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1303.1508 | Robert F. Bordley | Robert F. Bordley | A Bayesian Variant of Shafer's Commonalities For Modelling Unforeseen
Events | Appears in Proceedings of the Ninth Conference on Uncertainty in
Artificial Intelligence (UAI1993) | null | null | UAI-P-1993-PG-453-460 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Shafer's theory of belief and the Bayesian theory of probability are two
alternative and mutually inconsistent approaches toward modelling uncertainty
in artificial intelligence. To help reduce the conflict between these two
approaches, this paper reexamines expected utility theory-from which Bayesian
probability theory is derived. Expected utility theory requires the decision
maker to assign a utility to each decision conditioned on every possible event
that might occur. But frequently the decision maker cannot foresee all the
events that might occur, i.e., one of the possible events is the occurrence of
an unforeseen event. So once we acknowledge the existence of unforeseen events,
we need to develop some way of assigning utilities to decisions conditioned on
unforeseen events. The commonsensical solution to this problem is to assign
similar utilities to events which are similar. Implementing this commonsensical
solution is equivalent to replacing Bayesian subjective probabilities over the
space of foreseen and unforeseen events by random set theory probabilities over
the space of foreseen events. This leads to an expected utility principle in
which normalized variants of Shafer's commonalities play the role of subjective
probabilities. Hence allowing for unforeseen events in decision analysis causes
Bayesian probability theory to become much more similar to Shaferian theory.
| [
{
"version": "v1",
"created": "Wed, 6 Mar 2013 14:23:32 GMT"
}
] | 1,362,700,800,000 | [
[
"Bordley",
"Robert F.",
""
]
] |
1303.1509 | Craig Boutilier | Craig Boutilier | The Probability of a Possibility: Adding Uncertainty to Default Rules | Appears in Proceedings of the Ninth Conference on Uncertainty in
Artificial Intelligence (UAI1993) | null | null | UAI-P-1993-PG-461-468 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We present a semantics for adding uncertainty to conditional logics for
default reasoning and belief revision. We are able to treat conditional
sentences as statements of conditional probability, and express rules for
revision such as "If A were believed, then B would be believed to degree p."
This method of revision extends conditionalization by allowing meaningful
revision by sentences whose probability is zero. This is achieved through the
use of counterfactual probabilities. Thus, our system accounts for the best
properties of qualitative methods of update (in particular, the AGM theory of
revision) and probabilistic methods. We also show how our system can be viewed
as a unification of probability theory and possibility theory, highlighting
their orthogonality and providing a means for expressing the probability of a
possibility. We also demonstrate the connection to Lewis's method of imaging.
| [
{
"version": "v1",
"created": "Wed, 6 Mar 2013 14:23:38 GMT"
}
] | 1,362,700,800,000 | [
[
"Boutilier",
"Craig",
""
]
] |
1303.1510 | Dimiter Driankov | Dimiter Driankov, Jerome Lang | Possibilistic decreasing persistence | Appears in Proceedings of the Ninth Conference on Uncertainty in
Artificial Intelligence (UAI1993) | null | null | UAI-P-1993-PG-469-476 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | A key issue in the handling of temporal data is the treatment of persistence;
in most approaches it consists in inferring defeasible confusions by
extrapolating from the actual knowledge of the history of the world; we propose
here a gradual modelling of persistence, following the idea that persistence is
decreasing (the further we are from the last time point where a fluent is known
to be true, the less certainly true the fluent is); it is based on possibility
theory, which has strong relations with other well-known ordering-based
approaches to nonmonotonic reasoning. We compare our approach with Dean and
Kanazawa's probabilistic projection. We give a formal modelling of the
decreasing persistence problem. Lastly, we show how to infer nonmonotonic
conclusions using the principle of decreasing persistence.
| [
{
"version": "v1",
"created": "Wed, 6 Mar 2013 14:23:43 GMT"
}
] | 1,362,700,800,000 | [
[
"Driankov",
"Dimiter",
""
],
[
"Lang",
"Jerome",
""
]
] |
1303.1511 | Jiwen W. Guan | Jiwen W. Guan, David A. Bell | Discounting and Combination Operations in Evidential Reasoning | Appears in Proceedings of the Ninth Conference on Uncertainty in
Artificial Intelligence (UAI1993) | null | null | UAI-P-1993-PG-477-484 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Evidential reasoning is now a leading topic in Artificial Intelligence.
Evidence is represented by a variety of evidential functions. Evidential
reasoning is carried out by certain kinds of fundamental operation on these
functions. This paper discusses two of the basic operations on evidential
functions, the discount operation and the well-known orthogonal sum operation.
We show that the discount operation is not commutative with the orthogonal sum
operation, and derive expressions for the two operations applied to the various
evidential function.
| [
{
"version": "v1",
"created": "Wed, 6 Mar 2013 14:23:49 GMT"
}
] | 1,362,700,800,000 | [
[
"Guan",
"Jiwen W.",
""
],
[
"Bell",
"David A.",
""
]
] |
1303.1512 | Jurg Kohlas | Jurg Kohlas, Paul-Andre Monney | Probabilistic Assumption-Based Reasoning | Appears in Proceedings of the Ninth Conference on Uncertainty in
Artificial Intelligence (UAI1993) | null | null | UAI-P-1993-PG-485-491 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The classical propositional assumption-based model is extended to incorporate
probabilities for the assumptions. Then it is placed into the framework of
evidence theory. Several authors like Laskey, Lehner (1989) and Provan (1990)
already proposed a similar point of view, but the first paper is not as much
concerned with mathematical foundations, and Provan's paper develops into a
different direction. Here we thoroughly develop and present the mathematical
foundations of this theory, together with computational methods adapted from
Reiter, De Kleer (1987) and Inoue (1992). Finally, recently proposed techniques
for computing degrees of support are presented.
| [
{
"version": "v1",
"created": "Wed, 6 Mar 2013 14:23:56 GMT"
}
] | 1,362,700,800,000 | [
[
"Kohlas",
"Jurg",
""
],
[
"Monney",
"Paul-Andre",
""
]
] |
1303.1513 | Serafin Moral | Serafin Moral, Luis M. de Campos | Partially Specified Belief Functions | Appears in Proceedings of the Ninth Conference on Uncertainty in
Artificial Intelligence (UAI1993) | null | null | UAI-P-1993-PG-492-499 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper presents a procedure to determine a complete belief function from
the known values of belief for some of the subsets of the frame of discerment.
The method is based on the principle of minimum commitment and a new principle
called the focusing principle. This additional principle is based on the idea
that belief is specified for the most relevant sets: the focal elements. The
resulting procedure is compared with existing methods of building complete
belief functions: the minimum specificity principle and the least commitment
principle.
| [
{
"version": "v1",
"created": "Wed, 6 Mar 2013 14:24:02 GMT"
}
] | 1,362,700,800,000 | [
[
"Moral",
"Serafin",
""
],
[
"de Campos",
"Luis M.",
""
]
] |
1303.1514 | Philippe Smets | Philippe Smets | Jeffrey's rule of conditioning generalized to belief functions | Appears in Proceedings of the Ninth Conference on Uncertainty in
Artificial Intelligence (UAI1993) | null | null | UAI-P-1993-PG-500-505 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Jeffrey's rule of conditioning has been proposed in order to revise a
probability measure by another probability function. We generalize it within
the framework of the models based on belief functions. We show that several
forms of Jeffrey's conditionings can be defined that correspond to the
geometrical rule of conditioning and to Dempster's rule of conditioning,
respectively.
| [
{
"version": "v1",
"created": "Wed, 6 Mar 2013 14:24:07 GMT"
}
] | 1,362,700,800,000 | [
[
"Smets",
"Philippe",
""
]
] |
1303.1515 | Fengming Song | Fengming Song, Ping Liang | Inference with Possibilistic Evidence | Appears in Proceedings of the Ninth Conference on Uncertainty in
Artificial Intelligence (UAI1993) | null | null | UAI-P-1993-PG-506-514 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this paper, the concept of possibilistic evidence which is a possibility
distribution as well as a body of evidence is proposed over an infinite
universe of discourse. The inference with possibilistic evidence is
investigated based on a unified inference framework maintaining both the
compatibility of concepts and the consistency of the probability logic.
| [
{
"version": "v1",
"created": "Wed, 6 Mar 2013 14:24:13 GMT"
}
] | 1,362,700,800,000 | [
[
"Song",
"Fengming",
""
],
[
"Liang",
"Ping",
""
]
] |
1303.1516 | Carl G. Wagner | Carl G. Wagner, Bruce Tonn | Constructing Lower Probabilities | Appears in Proceedings of the Ninth Conference on Uncertainty in
Artificial Intelligence (UAI1993) | null | null | UAI-P-1993-PG-515-518 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | An elaboration of Dempster's method of constructing belief functions suggests
a broadly applicable strategy for constructing lower probabilities under a
variety of evidentiary constraints.
| [
{
"version": "v1",
"created": "Wed, 6 Mar 2013 14:24:18 GMT"
}
] | 1,362,700,800,000 | [
[
"Wagner",
"Carl G.",
""
],
[
"Tonn",
"Bruce",
""
]
] |
1303.1517 | Pei Wang | Pei Wang | Belief Revision in Probability Theory | Appears in Proceedings of the Ninth Conference on Uncertainty in
Artificial Intelligence (UAI1993) | null | null | UAI-P-1993-PG-519-526 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In a probability-based reasoning system, Bayes' theorem and its variations
are often used to revise the system's beliefs. However, if the explicit
conditions and the implicit conditions of probability assignments `me properly
distinguished, it follows that Bayes' theorem is not a generally applicable
revision rule. Upon properly distinguishing belief revision from belief
updating, we see that Jeffrey's rule and its variations are not revision rules,
either. Without these distinctions, the limitation of the Bayesian approach is
often ignored or underestimated. Revision, in its general form, cannot be done
in the Bayesian approach, because a probability distribution function alone
does not contain the information needed by the operation.
| [
{
"version": "v1",
"created": "Wed, 6 Mar 2013 14:24:24 GMT"
}
] | 1,362,700,800,000 | [
[
"Wang",
"Pei",
""
]
] |
1303.1518 | Nic Wilson | Nic Wilson | The Assumptions Behind Dempster's Rule | Appears in Proceedings of the Ninth Conference on Uncertainty in
Artificial Intelligence (UAI1993) | null | null | UAI-P-1993-PG-527-534 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper examines the concept of a combination rule for belief functions.
It is shown that two fairly simple and apparently reasonable assumptions
determine Dempster's rule, giving a new justification for it.
| [
{
"version": "v1",
"created": "Wed, 6 Mar 2013 14:24:29 GMT"
}
] | 1,362,700,800,000 | [
[
"Wilson",
"Nic",
""
]
] |
1303.1519 | Hong Xu | Hong Xu, Yen-Teh Hsia, Philippe Smets | A Belief-Function Based Decision Support System | Appears in Proceedings of the Ninth Conference on Uncertainty in
Artificial Intelligence (UAI1993) | null | null | UAI-P-1993-PG-535-542 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this paper, we present a decision support system based on belief functions
and the pignistic transformation. The system is an integration of an evidential
system for belief function propagation and a valuation-based system for
Bayesian decision analysis. The two subsystems are connected through the
pignistic transformation. The system takes as inputs the user's "gut feelings"
about a situation and suggests what, if any, are to be tested and in what
order, and it does so with a user friendly interface.
| [
{
"version": "v1",
"created": "Wed, 6 Mar 2013 14:24:35 GMT"
}
] | 1,362,700,800,000 | [
[
"Xu",
"Hong",
""
],
[
"Hsia",
"Yen-Teh",
""
],
[
"Smets",
"Philippe",
""
]
] |
1303.2013 | J. G. Wolff | J Gerard Wolff | Computing as compression: the SP theory of intelligence | 8 pages, 2 figures. arXiv admin note: text overlap with
arXiv:1212.0229 | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper provides an overview of the SP theory of intelligence and its
central idea that artificial intelligence, mainstream computing, and much of
human perception and cognition, may be understood as information compression.
The background and origins of the SP theory are described, and the main
elements of the theory, including the key concept of multiple alignment,
borrowed from bioinformatics but with important differences. Associated with
the SP theory is the idea that redundancy in information may be understood as
repetition of patterns, that compression of information may be achieved via the
matching and unification (merging) of patterns, and that computing and
information compression are both fundamentally probabilistic. It appears that
the SP system is Turing-equivalent in the sense that anything that may be
computed with a Turing machine may, in principle, also be computed with an SP
machine.
One of the main strengths of the SP theory and the multiple alignment concept
is in modelling concepts and phenomena in artificial intelligence. Within that
area, the SP theory provides a simple but versatile means of representing
different kinds of knowledge, it can model both the parsing and production of
natural language, with potential for the understanding and translation of
natural languages, it has strengths in pattern recognition, with potential in
computer vision, it can model several kinds of reasoning, and it has
capabilities in planning, problem solving, and unsupervised learning.
The paper includes two examples showing how alternative parsings of an
ambiguous sentence may be modelled as multiple alignments, and another example
showing how the concept of multiple alignment may be applied in medical
diagnosis.
| [
{
"version": "v1",
"created": "Fri, 8 Mar 2013 14:52:24 GMT"
}
] | 1,362,960,000,000 | [
[
"Wolff",
"J Gerard",
""
]
] |
1303.4183 | Lukasz Swierczewski | Lukasz Swierczewski | Generating extrema approximation of analytically incomputable functions
through usage of parallel computer aided genetic algorithms | 16 pages, 13 figures | null | null | null | cs.AI | http://creativecommons.org/licenses/publicdomain/ | This paper presents capabilities of using genetic algorithms to find
approximations of function extrema, which cannot be found using analytic ways.
To enhance effectiveness of calculations, algorithm has been parallelized using
OpenMP library. We gained much increase in speed on platforms using
multithreaded processors with shared memory free access. During analysis we
used different modifications of genetic operator, using them we obtained varied
evolution process of potential solutions. Results allow to choose best methods
among many applied in genetic algorithms and observation of acceleration on
Yorkfield, Bloomfield, Westmere-EX and most recent Sandy Bridge cores.
| [
{
"version": "v1",
"created": "Mon, 18 Mar 2013 08:49:48 GMT"
}
] | 1,363,651,200,000 | [
[
"Swierczewski",
"Lukasz",
""
]
] |
1303.5132 | Vania Bogorny | Vitor Cunha Fontes and Vania Bogorny | Discovering Semantic Spatial and Spatio-Temporal Outliers from Moving
Object Trajectories | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Several algorithms have been proposed for discovering patterns from
trajectories of moving objects, but only a few have concentrated on outlier
detection. Existing approaches, in general, discover spatial outliers, and do
not provide any further analysis of the patterns. In this paper we introduce
semantic spatial and spatio-temporal outliers and propose a new algorithm for
trajectory outlier detection. Semantic outliers are computed between regions of
interest, where objects have similar movement intention, and there exist
standard paths which connect the regions. We show with experiments on real data
that the method finds semantic outliers from trajectory data that are not
discovered by similar approaches.
| [
{
"version": "v1",
"created": "Thu, 21 Mar 2013 00:28:41 GMT"
}
] | 1,363,910,400,000 | [
[
"Fontes",
"Vitor Cunha",
""
],
[
"Bogorny",
"Vania",
""
]
] |
1303.5177 | Nabila Shikoun | Nabila Shikoun, Mohamed El Nahas and Samar Kassim | Model Based Framework for Estimating Mutation Rate of Hepatitis C Virus
in Egypt | 6 pages, 5 figures | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Hepatitis C virus (HCV) is a widely spread disease all over the world. HCV
has very high mutation rate that makes it resistant to antibodies. Modeling HCV
to identify the virus mutation process is essential to its detection and
predicting its evolution. This paper presents a model based framework for
estimating mutation rate of HCV in two steps. Firstly profile hidden Markov
model (PHMM) architecture was builder to select the sequences which represents
sequence per year. Secondly mutation rate was calculated by using pair-wise
distance method between sequences. A pilot study is conducted on NS5B zone of
HCV dataset of genotype 4 subtype a (HCV4a) in Egypt.
| [
{
"version": "v1",
"created": "Thu, 21 Mar 2013 06:49:05 GMT"
}
] | 1,363,910,400,000 | [
[
"Shikoun",
"Nabila",
""
],
[
"Nahas",
"Mohamed El",
""
],
[
"Kassim",
"Samar",
""
]
] |
1303.5391 | Zhi An | Zhi An, David A. Bell, John G. Hughes | RES - a Relative Method for Evidential Reasoning | Appears in Proceedings of the Eighth Conference on Uncertainty in
Artificial Intelligence (UAI1992) | null | null | UAI-P-1992-PG-1-8 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this paper we describe a novel method for evidential reasoning [1]. It
involves modelling the process of evidential reasoning in three steps, namely,
evidence structure construction, evidence accumulation, and decision making.
The proposed method, called RES, is novel in that evidence strength is
associated with an evidential support relationship (an argument) between a pair
of statements and such strength is carried by comparison between arguments.
This is in contrast to the onventional approaches, where evidence strength is
represented numerically and is associated with a statement.
| [
{
"version": "v1",
"created": "Wed, 13 Mar 2013 12:51:26 GMT"
}
] | 1,364,169,600,000 | [
[
"An",
"Zhi",
""
],
[
"Bell",
"David A.",
""
],
[
"Hughes",
"John G.",
""
]
] |
1303.5392 | Remco R. Bouckaert | Remco R. Bouckaert | Optimizing Causal Orderings for Generating DAGs from Data | Appears in Proceedings of the Eighth Conference on Uncertainty in
Artificial Intelligence (UAI1992) | null | null | UAI-P-1992-PG-9-16 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | An algorithm for generating the structure of a directed acyclic graph from
data using the notion of causal input lists is presented. The algorithm
manipulates the ordering of the variables with operations which very much
resemble arc reversal. Operations are only applied if the DAG after the
operation represents at least the independencies represented by the DAG before
the operation until no more arcs can be removed from the DAG. The resulting DAG
is a minimal l-map.
| [
{
"version": "v1",
"created": "Wed, 13 Mar 2013 12:51:32 GMT"
}
] | 1,364,169,600,000 | [
[
"Bouckaert",
"Remco R.",
""
]
] |
1303.5393 | Craig Boutilier | Craig Boutilier | Modal Logics for Qualitative Possibility and Beliefs | Appears in Proceedings of the Eighth Conference on Uncertainty in
Artificial Intelligence (UAI1992) | null | null | UAI-P-1992-PG-17-24 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Possibilistic logic has been proposed as a numerical formalism for reasoning
with uncertainty. There has been interest in developing qualitative accounts of
possibility, as well as an explanation of the relationship between possibility
and modal logics. We present two modal logics that can be used to represent and
reason with qualitative statements of possibility and necessity. Within this
modal framework, we are able to identify interesting relationships between
possibilistic logic, beliefs and conditionals. In particular, the most natural
conditional definable via possibilistic means for default reasoning is
identical to Pearl's conditional for e-semantics.
| [
{
"version": "v1",
"created": "Wed, 13 Mar 2013 12:51:38 GMT"
}
] | 1,364,169,600,000 | [
[
"Boutilier",
"Craig",
""
]
] |
1303.5394 | Brian Y. Chan | Brian Y. Chan, Ross D. Shachter | Structural Controllability and Observability in Influence Diagrams | Appears in Proceedings of the Eighth Conference on Uncertainty in
Artificial Intelligence (UAI1992) | null | null | UAI-P-1992-PG-25-32 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Influence diagram is a graphical representation of belief networks with
uncertainty. This article studies the structural properties of a probabilistic
model in an influence diagram. In particular, structural controllability
theorems and structural observability theorems are developed and algorithms are
formulated. Controllability and observability are fundamental concepts in
dynamic systems (Luenberger 1979). Controllability corresponds to the ability
to control a system while observability analyzes the inferability of its
variables. Both properties can be determined by the ranks of the system
matrices. Structural controllability and observability, on the other hand,
analyze the property of a system with its structure only, without the specific
knowledge of the values of its elements (tin 1974, Shields and Pearson 1976).
The structural analysis explores the connection between the structure of a
model and the functional dependence among its elements. It is useful in
comprehending problem and formulating solution by challenging the underlying
intuitions and detecting inconsistency in a model. This type of qualitative
reasoning can sometimes provide insight even when there is insufficient
numerical information in a model.
| [
{
"version": "v1",
"created": "Wed, 13 Mar 2013 12:51:44 GMT"
}
] | 1,364,169,600,000 | [
[
"Chan",
"Brian Y.",
""
],
[
"Shachter",
"Ross D.",
""
]
] |
1303.5395 | Philippe Chatalic | Philippe Chatalic, Christine Froidevaux | Lattice-Based Graded Logic: a Multimodal Approach | Appears in Proceedings of the Eighth Conference on Uncertainty in
Artificial Intelligence (UAI1992) | null | null | UAI-P-1992-PG-33-40 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Experts do not always feel very, comfortable when they have to give precise
numerical estimations of certainty degrees. In this paper we present a
qualitative approach which allows for attaching partially ordered symbolic
grades to logical formulas. Uncertain information is expressed by means of
parameterized modal operators. We propose a semantics for this multimodal logic
and give a sound and complete axiomatization. We study the links with related
approaches and suggest how this framework might be used to manage both
uncertain and incomplere knowledge.
| [
{
"version": "v1",
"created": "Wed, 13 Mar 2013 12:51:51 GMT"
}
] | 1,364,169,600,000 | [
[
"Chatalic",
"Philippe",
""
],
[
"Froidevaux",
"Christine",
""
]
] |
1303.5396 | Paul Dagum | Paul Dagum, Adam Galper, Eric J. Horvitz | Dynamic Network Models for Forecasting | Appears in Proceedings of the Eighth Conference on Uncertainty in
Artificial Intelligence (UAI1992) | null | null | UAI-P-1992-PG-41-48 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We have developed a probabilistic forecasting methodology through a synthesis
of belief network models and classical time-series analysis. We present the
dynamic network model (DNM) and describe methods for constructing, refining,
and performing inference with this representation of temporal probabilistic
knowledge. The DNM representation extends static belief-network models to more
general dynamic forecasting models by integrating and iteratively refining
contemporaneous and time-lagged dependencies. We discuss key concepts in terms
of a model for forecasting U.S. car sales in Japan.
| [
{
"version": "v1",
"created": "Wed, 13 Mar 2013 12:51:57 GMT"
}
] | 1,364,169,600,000 | [
[
"Dagum",
"Paul",
""
],
[
"Galper",
"Adam",
""
],
[
"Horvitz",
"Eric J.",
""
]
] |
1303.5397 | Paul Dagum | Paul Dagum, Eric J. Horvitz | Reformulating Inference Problems Through Selective Conditioning | Appears in Proceedings of the Eighth Conference on Uncertainty in
Artificial Intelligence (UAI1992) | null | null | UAI-P-1992-PG-49-54 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We describe how we selectively reformulate portions of a belief network that
pose difficulties for solution with a stochastic-simulation algorithm. With
employ the selective conditioning approach to target specific nodes in a belief
network for decomposition, based on the contribution the nodes make to the
tractability of stochastic simulation. We review previous work on BNRAS
algorithms- randomized approximation algorithms for probabilistic inference. We
show how selective conditioning can be employed to reformulate a single BNRAS
problem into multiple tractable BNRAS simulation problems. We discuss how we
can use another simulation algorithm-logic sampling-to solve a component of the
inference problem that provides a means for knitting the solutions of
individual subproblems into a final result. Finally, we analyze tradeoffs among
the computational subtasks associated with the selective conditioning approach
to reformulation.
| [
{
"version": "v1",
"created": "Wed, 13 Mar 2013 12:52:03 GMT"
}
] | 1,364,169,600,000 | [
[
"Dagum",
"Paul",
""
],
[
"Horvitz",
"Eric J.",
""
]
] |
1303.5398 | Norman C. Dalkey | Norman C. Dalkey | Entropy and Belief Networks | Appears in Proceedings of the Eighth Conference on Uncertainty in
Artificial Intelligence (UAI1992) | null | null | UAI-P-1992-PG-55-58 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The product expansion of conditional probabilities for belief nets is not
maximum entropy. This appears to deny a desirable kind of assurance for the
model. However, a kind of guarantee that is almost as strong as maximum entropy
can be derived. Surprisingly, a variant model also exhibits the guarantee, and
for many cases obtains a higher performance score than the product expansion.
| [
{
"version": "v1",
"created": "Wed, 13 Mar 2013 12:52:07 GMT"
}
] | 1,364,169,600,000 | [
[
"Dalkey",
"Norman C.",
""
]
] |
1303.5399 | Bruce D'Ambrosio | Bruce D'Ambrosio, Tony Fountain, Zhaoyu Li | Parallelizing Probabilistic Inference: Some Early Explorations | Appears in Proceedings of the Eighth Conference on Uncertainty in
Artificial Intelligence (UAI1992) | null | null | UAI-P-1992-PG-59-66 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We report on an experimental investigation into opportunities for parallelism
in beliefnet inference. Specifically, we report on a study performed of the
available parallelism, on hypercube style machines, of a set of randomly
generated belief nets, using factoring (SPI) style inference algorithms. Our
results indicate that substantial speedup is available, but that it is
available only through parallelization of individual conformal product
operations, and depends critically on finding an appropriate factoring. We find
negligible opportunity for parallelism at the topological, or clustering tree,
level.
| [
{
"version": "v1",
"created": "Wed, 13 Mar 2013 12:52:13 GMT"
}
] | 1,364,169,600,000 | [
[
"D'Ambrosio",
"Bruce",
""
],
[
"Fountain",
"Tony",
""
],
[
"Li",
"Zhaoyu",
""
]
] |
1303.5400 | Adnan Darwiche | Adnan Darwiche | Objection-Based Causal Networks | Appears in Proceedings of the Eighth Conference on Uncertainty in
Artificial Intelligence (UAI1992) | null | null | UAI-P-1992-PG-67-73 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper introduces the notion of objection-based causal networks which
resemble probabilistic causal networks except that they are quantified using
objections. An objection is a logical sentence and denotes a condition under
which a, causal dependency does not exist. Objection-based causal networks
enjoy almost all the properties that make probabilistic causal networks
popular, with the added advantage that objections are, arguably more intuitive
than probabilities.
| [
{
"version": "v1",
"created": "Wed, 13 Mar 2013 12:52:19 GMT"
}
] | 1,364,169,600,000 | [
[
"Darwiche",
"Adnan",
""
]
] |
1303.5401 | Didier Dubois | Didier Dubois, Henri Prade, Lluis Godo, Ramon Lopez de Mantaras | A Symbolic Approach to Reasoning with Linguistic Quantifiers | Appears in Proceedings of the Eighth Conference on Uncertainty in
Artificial Intelligence (UAI1992) | null | null | UAI-P-1992-PG-74-82 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper investigates the possibility of performing automated reasoning in
probabilistic logic when probabilities are expressed by means of linguistic
quantifiers. Each linguistic term is expressed as a prescribed interval of
proportions. Then instead of propagating numbers, qualitative terms are
propagated in accordance with the numerical interpretation of these terms. The
quantified syllogism, modelling the chaining of probabilistic rules, is studied
in this context. It is shown that a qualitative counterpart of this syllogism
makes sense, and is relatively independent of the threshold defining the
linguistically meaningful intervals, provided that these threshold values
remain in accordance with the intuition. The inference power is less than that
of a full-fledged probabilistic con-quaint propagation device but better
corresponds to what could be thought of as commonsense probabilistic reasoning.
| [
{
"version": "v1",
"created": "Wed, 13 Mar 2013 12:52:25 GMT"
}
] | 1,364,169,600,000 | [
[
"Dubois",
"Didier",
""
],
[
"Prade",
"Henri",
""
],
[
"Godo",
"Lluis",
""
],
[
"de Mantaras",
"Ramon Lopez",
""
]
] |
1303.5402 | Francesco Fulvio Monai | Francesco Fulvio Monai, Thomas Chehire | Possibilistic Assumption based Truth Maintenance System, Validation in a
Data Fusion Application | Appears in Proceedings of the Eighth Conference on Uncertainty in
Artificial Intelligence (UAI1992) | null | null | UAI-P-1992-PG-83-91 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Data fusion allows the elaboration and the evaluation of a situation
synthesized from low level informations provided by different kinds of sensors.
The fusion of the collected data will result in fewer and higher level
informations more easily assessed by a human operator and that will assist him
effectively in his decision process. In this paper we present the suitability
and the advantages of using a Possibilistic Assumption based Truth Maintenance
System (n-ATMS) in a data fusion military application. We first describe the
problem, the needed knowledge representation formalisms and problem solving
paradigms. Then we remind the reader of the basic concepts of ATMSs,
Possibilistic Logic and 11-ATMSs. Finally we detail the solution to the given
data fusion problem and conclude with the results and comparison with a
non-possibilistic solution.
| [
{
"version": "v1",
"created": "Wed, 13 Mar 2013 12:52:32 GMT"
}
] | 1,364,169,600,000 | [
[
"Monai",
"Francesco Fulvio",
""
],
[
"Chehire",
"Thomas",
""
]
] |
1303.5404 | Angelo Gilio | Angelo Gilio, Fulvio Spezzaferri | Knowledge Integration for Conditional Probability Assessments | Appears in Proceedings of the Eighth Conference on Uncertainty in
Artificial Intelligence (UAI1992) | null | null | UAI-P-1992-PG-98-103 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In the probabilistic approach to uncertainty management the input knowledge
is usually represented by means of some probability distributions. In this
paper we assume that the input knowledge is given by two discrete conditional
probability distributions, represented by two stochastic matrices P and Q. The
consistency of the knowledge base is analyzed. Coherence conditions and
explicit formulas for the extension to marginal distributions are obtained in
some special cases.
| [
{
"version": "v1",
"created": "Wed, 13 Mar 2013 12:52:43 GMT"
}
] | 1,364,169,600,000 | [
[
"Gilio",
"Angelo",
""
],
[
"Spezzaferri",
"Fulvio",
""
]
] |
1303.5405 | Robert P. Goldman | Robert P. Goldman, John S. Breese | Integrating Model Construction and Evaluation | Appears in Proceedings of the Eighth Conference on Uncertainty in
Artificial Intelligence (UAI1992) | null | null | UAI-P-1992-PG-104-111 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | To date, most probabilistic reasoning systems have relied on a fixed belief
network constructed at design time. The network is used by an application
program as a representation of (in)dependencies in the domain. Probabilistic
inference algorithms operate over the network to answer queries. Recognizing
the inflexibility of fixed models has led researchers to develop automated
network construction procedures that use an expressive knowledge base to
generate a network that can answer a query. Although more flexible than fixed
model approaches, these construction procedures separate construction and
evaluation into distinct phases. In this paper we develop an approach to
combining incremental construction and evaluation of a partial probability
model. The combined method holds promise for improved methods for control of
model construction based on a trade-off between fidelity of results and cost of
construction.
| [
{
"version": "v1",
"created": "Wed, 13 Mar 2013 12:52:48 GMT"
}
] | 1,364,169,600,000 | [
[
"Goldman",
"Robert P.",
""
],
[
"Breese",
"John S.",
""
]
] |
1303.5406 | Moises Goldszmidt | Moises Goldszmidt, Judea Pearl | Reasoning With Qualitative Probabilities Can Be Tractable | Appears in Proceedings of the Eighth Conference on Uncertainty in
Artificial Intelligence (UAI1992) | null | null | UAI-P-1992-PG-112-120 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We recently described a formalism for reasoning with if-then rules that re
expressed with different levels of firmness [18]. The formalism interprets
these rules as extreme conditional probability statements, specifying orders of
magnitude of disbelief, which impose constraints over possible rankings of
worlds. It was shown that, once we compute a priority function Z+ on the rules,
the degree to which a given query is confirmed or denied can be computed in
O(log n`) propositional satisfiability tests, where n is the number of rules in
the knowledge base. In this paper, we show that computing Z+ requires O(n2 X
log n) satisfiability tests, not an exponential number as was conjectured in
[18], which reduces to polynomial complexity in the case of Horn expressions.
We also show how reasoning with imprecise observations can be incorporated in
our formalism and how the popular notions of belief revision and epistemic
entrenchment are embodied naturally and tractably.
| [
{
"version": "v1",
"created": "Wed, 13 Mar 2013 12:52:54 GMT"
}
] | 1,364,169,600,000 | [
[
"Goldszmidt",
"Moises",
""
],
[
"Pearl",
"Judea",
""
]
] |
1303.5407 | Uffe Kj{\ae}rulff | Uffe Kj{\ae}rulff | A computational scheme for Reasoning in Dynamic Probabilistic Networks | Appears in Proceedings of the Eighth Conference on Uncertainty in
Artificial Intelligence (UAI1992) | null | null | UAI-P-1992-PG-121-129 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | A computational scheme for reasoning about dynamic systems using (causal)
probabilistic networks is presented. The scheme is based on the framework of
Lauritzen and Spiegelhalter (1988), and may be viewed as a generalization of
the inference methods of classical time-series analysis in the sense that it
allows description of non-linear, multivariate dynamic systems with complex
conditional independence structures. Further, the scheme provides a method for
efficient backward smoothing and possibilities for efficient, approximate
forecasting methods. The scheme has been implemented on top of the HUGIN shell.
| [
{
"version": "v1",
"created": "Wed, 13 Mar 2013 12:53:00 GMT"
}
] | 1,364,169,600,000 | [
[
"Kjærulff",
"Uffe",
""
]
] |
1303.5408 | Frank Klawonn | Frank Klawonn, Philippe Smets | The Dynamic of Belief in the Transferable Belief Model and
Specialization-Generalization Matrices | Appears in Proceedings of the Eighth Conference on Uncertainty in
Artificial Intelligence (UAI1992) | null | null | UAI-P-1992-PG-130-137 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The fundamental updating process in the transferable belief model is related
to the concept of specialization and can be described by a specialization
matrix. The degree of belief in the truth of a proposition is a degree of
justified support. The Principle of Minimal Commitment implies that one should
never give more support to the truth of a proposition than justified. We show
that Dempster's rule of conditioning corresponds essentially to the least
committed specialization, and that Dempster's rule of combination results
essentially from commutativity requirements. The concept of generalization,
dual to thc concept of specialization, is described.
| [
{
"version": "v1",
"created": "Wed, 13 Mar 2013 12:53:05 GMT"
}
] | 1,364,169,600,000 | [
[
"Klawonn",
"Frank",
""
],
[
"Smets",
"Philippe",
""
]
] |
1303.5409 | George J. Klir | George J. Klir, Behzad Parviz | A Note on the Measure of Discord | Appears in Proceedings of the Eighth Conference on Uncertainty in
Artificial Intelligence (UAI1992) | null | null | UAI-P-1992-PG-138-141 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | A new entropy-like measure as well as a new measure of total uncertainty
pertaining to the Dempster-Shafer theory are introduced. It is argued that
these measures are better justified than any of the previously proposed
candidates.
| [
{
"version": "v1",
"created": "Wed, 13 Mar 2013 12:53:11 GMT"
}
] | 1,364,169,600,000 | [
[
"Klir",
"George J.",
""
],
[
"Parviz",
"Behzad",
""
]
] |
1303.5410 | Henry E. Kyburg Jr. | Henry E. Kyburg Jr | Semantics for Probabilistic Inference | Appears in Proceedings of the Eighth Conference on Uncertainty in
Artificial Intelligence (UAI1992) | null | null | UAI-P-1992-PG-142-148 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | A number of writers(Joseph Halpern and Fahiem Bacchus among them) have
offered semantics for formal languages in which inferences concerning
probabilities can be made. Our concern is different. This paper provides a
formalization of nonmonotonic inferences in which the conclusion is supported
only to a certain degree. Such inferences are clearly 'invalid' since they must
allow the falsity of a conclusion even when the premises are true.
Nevertheless, such inferences can be characterized both syntactically and
semantically. The 'premises' of probabilistic arguments are sets of statements
(as in a database or knowledge base), the conclusions categorical statements in
the language. We provide standards for both this form of inference, for which
high probability is required, and for an inference in which the conclusion is
qualified by an intermediate interval of support.
| [
{
"version": "v1",
"created": "Wed, 13 Mar 2013 12:53:17 GMT"
}
] | 1,364,169,600,000 | [
[
"Kyburg",
"Henry E.",
"Jr"
]
] |
1303.5411 | Henry E. Kyburg Jr. | Henry E. Kyburg Jr., Michael Pittarelli | Some Problems for Convex Bayesians | Appears in Proceedings of the Eighth Conference on Uncertainty in
Artificial Intelligence (UAI1992) | null | null | UAI-P-1992-PG-149-154 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We discuss problems for convex Bayesian decision making and uncertainty
representation. These include the inability to accommodate various natural and
useful constraints and the possibility of an analog of the classical Dutch Book
being made against an agent behaving in accordance with convex Bayesian
prescriptions. A more general set-based Bayesianism may be as tractable and
would avoid the difficulties we raise.
| [
{
"version": "v1",
"created": "Wed, 13 Mar 2013 12:53:22 GMT"
}
] | 1,364,169,600,000 | [
[
"Kyburg",
"Henry E.",
"Jr."
],
[
"Pittarelli",
"Michael",
""
]
] |
1303.5412 | Kathryn Blackmond Laskey | Kathryn Blackmond Laskey | Bayesian Meta-Reasoning: Determining Model Adequacy from Within a Small
World | Appears in Proceedings of the Eighth Conference on Uncertainty in
Artificial Intelligence (UAI1992) | null | null | UAI-P-1992-PG-155-158 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper presents a Bayesian framework for assessing the adequacy of a
model without the necessity of explicitly enumerating a specific alternate
model. A test statistic is developed for tracking the performance of the model
across repeated problem instances. Asymptotic methods are used to derive an
approximate distribution for the test statistic. When the model is rejected,
the individual components of the test statistic can be used to guide search for
an alternate model.
| [
{
"version": "v1",
"created": "Wed, 13 Mar 2013 12:53:27 GMT"
}
] | 1,364,169,600,000 | [
[
"Laskey",
"Kathryn Blackmond",
""
]
] |
1303.5413 | Kathryn Blackmond Laskey | Kathryn Blackmond Laskey | The Bounded Bayesian | Appears in Proceedings of the Eighth Conference on Uncertainty in
Artificial Intelligence (UAI1992) | null | null | UAI-P-1992-PG-159-165 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The ideal Bayesian agent reasons from a global probability model, but real
agents are restricted to simplified models which they know to be adequate only
in restricted circumstances. Very little formal theory has been developed to
help fallibly rational agents manage the process of constructing and revising
small world models. The goal of this paper is to present a theoretical
framework for analyzing model management approaches. For a probability
forecasting problem, a search process over small world models is analyzed as an
approximation to a larger-world model which the agent cannot explicitly
enumerate or compute. Conditions are given under which the sequence of
small-world models converges to the larger-world probabilities.
| [
{
"version": "v1",
"created": "Wed, 13 Mar 2013 12:53:33 GMT"
}
] | 1,364,169,600,000 | [
[
"Laskey",
"Kathryn Blackmond",
""
]
] |
1303.5414 | Tze-Yun Leong | Tze-Yun Leong | Representing Context-Sensitive Knowledge in a Network Formalism: A
Preliminary Report | Appears in Proceedings of the Eighth Conference on Uncertainty in
Artificial Intelligence (UAI1992) | null | null | UAI-P-1992-PG-166-173 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Automated decision making is often complicated by the complexity of the
knowledge involved. Much of this complexity arises from the context sensitive
variations of the underlying phenomena. We propose a framework for representing
descriptive, context-sensitive knowledge. Our approach attempts to integrate
categorical and uncertain knowledge in a network formalism. This paper outlines
the basic representation constructs, examines their expressiveness and
efficiency, and discusses the potential applications of the framework.
| [
{
"version": "v1",
"created": "Wed, 13 Mar 2013 12:53:40 GMT"
}
] | 1,364,169,600,000 | [
[
"Leong",
"Tze-Yun",
""
]
] |
1303.5415 | Dekang Lin | Dekang Lin | A Probabilistic Network of Predicates | Appears in Proceedings of the Eighth Conference on Uncertainty in
Artificial Intelligence (UAI1992) | null | null | UAI-P-1992-PG-174-181 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Bayesian networks are directed acyclic graphs representing independence
relationships among a set of random variables. A random variable can be
regarded as a set of exhaustive and mutually exclusive propositions. We argue
that there are several drawbacks resulting from the propositional nature and
acyclic structure of Bayesian networks. To remedy these shortcomings, we
propose a probabilistic network where nodes represent unary predicates and
which may contain directed cycles. The proposed representation allows us to
represent domain knowledge in a single static network even though we cannot
determine the instantiations of the predicates before hand. The ability to deal
with cycles also enables us to handle cyclic causal tendencies and to recognize
recursive plans.
| [
{
"version": "v1",
"created": "Wed, 13 Mar 2013 12:53:47 GMT"
}
] | 1,364,169,600,000 | [
[
"Lin",
"Dekang",
""
]
] |
1303.5416 | Weiru Liu | Weiru Liu, John G. Hughes, Michael F. McTear | Representing Heuristic Knowledge in D-S Theory | Appears in Proceedings of the Eighth Conference on Uncertainty in
Artificial Intelligence (UAI1992) | null | null | UAI-P-1992-PG-182-190 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The Dempster-Shafer theory of evidence has been used intensively to deal with
uncertainty in knowledge-based systems. However the representation of uncertain
relationships between evidence and hypothesis groups (heuristic knowledge) is
still a major research problem. This paper presents an approach to representing
such heuristic knowledge by evidential mappings which are defined on the basis
of mass functions. The relationships between evidential mappings and multi
valued mappings, as well as between evidential mappings and Bayesian multi-
valued causal link models in Bayesian theory are discussed. Following this the
detailed procedures for constructing evidential mappings for any set of
heuristic rules are introduced. Several situations of belief propagation are
discussed.
| [
{
"version": "v1",
"created": "Wed, 13 Mar 2013 12:53:54 GMT"
}
] | 1,364,169,600,000 | [
[
"Liu",
"Weiru",
""
],
[
"Hughes",
"John G.",
""
],
[
"McTear",
"Michael F.",
""
]
] |
1303.5417 | Izhar Matzkevich | Izhar Matzkevich, Bruce Abramson | The Topological Fusion of Bayes Nets | Appears in Proceedings of the Eighth Conference on Uncertainty in
Artificial Intelligence (UAI1992) | null | null | UAI-P-1992-PG-191-198 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Bayes nets are relatively recent innovations. As a result, most of their
theoretical development has focused on the simplest class of single-author
models. The introduction of more sophisticated multiple-author settings raises
a variety of interesting questions. One such question involves the nature of
compromise and consensus. Posterior compromises let each model process all data
to arrive at an independent response, and then split the difference. Prior
compromises, on the other hand, force compromise to be reached on all points
before data is observed. This paper introduces prior compromises in a Bayes net
setting. It outlines the problem and develops an efficient algorithm for fusing
two directed acyclic graphs into a single, consensus structure, which may then
be used as the basis of a prior compromise.
| [
{
"version": "v1",
"created": "Wed, 13 Mar 2013 12:53:59 GMT"
}
] | 1,364,169,600,000 | [
[
"Matzkevich",
"Izhar",
""
],
[
"Abramson",
"Bruce",
""
]
] |
1303.5418 | Serafin Moral | Serafin Moral | Calculating Uncertainty Intervals From Conditional Convex Sets of
Probabilities | Appears in Proceedings of the Eighth Conference on Uncertainty in
Artificial Intelligence (UAI1992) | null | null | UAI-P-1992-PG-199-206 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In Moral, Campos (1991) and Cano, Moral, Verdegay-Lopez (1991) a new method
of conditioning convex sets of probabilities has been proposed. The result of
it is a convex set of non-necessarily normalized probability distributions. The
normalizing factor of each probability distribution is interpreted as the
possibility assigned to it by the conditioning information. From this, it is
deduced that the natural value for the conditional probability of an event is a
possibility distribution. The aim of this paper is to study methods of
transforming this possibility distribution into a probability (or uncertainty)
interval. These methods will be based on the use of Sugeno and Choquet
integrals. Their behaviour will be compared in basis to some selected examples.
| [
{
"version": "v1",
"created": "Wed, 13 Mar 2013 12:54:05 GMT"
}
] | 1,364,169,600,000 | [
[
"Moral",
"Serafin",
""
]
] |
1303.5419 | Ann Nicholson | Ann Nicholson, J. M. Brady | Sensor Validation Using Dynamic Belief Networks | Appears in Proceedings of the Eighth Conference on Uncertainty in
Artificial Intelligence (UAI1992) | null | null | UAI-P-1992-PG-207-214 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The trajectory of a robot is monitored in a restricted dynamic environment
using light beam sensor data. We have a Dynamic Belief Network (DBN), based on
a discrete model of the domain, which provides discrete monitoring analogous to
conventional quantitative filter techniques. Sensor observations are added to
the basic DBN in the form of specific evidence. However, sensor data is often
partially or totally incorrect. We show how the basic DBN, which infers only an
impossible combination of evidence, may be modified to handle specific types of
incorrect data which may occur in the domain. We then present an extension to
the DBN, the addition of an invalidating node, which models the status of the
sensor as working or defective. This node provides a qualitative explanation of
inconsistent data: it is caused by a defective sensor. The connection of
successive instances of the invalidating node models the status of a sensor
over time, allowing the DBN to handle both persistent and intermittent faults.
| [
{
"version": "v1",
"created": "Wed, 13 Mar 2013 12:54:11 GMT"
}
] | 1,364,169,600,000 | [
[
"Nicholson",
"Ann",
""
],
[
"Brady",
"J. M.",
""
]
] |
1303.5421 | Kristian G. Olesen | Kristian G. Olesen, Steffen L. Lauritzen, Finn Verner Jensen | aHUGIN: A System Creating Adaptive Causal Probabilistic Networks | Appears in Proceedings of the Eighth Conference on Uncertainty in
Artificial Intelligence (UAI1992) | null | null | UAI-P-1992-PG-223-229 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The paper describes aHUGIN, a tool for creating adaptive systems. aHUGIN is
an extension of the HUGIN shell, and is based on the methods reported by
Spiegelhalter and Lauritzen (1990a). The adaptive systems resulting from aHUGIN
are able to adjust the C011ditional probabilities in the model. A short
analysis of the adaptation task is given and the features of aHUGIN are
described. Finally a session with experiments is reported and the results are
discussed.
| [
{
"version": "v1",
"created": "Wed, 13 Mar 2013 12:54:22 GMT"
}
] | 1,364,169,600,000 | [
[
"Olesen",
"Kristian G.",
""
],
[
"Lauritzen",
"Steffen L.",
""
],
[
"Jensen",
"Finn Verner",
""
]
] |
1303.5422 | Gerhard Paa{\ss} | Gerhard Paa{\ss} | MESA: Maximum Entropy by Simulated Annealing | Appears in Proceedings of the Eighth Conference on Uncertainty in
Artificial Intelligence (UAI1992) | null | null | UAI-P-1992-PG-230-237 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Probabilistic reasoning systems combine different probabilistic rules and
probabilistic facts to arrive at the desired probability values of
consequences. In this paper we describe the MESA-algorithm (Maximum Entropy by
Simulated Annealing) that derives a joint distribution of variables or
propositions. It takes into account the reliability of probability values and
can resolve conflicts between contradictory statements. The joint distribution
is represented in terms of marginal distributions and therefore allows to
process large inference networks and to determine desired probability values
with high precision. The procedure derives a maximum entropy distribution
subject to the given constraints. It can be applied to inference networks of
arbitrary topology and may be extended into a number of directions.
| [
{
"version": "v1",
"created": "Wed, 13 Mar 2013 12:54:28 GMT"
}
] | 1,364,169,600,000 | [
[
"Paaß",
"Gerhard",
""
]
] |
1303.5423 | Thomas S. Paterson | Thomas S. Paterson, Michael R. Fehling | Decision Methods for Adaptive Task-Sharing in Associate Systems | Appears in Proceedings of the Eighth Conference on Uncertainty in
Artificial Intelligence (UAI1992) | null | null | UAI-P-1992-PG-238-243 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper describes some results of research on associate systems:
knowledge-based systems that flexibly and adaptively support their human users
in carrying out complex, time-dependent problem-solving tasks under
uncertainty. Based on principles derived from decision theory and decision
analysis, a problem-solving approach is presented which can overcome many of
the limitations of traditional expert-systems. This approach implements an
explicit model of the human user's problem-solving capabilities as an integral
element in the overall problem solving architecture. This integrated model,
represented as an influence diagram, is the basis for achieving adaptive task
sharing behavior between the associate system and the human user. This
associate system model has been applied toward ongoing research on a Mars Rover
Manager's Associate (MRMA). MRMA's role would be to manage a small fleet of
robotic rovers on the Martian surface. The paper describes results for a
specific scenario where MRMA examines the benefits and costs of consulting
human experts on Earth to assist a Mars rover with a complex resource
management decision.
| [
{
"version": "v1",
"created": "Wed, 13 Mar 2013 12:54:34 GMT"
}
] | 1,364,169,600,000 | [
[
"Paterson",
"Thomas S.",
""
],
[
"Fehling",
"Michael R.",
""
]
] |
1303.5424 | Luigi Portinale | Luigi Portinale | Modeling Uncertain Temporal Evolutions in Model-Based Diagnosis | Appears in Proceedings of the Eighth Conference on Uncertainty in
Artificial Intelligence (UAI1992) | null | null | UAI-P-1992-PG-244-251 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Although the notion of diagnostic problem has been extensively investigated
in the context of static systems, in most practical applications the behavior
of the modeled system is significantly variable during time. The goal of the
paper is to propose a novel approach to the modeling of uncertainty about
temporal evolutions of time-varying systems and a characterization of
model-based temporal diagnosis. Since in most real world cases knowledge about
the temporal evolution of the system to be diagnosed is uncertain, we consider
the case when probabilistic temporal knowledge is available for each component
of the system and we choose to model it by means of Markov chains. In fact, we
aim at exploiting the statistical assumptions underlying reliability theory in
the context of the diagnosis of timevarying systems. We finally show how to
exploit Markov chain theory in order to discard, in the diagnostic process,
very unlikely diagnoses.
| [
{
"version": "v1",
"created": "Wed, 13 Mar 2013 12:54:40 GMT"
}
] | 1,364,169,600,000 | [
[
"Portinale",
"Luigi",
""
]
] |
1303.5425 | Yuping Qiu | Yuping Qiu, Louis Anthony Cox, Jr., Lawrence Davis | Guess-And-Verify Heuristics for Reducing Uncertainties in Expert
Classification Systems | Appears in Proceedings of the Eighth Conference on Uncertainty in
Artificial Intelligence (UAI1992) | null | null | UAI-P-1992-PG-252-258 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | An expert classification system having statistical information about the
prior probabilities of the different classes should be able to use this
knowledge to reduce the amount of additional information that it must collect,
e.g., through questions, in order to make a correct classification. This paper
examines how best to use such prior information and additional
information-collection opportunities to reduce uncertainty about the class to
which a case belongs, thus minimizing the average cost or effort required to
correctly classify new cases.
| [
{
"version": "v1",
"created": "Wed, 13 Mar 2013 12:54:46 GMT"
}
] | 1,364,169,600,000 | [
[
"Qiu",
"Yuping",
""
],
[
"Cox,",
"Louis Anthony",
"Jr."
],
[
"Davis",
"Lawrence",
""
]
] |
1303.5426 | Peter J. Regan | Peter J. Regan, Samuel Holtzman | R&D Analyst: An Interactive Approach to Normative Decision System Model
Construction | Appears in Proceedings of the Eighth Conference on Uncertainty in
Artificial Intelligence (UAI1992) | null | null | UAI-P-1992-PG-259-267 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper describes the architecture of R&D Analyst, a commercial
intelligent decision system for evaluating corporate research and development
projects and portfolios. In analyzing projects, R&D Analyst interactively
guides a user in constructing an influence diagram model for an individual
research project. The system's interactive approach can be clearly explained
from a blackboard system perspective. The opportunistic reasoning emphasis of
blackboard systems satisfies the flexibility requirements of model
construction, thereby suggesting that a similar architecture would be valuable
for developing normative decision systems in other domains. Current research is
aimed at extending the system architecture to explicitly consider of sequential
decisions involving limited temporal, financial, and physical resources.
| [
{
"version": "v1",
"created": "Wed, 13 Mar 2013 12:54:52 GMT"
}
] | 1,364,169,600,000 | [
[
"Regan",
"Peter J.",
""
],
[
"Holtzman",
"Samuel",
""
]
] |
1303.5427 | Thomas Schiex | Thomas Schiex | Possibilistic Constraint Satisfaction Problems or "How to handle soft
constraints?" | Appears in Proceedings of the Eighth Conference on Uncertainty in
Artificial Intelligence (UAI1992) | null | null | UAI-P-1992-PG-268-275 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Many AI synthesis problems such as planning or scheduling may be modelized as
constraint satisfaction problems (CSP). A CSP is typically defined as the
problem of finding any consistent labeling for a fixed set of variables
satisfying all given constraints between these variables. However, for many
real tasks such as job-shop scheduling, time-table scheduling, design?, all
these constraints have not the same significance and have not to be necessarily
satisfied. A first distinction can be made between hard constraints, which
every solution should satisfy and soft constraints, whose satisfaction has not
to be certain. In this paper, we formalize the notion of possibilistic
constraint satisfaction problems that allows the modeling of uncertainly
satisfied constraints. We use a possibility distribution over labelings to
represent respective possibilities of each labeling. Necessity-valued
constraints allow a simple expression of the respective certainty degrees of
each constraint. The main advantage of our approach is its integration in the
CSP technical framework. Most classical techniques, such as Backtracking (BT),
arcconsistency enforcing (AC) or Forward Checking have been extended to handle
possibilistics CSP and are effectively implemented. The utility of our approach
is demonstrated on a simple design problem.
| [
{
"version": "v1",
"created": "Wed, 13 Mar 2013 12:54:58 GMT"
}
] | 1,364,169,600,000 | [
[
"Schiex",
"Thomas",
""
]
] |
1303.5428 | Ross D. Shachter | Ross D. Shachter, Mark Alan Peot | Decision Making Using Probabilistic Inference Methods | Appears in Proceedings of the Eighth Conference on Uncertainty in
Artificial Intelligence (UAI1992) | null | null | UAI-P-1992-PG-276-283 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The analysis of decision making under uncertainty is closely related to the
analysis of probabilistic inference. Indeed, much of the research into
efficient methods for probabilistic inference in expert systems has been
motivated by the fundamental normative arguments of decision theory. In this
paper we show how the developments underlying those efficient methods can be
applied immediately to decision problems. In addition to general approaches
which need know nothing about the actual probabilistic inference method, we
suggest some simple modifications to the clustering family of algorithms in
order to efficiently incorporate decision making capabilities.
| [
{
"version": "v1",
"created": "Wed, 13 Mar 2013 12:55:04 GMT"
}
] | 1,364,169,600,000 | [
[
"Shachter",
"Ross D.",
""
],
[
"Peot",
"Mark Alan",
""
]
] |
1303.5429 | Prakash P. Shenoy | Prakash P. Shenoy | Conditional Independence in Uncertainty Theories | Appears in Proceedings of the Eighth Conference on Uncertainty in
Artificial Intelligence (UAI1992) | null | null | UAI-P-1992-PG-284-291 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper introduces the notions of independence and conditional
independence in valuation-based systems (VBS). VBS is an axiomatic framework
capable of representing many different uncertainty calculi. We define
independence and conditional independence in terms of factorization of the
joint valuation. The definitions of independence and conditional independence
in VBS generalize the corresponding definitions in probability theory. Our
definitions apply not only to probability theory, but also to Dempster-Shafer's
belief-function theory, Spohn's epistemic-belief theory, and Zadeh's
possibility theory. In fact, they apply to any uncertainty calculi that fit in
the framework of valuation-based systems.
| [
{
"version": "v1",
"created": "Wed, 13 Mar 2013 12:55:11 GMT"
}
] | 1,364,169,600,000 | [
[
"Shenoy",
"Prakash P.",
""
]
] |
1303.5430 | Philippe Smets | Philippe Smets | The Nature of the Unnormalized Beliefs Encountered in the Transferable
Belief Model | Appears in Proceedings of the Eighth Conference on Uncertainty in
Artificial Intelligence (UAI1992) | null | null | UAI-P-1992-PG-292-297 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Within the transferable belief model, positive basic belief masses can be
allocated to the empty set, leading to unnormalized belief functions. The
nature of these unnormalized beliefs is analyzed.
| [
{
"version": "v1",
"created": "Wed, 13 Mar 2013 12:55:17 GMT"
}
] | 1,364,169,600,000 | [
[
"Smets",
"Philippe",
""
]
] |
1303.5431 | Paul Snow | Paul Snow | Intuitions about Ordered Beliefs Leading to Probabilistic Models | Appears in Proceedings of the Eighth Conference on Uncertainty in
Artificial Intelligence (UAI1992) | null | null | UAI-P-1992-PG-298-302 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The general use of subjective probabilities to model belief has been
justified using many axiomatic schemes. For example, ?consistent betting
behavior' arguments are well-known. To those not already convinced of the
unique fitness and generality of probability models, such justifications are
often unconvincing. The present paper explores another rationale for
probability models. ?Qualitative probability,' which is known to provide
stringent constraints on belief representation schemes, is derived from five
simple assumptions about relationships among beliefs. While counterparts of
familiar rationality concepts such as transitivity, dominance, and consistency
are used, the betting context is avoided. The gap between qualitative
probability and probability proper can be bridged by any of several additional
assumptions. The discussion here relies on results common in the recent AI
literature, introducing a sixth simple assumption. The narrative emphasizes
models based on unique complete orderings, but the rationale extends easily to
motivate set-valued representations of partial orderings as well.
| [
{
"version": "v1",
"created": "Wed, 13 Mar 2013 12:55:23 GMT"
}
] | 1,364,169,600,000 | [
[
"Snow",
"Paul",
""
]
] |
1303.5432 | Luis Enrique Sucar | Luis Enrique Sucar, Duncan F. Gillies | Expressing Relational and Temporal Knowledge in Visual Probabilistic
Networks | Appears in Proceedings of the Eighth Conference on Uncertainty in
Artificial Intelligence (UAI1992) | null | null | UAI-P-1992-PG-303-309 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Bayesian networks have been used extensively in diagnostic tasks such as
medicine, where they represent the dependency relations between a set of
symptoms and a set of diseases. A criticism of this type of knowledge
representation is that it is restricted to this kind of task, and that it
cannot cope with the knowledge required in other artificial intelligence
applications. For example, in computer vision, we require the ability to model
complex knowledge, including temporal and relational factors. In this paper we
extend Bayesian networks to model relational and temporal knowledge for
high-level vision. These extended networks have a simple structure which
permits us to propagate probability efficiently. We have applied them to the
domain of endoscopy, illustrating how the general modelling principles can be
used in specific cases.
| [
{
"version": "v1",
"created": "Wed, 13 Mar 2013 12:55:28 GMT"
}
] | 1,364,169,600,000 | [
[
"Sucar",
"Luis Enrique",
""
],
[
"Gillies",
"Duncan F.",
""
]
] |
1303.5433 | Chin-Wang Tao | Chin-Wang Tao, Wiley E. Thompson | A Fuzzy Logic Approach to Target Tracking | Appears in Proceedings of the Eighth Conference on Uncertainty in
Artificial Intelligence (UAI1992) | null | null | UAI-P-1992-PG-310-314 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper discusses a target tracking problem in which no dynamic
mathematical model is explicitly assumed. A nonlinear filter based on the fuzzy
If-then rules is developed. A comparison with a Kalman filter is made, and
empirical results show that the performance of the fuzzy filter is better.
Intensive simulations suggest that theoretical justification of the empirical
results is possible.
| [
{
"version": "v1",
"created": "Wed, 13 Mar 2013 12:55:34 GMT"
}
] | 1,364,169,600,000 | [
[
"Tao",
"Chin-Wang",
""
],
[
"Thompson",
"Wiley E.",
""
]
] |
1303.5434 | Helmut Thone | Helmut Thone, Ulrich Guntzer, Werner Kiessling | Towards Precision of Probabilistic Bounds Propagation | Appears in Proceedings of the Eighth Conference on Uncertainty in
Artificial Intelligence (UAI1992) | null | null | UAI-P-1992-PG-315-322 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The DUCK-calculus presented here is a recent approach to cope with
probabilistic uncertainty in a sound and efficient way. Uncertain rules with
bounds for probabilities and explicit conditional independences can be
maintained incrementally. The basic inference mechanism relies on local bounds
propagation, implementable by deductive databases with a bottom-up fixpoint
evaluation. In situations, where no precise bounds are deducible, it can be
combined with simple operations research techniques on a local scope. In
particular, we provide new precise analytical bounds for probabilistic
entailment.
| [
{
"version": "v1",
"created": "Wed, 13 Mar 2013 12:55:40 GMT"
}
] | 1,364,169,600,000 | [
[
"Thone",
"Helmut",
""
],
[
"Guntzer",
"Ulrich",
""
],
[
"Kiessling",
"Werner",
""
]
] |
1303.5435 | Tom S. Verma | Tom S. Verma, Judea Pearl | An Algorithm for Deciding if a Set of Observed Independencies Has a
Causal Explanation | Appears in Proceedings of the Eighth Conference on Uncertainty in
Artificial Intelligence (UAI1992) | null | null | UAI-P-1992-PG-323-330 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In a previous paper [Pearl and Verma, 1991] we presented an algorithm for
extracting causal influences from independence information, where a causal
influence was defined as the existence of a directed arc in all minimal causal
models consistent with the data. In this paper we address the question of
deciding whether there exists a causal model that explains ALL the observed
dependencies and independencies. Formally, given a list M of conditional
independence statements, it is required to decide whether there exists a
directed acyclic graph (dag) D that is perfectly consistent with M, namely,
every statement in M, and no other, is reflected via dseparation in D. We
present and analyze an effective algorithm that tests for the existence of such
a day, and produces one, if it exists.
| [
{
"version": "v1",
"created": "Wed, 13 Mar 2013 12:55:46 GMT"
}
] | 1,364,169,600,000 | [
[
"Verma",
"Tom S.",
""
],
[
"Pearl",
"Judea",
""
]
] |
1303.5436 | Carl G. Wagner | Carl G. Wagner | Generalizing Jeffrey Conditionalization | Appears in Proceedings of the Eighth Conference on Uncertainty in
Artificial Intelligence (UAI1992) | null | null | UAI-P-1992-PG-331-335 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Jeffrey's rule has been generalized by Wagner to the case in which new
evidence bounds the possible revisions of a prior probability below by a
Dempsterian lower probability. Classical probability kinematics arises within
this generalization as the special case in which the evidentiary focal elements
of the bounding lower probability are pairwise disjoint. We discuss a twofold
extension of this generalization, first allowing the lower bound to be any
two-monotone capacity and then allowing the prior to be a lower envelope.
| [
{
"version": "v1",
"created": "Wed, 13 Mar 2013 12:55:52 GMT"
}
] | 1,364,169,600,000 | [
[
"Wagner",
"Carl G.",
""
]
] |
1303.5437 | Michael S. K. M. Wong | Michael S. K. M. Wong, L. S. Wang, Y. Y. Yao | Interval Structure: A Framework for Representing Uncertain Information | Appears in Proceedings of the Eighth Conference on Uncertainty in
Artificial Intelligence (UAI1992) | null | null | UAI-P-1992-PG-336-343 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this paper, a unified framework for representing uncertain information
based on the notion of an interval structure is proposed. It is shown that the
lower and upper approximations of the rough-set model, the lower and upper
bounds of incidence calculus, and the belief and plausibility functions all
obey the axioms of an interval structure. An interval structure can be used to
synthesize the decision rules provided by the experts. An efficient algorithm
to find the desirable set of rules is developed from a set of sound and
complete inference axioms.
| [
{
"version": "v1",
"created": "Wed, 13 Mar 2013 12:55:58 GMT"
}
] | 1,364,169,600,000 | [
[
"Wong",
"Michael S. K. M.",
""
],
[
"Wang",
"L. S.",
""
],
[
"Yao",
"Y. Y.",
""
]
] |
1303.5438 | Yang Xiang | Yang Xiang, David L. Poole, Michael P. Beddoes | Exploring Localization in Bayesian Networks for Large Expert Systems | Appears in Proceedings of the Eighth Conference on Uncertainty in
Artificial Intelligence (UAI1992) | null | null | UAI-P-1992-PG-344-351 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Current Bayesian net representations do not consider structure in the domain
and include all variables in a homogeneous network. At any time, a human
reasoner in a large domain may direct his attention to only one of a number of
natural subdomains, i.e., there is ?localization' of queries and evidence. In
such a case, propagating evidence through a homogeneous network is inefficient
since the entire network has to be updated each time. This paper presents
multiply sectioned Bayesian networks that enable a (localization preserving)
representation of natural subdomains by separate Bayesian subnets. The subnets
are transformed into a set of permanent junction trees such that evidential
reasoning takes place at only one of them at a time. Probabilities obtained are
identical to those that would be obtained from the homogeneous network. We
discuss attention shift to a different junction tree and propagation of
previously acquired evidence. Although the overall system can be large,
computational requirements are governed by the size of only one junction tree.
| [
{
"version": "v1",
"created": "Wed, 13 Mar 2013 12:56:04 GMT"
}
] | 1,364,169,600,000 | [
[
"Xiang",
"Yang",
""
],
[
"Poole",
"David L.",
""
],
[
"Beddoes",
"Michael P.",
""
]
] |
1303.5439 | Hong Xu | Hong Xu | A Decision Calculus for Belief Functions in Valuation-Based Systems | Appears in Proceedings of the Eighth Conference on Uncertainty in
Artificial Intelligence (UAI1992) | null | null | UAI-P-1992-PG-352-359 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Valuation-based system (VBS) provides a general framework for representing
knowledge and drawing inferences under uncertainty. Recent studies have shown
that the semantics of VBS can represent and solve Bayesian decision problems
(Shenoy, 1991a). The purpose of this paper is to propose a decision calculus
for Dempster-Shafer (D-S) theory in the framework of VBS. The proposed calculus
uses a weighting factor whose role is similar to the probabilistic
interpretation of an assumption that disambiguates decision problems
represented with belief functions (Strat 1990). It will be shown that with the
presented calculus, if the decision problems are represented in the valuation
network properly, we can solve the problems by using fusion algorithm (Shenoy
1991a). It will also be shown the presented decision calculus can be reduced to
the calculus for Bayesian probability theory when probabilities, instead of
belief functions, are given.
| [
{
"version": "v1",
"created": "Wed, 13 Mar 2013 12:56:10 GMT"
}
] | 1,364,169,600,000 | [
[
"Xu",
"Hong",
""
]
] |
1303.5440 | Nevin Lianwen Zhang | Nevin Lianwen Zhang, David L. Poole | Sidestepping the Triangulation Problem in Bayesian Net Computations | Appears in Proceedings of the Eighth Conference on Uncertainty in
Artificial Intelligence (UAI1992) | null | null | UAI-P-1992-PG-360-367 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper presents a new approach for computing posterior probabilities in
Bayesian nets, which sidesteps the triangulation problem. The current state of
art is the clique tree propagation approach. When the underlying graph of a
Bayesian net is triangulated, this approach arranges its cliques into a tree
and computes posterior probabilities by appropriately passing around messages
in that tree. The computation in each clique is simply direct marginalization.
When the underlying graph is not triangulated, one has to first triangulated it
by adding edges. Referred to as the triangulation problem, the problem of
finding an optimal or even a ?good? triangulation proves to be difficult. In
this paper, we propose to first decompose a Bayesian net into smaller
components by making use of Tarjan's algorithm for decomposing an undirected
graph at all its minimal complete separators. Then, the components are arranged
into a tree and posterior probabilities are computed by appropriately passing
around messages in that tree. The computation in each component is carried out
by repeating the whole procedure from the beginning. Thus the triangulation
problem is sidestepped.
| [
{
"version": "v1",
"created": "Wed, 13 Mar 2013 12:56:16 GMT"
}
] | 1,364,169,600,000 | [
[
"Zhang",
"Nevin Lianwen",
""
],
[
"Poole",
"David L.",
""
]
] |
1303.5659 | Taisuke Sato | Taisuke Sato and Keiichi Kubota | Viterbi training in PRISM | 23 pages, 1 figure | Theory and Practice of Logic Programming 15 (2015) 147-168 | 10.1017/S1471068413000677 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | VT (Viterbi training), or hard EM, is an efficient way of parameter learning
for probabilistic models with hidden variables. Given an observation $y$, it
searches for a state of hidden variables $x$ that maximizes $p(x,y \mid
\theta)$ by coordinate ascent on parameters $\theta$ and $x$. In this paper we
introduce VT to PRISM, a logic-based probabilistic modeling system for
generative models. VT improves PRISM in three ways. First VT in PRISM converges
faster than EM in PRISM due to the VT's termination condition. Second,
parameters learned by VT often show good prediction performance compared to
those learned by EM. We conducted two parsing experiments with probabilistic
grammars while learning parameters by a variety of inference methods, i.e.\ VT,
EM, MAP and VB. The result is that VT achieved the best parsing accuracy among
them in both experiments. Also we conducted a similar experiment for
classification tasks where a hidden variable is not a prediction target unlike
probabilistic grammars. We found that in such a case VT does not necessarily
yield superior performance. Third since VT always deals with a single
probability of a single explanation, Viterbi explanation, the exclusiveness
condition that is imposed on PRISM programs is no more required if we learn
parameters by VT.
Last but not least we can say that as VT in PRISM is general and applicable
to any PRISM program, it largely reduces the need for the user to develop a
specific VT algorithm for a specific model. Furthermore since VT in PRISM can
be used just by setting a PRISM flag appropriately, it makes VT easily
accessible to (probabilistic) logic programmers. To appear in Theory and
Practice of Logic Programming (TPLP).
| [
{
"version": "v1",
"created": "Fri, 22 Mar 2013 16:22:43 GMT"
},
{
"version": "v2",
"created": "Fri, 29 Nov 2013 02:55:17 GMT"
}
] | 1,582,070,400,000 | [
[
"Sato",
"Taisuke",
""
],
[
"Kubota",
"Keiichi",
""
]
] |
1303.5704 | John Mark Agosta | John Mark Agosta | "Conditional Inter-Causally Independent" Node Distributions, a Property
of "Noisy-Or" Models | Appears in Proceedings of the Seventh Conference on Uncertainty in
Artificial Intelligence (UAI1991) | null | null | UAI-P-1991-PG-9-16 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper examines the interdependence generated between two parent nodes
with a common instantiated child node, such as two hypotheses sharing common
evidence. The relation so generated has been termed "intercausal." It is shown
by construction that inter-causal independence is possible for binary
distributions at one state of evidence. For such "CICI" distributions, the two
measures of inter-causal effect, "multiplicative synergy" and "additive
synergy" are equal. The well known "noisy-or" model is an example of such a
distribution. This introduces novel semantics for the noisy-or, as a model of
the degree of conflict among competing hypotheses of a common observation.
| [
{
"version": "v1",
"created": "Wed, 20 Mar 2013 15:29:29 GMT"
}
] | 1,364,256,000,000 | [
[
"Agosta",
"John Mark",
""
]
] |
1303.5705 | Jaume Agust\'i-Cullell | Jaume Agust\'i-Cullell, Francesc Esteva, Pere Garcia, Lluis Godo,
Carles Sierra | Combining Multiple-Valued Logics in Modular Expert Systems | Appears in Proceedings of the Seventh Conference on Uncertainty in
Artificial Intelligence (UAI1991) | null | null | UAI-P-1991-PG-17-25 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The way experts manage uncertainty usually changes depending on the task they
are performing. This fact has lead us to consider the problem of communicating
modules (task implementations) in a large and structured knowledge based system
when modules have different uncertainty calculi. In this paper, the analysis of
the communication problem is made assuming that (i) each uncertainty calculus
is an inference mechanism defining an entailment relation, and therefore the
communication is considered to be inference-preserving, and (ii) we restrict
ourselves to the case which the different uncertainty calculi are given by a
class of truth functional Multiple-valued Logics.
| [
{
"version": "v1",
"created": "Wed, 20 Mar 2013 15:29:35 GMT"
}
] | 1,364,256,000,000 | [
[
"Agustí-Cullell",
"Jaume",
""
],
[
"Esteva",
"Francesc",
""
],
[
"Garcia",
"Pere",
""
],
[
"Godo",
"Lluis",
""
],
[
"Sierra",
"Carles",
""
]
] |
1303.5706 | Stephane Amarger | Stephane Amarger, Didier Dubois, Henri Prade | Constraint Propagation with Imprecise Conditional Probabilities | Appears in Proceedings of the Seventh Conference on Uncertainty in
Artificial Intelligence (UAI1991) | null | null | UAI-P-1991-PG-26-34 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | An approach to reasoning with default rules where the proportion of
exceptions, or more generally the probability of encountering an exception, can
be at least roughly assessed is presented. It is based on local uncertainty
propagation rules which provide the best bracketing of a conditional
probability of interest from the knowledge of the bracketing of some other
conditional probabilities. A procedure that uses two such propagation rules
repeatedly is proposed in order to estimate any simple conditional probability
of interest from the available knowledge. The iterative procedure, that does
not require independence assumptions, looks promising with respect to the
linear programming method. Improved bounds for conditional probabilities are
given when independence assumptions hold.
| [
{
"version": "v1",
"created": "Wed, 20 Mar 2013 15:29:40 GMT"
}
] | 1,364,256,000,000 | [
[
"Amarger",
"Stephane",
""
],
[
"Dubois",
"Didier",
""
],
[
"Prade",
"Henri",
""
]
] |
1303.5708 | Wray L. Buntine | Wray L. Buntine | Some Properties of Plausible Reasoning | Appears in Proceedings of the Seventh Conference on Uncertainty in
Artificial Intelligence (UAI1991) | null | null | UAI-P-1991-PG-44-51 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper presents a plausible reasoning system to illustrate some broad
issues in knowledge representation: dualities between different reasoning
forms, the difficulty of unifying complementary reasoning styles, and the
approximate nature of plausible reasoning. These issues have a common
underlying theme: there should be an underlying belief calculus of which the
many different reasoning forms are special cases, sometimes approximate. The
system presented allows reasoning about defaults, likelihood, necessity and
possibility in a manner similar to the earlier work of Adams. The system is
based on the belief calculus of subjective Bayesian probability which itself is
based on a few simple assumptions about how belief should be manipulated.
Approximations, semantics, consistency and consequence results are presented
for the system. While this puts these often discussed plausible reasoning forms
on a probabilistic footing, useful application to practical problems remains an
issue.
| [
{
"version": "v1",
"created": "Wed, 20 Mar 2013 15:29:51 GMT"
}
] | 1,364,256,000,000 | [
[
"Buntine",
"Wray L.",
""
]
] |
1303.5709 | Wray L. Buntine | Wray L. Buntine | Theory Refinement on Bayesian Networks | Appears in Proceedings of the Seventh Conference on Uncertainty in
Artificial Intelligence (UAI1991) | null | null | UAI-P-1991-PG-52-60 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Theory refinement is the task of updating a domain theory in the light of new
cases, to be done automatically or with some expert assistance. The problem of
theory refinement under uncertainty is reviewed here in the context of Bayesian
statistics, a theory of belief revision. The problem is reduced to an
incremental learning task as follows: the learning system is initially primed
with a partial theory supplied by a domain expert, and thereafter maintains its
own internal representation of alternative theories which is able to be
interrogated by the domain expert and able to be incrementally refined from
data. Algorithms for refinement of Bayesian networks are presented to
illustrate what is meant by "partial theory", "alternative theory
representation", etc. The algorithms are an incremental variant of batch
learning algorithms from the literature so can work well in batch and
incremental mode.
| [
{
"version": "v1",
"created": "Wed, 20 Mar 2013 15:29:57 GMT"
}
] | 1,364,256,000,000 | [
[
"Buntine",
"Wray L.",
""
]
] |
1303.5710 | Jose E. Cano | Jose E. Cano, Serafin Moral, Juan F. Verdegay-Lopez | Combination of Upper and Lower Probabilities | Appears in Proceedings of the Seventh Conference on Uncertainty in
Artificial Intelligence (UAI1991) | null | null | UAI-P-1991-PG-61-68 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this paper, we consider several types of information and methods of
combination associated with incomplete probabilistic systems. We discriminate
between 'a priori' and evidential information. The former one is a description
of the whole population, the latest is a restriction based on observations for
a particular case. Then, we propose different combination methods for each one
of them. We also consider conditioning as the heterogeneous combination of 'a
priori' and evidential information. The evidential information is represented
as a convex set of likelihood functions. These will have an associated
possibility distribution with behavior according to classical Possibility
Theory.
| [
{
"version": "v1",
"created": "Wed, 20 Mar 2013 15:30:02 GMT"
}
] | 1,364,256,000,000 | [
[
"Cano",
"Jose E.",
""
],
[
"Moral",
"Serafin",
""
],
[
"Verdegay-Lopez",
"Juan F.",
""
]
] |
1303.5711 | Glenn Carroll | Glenn Carroll, Eugene Charniak | A Probabilistic Analysis of Marker-Passing Techniques for
Plan-Recognition | Appears in Proceedings of the Seventh Conference on Uncertainty in
Artificial Intelligence (UAI1991) | null | null | UAI-P-1991-PG-69-76 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Useless paths are a chronic problem for marker-passing techniques. We use a
probabilistic analysis to justify a method for quickly identifying and
rejecting useless paths. Using the same analysis, we identify key conditions
and assumptions necessary for marker-passing to perform well.
| [
{
"version": "v1",
"created": "Wed, 20 Mar 2013 15:30:07 GMT"
}
] | 1,364,256,000,000 | [
[
"Carroll",
"Glenn",
""
],
[
"Charniak",
"Eugene",
""
]
] |
1303.5712 | Kuo-Chu Chang | Kuo-Chu Chang, Robert Fung | Symbolic Probabilistic Inference with Continuous Variables | Appears in Proceedings of the Seventh Conference on Uncertainty in
Artificial Intelligence (UAI1991) | null | null | UAI-P-1991-PG-77-81 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Research on Symbolic Probabilistic Inference (SPI) [2, 3] has provided an
algorithm for resolving general queries in Bayesian networks. SPI applies the
concept of dependency directed backward search to probabilistic inference, and
is incremental with respect to both queries and observations. Unlike
traditional Bayesian network inferencing algorithms, SPI algorithm is goal
directed, performing only those calculations that are required to respond to
queries. Research to date on SPI applies to Bayesian networks with
discrete-valued variables and does not address variables with continuous
values. In this papers, we extend the SPI algorithm to handle Bayesian networks
made up of continuous variables where the relationships between the variables
are restricted to be ?linear gaussian?. We call this variation of the SPI
algorithm, SPI Continuous (SPIC). SPIC modifies the three basic SPI operations:
multiplication, summation, and substitution. However, SPIC retains the
framework of the SPI algorithm, namely building the search tree and recursive
query mechanism and therefore retains the goal-directed and incrementality
features of SPI.
| [
{
"version": "v1",
"created": "Wed, 20 Mar 2013 15:30:11 GMT"
}
] | 1,364,256,000,000 | [
[
"Chang",
"Kuo-Chu",
""
],
[
"Fung",
"Robert",
""
]
] |
1303.5713 | Kuo-Chu Chang | Kuo-Chu Chang, Robert Fung | Symbolic Probabilistic Inference with Evidence Potential | Appears in Proceedings of the Seventh Conference on Uncertainty in
Artificial Intelligence (UAI1991) | null | null | UAI-P-1991-PG-82-85 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Recent research on the Symbolic Probabilistic Inference (SPI) algorithm[2]
has focused attention on the importance of resolving general queries in
Bayesian networks. SPI applies the concept of dependency-directed backward
search to probabilistic inference, and is incremental with respect to both
queries and observations. In response to this research we have extended the
evidence potential algorithm [3] with the same features. We call the extension
symbolic evidence potential inference (SEPI). SEPI like SPI can handle generic
queries and is incremental with respect to queries and observations. While in
SPI, operations are done on a search tree constructed from the nodes of the
original network, in SEPI, a clique-tree structure obtained from the evidence
potential algorithm [3] is the basic framework for recursive query processing.
In this paper, we describe the systematic query and caching procedure of SEPI.
SEPI begins with finding a clique tree from a Bayesian network-the standard
procedure of the evidence potential algorithm. With the clique tree, various
probability distributions are computed and stored in each clique. This is the
?pre-processing? step of SEPI. Once this step is done, the query can then be
computed. To process a query, a recursive process similar to the SPI algorithm
is used. The queries are directed to the root clique and decomposed into
queries for the clique's subtrees until a particular query can be answered at
the clique at which it is directed. The algorithm and the computation are
simple. The SEPI algorithm will be presented in this paper along with several
examples.
| [
{
"version": "v1",
"created": "Wed, 20 Mar 2013 15:30:16 GMT"
}
] | 1,364,256,000,000 | [
[
"Chang",
"Kuo-Chu",
""
],
[
"Fung",
"Robert",
""
]
] |
1303.5714 | Gregory F. Cooper | Gregory F. Cooper, Edward H. Herskovits | A Bayesian Method for Constructing Bayesian Belief Networks from
Databases | Appears in Proceedings of the Seventh Conference on Uncertainty in
Artificial Intelligence (UAI1991) | null | null | UAI-P-1991-PG-86-94 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper presents a Bayesian method for constructing Bayesian belief
networks from a database of cases. Potential applications include
computer-assisted hypothesis testing, automated scientific discovery, and
automated construction of probabilistic expert systems. Results are presented
of a preliminary evaluation of an algorithm for constructing a belief network
from a database of cases. We relate the methods in this paper to previous work,
and we discuss open problems.
| [
{
"version": "v1",
"created": "Wed, 20 Mar 2013 15:30:21 GMT"
}
] | 1,364,256,000,000 | [
[
"Cooper",
"Gregory F.",
""
],
[
"Herskovits",
"Edward H.",
""
]
] |
1303.5715 | Bruce D'Ambrosio | Bruce D'Ambrosio | Local Expression Languages for Probabilistic Dependence: a Preliminary
Report | Appears in Proceedings of the Seventh Conference on Uncertainty in
Artificial Intelligence (UAI1991) | null | null | UAI-P-1991-PG-95-102 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We present a generalization of the local expression language used in the
Symbolic Probabilistic Inference (SPI) approach to inference in belief nets
[1l, [8]. The local expression language in SPI is the language in which the
dependence of a node on its antecedents is described. The original language
represented the dependence as a single monolithic conditional probability
distribution. The extended language provides a set of operators (*, +, and -)
which can be used to specify methods for combining partial conditional
distributions. As one instance of the utility of this extension, we show how
this extended language can be used to capture the semantics, representational
advantages, and inferential complexity advantages of the "noisy or"
relationship.
| [
{
"version": "v1",
"created": "Wed, 20 Mar 2013 15:30:27 GMT"
}
] | 1,364,256,000,000 | [
[
"D'Ambrosio",
"Bruce",
""
]
] |
1303.5716 | John Fox | John Fox, Paul J. Krause | Symbolic Decision Theory and Autonomous Systems | Appears in Proceedings of the Seventh Conference on Uncertainty in
Artificial Intelligence (UAI1991) | null | null | UAI-P-1991-PG-103-110 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The ability to reason under uncertainty and with incomplete information is a
fundamental requirement of decision support technology. In this paper we argue
that the concentration on theoretical techniques for the evaluation and
selection of decision options has distracted attention from many of the wider
issues in decision making. Although numerical methods of reasoning under
uncertainty have strong theoretical foundations, they are representationally
weak and only deal with a small part of the decision process. Knowledge based
systems, on the other hand, offer greater flexibility but have not been
accompanied by a clear decision theory. We describe here work which is under
way towards providing a theoretical framework for symbolic decision procedures.
A central proposal is an extended form of inference which we call
argumentation; reasoning for and against decision options from generalised
domain theories. The approach has been successfully used in several decision
support applications, but it is argued that a comprehensive decision theory
must cover autonomous decision making, where the agent can formulate questions
as well as take decisions. A major theoretical challenge for this theory is to
capture the idea of reflection to permit decision agents to reason about their
goals, what they believe and why, and what they need to know or do in order to
achieve their goals.
| [
{
"version": "v1",
"created": "Wed, 20 Mar 2013 15:30:32 GMT"
}
] | 1,364,256,000,000 | [
[
"Fox",
"John",
""
],
[
"Krause",
"Paul J.",
""
]
] |
1303.5717 | B. Fringuelli | B. Fringuelli, S. Marcugini, A. Milani, S. Rivoira | A Reason Maintenace System Dealing with Vague Data | Appears in Proceedings of the Seventh Conference on Uncertainty in
Artificial Intelligence (UAI1991) | null | null | UAI-P-1991-PG-111-117 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | A reason maintenance system which extends an ATMS through Mukaidono's fuzzy
logic is described. It supports a problem solver in situations affected by
incomplete information and vague data, by allowing nonmonotonic inferences and
the revision of previous conclusions when contradictions are detected.
| [
{
"version": "v1",
"created": "Wed, 20 Mar 2013 15:30:37 GMT"
}
] | 1,364,256,000,000 | [
[
"Fringuelli",
"B.",
""
],
[
"Marcugini",
"S.",
""
],
[
"Milani",
"A.",
""
],
[
"Rivoira",
"S.",
""
]
] |
1303.5718 | Dan Geiger | Dan Geiger, David Heckerman | Advances in Probabilistic Reasoning | Appears in Proceedings of the Seventh Conference on Uncertainty in
Artificial Intelligence (UAI1991) | null | null | UAI-P-1991-PG-118-126 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper discuses multiple Bayesian networks representation paradigms for
encoding asymmetric independence assertions. We offer three contributions: (1)
an inference mechanism that makes explicit use of asymmetric independence to
speed up computations, (2) a simplified definition of similarity networks and
extensions of their theory, and (3) a generalized representation scheme that
encodes more types of asymmetric independence assertions than do similarity
networks.
| [
{
"version": "v1",
"created": "Wed, 20 Mar 2013 15:30:42 GMT"
},
{
"version": "v2",
"created": "Sat, 16 May 2015 23:56:18 GMT"
}
] | 1,431,993,600,000 | [
[
"Geiger",
"Dan",
""
],
[
"Heckerman",
"David",
""
]
] |
1303.5719 | Adam J. Grove | Adam J. Grove, Daphne Koller | Probability Estimation in Face of Irrelevant Information | Appears in Proceedings of the Seventh Conference on Uncertainty in
Artificial Intelligence (UAI1991) | null | null | UAI-P-1991-PG-127-134 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this paper, we consider one aspect of the problem of applying decision
theory to the design of agents that learn how to make decisions under
uncertainty. This aspect concerns how an agent can estimate probabilities for
the possible states of the world, given that it only makes limited observations
before committing to a decision. We show that the naive application of
statistical tools can be improved upon if the agent can determine which of his
observations are truly relevant to the estimation problem at hand. We give a
framework in which such determinations can be made, and define an estimation
procedure to use them. Our framework also suggests several extensions, which
show how additional knowledge can be used to improve tile estimation procedure
still further.
| [
{
"version": "v1",
"created": "Wed, 20 Mar 2013 15:30:46 GMT"
}
] | 1,364,256,000,000 | [
[
"Grove",
"Adam J.",
""
],
[
"Koller",
"Daphne",
""
]
] |
1303.5720 | David Heckerman | David Heckerman, Eric J. Horvitz, Blackford Middleton | An Approximate Nonmyopic Computation for Value of Information | Appears in Proceedings of the Seventh Conference on Uncertainty in
Artificial Intelligence (UAI1991) | null | null | UAI-P-1991-PG-135-141 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Value-of-information analyses provide a straightforward means for selecting
the best next observation to make, and for determining whether it is better to
gather additional information or to act immediately. Determining the next best
test to perform, given a state of uncertainty about the world, requires a
consideration of the value of making all possible sequences of observations. In
practice, decision analysts and expert-system designers have avoided the
intractability of exact computation of the value of information by relying on a
myopic approximation. Myopic analyses are based on the assumption that only one
additional test will be performed, even when there is an opportunity to make a
large number of observations. We present a nonmyopic approximation for value of
information that bypasses the traditional myopic analyses by exploiting the
statistical properties of large samples.
| [
{
"version": "v1",
"created": "Wed, 20 Mar 2013 15:30:51 GMT"
},
{
"version": "v2",
"created": "Sat, 16 May 2015 23:55:05 GMT"
}
] | 1,431,993,600,000 | [
[
"Heckerman",
"David",
""
],
[
"Horvitz",
"Eric J.",
""
],
[
"Middleton",
"Blackford",
""
]
] |
1303.5721 | Max Henrion | Max Henrion | Search-based Methods to Bound Diagnostic Probabilities in Very Large
Belief Nets | Appears in Proceedings of the Seventh Conference on Uncertainty in
Artificial Intelligence (UAI1991) | null | null | UAI-P-1991-PG-142-150 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Since exact probabilistic inference is intractable in general for large
multiply connected belief nets, approximate methods are required. A promising
approach is to use heuristic search among hypotheses (instantiations of the
network) to find the most probable ones, as in the TopN algorithm. Search is
based on the relative probabilities of hypotheses which are efficient to
compute. Given upper and lower bounds on the relative probability of partial
hypotheses, it is possible to obtain bounds on the absolute probabilities of
hypotheses. Best-first search aimed at reducing the maximum error progressively
narrows the bounds as more hypotheses are examined. Here, qualitative
probabilistic analysis is employed to obtain bounds on the relative probability
of partial hypotheses for the BN20 class of networks networks and a
generalization replacing the noisy OR assumption by negative synergy. The
approach is illustrated by application to a very large belief network, QMR-BN,
which is a reformulation of the Internist-1 system for diagnosis in internal
medicine.
| [
{
"version": "v1",
"created": "Wed, 20 Mar 2013 15:30:56 GMT"
}
] | 1,364,256,000,000 | [
[
"Henrion",
"Max",
""
]
] |
1303.5722 | Eric J. Horvitz | Eric J. Horvitz, Geoffrey Rutledge | Time-Dependent Utility and Action Under Uncertainty | Appears in Proceedings of the Seventh Conference on Uncertainty in
Artificial Intelligence (UAI1991) | null | null | UAI-P-1991-PG-151-158 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We discuss representing and reasoning with knowledge about the time-dependent
utility of an agent's actions. Time-dependent utility plays a crucial role in
the interaction between computation and action under bounded resources. We
present a semantics for time-dependent utility and describe the use of
time-dependent information in decision contexts. We illustrate our discussion
with examples of time-pressured reasoning in Protos, a system constructed to
explore the ideal control of inference by reasoners with limit abilities.
| [
{
"version": "v1",
"created": "Wed, 20 Mar 2013 15:31:01 GMT"
}
] | 1,364,256,000,000 | [
[
"Horvitz",
"Eric J.",
""
],
[
"Rutledge",
"Geoffrey",
""
]
] |
1303.5723 | Daniel Hunter | Daniel Hunter | Non-monotonic Reasoning and the Reversibility of Belief Change | Appears in Proceedings of the Seventh Conference on Uncertainty in
Artificial Intelligence (UAI1991) | null | null | UAI-P-1991-PG-159-164 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Traditional approaches to non-monotonic reasoning fail to satisfy a number of
plausible axioms for belief revision and suffer from conceptual difficulties as
well. Recent work on ranked preferential models (RPMs) promises to overcome
some of these difficulties. Here we show that RPMs are not adequate to handle
iterated belief change. Specifically, we show that RPMs do not always allow for
the reversibility of belief change. This result indicates the need for
numerical strengths of belief.
| [
{
"version": "v1",
"created": "Wed, 20 Mar 2013 15:31:06 GMT"
}
] | 1,364,256,000,000 | [
[
"Hunter",
"Daniel",
""
]
] |
1303.5724 | Yen-Teh Hsia | Yen-Teh Hsia | Belief and Surprise - A Belief-Function Formulation | Appears in Proceedings of the Seventh Conference on Uncertainty in
Artificial Intelligence (UAI1991) | null | null | UAI-P-1991-PG-165-173 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We motivate and describe a theory of belief in this paper. This theory is
developed with the following view of human belief in mind. Consider the belief
that an event E will occur (or has occurred or is occurring). An agent either
entertains this belief or does not entertain this belief (i.e., there is no
"grade" in entertaining the belief). If the agent chooses to exercise "the will
to believe" and entertain this belief, he/she/it is entitled to a degree of
confidence c (1 > c > 0) in doing so. Adopting this view of human belief, we
conjecture that whenever an agent entertains the belief that E will occur with
c degree of confidence, the agent will be surprised (to the extent c) upon
realizing that E did not occur.
| [
{
"version": "v1",
"created": "Wed, 20 Mar 2013 15:31:10 GMT"
}
] | 1,364,256,000,000 | [
[
"Hsia",
"Yen-Teh",
""
]
] |
1303.5725 | Robert Kennes | Robert Kennes | Evidential Reasoning in a Categorial Perspective: Conjunction and
Disjunction of Belief Functions | Appears in Proceedings of the Seventh Conference on Uncertainty in
Artificial Intelligence (UAI1991) | null | null | UAI-P-1991-PG-174-181 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The categorial approach to evidential reasoning can be seen as a combination
of the probability kinematics approach of Richard Jeffrey (1965) and the
maximum (cross-) entropy inference approach of E. T. Jaynes (1957). As a
consequence of that viewpoint, it is well known that category theory provides
natural definitions for logical connectives. In particular, disjunction and
conjunction are modelled by general categorial constructions known as products
and coproducts. In this paper, I focus mainly on Dempster-Shafer theory of
belief functions for which I introduce a category I call Dempster?s category. I
prove the existence of and give explicit formulas for conjunction and
disjunction in the subcategory of separable belief functions. In Dempster?s
category, the new defined conjunction can be seen as the most cautious
conjunction of beliefs, and thus no assumption about distinctness (of the
sources) of beliefs is needed as opposed to Dempster?s rule of combination,
which calls for distinctness (of the sources) of beliefs.
| [
{
"version": "v1",
"created": "Wed, 20 Mar 2013 15:31:15 GMT"
}
] | 1,364,256,000,000 | [
[
"Kennes",
"Robert",
""
]
] |
1303.5726 | Rudolf Kruse | Rudolf Kruse, Detlef Nauck, Frank Klawonn | Reasoning with Mass Distributions | Appears in Proceedings of the Seventh Conference on Uncertainty in
Artificial Intelligence (UAI1991) | null | null | UAI-P-1991-PG-182-187 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The concept of movable evidence masses that flow from supersets to subsets as
specified by experts represents a suitable framework for reasoning under
uncertainty. The mass flow is controlled by specialization matrices. New
evidence is integrated into the frame of discernment by conditioning or
revision (Dempster's rule of conditioning), for which special specialization
matrices exist. Even some aspects of non-monotonic reasoning can be represented
by certain specialization matrices.
| [
{
"version": "v1",
"created": "Wed, 20 Mar 2013 15:31:20 GMT"
}
] | 1,364,256,000,000 | [
[
"Kruse",
"Rudolf",
""
],
[
"Nauck",
"Detlef",
""
],
[
"Klawonn",
"Frank",
""
]
] |
1303.5728 | Kathryn Blackmond Laskey | Kathryn Blackmond Laskey | Conflict and Surprise: Heuristics for Model Revision | Appears in Proceedings of the Seventh Conference on Uncertainty in
Artificial Intelligence (UAI1991) | null | null | UAI-P-1991-PG-197-204 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Any probabilistic model of a problem is based on assumptions which, if
violated, invalidate the model. Users of probability based decision aids need
to be alerted when cases arise that are not covered by the aid's model.
Diagnosis of model failure is also necessary to control dynamic model
construction and revision. This paper presents a set of decision theoretically
motivated heuristics for diagnosing situations in which a model is likely to
provide an inadequate representation of the process being modeled.
| [
{
"version": "v1",
"created": "Wed, 20 Mar 2013 15:31:31 GMT"
}
] | 1,364,256,000,000 | [
[
"Laskey",
"Kathryn Blackmond",
""
]
] |
1303.5729 | Paul E. Lehner | Paul E. Lehner, Azar Sadigh | Reasoning under Uncertainty: Some Monte Carlo Results | Appears in Proceedings of the Seventh Conference on Uncertainty in
Artificial Intelligence (UAI1991) | null | null | UAI-P-1991-PG-205-211 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | A series of monte carlo studies were performed to compare the behavior of
some alternative procedures for reasoning under uncertainty. The behavior of
several Bayesian, linear model and default reasoning procedures were examined
in the context of increasing levels of calibration error. The most interesting
result is that Bayesian procedures tended to output more extreme posterior
belief values (posterior beliefs near 0.0 or 1.0) than other techniques, but
the linear models were relatively less likely to output strong support for an
erroneous conclusion. Also, accounting for the probabilistic dependencies
between evidence items was important for both Bayesian and linear updating
procedures.
| [
{
"version": "v1",
"created": "Wed, 20 Mar 2013 15:31:35 GMT"
}
] | 1,364,256,000,000 | [
[
"Lehner",
"Paul E.",
""
],
[
"Sadigh",
"Azar",
""
]
] |
1303.5730 | Tze-Yun Leong | Tze-Yun Leong | Representation Requirements for Supporting Decision Model Formulation | Appears in Proceedings of the Seventh Conference on Uncertainty in
Artificial Intelligence (UAI1991) | null | null | UAI-P-1991-PG-212-219 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper outlines a methodology for analyzing the representational support
for knowledge-based decision-modeling in a broad domain. A relevant set of
inference patterns and knowledge types are identified. By comparing the
analysis results to existing representations, some insights are gained into a
design approach for integrating categorical and uncertain knowledge in a
context sensitive manner.
| [
{
"version": "v1",
"created": "Wed, 20 Mar 2013 15:31:41 GMT"
}
] | 1,364,256,000,000 | [
[
"Leong",
"Tze-Yun",
""
]
] |
1303.5731 | Nathaniel G. Martin | Nathaniel G. Martin, James F. Allen | A Language for Planning with Statistics | Appears in Proceedings of the Seventh Conference on Uncertainty in
Artificial Intelligence (UAI1991) | null | null | UAI-P-1991-PG-220-227 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | When a planner must decide whether it has enough evidence to make a decision
based on probability, it faces the sample size problem. Current planners using
probabilities need not deal with this problem because they do not generate
their probabilities from observations. This paper presents an event based
language in which the planner's probabilities are calculated from the binomial
random variable generated by the observed ratio of one type of event to
another. Such probabilities are subject to error, so the planner must
introspect about their validity. Inferences about the probability of these
events can be made using statistics. Inferences about the validity of the
approximations can be made using interval estimation. Interval estimation
allows the planner to avoid making choices that are only weakly supported by
the planner's evidence.
| [
{
"version": "v1",
"created": "Wed, 20 Mar 2013 15:31:46 GMT"
}
] | 1,364,256,000,000 | [
[
"Martin",
"Nathaniel G.",
""
],
[
"Allen",
"James F.",
""
]
] |
1303.5732 | B\"ulent Murtezaoglu | B\"ulent Murtezao\u{g}lu, Henry E. Kyburg Jr | A Modification to Evidential Probability | Appears in Proceedings of the Seventh Conference on Uncertainty in
Artificial Intelligence (UAI1991) | null | null | UAI-P-1991-PG-228-231 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Selecting the right reference class and the right interval when faced with
conflicting candidates and no possibility of establishing subset style
dominance has been a problem for Kyburg's Evidential Probability system.
Various methods have been proposed by Loui and Kyburg to solve this problem in
a way that is both intuitively appealing and justifiable within Kyburg's
framework. The scheme proposed in this paper leads to stronger statistical
assertions without sacrificing too much of the intuitive appeal of Kyburg's
latest proposal.
| [
{
"version": "v1",
"created": "Wed, 20 Mar 2013 15:31:51 GMT"
}
] | 1,364,256,000,000 | [
[
"Murtezaoğlu",
"Bülent",
""
],
[
"Kyburg",
"Henry E.",
"Jr"
]
] |
1303.5733 | Richard E. Neapolitan | Richard E. Neapolitan, James Kenevan | Investigation of Variances in Belief Networks | Appears in Proceedings of the Seventh Conference on Uncertainty in
Artificial Intelligence (UAI1991) | null | null | UAI-P-1991-PG-232-241 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The belief network is a well-known graphical structure for representing
independences in a joint probability distribution. The methods, which perform
probabilistic inference in belief networks, often treat the conditional
probabilities which are stored in the network as certain values. However, if
one takes either a subjectivistic or a limiting frequency approach to
probability, one can never be certain of probability values. An algorithm
should not only be capable of reporting the probabilities of the alternatives
of remaining nodes when other nodes are instantiated; it should also be capable
of reporting the uncertainty in these probabilities relative to the uncertainty
in the probabilities which are stored in the network. In this paper a method
for determining the variances in inferred probabilities is obtained under the
assumption that a posterior distribution on the uncertainty variables can be
approximated by the prior distribution. It is shown that this assumption is
plausible if their is a reasonable amount of confidence in the probabilities
which are stored in the network. Furthermore in this paper, a surprising upper
bound for the prior variances in the probabilities of the alternatives of all
nodes is obtained in the case where the probability distributions of the
probabilities of the alternatives are beta distributions. It is shown that the
prior variance in the probability at an alternative of a node is bounded above
by the largest variance in an element of the conditional probability
distribution for that node.
| [
{
"version": "v1",
"created": "Wed, 20 Mar 2013 15:31:56 GMT"
}
] | 1,364,256,000,000 | [
[
"Neapolitan",
"Richard E.",
""
],
[
"Kenevan",
"James",
""
]
] |
1303.5734 | Keung-Chi Ng | Keung-Chi Ng, Bruce Abramson | A Sensitivity Analysis of Pathfinder: A Follow-up Study | Appears in Proceedings of the Seventh Conference on Uncertainty in
Artificial Intelligence (UAI1991) | null | null | UAI-P-1991-PG-242-248 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | At last year?s Uncertainty in AI Conference, we reported the results of a
sensitivity analysis study of Pathfinder. Our findings were quite
unexpected-slight variations to Pathfinder?s parameters appeared to lead to
substantial degradations in system performance. A careful look at our first
analysis, together with the valuable feedback provided by the participants of
last year?s conference, led us to conduct a follow-up study. Our follow-up
differs from our initial study in two ways: (i) the probabilities 0.0 and 1.0
remained unchanged, and (ii) the variations to the probabilities that are close
to both ends (0.0 or 1.0) were less than the ones close to the middle (0.5).
The results of the follow-up study look more reasonable-slight variations to
Pathfinder?s parameters now have little effect on its performance. Taken
together, these two sets of results suggest a viable extension of a common
decision analytic sensitivity analysis to the larger, more complex settings
generally encountered in artificial intelligence.
| [
{
"version": "v1",
"created": "Wed, 20 Mar 2013 15:32:02 GMT"
}
] | 1,364,256,000,000 | [
[
"Ng",
"Keung-Chi",
""
],
[
"Abramson",
"Bruce",
""
]
] |
1303.5735 | Raymond T. Ng | Raymond T. Ng, V. S. Subrahmanian | Non-monotonic Negation in Probabilistic Deductive Databases | Appears in Proceedings of the Seventh Conference on Uncertainty in
Artificial Intelligence (UAI1991) | null | null | UAI-P-1991-PG-249-256 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this paper we study the uses and the semantics of non-monotonic negation
in probabilistic deductive data bases. Based on the stable semantics for
classical logic programming, we introduce the notion of stable formula,
functions. We show that stable formula, functions are minimal fixpoints of
operators associated with probabilistic deductive databases with negation.
Furthermore, since a. probabilistic deductive database may not necessarily have
a stable formula function, we provide a stable class semantics for such
databases. Finally, we demonstrate that the proposed semantics can handle
default reasoning naturally in the context of probabilistic deduction.
| [
{
"version": "v1",
"created": "Wed, 20 Mar 2013 15:32:08 GMT"
}
] | 1,364,256,000,000 | [
[
"Ng",
"Raymond T.",
""
],
[
"Subrahmanian",
"V. S.",
""
]
] |
1303.5736 | Robert K. Paasch | Robert K. Paasch, Alice M. Agogino | Management of Uncertainty in the Multi-Level Monitoring and Diagnosis of
the Time of Flight Scintillation Array | Appears in Proceedings of the Seventh Conference on Uncertainty in
Artificial Intelligence (UAI1991) | null | null | UAI-P-1991-PG-257-263 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We present a general architecture for the monitoring and diagnosis of large
scale sensor-based systems with real time diagnostic constraints. This
architecture is multileveled, combining a single monitoring level based on
statistical methods with two model based diagnostic levels. At each level,
sources of uncertainty are identified, and integrated methodologies for
uncertainty management are developed. The general architecture was applied to
the monitoring and diagnosis of a specific nuclear physics detector at Lawrence
Berkeley National Laboratory that contained approximately 5000 components and
produced over 500 channels of output data. The general architecture is
scalable, and work is ongoing to apply it to detector systems one and two
orders of magnitude more complex.
| [
{
"version": "v1",
"created": "Wed, 20 Mar 2013 15:32:13 GMT"
}
] | 1,364,256,000,000 | [
[
"Paasch",
"Robert K.",
""
],
[
"Agogino",
"Alice M.",
""
]
] |
1303.5737 | Gerhard Paa{\ss} | Gerhard Paass | Integrating Probabilistic Rules into Neural Networks: A Stochastic EM
Learning Algorithm | Appears in Proceedings of the Seventh Conference on Uncertainty in
Artificial Intelligence (UAI1991) | null | null | UAI-P-1991-PG-264-270 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The EM-algorithm is a general procedure to get maximum likelihood estimates
if part of the observations on the variables of a network are missing. In this
paper a stochastic version of the algorithm is adapted to probabilistic neural
networks describing the associative dependency of variables. These networks
have a probability distribution, which is a special case of the distribution
generated by probabilistic inference networks. Hence both types of networks can
be combined allowing to integrate probabilistic rules as well as unspecified
associations in a sound way. The resulting network may have a number of
interesting features including cycles of probabilistic rules, hidden
'unobservable' variables, and uncertain and contradictory evidence.
| [
{
"version": "v1",
"created": "Wed, 20 Mar 2013 15:32:18 GMT"
}
] | 1,364,256,000,000 | [
[
"Paass",
"Gerhard",
""
]
] |
1303.5738 | David L Poole | David L. Poole | Representing Bayesian Networks within Probabilistic Horn Abduction | Appears in Proceedings of the Seventh Conference on Uncertainty in
Artificial Intelligence (UAI1991) | null | null | UAI-P-1991-PG-271-278 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper presents a simple framework for Horn clause abduction, with
probabilities associated with hypotheses. It is shown how this representation
can represent any probabilistic knowledge representable in a Bayesian belief
network. The main contributions are in finding a relationship between logical
and probabilistic notions of evidential reasoning. This can be used as a basis
for a new way to implement Bayesian Networks that allows for approximations to
the value of the posterior probabilities, and also points to a way that
Bayesian networks can be extended beyond a propositional language.
| [
{
"version": "v1",
"created": "Wed, 20 Mar 2013 15:32:22 GMT"
}
] | 1,364,256,000,000 | [
[
"Poole",
"David L.",
""
]
] |
1303.5739 | Gregory M. Provan | Gregory M. Provan | Dynamic Network Updating Techniques For Diagnostic Reasoning | Appears in Proceedings of the Seventh Conference on Uncertainty in
Artificial Intelligence (UAI1991) | null | null | UAI-P-1991-PG-279-286 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | A new probabilistic network construction system, DYNASTY, is proposed for
diagnostic reasoning given variables whose probabilities change over time.
Diagnostic reasoning is formulated as a sequential stochastic process, and is
modeled using influence diagrams. Given a set O of observations, DYNASTY
creates an influence diagram in order to devise the best action given O.
Sensitivity analyses are conducted to determine if the best network has been
created, given the uncertainty in network parameters and topology. DYNASTY uses
an equivalence class approach to provide decision thresholds for the
sensitivity analysis. This equivalence-class approach to diagnostic reasoning
differentiates diagnoses only if the required actions are different. A set of
network-topology updating algorithms are proposed for dynamically updating the
network when necessary.
| [
{
"version": "v1",
"created": "Wed, 20 Mar 2013 15:32:27 GMT"
}
] | 1,364,256,000,000 | [
[
"Provan",
"Gregory M.",
""
]
] |
1303.5740 | Runping Qi | Runping Qi, David L. Poole | High Level Path Planning with Uncertainty | Appears in Proceedings of the Seventh Conference on Uncertainty in
Artificial Intelligence (UAI1991) | null | null | UAI-P-1991-PG-287-294 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | For high level path planning, environments are usually modeled as distance
graphs, and path planning problems are reduced to computing the shortest path
in distance graphs. One major drawback of this modeling is the inability to
model uncertainties, which are often encountered in practice. In this paper, a
new tool, called U-yraph, is proposed for environment modeling. A U-graph is an
extension of distance graphs with the ability to handle a kind of uncertainty.
By modeling an uncertain environment as a U-graph, and a navigation problem as
a Markovian decision process, we can precisely define a new optimality
criterion for navigation plans, and more importantly, we can come up with a
general algorithm for computing optimal plans for navigation tasks.
| [
{
"version": "v1",
"created": "Wed, 20 Mar 2013 15:32:32 GMT"
}
] | 1,364,256,000,000 | [
[
"Qi",
"Runping",
""
],
[
"Poole",
"David L.",
""
]
] |
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