null | UAI-P-1997-PG-445-452 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | A stable joint plan should guarantee the achievement of a designer's goal in
a multi-agent environment, while ensuring that deviations from the prescribed
plan would be detected. We present a computational framework where stable joint
plans can be studied, as well as several basic results about the
representation, verification and synthesis of stable joint plans.
| [
{
"version": "v1",
"created": "Wed, 6 Feb 2013 15:59:13 GMT"
}
] | 1,360,281,600,000 | [
[
"Tennenholtz",
"Moshe",
""
]
] |
1302.1573 | Nevin Lianwen Zhang | Nevin Lianwen Zhang, Wenju Liu | Region-Based Approximations for Planning in Stochastic Domains | Appears in Proceedings of the Thirteenth Conference on Uncertainty in
Artificial Intelligence (UAI1997) | null | null | UAI-P-1997-PG-472-480 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper is concerned with planning in stochastic domains by means of
partially observable Markov decision processes (POMDPs). POMDPs are difficult
to solve. This paper identifies a subclass of POMDPs called region observable
POMDPs, which are easier to solve and can be used to approximate general POMDPs
to arbitrary accuracy.
| [
{
"version": "v1",
"created": "Wed, 6 Feb 2013 15:59:30 GMT"
}
] | 1,360,281,600,000 | [
[
"Zhang",
"Nevin Lianwen",
""
],
[
"Liu",
"Wenju",
""
]
] |
1302.1574 | Nevin Lianwen Zhang | Nevin Lianwen Zhang, Li Yan | Independence of Causal Influence and Clique Tree Propagation | Appears in Proceedings of the Thirteenth Conference on Uncertainty in
Artificial Intelligence (UAI1997) | null | null | UAI-P-1997-PG-481-488 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper explores the role of independence of causal influence (ICI) in
Bayesian network inference. ICI allows one to factorize a conditional
probability table into smaller pieces. We describe a method for exploiting the
factorization in clique tree propagation (CTP) - the state-of-the-art exact
inference algorithm for Bayesian networks. We also present empirical results
showing that the resulting algorithm is significantly more efficient than the
combination of CTP and previous techniques for exploiting ICI.
| [
{
"version": "v1",
"created": "Wed, 6 Feb 2013 15:59:36 GMT"
}
] | 1,360,281,600,000 | [
[
"Zhang",
"Nevin Lianwen",
""
],
[
"Yan",
"Li",
""
]
] |
1302.1575 | Nevin Lianwen Zhang | Nevin Lianwen Zhang, Weihong Zhang | Fast Value Iteration for Goal-Directed Markov Decision Processes | Appears in Proceedings of the Thirteenth Conference on Uncertainty in
Artificial Intelligence (UAI1997) | null | null | UAI-P-1997-PG-489-494 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Planning problems where effects of actions are non-deterministic can be
modeled as Markov decision processes. Planning problems are usually
goal-directed. This paper proposes several techniques for exploiting the
goal-directedness to accelerate value iteration, a standard algorithm for
solving Markov decision processes. Empirical studies have shown that the
techniques can bring about significant speedups.
| [
{
"version": "v1",
"created": "Wed, 6 Feb 2013 15:59:41 GMT"
}
] | 1,360,281,600,000 | [
[
"Zhang",
"Nevin Lianwen",
""
],
[
"Zhang",
"Weihong",
""
]
] |
1302.2056 | Jose Hernandez-Orallo | Jose Hernandez-Orallo | Complexity distribution of agent policies | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We analyse the complexity of environments according to the policies that need
to be used to achieve high performance. The performance results for a
population of policies leads to a distribution that is examined in terms of
policy complexity and analysed through several diagrams and indicators. The
notion of environment response curve is also introduced, by inverting the
performance results into an ability scale. We apply all these concepts,
diagrams and indicators to a minimalistic environment class, agent-populated
elementary cellular automata, showing how the difficulty, discriminating power
and ranges (previous to normalisation) may vary for several environments.
| [
{
"version": "v1",
"created": "Fri, 8 Feb 2013 15:01:20 GMT"
}
] | 1,360,540,800,000 | [
[
"Hernandez-Orallo",
"Jose",
""
]
] |
1302.2465 | Patrick Rodler | Patrick Rodler and Kostyantyn Shchekotykhin and Philipp Fleiss and
Gerhard Friedrich | RIO: Minimizing User Interaction in Debugging of Knowledge Bases | arXiv admin note: substantial text overlap with arXiv:1209.3734 | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The best currently known interactive debugging systems rely upon some
meta-information in terms of fault probabilities in order to improve their
efficiency. However, misleading meta information might result in a dramatic
decrease of the performance and its assessment is only possible a-posteriori.
Consequently, as long as the actual fault is unknown, there is always some risk
of suboptimal interactions. In this work we present a reinforcement learning
strategy that continuously adapts its behavior depending on the performance
achieved and minimizes the risk of using low-quality meta information.
Therefore, this method is suitable for application scenarios where reliable
prior fault estimates are difficult to obtain. Using diverse real-world
knowledge bases, we show that the proposed interactive query strategy is
scalable, features decent reaction time, and outperforms both entropy-based and
no-risk strategies on average w.r.t. required amount of user interaction.
| [
{
"version": "v1",
"created": "Mon, 11 Feb 2013 12:53:47 GMT"
},
{
"version": "v2",
"created": "Wed, 6 Mar 2013 14:46:03 GMT"
}
] | 1,362,614,400,000 | [
[
"Rodler",
"Patrick",
""
],
[
"Shchekotykhin",
"Kostyantyn",
""
],
[
"Fleiss",
"Philipp",
""
],
[
"Friedrich",
"Gerhard",
""
]
] |
1302.3549 | Silvia Acid | Silvia Acid, Luis M. de Campos | An Algorithm for Finding Minimum d-Separating Sets in Belief Networks | Appears in Proceedings of the Twelfth Conference on Uncertainty in
Artificial Intelligence (UAI1996) | null | null | UAI-P-1996-PG-3-10 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The criterion commonly used in directed acyclic graphs (dags) for testing
graphical independence is the well-known d-separation criterion. It allows us
to build graphical representations of dependency models (usually probabilistic
dependency models) in the form of belief networks, which make easy
interpretation and management of independence relationships possible, without
reference to numerical parameters (conditional probabilities). In this paper,
we study the following combinatorial problem: finding the minimum d-separating
set for two nodes in a dag. This set would represent the minimum information
(in the sense of minimum number of variables) necessary to prevent these two
nodes from influencing each other. The solution to this basic problem and some
of its extensions can be useful in several ways, as we shall see later. Our
solution is based on a two-step process: first, we reduce the original problem
to the simpler one of finding a minimum separating set in an undirected graph,
and second, we develop an algorithm for solving it.
| [
{
"version": "v1",
"created": "Wed, 13 Feb 2013 14:11:19 GMT"
}
] | 1,361,145,600,000 | [
[
"Acid",
"Silvia",
""
],
[
"de Campos",
"Luis M.",
""
]
] |
1302.3550 | John Mark Agosta | John Mark Agosta | Constraining Influence Diagram Structure by Generative Planning: An
Application to the Optimization of Oil Spill Response | Appears in Proceedings of the Twelfth Conference on Uncertainty in
Artificial Intelligence (UAI1996) | null | null | UAI-P-1996-PG-11-19 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper works through the optimization of a real world planning problem,
with a combination of a generative planning tool and an influence diagram
solver. The problem is taken from an existing application in the domain of oil
spill emergency response. The planning agent manages constraints that order
sets of feasible equipment employment actions. This is mapped at an
intermediate level of abstraction onto an influence diagram. In addition, the
planner can apply a surveillance operator that determines observability of the
state---the unknown trajectory of the oil. The uncertain world state and the
objective function properties are part of the influence diagram structure, but
not represented in the planning agent domain. By exploiting this structure
under the constraints generated by the planning agent, the influence diagram
solution complexity simplifies considerably, and an optimum solution to the
employment problem based on the objective function is found. Finding this
optimum is equivalent to the simultaneous evaluation of a range of plans. This
result is an example of bounded optimality, within the limitations of this
hybrid generative planner and influence diagram architecture.
| [
{
"version": "v1",
"created": "Wed, 13 Feb 2013 14:11:25 GMT"
}
] | 1,361,145,600,000 | [
[
"Agosta",
"John Mark",
""
]
] |
1302.3551 | Satnam Alag | Satnam Alag, Alice M. Agogino | Inference Using Message Propagation and Topology Transformation in
Vector Gaussian Continuous Networks | Appears in Proceedings of the Twelfth Conference on Uncertainty in
Artificial Intelligence (UAI1996) | null | null | UAI-P-1996-PG-20-27 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We extend Gaussian networks - directed acyclic graphs that encode
probabilistic relationships between variables - to its vector form. Vector
Gaussian continuous networks consist of composite nodes representing
multivariates, that take continuous values. These vector or composite nodes can
represent correlations between parents, as opposed to conventional univariate
nodes. We derive rules for inference in these networks based on two methods:
message propagation and topology transformation. These two approaches lead to
the development of algorithms, that can be implemented in either a centralized
or a decentralized manner. The domain of application of these networks are
monitoring and estimation problems. This new representation along with the
rules for inference developed here can be used to derive current Bayesian
algorithms such as the Kalman filter, and provide a rich foundation to develop
new algorithms. We illustrate this process by deriving the decentralized form
of the Kalman filter. This work unifies concepts from artificial intelligence
and modern control theory.
| [
{
"version": "v1",
"created": "Wed, 13 Feb 2013 14:11:31 GMT"
}
] | 1,361,145,600,000 | [
[
"Alag",
"Satnam",
""
],
[
"Agogino",
"Alice M.",
""
]
] |
1302.3552 | Constantin F. Aliferis | Constantin F. Aliferis, Gregory F. Cooper | A Structurally and Temporally Extended Bayesian Belief Network Model:
Definitions, Properties, and Modeling Techniques | Appears in Proceedings of the Twelfth Conference on Uncertainty in
Artificial Intelligence (UAI1996) | null | null | UAI-P-1996-PG-28-39 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We developed the language of Modifiable Temporal Belief Networks (MTBNs) as a
structural and temporal extension of Bayesian Belief Networks (BNs) to
facilitate normative temporal and causal modeling under uncertainty. In this
paper we present definitions of the model, its components, and its fundamental
properties. We also discuss how to represent various types of temporal
knowledge, with an emphasis on hybrid temporal-explicit time modeling, dynamic
structures, avoiding causal temporal inconsistencies, and dealing with models
that involve simultaneously actions (decisions) and causal and non-causal
associations. We examine the relationships among BNs, Modifiable Belief
Networks, and MTBNs with a single temporal granularity, and suggest areas of
application suitable to each one of them.
| [
{
"version": "v1",
"created": "Wed, 13 Feb 2013 14:11:37 GMT"
}
] | 1,361,145,600,000 | [
[
"Aliferis",
"Constantin F.",
""
],
[
"Cooper",
"Gregory F.",
""
]
] |
1302.3553 | Steen A. Andersson | Steen A. Andersson, David Madigan, Michael D. Perlman | An Alternative Markov Property for Chain Graphs | Appears in Proceedings of the Twelfth Conference on Uncertainty in
Artificial Intelligence (UAI1996) | null | null | UAI-P-1996-PG-40-48 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Graphical Markov models use graphs, either undirected, directed, or mixed, to
represent possible dependences among statistical variables. Applications of
undirected graphs (UDGs) include models for spatial dependence and image
analysis, while acyclic directed graphs (ADGs), which are especially convenient
for statistical analysis, arise in such fields as genetics and psychometrics
and as models for expert systems and Bayesian belief networks. Lauritzen,
Wermuth and Frydenberg (LWF) introduced a Markov property for chain graphs,
which are mixed graphs that can be used to represent simultaneously both causal
and associative dependencies and which include both UDGs and ADGs as special
cases. In this paper an alternative Markov property (AMP) for chain graphs is
introduced, which in some ways is a more direct extension of the ADG Markov
property than is the LWF property for chain graph.
| [
{
"version": "v1",
"created": "Wed, 13 Feb 2013 14:11:42 GMT"
}
] | 1,361,145,600,000 | [
[
"Andersson",
"Steen A.",
""
],
[
"Madigan",
"David",
""
],
[
"Perlman",
"Michael D.",
""
]
] |
1302.3554 | Ella M. Atkins | Ella M. Atkins, Edmund H. Durfee, Kang G. Shin | Plan Development using Local Probabilistic Models | Appears in Proceedings of the Twelfth Conference on Uncertainty in
Artificial Intelligence (UAI1996) | null | null | UAI-P-1996-PG-49-56 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Approximate models of world state transitions are necessary when building
plans for complex systems operating in dynamic environments. External event
probabilities can depend on state feature values as well as time spent in that
particular state. We assign temporally -dependent probability functions to
state transitions. These functions are used to locally compute state
probabilities, which are then used to select highly probable goal paths and
eliminate improbable states. This probabilistic model has been implemented in
the Cooperative Intelligent Real-time Control Architecture (CIRCA), which
combines an AI planner with a separate real-time system such that plans are
developed, scheduled, and executed with real-time guarantees. We present flight
simulation tests that demonstrate how our probabilistic model may improve CIRCA
performance.
| [
{
"version": "v1",
"created": "Wed, 13 Feb 2013 14:11:48 GMT"
}
] | 1,361,145,600,000 | [
[
"Atkins",
"Ella M.",
""
],
[
"Durfee",
"Edmund H.",
""
],
[
"Shin",
"Kang G.",
""
]
] |
1302.3555 | Donald Bamber | Donald Bamber | Entailment in Probability of Thresholded Generalizations | Appears in Proceedings of the Twelfth Conference on Uncertainty in
Artificial Intelligence (UAI1996) | null | null | UAI-P-1996-PG-57-64 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | A nonmonotonic logic of thresholded generalizations is presented. Given
propositions A and B from a language L and a positive integer k, the
thresholded generalization A=>B{k} means that the conditional probability
P(B|A) falls short of one by no more than c*d^k. A two-level probability
structure is defined. At the lower level, a model is defined to be a
probability function on L. At the upper level, there is a probability
distribution over models. A definition is given of what it means for a
collection of thresholded generalizations to entail another thresholded
generalization. This nonmonotonic entailment relation, called "entailment in
probability", has the feature that its conclusions are "probabilistically
trustworthy" meaning that, given true premises, it is improbable that an
entailed conclusion would be false. A procedure is presented for ascertaining
whether any given collection of premises entails any given conclusion. It is
shown that entailment in probability is closely related to Goldszmidt and
Pearl's System-Z^+, thereby demonstrating that the conclusions of System-Z^+
are probabilistically trustworthy.
| [
{
"version": "v1",
"created": "Wed, 13 Feb 2013 14:11:54 GMT"
}
] | 1,361,145,600,000 | [
[
"Bamber",
"Donald",
""
]
] |
1302.3557 | Mathias Bauer | Mathias Bauer | Approximations for Decision Making in the Dempster-Shafer Theory of
Evidence | Appears in Proceedings of the Twelfth Conference on Uncertainty in
Artificial Intelligence (UAI1996) | null | null | UAI-P-1996-PG-73-80 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The computational complexity of reasoning within the Dempster-Shafer theory
of evidence is one of the main points of criticism this formalism has to face.
To overcome this difficulty various approximation algorithms have been
suggested that aim at reducing the number of focal elements in the belief
functions involved. Besides introducing a new algorithm using this method, this
paper describes an empirical study that examines the appropriateness of these
approximation procedures in decision making situations. It presents the
empirical findings and discusses the various tradeoffs that have to be taken
into account when actually applying one of these methods.
| [
{
"version": "v1",
"created": "Wed, 13 Feb 2013 14:12:06 GMT"
}
] | 1,361,145,600,000 | [
[
"Bauer",
"Mathias",
""
]
] |
1302.3559 | Salem Benferhat | Salem Benferhat, Didier Dubois, Henri Prade | Coping with the Limitations of Rational Inference in the Framework of
Possibility Theory | Appears in Proceedings of the Twelfth Conference on Uncertainty in
Artificial Intelligence (UAI1996) | null | null | UAI-P-1996-PG-90-97 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Possibility theory offers a framework where both Lehmann's "preferential
inference" and the more productive (but less cautious) "rational closure
inference" can be represented. However, there are situations where the second
inference does not provide expected results either because it cannot produce
them, or even provide counter-intuitive conclusions. This state of facts is not
due to the principle of selecting a unique ordering of interpretations (which
can be encoded by one possibility distribution), but rather to the absence of
constraints expressing pieces of knowledge we have implicitly in mind. It is
advocated in this paper that constraints induced by independence information
can help finding the right ordering of interpretations. In particular,
independence constraints can be systematically assumed with respect to formulas
composed of literals which do not appear in the conditional knowledge base, or
for default rules with respect to situations which are "normal" according to
the other default rules in the base. The notion of independence which is used
can be easily expressed in the qualitative setting of possibility theory.
Moreover, when a counter-intuitive plausible conclusion of a set of defaults,
is in its rational closure, but not in its preferential closure, it is always
possible to repair the set of defaults so as to produce the desired conclusion.
| [
{
"version": "v1",
"created": "Wed, 13 Feb 2013 14:12:18 GMT"
}
] | 1,361,145,600,000 | [
[
"Benferhat",
"Salem",
""
],
[
"Dubois",
"Didier",
""
],
[
"Prade",
"Henri",
""
]
] |
1302.3560 | Blai Bonet | Blai Bonet, Hector Geffner | Arguing for Decisions: A Qualitative Model of Decision Making | Appears in Proceedings of the Twelfth Conference on Uncertainty in
Artificial Intelligence (UAI1996) | null | null | UAI-P-1996-PG-98-105 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We develop a qualitative model of decision making with two aims: to describe
how people make simple decisions and to enable computer programs to do the
same. Current approaches based on Planning or Decisions Theory either ignore
uncertainty and tradeoffs, or provide languages and algorithms that are too
complex for this task. The proposed model provides a language based on rules, a
semantics based on high probabilities and lexicographical preferences, and a
transparent decision procedure where reasons for and against decisions
interact. The model is no substitude for Decision Theory, yet for decisions
that people find easy to explain it may provide an appealing alternative.
| [
{
"version": "v1",
"created": "Wed, 13 Feb 2013 14:12:23 GMT"
}
] | 1,361,145,600,000 | [
[
"Bonet",
"Blai",
""
],
[
"Geffner",
"Hector",
""
]
] |
1302.3562 | Craig Boutilier | Craig Boutilier, Nir Friedman, Moises Goldszmidt, Daphne Koller | Context-Specific Independence in Bayesian Networks | Appears in Proceedings of the Twelfth Conference on Uncertainty in
Artificial Intelligence (UAI1996) | null | null | UAI-P-1996-PG-115-123 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Bayesian networks provide a language for qualitatively representing the
conditional independence properties of a distribution. This allows a natural
and compact representation of the distribution, eases knowledge acquisition,
and supports effective inference algorithms. It is well-known, however, that
there are certain independencies that we cannot capture qualitatively within
the Bayesian network structure: independencies that hold only in certain
contexts, i.e., given a specific assignment of values to certain variables. In
this paper, we propose a formal notion of context-specific independence (CSI),
based on regularities in the conditional probability tables (CPTs) at a node.
We present a technique, analogous to (and based on) d-separation, for
determining when such independence holds in a given network. We then focus on a
particular qualitative representation scheme - tree-structured CPTs - for
capturing CSI. We suggest ways in which this representation can be used to
support effective inference algorithms. In particular, we present a structural
decomposition of the resulting network which can improve the performance of
clustering algorithms, and an alternative algorithm based on cutset
conditioning.
| [
{
"version": "v1",
"created": "Wed, 13 Feb 2013 14:12:34 GMT"
}
] | 1,361,145,600,000 | [
[
"Boutilier",
"Craig",
""
],
[
"Friedman",
"Nir",
""
],
[
"Goldszmidt",
"Moises",
""
],
[
"Koller",
"Daphne",
""
]
] |
1302.3563 | John Breese | John S. Breese, David Heckerman | Decision-Theoretic Troubleshooting: A Framework for Repair and
Experiment | Appears in Proceedings of the Twelfth Conference on Uncertainty in
Artificial Intelligence (UAI1996) | null | null | UAI-P-1996-PG-124-132 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We develop and extend existing decision-theoretic methods for troubleshooting
a nonfunctioning device. Traditionally, diagnosis with Bayesian networks has
focused on belief updating---determining the probabilities of various faults
given current observations. In this paper, we extend this paradigm to include
taking actions. In particular, we consider three classes of actions: (1) we can
make observations regarding the behavior of a device and infer likely faults as
in traditional diagnosis, (2) we can repair a component and then observe the
behavior of the device to infer likely faults, and (3) we can change the
configuration of the device, observe its new behavior, and infer the likelihood
of faults. Analysis of latter two classes of troubleshooting actions requires
incorporating notions of persistence into the belief-network formalism used for
probabilistic inference.
| [
{
"version": "v1",
"created": "Wed, 13 Feb 2013 14:12:40 GMT"
},
{
"version": "v2",
"created": "Sun, 17 May 2015 23:18:21 GMT"
}
] | 1,431,993,600,000 | [
[
"Breese",
"John S.",
""
],
[
"Heckerman",
"David",
""
]
] |
1302.3568 | Lonnie Chrisman | Lonnie Chrisman | Independence with Lower and Upper Probabilities | Appears in Proceedings of the Twelfth Conference on Uncertainty in
Artificial Intelligence (UAI1996) | null | null | UAI-P-1996-PG-169-177 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | It is shown that the ability of the interval probability representation to
capture epistemological independence is severely limited. Two events are
epistemologically independent if knowledge of the first event does not alter
belief (i.e., probability bounds) about the second. However, independence in
this form can only exist in a 2-monotone probability function in degenerate
cases i.e., if the prior bounds are either point probabilities or entirely
vacuous. Additional limitations are characterized for other classes of lower
probabilities as well. It is argued that these phenomena are simply a matter of
interpretation. They appear to be limitations when one interprets probability
bounds as a measure of epistemological indeterminacy (i.e., uncertainty arising
from a lack of knowledge), but are exactly as one would expect when probability
intervals are interpreted as representations of ontological indeterminacy
(indeterminacy introduced by structural approximations). The ontological
interpretation is introduced and discussed.
| [
{
"version": "v1",
"created": "Wed, 13 Feb 2013 14:13:09 GMT"
}
] | 1,361,145,600,000 | [
[
"Chrisman",
"Lonnie",
""
]
] |
1302.3569 | Lonnie Chrisman | Lonnie Chrisman | Propagation of 2-Monotone Lower Probabilities on an Undirected Graph | Appears in Proceedings of the Twelfth Conference on Uncertainty in
Artificial Intelligence (UAI1996) | null | null | UAI-P-1996-PG-178-185 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Lower and upper probabilities, also known as Choquet capacities, are widely
used as a convenient representation for sets of probability distributions. This
paper presents a graphical decomposition and exact propagation algorithm for
computing marginal posteriors of 2-monotone lower probabilities (equivalently,
2-alternating upper probabilities).
| [
{
"version": "v1",
"created": "Wed, 13 Feb 2013 14:13:15 GMT"
}
] | 1,361,145,600,000 | [
[
"Chrisman",
"Lonnie",
""
]
] |
1302.3570 | Fabio Gagliardi Cozman | Fabio Gagliardi Cozman, Eric Krotkov | Quasi-Bayesian Strategies for Efficient Plan Generation: Application to
the Planning to Observe Problem | Appears in Proceedings of the Twelfth Conference on Uncertainty in
Artificial Intelligence (UAI1996) | null | null | UAI-P-1996-PG-186-193 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Quasi-Bayesian theory uses convex sets of probability distributions and
expected loss to represent preferences about plans. The theory focuses on
decision robustness, i.e., the extent to which plans are affected by deviations
in subjective assessments of probability. The present work presents solutions
for plan generation when robustness of probability assessments must be
included: plans contain information about the robustness of certain actions.
The surprising result is that some problems can be solved faster in the
Quasi-Bayesian framework than within usual Bayesian theory. We investigate this
on the planning to observe problem, i.e., an agent must decide whether to take
new observations or not. The fundamental question is: How, and how much, to
search for a "best" plan, based on the robustness of probability assessments?
Plan generation algorithms are derived in the context of material
classification with an acoustic robotic probe. A package that constructs
Quasi-Bayesian plans is available through anonymous ftp.
| [
{
"version": "v1",
"created": "Wed, 13 Feb 2013 14:13:21 GMT"
}
] | 1,478,217,600,000 | [
[
"Cozman",
"Fabio Gagliardi",
""
],
[
"Krotkov",
"Eric",
""
]
] |
1302.3571 | Bruce D'Ambrosio | Bruce D'Ambrosio, Scott Burgess | Some Experiments with Real-Time Decision Algorithms | Appears in Proceedings of the Twelfth Conference on Uncertainty in
Artificial Intelligence (UAI1996) | null | null | UAI-P-1996-PG-194-202 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Real-time Decision algorithms are a class of incremental resource-bounded
[Horvitz, 89] or anytime [Dean, 93] algorithms for evaluating influence
diagrams. We present a test domain for real-time decision algorithms, and the
results of experiments with several Real-time Decision Algorithms in this
domain. The results demonstrate high performance for two algorithms, a
decision-evaluation variant of Incremental Probabilisitic Inference [D'Ambrosio
93] and a variant of an algorithm suggested by Goldszmidt, [Goldszmidt, 95],
PK-reduced. We discuss the implications of these experimental results and
explore the broader applicability of these algorithms.
| [
{
"version": "v1",
"created": "Wed, 13 Feb 2013 14:13:27 GMT"
}
] | 1,361,145,600,000 | [
[
"D'Ambrosio",
"Bruce",
""
],
[
"Burgess",
"Scott",
""
]
] |
1302.3572 | Rina Dechter | Rina Dechter | Bucket Elimination: A Unifying Framework for Several Probabilistic
Inference | Appears in Proceedings of the Twelfth Conference on Uncertainty in
Artificial Intelligence (UAI1996) | null | null | UAI-P-1996-PG-211-219 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Probabilistic inference algorithms for finding the most probable explanation,
the maximum aposteriori hypothesis, and the maximum expected utility and for
updating belief are reformulated as an elimination--type algorithm called
bucket elimination. This emphasizes the principle common to many of the
algorithms appearing in that literature and clarifies their relationship to
nonserial dynamic programming algorithms. We also present a general way of
combining conditioning and elimination within this framework. Bounds on
complexity are given for all the algorithms as a function of the problem's
structure.
| [
{
"version": "v1",
"created": "Wed, 13 Feb 2013 14:13:33 GMT"
}
] | 1,361,145,600,000 | [
[
"Dechter",
"Rina",
""
]
] |
1302.3573 | Rina Dechter | Rina Dechter | Topological Parameters for Time-Space Tradeoff | Appears in Proceedings of the Twelfth Conference on Uncertainty in
Artificial Intelligence (UAI1996) | null | null | UAI-P-1996-PG-220-227 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this paper we propose a family of algorithms combining tree-clustering
with conditioning that trade space for time. Such algorithms are useful for
reasoning in probabilistic and deterministic networks as well as for
accomplishing optimization tasks. By analyzing the problem structure it will be
possible to select from a spectrum the algorithm that best meets a given
time-space specification.
| [
{
"version": "v1",
"created": "Wed, 13 Feb 2013 14:13:38 GMT"
}
] | 1,361,145,600,000 | [
[
"Dechter",
"Rina",
""
]
] |
1302.3574 | AnHai Doan | AnHai Doan, Peter Haddawy | Sound Abstraction of Probabilistic Actions in The Constraint Mass
Assignment Framework | Appears in Proceedings of the Twelfth Conference on Uncertainty in
Artificial Intelligence (UAI1996) | null | null | UAI-P-1996-PG-228-235 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper provides a formal and practical framework for sound abstraction of
probabilistic actions. We start by precisely defining the concept of sound
abstraction within the context of finite-horizon planning (where each plan is a
finite sequence of actions). Next we show that such abstraction cannot be
performed within the traditional probabilistic action representation, which
models a world with a single probability distribution over the state space. We
then present the constraint mass assignment representation, which models the
world with a set of probability distributions and is a generalization of mass
assignment representations. Within this framework, we present sound abstraction
procedures for three types of action abstraction. We end the paper with
discussions and related work on sound and approximate abstraction. We give
pointers to papers in which we discuss other sound abstraction-related issues,
including applications, estimating loss due to abstraction, and automatically
generating abstraction hierarchies.
| [
{
"version": "v1",
"created": "Wed, 13 Feb 2013 14:13:44 GMT"
}
] | 1,361,145,600,000 | [
[
"Doan",
"AnHai",
""
],
[
"Haddawy",
"Peter",
""
]
] |
1302.3575 | Didier Dubois | Didier Dubois, Henri Prade | Belief Revision with Uncertain Inputs in the Possibilistic Setting | Appears in Proceedings of the Twelfth Conference on Uncertainty in
Artificial Intelligence (UAI1996) | null | null | UAI-P-1996-PG-236-243 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper discusses belief revision under uncertain inputs in the framework
of possibility theory. Revision can be based on two possible definitions of the
conditioning operation, one based on min operator which requires a purely
ordinal scale only, and another based on product, for which a richer structure
is needed, and which is a particular case of Dempster's rule of conditioning.
Besides, revision under uncertain inputs can be understood in two different
ways depending on whether the input is viewed, or not, as a constraint to
enforce. Moreover, it is shown that M.A. Williams' transmutations, originally
defined in the setting of Spohn's functions, can be captured in this framework,
as well as Boutilier's natural revision.
| [
{
"version": "v1",
"created": "Wed, 13 Feb 2013 14:13:49 GMT"
}
] | 1,361,145,600,000 | [
[
"Dubois",
"Didier",
""
],
[
"Prade",
"Henri",
""
]
] |
1302.3576 | Yousri El Fattah | Yousri El Fattah, Rina Dechter | An Evaluation of Structural Parameters for Probabilistic Reasoning:
Results on Benchmark Circuits | Appears in Proceedings of the Twelfth Conference on Uncertainty in
Artificial Intelligence (UAI1996) | null | null | UAI-P-1996-PG-244-251 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Many algorithms for processing probabilistic networks are dependent on the
topological properties of the problem's structure. Such algorithms (e.g.,
clustering, conditioning) are effective only if the problem has a sparse graph
captured by parameters such as tree width and cycle-cut set size. In this paper
we initiate a study to determine the potential of structure-based algorithms in
real-life applications. We analyze empirically the structural properties of
problems coming from the circuit diagnosis domain. Specifically, we locate
those properties that capture the effectiveness of clustering and conditioning
as well as of a family of conditioning+clustering algorithms designed to
gradually trade space for time. We perform our analysis on 11 benchmark
circuits widely used in the testing community. We also report on the effect of
ordering heuristics on tree-clustering and show that, on our benchmarks, the
well-known max-cardinality ordering is substantially inferior to an ordering
called min-degree.
| [
{
"version": "v1",
"created": "Wed, 13 Feb 2013 14:13:55 GMT"
}
] | 1,361,145,600,000 | [
[
"Fattah",
"Yousri El",
""
],
[
"Dechter",
"Rina",
""
]
] |
1302.3578 | Nir Friedman | Nir Friedman, Joseph Y. Halpern | A Qualitative Markov Assumption and its Implications for Belief Change | Appears in Proceedings of the Twelfth Conference on Uncertainty in
Artificial Intelligence (UAI1996) | null | null | UAI-P-1996-PG-263-273 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The study of belief change has been an active area in philosophy and AI. In
recent years two special cases of belief change, belief revision and belief
update, have been studied in detail. Roughly, revision treats a surprising
observation as a sign that previous beliefs were wrong, while update treats a
surprising observation as an indication that the world has changed. In general,
we would expect that an agent making an observation may both want to revise
some earlier beliefs and assume that some change has occurred in the world. We
define a novel approach to belief change that allows us to do this, by applying
ideas from probability theory in a qualitative setting. The key idea is to use
a qualitative Markov assumption, which says that state transitions are
independent. We show that a recent approach to modeling qualitative uncertainty
using plausibility measures allows us to make such a qualitative Markov
assumption in a relatively straightforward way, and show how the Markov
assumption can be used to provide an attractive belief-change model.
| [
{
"version": "v1",
"created": "Wed, 13 Feb 2013 14:14:08 GMT"
}
] | 1,361,145,600,000 | [
[
"Friedman",
"Nir",
""
],
[
"Halpern",
"Joseph Y.",
""
]
] |
1302.3581 | Vu A. Ha | Vu A. Ha, Peter Haddawy | Theoretical Foundations for Abstraction-Based Probabilistic Planning | Appears in Proceedings of the Twelfth Conference on Uncertainty in
Artificial Intelligence (UAI1996) | null | null | UAI-P-1996-PG-291-298 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Modeling worlds and actions under uncertainty is one of the central problems
in the framework of decision-theoretic planning. The representation must be
general enough to capture real-world problems but at the same time it must
provide a basis upon which theoretical results can be derived. The central
notion in the framework we propose here is that of the affine-operator, which
serves as a tool for constructing (convex) sets of probability distributions,
and which can be considered as a generalization of belief functions and
interval mass assignments. Uncertainty in the state of the worlds is modeled
with sets of probability distributions, represented by affine-trees while
actions are defined as tree-manipulators. A small set of key properties of the
affine-operator is presented, forming the basis for most existing
operator-based definitions of probabilistic action projection and action
abstraction. We derive and prove correct three projection rules, which vividly
illustrate the precision-complexity tradeoff in plan projection. Finally, we
show how the three types of action abstraction identified by Haddawy and Doan
are manifested in the present framework.
| [
{
"version": "v1",
"created": "Wed, 13 Feb 2013 14:14:25 GMT"
}
] | 1,361,145,600,000 | [
[
"Ha",
"Vu A.",
""
],
[
"Haddawy",
"Peter",
""
]
] |
1302.3582 | Max Henrion | Max Henrion, Malcolm Pradhan, Brendan del Favero, Kurt Huang, Gregory
M. Provan, Paul O'Rorke | Why Is Diagnosis Using Belief Networks Insensitive to Imprecision In
Probabilities? | Appears in Proceedings of the Twelfth Conference on Uncertainty in
Artificial Intelligence (UAI1996) | null | null | UAI-P-1996-PG-307-314 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Recent research has found that diagnostic performance with Bayesian belief
networks is often surprisingly insensitive to imprecision in the numerical
probabilities. For example, the authors have recently completed an extensive
study in which they applied random noise to the numerical probabilities in a
set of belief networks for medical diagnosis, subsets of the CPCS network, a
subset of the QMR (Quick Medical Reference) focused on liver and bile diseases.
The diagnostic performance in terms of the average probabilities assigned to
the actual diseases showed small sensitivity even to large amounts of noise. In
this paper, we summarize the findings of this study and discuss possible
explanations of this low sensitivity. One reason is that the criterion for
performance is average probability of the true hypotheses, rather than average
error in probability, which is insensitive to symmetric noise distributions.
But, we show that even asymmetric, logodds-normal noise has modest effects. A
second reason is that the gold-standard posterior probabilities are often near
zero or one, and are little disturbed by noise.
| [
{
"version": "v1",
"created": "Wed, 13 Feb 2013 14:14:34 GMT"
}
] | 1,361,145,600,000 | [
[
"Henrion",
"Max",
""
],
[
"Pradhan",
"Malcolm",
""
],
[
"del Favero",
"Brendan",
""
],
[
"Huang",
"Kurt",
""
],
[
"Provan",
"Gregory M.",
""
],
[
"O'Rorke",
"Paul",
""
]
] |
1302.3583 | Michael C. Horsch | Michael C. Horsch, David L. Poole | Flexible Policy Construction by Information Refinement | Appears in Proceedings of the Twelfth Conference on Uncertainty in
Artificial Intelligence (UAI1996) | null | null | UAI-P-1996-PG-315-324 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We report on work towards flexible algorithms for solving decision problems
represented as influence diagrams. An algorithm is given to construct a tree
structure for each decision node in an influence diagram. Each tree represents
a decision function and is constructed incrementally. The improvements to the
tree converge to the optimal decision function (neglecting computational costs)
and the asymptotic behaviour is only a constant factor worse than dynamic
programming techniques, counting the number of Bayesian network queries.
Empirical results show how expected utility increases with the size of the tree
and the number of Bayesian net calculations.
| [
{
"version": "v1",
"created": "Wed, 13 Feb 2013 14:14:40 GMT"
}
] | 1,361,145,600,000 | [
[
"Horsch",
"Michael C.",
""
],
[
"Poole",
"David L.",
""
]
] |
1302.3584 | Kurt Huang | Kurt Huang, Max Henrion | Efficient Search-Based Inference for Noisy-OR Belief Networks:
TopEpsilon | Appears in Proceedings of the Twelfth Conference on Uncertainty in
Artificial Intelligence (UAI1996) | null | null | UAI-P-1996-PG-325-331 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Inference algorithms for arbitrary belief networks are impractical for large,
complex belief networks. Inference algorithms for specialized classes of belief
networks have been shown to be more efficient. In this paper, we present a
search-based algorithm for approximate inference on arbitrary, noisy-OR belief
networks, generalizing earlier work on search-based inference for two-level,
noisy-OR belief networks. Initial experimental results appear promising.
| [
{
"version": "v1",
"created": "Wed, 13 Feb 2013 14:14:45 GMT"
}
] | 1,361,145,600,000 | [
[
"Huang",
"Kurt",
""
],
[
"Henrion",
"Max",
""
]
] |
1302.3585 | Pablo H. Ibarguengoytia | Pablo H. Ibarguengoytia, Luis Enrique Sucar, Sunil Vadera | A Probabilistic Model For Sensor Validation | Appears in Proceedings of the Twelfth Conference on Uncertainty in
Artificial Intelligence (UAI1996) | null | null | UAI-P-1996-PG-332-339 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The validation of data from sensors has become an important issue in the
operation and control of modern industrial plants. One approach is to use
knowledge based techniques to detect inconsistencies in measured data. This
article presents a probabilistic model for the detection of such
inconsistencies. Based on probability propagation, this method is able to find
the existence of a possible fault among the set of sensors. That is, if an
error exists, many sensors present an apparent fault due to the propagation
from the sensor(s) with a real fault. So the fault detection mechanism can only
tell if a sensor has a potential fault, but it can not tell if the fault is
real or apparent. So the central problem is to develop a theory, and then an
algorithm, for distinguishing real and apparent faults, given that one or more
sensors can fail at the same time. This article then, presents an approach
based on two levels: (i) probabilistic reasoning, to detect a potential fault,
and (ii) constraint management, to distinguish the real fault from the apparent
ones. The proposed approach is exemplified by applying it to a power plant
model.
| [
{
"version": "v1",
"created": "Wed, 13 Feb 2013 14:14:51 GMT"
}
] | 1,361,145,600,000 | [
[
"Ibarguengoytia",
"Pablo H.",
""
],
[
"Sucar",
"Luis Enrique",
""
],
[
"Vadera",
"Sunil",
""
]
] |
1302.3586 | Tommi S. Jaakkola | Tommi S. Jaakkola, Michael I. Jordan | Computing Upper and Lower Bounds on Likelihoods in Intractable Networks | Appears in Proceedings of the Twelfth Conference on Uncertainty in
Artificial Intelligence (UAI1996) | null | null | UAI-P-1996-PG-340-348 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We present deterministic techniques for computing upper and lower bounds on
marginal probabilities in sigmoid and noisy-OR networks. These techniques
become useful when the size of the network (or clique size) precludes exact
computations. We illustrate the tightness of the bounds by numerical
experiments.
| [
{
"version": "v1",
"created": "Wed, 13 Feb 2013 14:14:57 GMT"
}
] | 1,361,145,600,000 | [
[
"Jaakkola",
"Tommi S.",
""
],
[
"Jordan",
"Michael I.",
""
]
] |
1302.3587 | Allan Leck Jensen | Allan Leck Jensen, Finn Verner Jensen | MIDAS - An Influence Diagram for Management of Mildew in Winter Wheat | Appears in Proceedings of the Twelfth Conference on Uncertainty in
Artificial Intelligence (UAI1996) | null | null | UAI-P-1996-PG-349-356 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We present a prototype of a decision support system for management of the
fungal disease mildew in winter wheat. The prototype is based on an influence
diagram which is used to determine the optimal time and dose of mildew
treatments. This involves multiple decision opportunities over time,
stochasticity, inaccurate information and incomplete knowledge. The paper
describes the practical and theoretical problems encountered during the
construction of the influence diagram, and also the experience with the
prototype.
| [
{
"version": "v1",
"created": "Wed, 13 Feb 2013 14:15:02 GMT"
}
] | 1,361,145,600,000 | [
[
"Jensen",
"Allan Leck",
""
],
[
"Jensen",
"Finn Verner",
""
]
] |
1302.3588 | Alexander V. Kozlov | Alexander V. Kozlov, Jaswinder Pal Singh | Computational Complexity Reduction for BN2O Networks Using Similarity of
States | Appears in Proceedings of the Twelfth Conference on Uncertainty in
Artificial Intelligence (UAI1996) | null | null | UAI-P-1996-PG-357-364 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Although probabilistic inference in a general Bayesian belief network is an
NP-hard problem, computation time for inference can be reduced in most
practical cases by exploiting domain knowledge and by making approximations in
the knowledge representation. In this paper we introduce the property of
similarity of states and a new method for approximate knowledge representation
and inference which is based on this property. We define two or more states of
a node to be similar when the ratio of their probabilities, the likelihood
ratio, does not depend on the instantiations of the other nodes in the network.
We show that the similarity of states exposes redundancies in the joint
probability distribution which can be exploited to reduce the computation time
of probabilistic inference in networks with multiple similar states, and that
the computational complexity in the networks with exponentially many similar
states might be polynomial. We demonstrate our ideas on the example of a BN2O
network -- a two layer network often used in diagnostic problems -- by reducing
it to a very close network with multiple similar states. We show that the
answers to practical queries converge very fast to the answers obtained with
the original network. The maximum error is as low as 5% for models that require
only 10% of the computation time needed by the original BN2O model.
| [
{
"version": "v1",
"created": "Wed, 13 Feb 2013 14:15:08 GMT"
}
] | 1,478,217,600,000 | [
[
"Kozlov",
"Alexander V.",
""
],
[
"Singh",
"Jaswinder Pal",
""
]
] |
1302.3589 | Henry E. Kyburg Jr. | Henry E. Kyburg Jr | Uncertain Inferences and Uncertain Conclusions | Appears in Proceedings of the Twelfth Conference on Uncertainty in
Artificial Intelligence (UAI1996) | null | null | UAI-P-1996-PG-365-372 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Uncertainty may be taken to characterize inferences, their conclusions, their
premises or all three. Under some treatments of uncertainty, the inferences
itself is never characterized by uncertainty. We explore both the significance
of uncertainty in the premises and in the conclusion of an argument that
involves uncertainty. We argue that for uncertainty to characterize the
conclusion of an inference is natural, but that there is an interplay between
uncertainty in the premises and uncertainty in the procedure of argument
itself. We show that it is possible in principle to incorporate all uncertainty
in the premises, rendering uncertainty arguments deductively valid. But we then
argue (1) that this does not reflect human argument, (2) that it is
computationally costly, and (3) that the gain in simplicity obtained by
allowing uncertainty inference can sometimes outweigh the loss of flexibility
it entails.
| [
{
"version": "v1",
"created": "Wed, 13 Feb 2013 14:15:14 GMT"
}
] | 1,361,145,600,000 | [
[
"Kyburg",
"Henry E.",
"Jr"
]
] |
1302.3591 | Suzanne M. Mahoney | Suzanne M. Mahoney, Kathryn Blackmond Laskey | Network Engineering for Complex Belief Networks | Appears in Proceedings of the Twelfth Conference on Uncertainty in
Artificial Intelligence (UAI1996) | null | null | UAI-P-1996-PG-389-396 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Like any large system development effort, the construction of a complex
belief network model requires systems engineering to manage the design and
construction process. We propose a rapid prototyping approach to network
engineering. We describe criteria for identifying network modules and the use
of "stubs" to represent not-yet-constructed modules. We propose an object
oriented representation for belief networks which captures the semantics of the
problem in addition to conditional independencies and probabilities. Methods
for evaluating complex belief network models are discussed. The ideas are
illustrated with examples from a large belief network construction problem in
the military intelligence domain.
| [
{
"version": "v1",
"created": "Wed, 13 Feb 2013 14:15:26 GMT"
}
] | 1,361,145,600,000 | [
[
"Mahoney",
"Suzanne M.",
""
],
[
"Laskey",
"Kathryn Blackmond",
""
]
] |
1302.3592 | Liem Ngo | Liem Ngo | Probabilistic Disjunctive Logic Programming | Appears in Proceedings of the Twelfth Conference on Uncertainty in
Artificial Intelligence (UAI1996) | null | null | UAI-P-1996-PG-397-404 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this paper we propose a framework for combining Disjunctive Logic
Programming and Poole's Probabilistic Horn Abduction. We use the concept of
hypothesis to specify the probability structure. We consider the case in which
probabilistic information is not available. Instead of using probability
intervals, we allow for the specification of the probabilities of disjunctions.
Because minimal models are used as characteristic models in disjunctive logic
programming, we apply the principle of indifference on the set of minimal
models to derive default probability values. We define the concepts of
explanation and partial explanation of a formula, and use them to determine the
default probability distribution(s) induced by a program. An algorithm for
calculating the default probability of a goal is presented.
| [
{
"version": "v1",
"created": "Wed, 13 Feb 2013 14:15:32 GMT"
}
] | 1,361,145,600,000 | [
[
"Ngo",
"Liem",
""
]
] |
1302.3594 | Mark Alan Peot | Mark Alan Peot | Geometric Implications of the Naive Bayes Assumption | Appears in Proceedings of the Twelfth Conference on Uncertainty in
Artificial Intelligence (UAI1996) | null | null | UAI-P-1996-PG-414-419 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | A naive (or Idiot) Bayes network is a network with a single hypothesis node
and several observations that are conditionally independent given the
hypothesis. We recently surveyed a number of members of the UAI community and
discovered a general lack of understanding of the implications of the Naive
Bayes assumption on the kinds of problems that can be solved by these networks.
It has long been recognized [Minsky 61] that if observations are binary, the
decision surfaces in these networks are hyperplanes. We extend this result
(hyperplane separability) to Naive Bayes networks with m-ary observations. In
addition, we illustrate the effect of observation-observation dependencies on
decision surfaces. Finally, we discuss the implications of these results on
knowledge acquisition and research in learning.
| [
{
"version": "v1",
"created": "Wed, 13 Feb 2013 14:15:44 GMT"
}
] | 1,361,145,600,000 | [
[
"Peot",
"Mark Alan",
""
]
] |
1302.3595 | Judea Pearl | Judea Pearl, Rina Dechter | Identifying Independencies in Causal Graphs with Feedback | Appears in Proceedings of the Twelfth Conference on Uncertainty in
Artificial Intelligence (UAI1996) | null | null | UAI-P-1996-PG-420-426 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We show that the d -separation criterion constitutes a valid test for
conditional independence relationships that are induced by feedback systems
involving discrete variables.
| [
{
"version": "v1",
"created": "Wed, 13 Feb 2013 14:15:49 GMT"
}
] | 1,361,145,600,000 | [
[
"Pearl",
"Judea",
""
],
[
"Dechter",
"Rina",
""
]
] |
1302.3596 | Kim-Leng Poh | Kim-Leng Poh, Eric J. Horvitz | A Graph-Theoretic Analysis of Information Value | Appears in Proceedings of the Twelfth Conference on Uncertainty in
Artificial Intelligence (UAI1996) | null | null | UAI-P-1996-PG-427-435 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We derive qualitative relationships about the informational relevance of
variables in graphical decision models based on a consideration of the topology
of the models. Specifically, we identify dominance relations for the expected
value of information on chance variables in terms of their position and
relationships in influence diagrams. The qualitative relationships can be
harnessed to generate nonnumerical procedures for ordering uncertain variables
in a decision model by their informational relevance.
| [
{
"version": "v1",
"created": "Wed, 13 Feb 2013 14:15:55 GMT"
}
] | 1,361,145,600,000 | [
[
"Poh",
"Kim-Leng",
""
],
[
"Horvitz",
"Eric J.",
""
]
] |
1302.3597 | David L Poole | David L. Poole | A Framework for Decision-Theoretic Planning I: Combining the Situation
Calculus, Conditional Plans, Probability and Utility | Appears in Proceedings of the Twelfth Conference on Uncertainty in
Artificial Intelligence (UAI1996) | null | null | UAI-P-1996-PG-436-445 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper shows how we can combine logical representations of actions and
decision theory in such a manner that seems natural for both. In particular we
assume an axiomatization of the domain in terms of situation calculus, using
what is essentially Reiter's solution to the frame problem, in terms of the
completion of the axioms defining the state change. Uncertainty is handled in
terms of the independent choice logic, which allows for independent choices and
a logic program that gives the consequences of the choices. As part of the
consequences are a specification of the utility of (final) states. The robot
adopts robot plans, similar to the GOLOG programming language. Within this
logic, we can define the expected utility of a conditional plan, based on the
axiomatization of the actions, the uncertainty and the utility. The ?planning'
problem is to find the plan with the highest expected utility. This is related
to recent structured representations for POMDPs; here we use stochastic
situation calculus rules to specify the state transition function and the
reward/value function. Finally we show that with stochastic frame axioms,
actions representations in probabilistic STRIPS are exponentially larger than
using the representation proposed here.
| [
{
"version": "v1",
"created": "Wed, 13 Feb 2013 14:16:00 GMT"
}
] | 1,361,145,600,000 | [
[
"Poole",
"David L.",
""
]
] |
1302.3598 | Malcolm Pradhan | Malcolm Pradhan, Paul Dagum | Optimal Monte Carlo Estimation of Belief Network Inference | Appears in Proceedings of the Twelfth Conference on Uncertainty in
Artificial Intelligence (UAI1996) | null | null | UAI-P-1996-PG-446-453 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We present two Monte Carlo sampling algorithms for probabilistic inference
that guarantee polynomial-time convergence for a larger class of network than
current sampling algorithms provide. These new methods are variants of the
known likelihood weighting algorithm. We use of recent advances in the theory
of optimal stopping rules for Monte Carlo simulation to obtain an inference
approximation with relative error epsilon and a small failure probability
delta. We present an empirical evaluation of the algorithms which demonstrates
their improved performance.
| [
{
"version": "v1",
"created": "Wed, 13 Feb 2013 14:16:06 GMT"
}
] | 1,361,145,600,000 | [
[
"Pradhan",
"Malcolm",
""
],
[
"Dagum",
"Paul",
""
]
] |
1302.3599 | Thomas S. Richardson | Thomas S. Richardson | A Discovery Algorithm for Directed Cyclic Graphs | Appears in Proceedings of the Twelfth Conference on Uncertainty in
Artificial Intelligence (UAI1996) | null | null | UAI-P-1996-PG-454-461 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Directed acyclic graphs have been used fruitfully to represent causal
strucures (Pearl 1988). However, in the social sciences and elsewhere models
are often used which correspond both causally and statistically to directed
graphs with directed cycles (Spirtes 1995). Pearl (1993) discussed predicting
the effects of intervention in models of this kind, so-called linear
non-recursive structural equation models. This raises the question of whether
it is possible to make inferences about causal structure with cycles, form
sample data. In particular do there exist general, informative, feasible and
reliable precedures for inferring causal structure from conditional
independence relations among variables in a sample generated by an unknown
causal structure? In this paper I present a discovery algorithm that is correct
in the large sample limit, given commonly (but often implicitly) made plausible
assumptions, and which provides information about the existence or
non-existence of causal pathways from one variable to another. The algorithm is
polynomial on sparse graphs.
| [
{
"version": "v1",
"created": "Wed, 13 Feb 2013 14:16:12 GMT"
}
] | 1,603,497,600,000 | [
[
"Richardson",
"Thomas S.",
""
]
] |
1302.3600 | Thomas S. Richardson | Thomas S. Richardson | A Polynomial-Time Algorithm for Deciding Markov Equivalence of Directed
Cyclic Graphical Models | Appears in Proceedings of the Twelfth Conference on Uncertainty in
Artificial Intelligence (UAI1996) | null | null | UAI-P-1996-PG-462-469 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Although the concept of d-separation was originally defined for directed
acyclic graphs (see Pearl 1988), there is a natural extension of he concept to
directed cyclic graphs. When exactly the same set of d-separation relations
hold in two directed graphs, no matter whether respectively cyclic or acyclic,
we say that they are Markov equivalent. In other words, when two directed
cyclic graphs are Markov equivalent, the set of distributions that satisfy a
natural extension of the Global Directed Markov condition (Lauritzen et al.
1990) is exactly the same for each graph. There is an obvious exponential (in
the number of vertices) time algorithm for deciding Markov equivalence of two
directed cyclic graphs; simply chech all of the d-separation relations in each
graph. In this paper I state a theorem that gives necessary and sufficient
conditions for the Markov equivalence of two directed cyclic graphs, where each
of the conditions can be checked in polynomial time. Hence, the theorem can be
easily adapted into a polynomial time algorithm for deciding the Markov
equivalence of two directed cyclic graphs. Although space prohibits inclusion
of correctness proofs, they are fully described in Richardson (1994b).
| [
{
"version": "v1",
"created": "Wed, 13 Feb 2013 14:16:18 GMT"
}
] | 1,361,145,600,000 | [
[
"Richardson",
"Thomas S.",
""
]
] |
1302.3601 | Wilhelm Roedder | Wilhelm Roedder, Carl-Heinz Meyer | Coherent Knowledge Processing at Maximum Entropy by SPIRIT | Appears in Proceedings of the Twelfth Conference on Uncertainty in
Artificial Intelligence (UAI1996) | null | null | UAI-P-1996-PG-470-476 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | SPIRIT is an expert system shell for probabilistic knowledge bases. Knowledge
acquisition is performed by processing facts and rules on discrete variables in
a rich syntax. The shell generates a probability distribution which respects
all acquired facts and rules and which maximizes entropy. The user-friendly
devices of SPIRIT to define variables, formulate rules and create the knowledge
base are revealed in detail. Inductive learning is possible. Medium sized
applications show the power of the system.
| [
{
"version": "v1",
"created": "Wed, 13 Feb 2013 14:16:23 GMT"
}
] | 1,361,145,600,000 | [
[
"Roedder",
"Wilhelm",
""
],
[
"Meyer",
"Carl-Heinz",
""
]
] |
1302.3602 | Eugene Santos Jr. | Eugene Santos Jr., Solomon Eyal Shimony, Edward Williams | Sample-and-Accumulate Algorithms for Belief Updating in Bayes Networks | Appears in Proceedings of the Twelfth Conference on Uncertainty in
Artificial Intelligence (UAI1996) | null | null | UAI-P-1996-PG-477-484 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Belief updating in Bayes nets, a well known computationally hard problem, has
recently been approximated by several deterministic algorithms, and by various
randomized approximation algorithms. Deterministic algorithms usually provide
probability bounds, but have an exponential runtime. Some randomized schemes
have a polynomial runtime, but provide only probability estimates. We present
randomized algorithms that enumerate high-probability partial instantiations,
resulting in probability bounds. Some of these algorithms are also sampling
algorithms. Specifically, we introduce and evaluate a variant of backward
sampling, both as a sampling algorithm and as a randomized enumeration
algorithm. We also relax the implicit assumption used by both sampling and
accumulation algorithms, that query nodes must be instantiated in all the
samples.
| [
{
"version": "v1",
"created": "Wed, 13 Feb 2013 14:16:30 GMT"
}
] | 1,361,145,600,000 | [
[
"Santos",
"Eugene",
"Jr."
],
[
"Shimony",
"Solomon Eyal",
""
],
[
"Williams",
"Edward",
""
]
] |
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