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1205.2642 | Peter Hooper | Peter Hooper, Yasin Abbasi-Yadkori, Russell Greiner, Bret Hoehn | Improved Mean and Variance Approximations for Belief Net Responses via
Network Doubling | Appears in Proceedings of the Twenty-Fifth Conference on Uncertainty
in Artificial Intelligence (UAI2009) | null | null | UAI-P-2009-PG-232-239 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | A Bayesian belief network models a joint distribution with an directed
acyclic graph representing dependencies among variables and network parameters
characterizing conditional distributions. The parameters are viewed as random
variables to quantify uncertainty about their values. Belief nets are used to
compute responses to queries; i.e., conditional probabilities of interest. A
query is a function of the parameters, hence a random variable. Van Allen et
al. (2001, 2008) showed how to quantify uncertainty about a query via a delta
method approximation of its variance. We develop more accurate approximations
for both query mean and variance. The key idea is to extend the query mean
approximation to a "doubled network" involving two independent replicates. Our
method assumes complete data and can be applied to discrete, continuous, and
hybrid networks (provided discrete variables have only discrete parents). We
analyze several improvements, and provide empirical studies to demonstrate
their effectiveness.
| [
{
"version": "v1",
"created": "Wed, 9 May 2012 15:28:28 GMT"
}
] | 1,336,953,600,000 | [
[
"Hooper",
"Peter",
""
],
[
"Abbasi-Yadkori",
"Yasin",
""
],
[
"Greiner",
"Russell",
""
],
[
"Hoehn",
"Bret",
""
]
] |
1205.2647 | Christian Fritz | Christian Fritz, Sheila McIlraith | Generating Optimal Plans in Highly-Dynamic Domains | Appears in Proceedings of the Twenty-Fifth Conference on Uncertainty
in Artificial Intelligence (UAI2009) | null | null | UAI-P-2009-PG-177-184 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Generating optimal plans in highly dynamic environments is challenging. Plans
are predicated on an assumed initial state, but this state can change
unexpectedly during plan generation, potentially invalidating the planning
effort. In this paper we make three contributions: (1) We propose a novel
algorithm for generating optimal plans in settings where frequent, unexpected
events interfere with planning. It is able to quickly distinguish relevant from
irrelevant state changes, and to update the existing planning search tree if
necessary. (2) We argue for a new criterion for evaluating plan adaptation
techniques: the relative running time compared to the "size" of changes. This
is significant since during recovery more changes may occur that need to be
recovered from subsequently, and in order for this process of repeated recovery
to terminate, recovery time has to converge. (3) We show empirically that our
approach can converge and find optimal plans in environments that would
ordinarily defy planning due to their high dynamics.
| [
{
"version": "v1",
"created": "Wed, 9 May 2012 13:51:50 GMT"
}
] | 1,336,953,600,000 | [
[
"Fritz",
"Christian",
""
],
[
"McIlraith",
"Sheila",
""
]
] |
1205.2651 | Mark Crowley | Mark Crowley, John Nelson, David L Poole | Seeing the Forest Despite the Trees: Large Scale Spatial-Temporal
Decision Making | Appears in Proceedings of the Twenty-Fifth Conference on Uncertainty
in Artificial Intelligence (UAI2009) | null | null | UAI-P-2009-PG-126-134 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We introduce a challenging real-world planning problem where actions must be
taken at each location in a spatial area at each point in time. We use forestry
planning as the motivating application. In Large Scale Spatial-Temporal (LSST)
planning problems, the state and action spaces are defined as the
cross-products of many local state and action spaces spread over a large
spatial area such as a city or forest. These problems possess state
uncertainty, have complex utility functions involving spatial constraints and
we generally must rely on simulations rather than an explicit transition model.
We define LSST problems as reinforcement learning problems and present a
solution using policy gradients. We compare two different policy formulations:
an explicit policy that identifies each location in space and the action to
take there; and an abstract policy that defines the proportion of actions to
take across all locations in space. We show that the abstract policy is more
robust and achieves higher rewards with far fewer parameters than the
elementary policy. This abstract policy is also a better fit to the properties
that practitioners in LSST problem domains require for such methods to be
widely useful.
| [
{
"version": "v1",
"created": "Wed, 9 May 2012 15:08:18 GMT"
}
] | 1,336,953,600,000 | [
[
"Crowley",
"Mark",
""
],
[
"Nelson",
"John",
""
],
[
"Poole",
"David L",
""
]
] |
1205.2652 | Fabio Gagliardi Cozman | Fabio Gagliardi Cozman, Rodrigo Bellizia Polastro | Complexity Analysis and Variational Inference for Interpretation-based
Probabilistic Description Logic | Appears in Proceedings of the Twenty-Fifth Conference on Uncertainty
in Artificial Intelligence (UAI2009) | null | null | UAI-P-2009-PG-117-125 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper presents complexity analysis and variational methods for inference
in probabilistic description logics featuring Boolean operators,
quantification, qualified number restrictions, nominals, inverse roles and role
hierarchies. Inference is shown to be PEXP-complete, and variational methods
are designed so as to exploit logical inference whenever possible.
| [
{
"version": "v1",
"created": "Wed, 9 May 2012 15:05:48 GMT"
}
] | 1,336,953,600,000 | [
[
"Cozman",
"Fabio Gagliardi",
""
],
[
"Polastro",
"Rodrigo Bellizia",
""
]
] |
1205.2655 | Ido Cohn | Ido Cohn, Tal El-Hay, Nir Friedman, Raz Kupferman | Mean Field Variational Approximation for Continuous-Time Bayesian
Networks | Appears in Proceedings of the Twenty-Fifth Conference on Uncertainty
in Artificial Intelligence (UAI2009) | null | null | UAI-P-2009-PG-91-100 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Continuous-time Bayesian networks is a natural structured representation
language for multicomponent stochastic processes that evolve continuously over
time. Despite the compact representation, inference in such models is
intractable even in relatively simple structured networks. Here we introduce a
mean field variational approximation in which we use a product of inhomogeneous
Markov processes to approximate a distribution over trajectories. This
variational approach leads to a globally consistent distribution, which can be
efficiently queried. Additionally, it provides a lower bound on the probability
of observations, thus making it attractive for learning tasks. We provide the
theoretical foundations for the approximation, an efficient implementation that
exploits the wide range of highly optimized ordinary differential equations
(ODE) solvers, experimentally explore characterizations of processes for which
this approximation is suitable, and show applications to a large-scale
realworld inference problem.
| [
{
"version": "v1",
"created": "Wed, 9 May 2012 14:57:02 GMT"
}
] | 1,336,953,600,000 | [
[
"Cohn",
"Ido",
""
],
[
"El-Hay",
"Tal",
""
],
[
"Friedman",
"Nir",
""
],
[
"Kupferman",
"Raz",
""
]
] |
1205.2659 | Blai Bonet | Blai Bonet | Deterministic POMDPs Revisited | Appears in Proceedings of the Twenty-Fifth Conference on Uncertainty
in Artificial Intelligence (UAI2009) | null | null | UAI-P-2009-PG-59-66 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We study a subclass of POMDPs, called Deterministic POMDPs, that is
characterized by deterministic actions and observations. These models do not
provide the same generality of POMDPs yet they capture a number of interesting
and challenging problems, and permit more efficient algorithms. Indeed, some of
the recent work in planning is built around such assumptions mainly by the
quest of amenable models more expressive than the classical deterministic
models. We provide results about the fundamental properties of Deterministic
POMDPs, their relation with AND/OR search problems and algorithms, and their
computational complexity.
| [
{
"version": "v1",
"created": "Wed, 9 May 2012 14:50:18 GMT"
}
] | 1,336,953,600,000 | [
[
"Bonet",
"Blai",
""
]
] |
1205.2665 | Daniel Andrade | Daniel Andrade, Bernhard Sick | Lower Bound Bayesian Networks - An Efficient Inference of Lower Bounds
on Probability Distributions in Bayesian Networks | Appears in Proceedings of the Twenty-Fifth Conference on Uncertainty
in Artificial Intelligence (UAI2009) | null | null | UAI-P-2009-PG-10-18 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We present a new method to propagate lower bounds on conditional probability
distributions in conventional Bayesian networks. Our method guarantees to
provide outer approximations of the exact lower bounds. A key advantage is that
we can use any available algorithms and tools for Bayesian networks in order to
represent and infer lower bounds. This new method yields results that are
provable exact for trees with binary variables, and results which are
competitive to existing approximations in credal networks for all other network
structures. Our method is not limited to a specific kind of network structure.
Basically, it is also not restricted to a specific kind of inference, but we
restrict our analysis to prognostic inference in this article. The
computational complexity is superior to that of other existing approaches.
| [
{
"version": "v1",
"created": "Wed, 9 May 2012 14:40:39 GMT"
}
] | 1,336,953,600,000 | [
[
"Andrade",
"Daniel",
""
],
[
"Sick",
"Bernhard",
""
]
] |
1205.2857 | Ping Zhu | Ping Zhu and Qiaoyan Wen | Operations on soft sets revisited | 8 pages | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Soft sets, as a mathematical tool for dealing with uncertainty, have recently
gained considerable attention, including some successful applications in
information processing, decision, demand analysis, and forecasting. To
construct new soft sets from given soft sets, some operations on soft sets have
been proposed. Unfortunately, such operations cannot keep all classical
set-theoretic laws true for soft sets. In this paper, we redefine the
intersection, complement, and difference of soft sets and investigate the
algebraic properties of these operations along with a known union operation. We
find that the new operation system on soft sets inherits all basic properties
of operations on classical sets, which justifies our definitions.
| [
{
"version": "v1",
"created": "Sun, 13 May 2012 13:21:59 GMT"
}
] | 1,426,809,600,000 | [
[
"Zhu",
"Ping",
""
],
[
"Wen",
"Qiaoyan",
""
]
] |
1205.3054 | Bruno Scherrer | Bruno Scherrer (INRIA Lorraine - LORIA), Victor Gabillon (INRIA Lille
- Nord Europe), Mohammad Ghavamzadeh (INRIA Lille - Nord Europe), Matthieu
Geist (UMI2958) | Approximate Modified Policy Iteration | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Modified policy iteration (MPI) is a dynamic programming (DP) algorithm that
contains the two celebrated policy and value iteration methods. Despite its
generality, MPI has not been thoroughly studied, especially its approximation
form which is used when the state and/or action spaces are large or infinite.
In this paper, we propose three implementations of approximate MPI (AMPI) that
are extensions of well-known approximate DP algorithms: fitted-value iteration,
fitted-Q iteration, and classification-based policy iteration. We provide error
propagation analyses that unify those for approximate policy and value
iteration. On the last classification-based implementation, we develop a
finite-sample analysis that shows that MPI's main parameter allows to control
the balance between the estimation error of the classifier and the overall
value function approximation.
| [
{
"version": "v1",
"created": "Mon, 14 May 2012 15:01:31 GMT"
},
{
"version": "v2",
"created": "Fri, 18 May 2012 06:56:47 GMT"
}
] | 1,337,558,400,000 | [
[
"Scherrer",
"Bruno",
"",
"INRIA Lorraine - LORIA"
],
[
"Gabillon",
"Victor",
"",
"INRIA Lille\n - Nord Europe"
],
[
"Ghavamzadeh",
"Mohammad",
"",
"INRIA Lille - Nord Europe"
],
[
"Geist",
"Matthieu",
"",
"UMI2958"
]
] |
1205.3964 | Yusuf Perwej | Yusuf Perwej, Ashish Chaturvedi | Machine Recognition of Hand Written Characters using Neural Networks | 4 pages, 1 Figure, ISSN:0975 - 8887 | International Journal of Computer Applications (IJCA) ,January
2011 Volume 14, Number 2, Pages 6-9 | 10.5120/1819-2380 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Even today in Twenty First Century Handwritten communication has its own
stand and most of the times, in daily life it is globally using as means of
communication and recording the information like to be shared with others.
Challenges in handwritten characters recognition wholly lie in the variation
and distortion of handwritten characters, since different people may use
different style of handwriting, and direction to draw the same shape of the
characters of their known script. This paper demonstrates the nature of
handwritten characters, conversion of handwritten data into electronic data,
and the neural network approach to make machine capable of recognizing hand
written characters.
| [
{
"version": "v1",
"created": "Thu, 17 May 2012 15:50:08 GMT"
}
] | 1,337,299,200,000 | [
[
"Perwej",
"Yusuf",
""
],
[
"Chaturvedi",
"Ashish",
""
]
] |
1205.5098 | Balwinder Sodhi | Balwinder Sodhi and Prabhakar T.V | A Simplified Description of Fuzzy TOPSIS | 3 pages | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | A simplified description of Fuzzy TOPSIS (Technique for Order Preference by
Similarity to Ideal Situation) is presented. We have adapted the TOPSIS
description from existing Fuzzy theory literature and distilled the bare
minimum concepts required for understanding and applying TOPSIS. An example has
been worked out to illustrate the application of TOPSIS for a multi-criteria
group decision making scenario.
| [
{
"version": "v1",
"created": "Wed, 23 May 2012 06:21:54 GMT"
},
{
"version": "v2",
"created": "Sat, 3 Jun 2017 07:18:30 GMT"
}
] | 1,496,707,200,000 | [
[
"Sodhi",
"Balwinder",
""
],
[
"T.",
"Prabhakar",
"V"
]
] |
1205.5866 | G K Panda | B. K. Tripathy, G. K. Panda | Approximate Equalities on Rough Intuitionistic Fuzzy Sets and an
Analysis of Approximate Equalities | null | IJCSI International Journal of Computer Science Issues, Vol. 9,
Issue 2, No 3, March 2012 ISSN (Online): 1694-0814 www.IJCSI.org | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In order to involve user knowledge in determining equality of sets, which may
not be equal in the mathematical sense, three types of approximate (rough)
equalities were introduced by Novotny and Pawlak ([8, 9, 10]). These notions
were generalized by Tripathy, Mitra and Ojha ([13]), who introduced the
concepts of approximate (rough) equivalences of sets. Rough equivalences
capture equality of sets at a higher level than rough equalities. More
properties of these concepts were established in [14]. Combining the conditions
for the two types of approximate equalities, two more approximate equalities
were introduced by Tripathy [12] and a comparative analysis of their relative
efficiency was provided. In [15], the four types of approximate equalities were
extended by considering rough fuzzy sets instead of only rough sets. In fact
the concepts of leveled approximate equalities were introduced and properties
were studied. In this paper we proceed further by introducing and studying the
approximate equalities based on rough intuitionistic fuzzy sets instead of
rough fuzzy sets. That is we introduce the concepts of approximate
(rough)equalities of intuitionistic fuzzy sets and study their properties. We
provide some real life examples to show the applications of rough equalities of
fuzzy sets and rough equalities of intuitionistic fuzzy sets.
| [
{
"version": "v1",
"created": "Sat, 26 May 2012 09:49:38 GMT"
}
] | 1,338,249,600,000 | [
[
"Tripathy",
"B. K.",
""
],
[
"Panda",
"G. K.",
""
]
] |
1206.0259 | Stevan Harnad | Stevan Harnad | The Causal Topography of Cognition | 11 pages, 0 figures, 10 references, Journal of Cognitive Science 13
2012 | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The causal structure of cognition can be simulated but not implemented
computationally, just as the causal structure of a comet can be simulated but
not implemented computationally. The only thing that allows us even to imagine
otherwise is that cognition, unlike a comet, is invisible (to all but the
cognizer).
| [
{
"version": "v1",
"created": "Sat, 25 Feb 2012 14:59:49 GMT"
},
{
"version": "v2",
"created": "Mon, 4 Jun 2012 00:45:35 GMT"
}
] | 1,338,854,400,000 | [
[
"Harnad",
"Stevan",
""
]
] |
1206.0918 | Omri Mohamed Nazih | Abdelkader Heni, Mohamed Nazih Omri and Adel Alimi | Fuzzy Knowledge Representation Based on Possibilistic and Necessary
Bayesian Networks | ISSN: 1790-0832 | WSEAS Transactions on Information Science & Applications Issue 2,
Volume 3, February 2006, 224-231 | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Within the framework proposed in this paper, we address the issue of
extending the certain networks to a fuzzy certain networks in order to cope
with a vagueness and limitations of existing models for decision under
imprecise and uncertain knowledge. This paper proposes a framework that
combines two disciplines to exploit their own advantages in uncertain and
imprecise knowledge representation problems. The framework proposed is a
possibilistic logic based one in which Bayesian nodes and their properties are
represented by local necessity-valued knowledge base. Data in properties are
interpreted as set of valuated formulas. In our contribution possibilistic
Bayesian networks have a qualitative part and a quantitative part, represented
by local knowledge bases. The general idea is to study how a fusion of these
two formalisms would permit representing compact way to solve efficiently
problems for knowledge representation. We show how to apply possibility and
necessity measures to the problem of knowledge representation with large scale
data. On the other hand fuzzification of crisp certainty degrees to fuzzy
variables improves the quality of the network and tends to bring smoothness and
robustness in the network performance. The general aim is to provide a new
approach for decision under uncertainty that combines three methodologies:
Bayesian networks certainty distribution and fuzzy logic.
| [
{
"version": "v1",
"created": "Tue, 5 Jun 2012 13:13:21 GMT"
}
] | 1,338,940,800,000 | [
[
"Heni",
"Abdelkader",
""
],
[
"Omri",
"Mohamed Nazih",
""
],
[
"Alimi",
"Adel",
""
]
] |
1206.1061 | Omri Mohamed Nazih | Mohamed Nazih Omri and Mohamed Ali Mahjoub | Use of Fuzzy Sets in Semantic Nets for Providing On-Line Assistance to
User of Technological Systems | null | International Workshop on Intelligent Knowledge Management
Techniques I-KOMAT'2002-KES'2002. p. 1444-1449. Podere d'Ombriano, Crema,
Italy, (2002) | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The main objective of this paper is to develop a new semantic Network
structure, based on the fuzzy sets theory, used in Artificial Intelligent
system in order to provide effective on-line assistance to users of new
technological systems. This Semantic Networks is used to describe the knowledge
of an "ideal" expert while fuzzy sets are used both to describe the approximate
and uncertain knowledge of novice users who intervene to match fuzzy labels of
a query with categories from an "ideal" expert. The technical system we
consider is a word processor software, with Objects such as "Word" and Goals
such as "Cut" or "Copy". We suggest to consider the set of the system's Goals
as a set of linguistic variables to which corresponds a set of possible
linguistic values based on the fuzzy set. We consider, therefore, a set of
interpretation's levels for these possible values to which corresponds a set of
membership functions. We also propose a method to measure the similarity degree
between different fuzzy linguistic variables for the partition of the semantic
network in class of similar objects to make easy the diagnosis of the user's
fuzzy queries.
| [
{
"version": "v1",
"created": "Tue, 5 Jun 2012 20:05:48 GMT"
}
] | 1,339,027,200,000 | [
[
"Omri",
"Mohamed Nazih",
""
],
[
"Mahjoub",
"Mohamed Ali",
""
]
] |
1206.1291 | Reza Tavoli | Mohammadreza Keyvanpour, Reza Tavoli | Feature Weighting for Improving Document Image Retrieval System
Performance | null | International Journal of Computer Science Issues, Vol 9, Issue 3,
No 3 (2012) 125-130 | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Feature weighting is a technique used to approximate the optimal degree of
influence of individual features. This paper presents a feature weighting
method for Document Image Retrieval System (DIRS) based on keyword spotting. In
this method, we weight the feature using coefficient of multiple correlations.
Coefficient of multiple correlations can be used to describe the synthesized
effects and correlation of each feature. The aim of this paper is to show that
feature weighting increases the performance of DIRS. After applying the feature
weighting method to DIRS the average precision is 93.23% and average recall
become 98.66% respectively
| [
{
"version": "v1",
"created": "Wed, 6 Jun 2012 18:20:27 GMT"
}
] | 1,339,372,800,000 | [
[
"Keyvanpour",
"Mohammadreza",
""
],
[
"Tavoli",
"Reza",
""
]
] |
1206.1319 | Omri Mohamed Nazih | Abdelkader Heni, Mohamed Nazih Omri and Adel Alimi | Certain Bayesian Network based on Fuzzy knowledge Bases | arXiv admin note: substantial text overlap with 1206.0918 | International Conference on Internet &, Information Technology in
Modern Organizations (5th IBIMA), p. 826-832, Cairo, Egypt, December, 13-15
(2005) | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this paper, we are trying to examine trade offs between fuzzy logic and
certain Bayesian networks and we propose to combine their respective advantages
into fuzzy certain Bayesian networks (FCBN), a certain Bayesian networks of
fuzzy random variables. This paper deals with different definitions and
classifications of uncertainty, sources of uncertainty, and theories and
methodologies presented to deal with uncertainty. Fuzzification of crisp
certainty degrees to fuzzy variables improves the quality of the network and
tends to bring smoothness and robustness in the network performance. The aim is
to provide a new approach for decision under uncertainty that combines three
methodologies: Bayesian networks certainty distribution and fuzzy logic. Within
the framework proposed in this paper, we address the issue of extending the
certain networks to a fuzzy certain networks in order to cope with a vagueness
and limitations of existing models for decision under imprecise and uncertain
knowledge.
| [
{
"version": "v1",
"created": "Tue, 5 Jun 2012 19:53:43 GMT"
}
] | 1,339,027,200,000 | [
[
"Heni",
"Abdelkader",
""
],
[
"Omri",
"Mohamed Nazih",
""
],
[
"Alimi",
"Adel",
""
]
] |
1206.1414 | Amin Nezarat | Shahab Firouzi (Department of Computer engineering, Yazd Branch,
Islamic Azad University, Yazd, Iran), Amin Nezarat (Department of Computer
engineering, Yazd Branch, Islamic Azad University, Yazd, Iran) | An Intelligent Approach for Negotiating between chains in Supply Chain
Management Systems | 10 | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Holding commercial negotiations and selecting the best supplier in supply
chain management systems are among weaknesses of producers in production
process. Therefore, applying intelligent systems may have an effective role in
increased speed and improved quality in the selections .This paper introduces a
system which tries to trade using multi-agents systems and holding negotiations
between any agents. In this system, an intelligent agent is considered for each
segment of chains which it tries to send order and receive the response with
attendance in negotiation medium and communication with other agents .This
paper introduces how to communicate between agents, characteristics of
multi-agent and standard registration medium of each agent in the environment.
JADE (Java Application Development Environment) was used for implementation and
simulation of agents cooperation.
| [
{
"version": "v1",
"created": "Thu, 7 Jun 2012 07:50:43 GMT"
}
] | 1,339,113,600,000 | [
[
"Firouzi",
"Shahab",
"",
"Department of Computer engineering, Yazd Branch,\n Islamic Azad University, Yazd, Iran"
],
[
"Nezarat",
"Amin",
"",
"Department of Computer\n engineering, Yazd Branch, Islamic Azad University, Yazd, Iran"
]
] |
1206.1418 | Dinh Que Tran | Thuy Van T. Duong, Dinh Que Tran and Cong Hung Tran | A weighted combination similarity measure for mobility patterns in
wireless networks | 15 pages, 2 figures; International Journal of Computer Networks &
Communications (IJCNC) http://airccse.org/journal/ijc2012.html | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The similarity between trajectory patterns in clustering has played an
important role in discovering movement behaviour of different groups of mobile
objects. Several approaches have been proposed to measure the similarity
between sequences in trajectory data. Most of these measures are based on
Euclidean space or on spatial network and some of them have been concerned with
temporal aspect or ordering types. However, they are not appropriate to
characteristics of spatiotemporal mobility patterns in wireless networks. In
this paper, we propose a new similarity measure for mobility patterns in
cellular space of wireless network. The framework for constructing our measure
is composed of two phases as follows. First, we present formal definitions to
capture mathematically two spatial and temporal similarity measures for
mobility patterns. And then, we define the total similarity measure by means of
a weighted combination of these similarities. The truth of the partial and
total similarity measures are proved in mathematics. Furthermore, instead of
the time interval or ordering, our work makes use of the timestamp at which two
mobility patterns share the same cell. A case study is also described to give a
comparison of the combination measure with other ones.
| [
{
"version": "v1",
"created": "Thu, 7 Jun 2012 07:58:18 GMT"
}
] | 1,339,113,600,000 | [
[
"Duong",
"Thuy Van T.",
""
],
[
"Tran",
"Dinh Que",
""
],
[
"Tran",
"Cong Hung",
""
]
] |
1206.1458 | Shervan Fekri ershad | Shervan Fekri Ershad and Sattar Hashemi | Dispelling Classes Gradually to Improve Quality of Feature Reduction
Approaches | 11 Pages, 5 Figure, 7 Tables; Advanced Computing: An International
Journal (ACIJ), Vol.3, No.3, May 2012 | null | 10.5121/acij.2012.3310 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Feature reduction is an important concept which is used for reducing
dimensions to decrease the computation complexity and time of classification.
Since now many approaches have been proposed for solving this problem, but
almost all of them just presented a fix output for each input dataset that some
of them aren't satisfied cases for classification. In this we proposed an
approach as processing input dataset to increase accuracy rate of each feature
extraction methods. First of all, a new concept called dispelling classes
gradually (DCG) is proposed to increase separability of classes based on their
labels. Next, this method is used to process input dataset of the feature
reduction approaches to decrease the misclassification error rate of their
outputs more than when output is achieved without any processing. In addition
our method has a good quality to collate with noise based on adapting dataset
with feature reduction approaches. In the result part, two conditions (With
process and without that) are compared to support our idea by using some of UCI
datasets.
| [
{
"version": "v1",
"created": "Thu, 7 Jun 2012 11:52:21 GMT"
}
] | 1,339,113,600,000 | [
[
"Ershad",
"Shervan Fekri",
""
],
[
"Hashemi",
"Sattar",
""
]
] |
1206.1534 | Sumathi Gnanasekaran | G. Sumathi and R. Raju | Software Aging Analysis of Web Server Using Neural Networks | 11 pages, 8 figures, 1 table; International Journal of Artificial
Intelligence & Applications (IJAIA), Vol.3, No.3, May 2012 | null | 10.5121/ijaia.2012.3302 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Software aging is a phenomenon that refers to progressive performance
degradation or transient failures or even crashes in long running software
systems such as web servers. It mainly occurs due to the deterioration of
operating system resource, fragmentation and numerical error accumulation. A
primitive method to fight against software aging is software rejuvenation.
Software rejuvenation is a proactive fault management technique aimed at
cleaning up the system internal state to prevent the occurrence of more severe
crash failures in the future. It involves occasionally stopping the running
software, cleaning its internal state and restarting it. An optimized schedule
for performing the software rejuvenation has to be derived in advance because a
long running application could not be put down now and then as it may lead to
waste of cost. This paper proposes a method to derive an accurate and optimized
schedule for rejuvenation of a web server (Apache) by using Radial Basis
Function (RBF) based Feed Forward Neural Network, a variant of Artificial
Neural Networks (ANN). Aging indicators are obtained through experimental setup
involving Apache web server and clients, which acts as input to the neural
network model. This method is better than existing ones because usage of RBF
leads to better accuracy and speed in convergence.
| [
{
"version": "v1",
"created": "Thu, 7 Jun 2012 15:52:46 GMT"
}
] | 1,339,113,600,000 | [
[
"Sumathi",
"G.",
""
],
[
"Raju",
"R.",
""
]
] |
1206.1678 | Magesh George | G. Mageshwari and E. Grace Mary Kanaga | A Distributed Optimized Patient Scheduling using Partial Information | 11 pages, 8 figures | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | A software agent may be a member of a Multi-Agent System (MAS) which is
collectively performing a range of complex and intelligent tasks. In the
hospital, scheduling decisions are finding difficult to schedule because of the
dynamic changes and distribution. In order to face this problem with dynamic
changes in the hospital, a new method, Distributed Optimized Patient Scheduling
with Grouping (DOPSG) has been proposed. The goal of this method is that there
is no necessity for knowing patient agents information globally. With minimal
information this method works effectively. Scheduling problem can be solved for
multiple departments in the hospital. Patient agents have been scheduled to the
resource agent based on the patient priority to reduce the waiting time of
patient agent and to reduce idle time of resources.
| [
{
"version": "v1",
"created": "Fri, 8 Jun 2012 07:02:06 GMT"
}
] | 1,339,372,800,000 | [
[
"Mageshwari",
"G.",
""
],
[
"Kanaga",
"E. Grace Mary",
""
]
] |
1206.1724 | Omri Mohamed Nazih | Mohamed Nazih Omri | Softening Fuzzy Knowledge Representation Tool with the Learning of New
Words in Natural Language | null | International Conference on Artificial and Computational
Intelligence for Decision, Control and Automation in Engineering and
Industrial Applications,(ACIDCA'2000). p. 190-194. Monastir, Tunisia,2000 | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The approach described here allows using membership function to represent
imprecise and uncertain knowledge by learning in Fuzzy Semantic Networks. This
representation has a great practical interest due to the possibility to realize
on the one hand, the construction of this membership function from a simple
value expressing the degree of interpretation of an Object or a Goal as
compared to an other and on the other hand, the adjustment of the membership
function during the apprenticeship. We show, how to use these membership
functions to represent the interpretation of an Object (respectively of a Goal)
user as compared to an system Object (respectively to a Goal). We also show the
possibility to make decision for each representation of an user Object compared
to a system Object. This decision is taken by determining decision coefficient
calculates according to the nucleus of the membership function of the user
Object.
| [
{
"version": "v1",
"created": "Fri, 8 Jun 2012 10:51:51 GMT"
}
] | 1,339,372,800,000 | [
[
"Omri",
"Mohamed Nazih",
""
]
] |
1206.1794 | Omri Mohamed Nazih | Mohamed Nazih Omri | Fuzzy Knowledge Representation, Learning and Optimization with Bayesian
Analysis in Fuzzy Semantic Networks | null | The 6th International Conference of Neural Information Processing.
ICONIP'99. p. 345-351. Perth. Austria, 1999 | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper presents a method of optimization, based on both Bayesian Analysis
technical and Gallois Lattice, of a Fuzzy Semantic Networks. The technical
System we use learn by interpreting an unknown word using the links created
between this new word and known words. The main link is provided by the context
of the query. When novice's query is confused with an unknown verb (goal)
applied to a known noun denoting either an object in the ideal user's Network
or an object in the user's Network, the system infer that this new verb
corresponds to one of the known goal. With the learning of new words in natural
language as the interpretation, which was produced in agreement with the user,
the system improves its representation scheme at each experiment with a new
user and, in addition, takes advantage of previous discussions with users. The
semantic Net of user objects thus obtained by these kinds of learning is not
always optimal because some relationships between couple of user objects can be
generalized and others suppressed according to values of forces that
characterize them. Indeed, to simplify the obtained Net, we propose to proceed
to an inductive Bayesian analysis, on the Net obtained from Gallois lattice.
The objective of this analysis can be seen as an operation of filtering of the
obtained descriptive graph.
| [
{
"version": "v1",
"created": "Fri, 8 Jun 2012 15:41:03 GMT"
}
] | 1,339,372,800,000 | [
[
"Omri",
"Mohamed Nazih",
""
]
] |
1206.2347 | Omri Mohamed Nazih | Mohamed Nazih Omri | Uncertain and Approximative Knowledge Representation to Reasoning on
Classification with a Fuzzy Networks Based System | arXiv admin note: text overlap with arXiv:1206.1794 | The 8th IEEE International Conference on Fuzzy Systems,
FUZZ-IEEE'99. p. 1632-1637. Seoul. Korea,1999 | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The approach described here allows to use the fuzzy Object Based
Representation of imprecise and uncertain knowledge. This representation has a
great practical interest due to the possibility to realize reasoning on
classification with a fuzzy semantic network based system. For instance, the
distinction between necessary, possible and user classes allows to take into
account exceptions that may appear on fuzzy knowledge-base and facilitates
integration of user's Objects in the base. This approach describes the
theoretical aspects of the architecture of the whole experimental A.I. system
we built in order to provide effective on-line assistance to users of new
technological systems: the understanding of "how it works" and "how to complete
tasks" from queries in quite natural languages. In our model, procedural
semantic networks are used to describe the knowledge of an "ideal" expert while
fuzzy sets are used both to describe the approximative and uncertain knowledge
of novice users in fuzzy semantic networks which intervene to match fuzzy
labels of a query with categories from our "ideal" expert.
| [
{
"version": "v1",
"created": "Mon, 11 Jun 2012 20:51:51 GMT"
}
] | 1,339,545,600,000 | [
[
"Omri",
"Mohamed Nazih",
""
]
] |
1206.3111 | Francesco Calimeri | Francesco Calimeri, Giovambattista Ianni, Francesco Ricca | The third open Answer Set Programming competition | 37 pages, 12 figures, 1 table - To appear in Theory and Practice of
Logic Programming (TPLP) | Theory and Practice of Logic Programming 14 (2014) 117-135 | 10.1017/S1471068412000105 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Answer Set Programming (ASP) is a well-established paradigm of declarative
programming in close relationship with other declarative formalisms such as SAT
Modulo Theories, Constraint Handling Rules, FO(.), PDDL and many others. Since
its first informal editions, ASP systems have been compared in the now
well-established ASP Competition. The Third (Open) ASP Competition, as the
sequel to the ASP Competitions Series held at the University of Potsdam in
Germany (2006-2007) and at the University of Leuven in Belgium in 2009, took
place at the University of Calabria (Italy) in the first half of 2011.
Participants competed on a pre-selected collection of benchmark problems, taken
from a variety of domains as well as real world applications. The Competition
ran on two tracks: the Model and Solve (M&S) Track, based on an open problem
encoding, and open language, and open to any kind of system based on a
declarative specification paradigm; and the System Track, run on the basis of
fixed, public problem encodings, written in a standard ASP language. This paper
discusses the format of the Competition and the rationale behind it, then
reports the results for both tracks. Comparison with the second ASP competition
and state-of-the-art solutions for some of the benchmark domains is eventually
discussed.
To appear in Theory and Practice of Logic Programming (TPLP).
| [
{
"version": "v1",
"created": "Thu, 14 Jun 2012 14:03:28 GMT"
}
] | 1,582,070,400,000 | [
[
"Calimeri",
"Francesco",
""
],
[
"Ianni",
"Giovambattista",
""
],
[
"Ricca",
"Francesco",
""
]
] |
1206.3232 | Vibhav Gogate | Vibhav Gogate, Rina Dechter | AND/OR Importance Sampling | Appears in Proceedings of the Twenty-Fourth Conference on Uncertainty
in Artificial Intelligence (UAI2008) | null | null | UAI-P-2008-PG-212-219 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The paper introduces AND/OR importance sampling for probabilistic graphical
models. In contrast to importance sampling, AND/OR importance sampling caches
samples in the AND/OR space and then extracts a new sample mean from the stored
samples. We prove that AND/OR importance sampling may have lower variance than
importance sampling; thereby providing a theoretical justification for
preferring it over importance sampling. Our empirical evaluation demonstrates
that AND/OR importance sampling is far more accurate than importance sampling
in many cases.
| [
{
"version": "v1",
"created": "Wed, 13 Jun 2012 12:33:40 GMT"
}
] | 1,339,977,600,000 | [
[
"Gogate",
"Vibhav",
""
],
[
"Dechter",
"Rina",
""
]
] |
1206.3233 | Alejandro Isaza | Alejandro Isaza, Csaba Szepesvari, Vadim Bulitko, Russell Greiner | Speeding Up Planning in Markov Decision Processes via Automatically
Constructed Abstractions | Appears in Proceedings of the Twenty-Fourth Conference on Uncertainty
in Artificial Intelligence (UAI2008) | null | null | UAI-P-2008-PG-306-314 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this paper, we consider planning in stochastic shortest path (SSP)
problems, a subclass of Markov Decision Problems (MDP). We focus on medium-size
problems whose state space can be fully enumerated. This problem has numerous
important applications, such as navigation and planning under uncertainty. We
propose a new approach for constructing a multi-level hierarchy of
progressively simpler abstractions of the original problem. Once computed, the
hierarchy can be used to speed up planning by first finding a policy for the
most abstract level and then recursively refining it into a solution to the
original problem. This approach is fully automated and delivers a speed-up of
two orders of magnitude over a state-of-the-art MDP solver on sample problems
while returning near-optimal solutions. We also prove theoretical bounds on the
loss of solution optimality resulting from the use of abstractions.
| [
{
"version": "v1",
"created": "Wed, 13 Jun 2012 12:34:35 GMT"
}
] | 1,339,977,600,000 | [
[
"Isaza",
"Alejandro",
""
],
[
"Szepesvari",
"Csaba",
""
],
[
"Bulitko",
"Vadim",
""
],
[
"Greiner",
"Russell",
""
]
] |
1206.3244 | James Cussens | James Cussens | Bayesian network learning by compiling to weighted MAX-SAT | Appears in Proceedings of the Twenty-Fourth Conference on Uncertainty
in Artificial Intelligence (UAI2008) | null | null | UAI-P-2008-PG-105-112 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The problem of learning discrete Bayesian networks from data is encoded as a
weighted MAX-SAT problem and the MaxWalkSat local search algorithm is used to
address it. For each dataset, the per-variable summands of the (BDeu) marginal
likelihood for different choices of parents ('family scores') are computed
prior to applying MaxWalkSat. Each permissible choice of parents for each
variable is encoded as a distinct propositional atom and the associated family
score encoded as a 'soft' weighted single-literal clause. Two approaches to
enforcing acyclicity are considered: either by encoding the ancestor relation
or by attaching a total order to each graph and encoding that. The latter
approach gives better results. Learning experiments have been conducted on 21
synthetic datasets sampled from 7 BNs. The largest dataset has 10,000
datapoints and 60 variables producing (for the 'ancestor' encoding) a weighted
CNF input file with 19,932 atoms and 269,367 clauses. For most datasets,
MaxWalkSat quickly finds BNs with higher BDeu score than the 'true' BN. The
effect of adding prior information is assessed. It is further shown that
Bayesian model averaging can be effected by collecting BNs generated during the
search.
| [
{
"version": "v1",
"created": "Wed, 13 Jun 2012 15:06:22 GMT"
}
] | 1,339,977,600,000 | [
[
"Cussens",
"James",
""
]
] |
1206.3246 | Cassio Polpo de Campos | Cassio Polpo de Campos, Qiang Ji | Strategy Selection in Influence Diagrams using Imprecise Probabilities | Appears in Proceedings of the Twenty-Fourth Conference on Uncertainty
in Artificial Intelligence (UAI2008) | null | null | UAI-P-2008-PG-121-128 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper describes a new algorithm to solve the decision making problem in
Influence Diagrams based on algorithms for credal networks. Decision nodes are
associated to imprecise probability distributions and a reformulation is
introduced that finds the global maximum strategy with respect to the expected
utility. We work with Limited Memory Influence Diagrams, which generalize most
Influence Diagram proposals and handle simultaneous decisions. Besides the
global optimum method, we explore an anytime approximate solution with a
guaranteed maximum error and show that imprecise probabilities are handled in a
straightforward way. Complexity issues and experiments with random diagrams and
an effects-based military planning problem are discussed.
| [
{
"version": "v1",
"created": "Wed, 13 Jun 2012 15:08:24 GMT"
}
] | 1,339,977,600,000 | [
[
"de Campos",
"Cassio Polpo",
""
],
[
"Ji",
"Qiang",
""
]
] |
1206.3248 | Quang Duong | Quang Duong, Michael P. Wellman, Satinder Singh | Knowledge Combination in Graphical Multiagent Model | Appears in Proceedings of the Twenty-Fourth Conference on Uncertainty
in Artificial Intelligence (UAI2008) | null | null | UAI-P-2008-PG-145-152 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | A graphical multiagent model (GMM) represents a joint distribution over the
behavior of a set of agents. One source of knowledge about agents' behavior may
come from gametheoretic analysis, as captured by several graphical game
representations developed in recent years. GMMs generalize this approach to
express arbitrary distributions, based on game descriptions or other sources of
knowledge bearing on beliefs about agent behavior. To illustrate the
flexibility of GMMs, we exhibit game-derived models that allow probabilistic
deviation from equilibrium, as well as models based on heuristic action choice.
We investigate three different methods of integrating these models into a
single model representing the combined knowledge sources. To evaluate the
predictive performance of the combined model, we treat as actual outcome the
behavior produced by a reinforcement learning process. We find that combining
the two knowledge sources, using any of the methods, provides better
predictions than either source alone. Among the combination methods, mixing
data outperforms the opinion pool and direct update methods investigated in
this empirical trial.
| [
{
"version": "v1",
"created": "Wed, 13 Jun 2012 15:09:25 GMT"
}
] | 1,339,977,600,000 | [
[
"Duong",
"Quang",
""
],
[
"Wellman",
"Michael P.",
""
],
[
"Singh",
"Satinder",
""
]
] |
1206.3250 | Frederick Eberhardt | Frederick Eberhardt | Almost Optimal Intervention Sets for Causal Discovery | Appears in Proceedings of the Twenty-Fourth Conference on Uncertainty
in Artificial Intelligence (UAI2008) | null | null | UAI-P-2008-PG-161-168 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We conjecture that the worst case number of experiments necessary and
sufficient to discover a causal graph uniquely given its observational Markov
equivalence class can be specified as a function of the largest clique in the
Markov equivalence class. We provide an algorithm that computes intervention
sets that we believe are optimal for the above task. The algorithm builds on
insights gained from the worst case analysis in Eberhardt et al. (2005) for
sequences of experiments when all possible directed acyclic graphs over N
variables are considered. A simulation suggests that our conjecture is correct.
We also show that a generalization of our conjecture to other classes of
possible graph hypotheses cannot be given easily, and in what sense the
algorithm is then no longer optimal.
| [
{
"version": "v1",
"created": "Wed, 13 Jun 2012 15:10:21 GMT"
}
] | 1,339,977,600,000 | [
[
"Eberhardt",
"Frederick",
""
]
] |
1206.3263 | Eric A. Hansen | Eric A. Hansen | Sparse Stochastic Finite-State Controllers for POMDPs | Appears in Proceedings of the Twenty-Fourth Conference on Uncertainty
in Artificial Intelligence (UAI2008) | null | null | UAI-P-2008-PG-256-263 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Bounded policy iteration is an approach to solving infinite-horizon POMDPs
that represents policies as stochastic finite-state controllers and iteratively
improves a controller by adjusting the parameters of each node using linear
programming. In the original algorithm, the size of the linear programs, and
thus the complexity of policy improvement, depends on the number of parameters
of each node, which grows with the size of the controller. But in practice, the
number of parameters of a node with non-zero values is often very small, and
does not grow with the size of the controller. Based on this observation, we
develop a version of bounded policy iteration that leverages the sparse
structure of a stochastic finite-state controller. In each iteration, it
improves a policy by the same amount as the original algorithm, but with much
better scalability.
| [
{
"version": "v1",
"created": "Wed, 13 Jun 2012 15:34:42 GMT"
}
] | 1,339,977,600,000 | [
[
"Hansen",
"Eric A.",
""
]
] |
1206.3264 | Hannaneh Hajishirzi | Hannaneh Hajishirzi, Eyal Amir | Sampling First Order Logical Particles | Appears in Proceedings of the Twenty-Fourth Conference on Uncertainty
in Artificial Intelligence (UAI2008) | null | null | UAI-P-2008-PG-248-255 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Approximate inference in dynamic systems is the problem of estimating the
state of the system given a sequence of actions and partial observations. High
precision estimation is fundamental in many applications like diagnosis,
natural language processing, tracking, planning, and robotics. In this paper we
present an algorithm that samples possible deterministic executions of a
probabilistic sequence. The algorithm takes advantage of a compact
representation (using first order logic) for actions and world states to
improve the precision of its estimation. Theoretical and empirical results show
that the algorithm's expected error is smaller than propositional sampling and
Sequential Monte Carlo (SMC) sampling techniques.
| [
{
"version": "v1",
"created": "Wed, 13 Jun 2012 15:35:12 GMT"
}
] | 1,339,977,600,000 | [
[
"Hajishirzi",
"Hannaneh",
""
],
[
"Amir",
"Eyal",
""
]
] |
1206.3265 | Johan Kwisthout | Johan Kwisthout, Linda C. van der Gaag | The Computational Complexity of Sensitivity Analysis and Parameter
Tuning | Appears in Proceedings of the Twenty-Fourth Conference on Uncertainty
in Artificial Intelligence (UAI2008) | null | null | UAI-P-2008-PG-349-356 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | While known algorithms for sensitivity analysis and parameter tuning in
probabilistic networks have a running time that is exponential in the size of
the network, the exact computational complexity of these problems has not been
established as yet. In this paper we study several variants of the tuning
problem and show that these problems are NPPP-complete in general. We further
show that the problems remain NP-complete or PP-complete, for a number of
restricted variants. These complexity results provide insight in whether or not
recent achievements in sensitivity analysis and tuning can be extended to more
general, practicable methods.
| [
{
"version": "v1",
"created": "Wed, 13 Jun 2012 15:35:54 GMT"
}
] | 1,339,977,600,000 | [
[
"Kwisthout",
"Johan",
""
],
[
"van der Gaag",
"Linda C.",
""
]
] |
1206.3266 | Branislav Kveton | Branislav Kveton, Milos Hauskrecht | Partitioned Linear Programming Approximations for MDPs | Appears in Proceedings of the Twenty-Fourth Conference on Uncertainty
in Artificial Intelligence (UAI2008) | null | null | UAI-P-2008-PG-341-348 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Approximate linear programming (ALP) is an efficient approach to solving
large factored Markov decision processes (MDPs). The main idea of the method is
to approximate the optimal value function by a set of basis functions and
optimize their weights by linear programming (LP). This paper proposes a new
ALP approximation. Comparing to the standard ALP formulation, we decompose the
constraint space into a set of low-dimensional spaces. This structure allows
for solving the new LP efficiently. In particular, the constraints of the LP
can be satisfied in a compact form without an exponential dependence on the
treewidth of ALP constraints. We study both practical and theoretical aspects
of the proposed approach. Moreover, we demonstrate its scale-up potential on an
MDP with more than 2^100 states.
| [
{
"version": "v1",
"created": "Wed, 13 Jun 2012 15:36:14 GMT"
}
] | 1,339,977,600,000 | [
[
"Kveton",
"Branislav",
""
],
[
"Hauskrecht",
"Milos",
""
]
] |
1206.3271 | Daniel Lowd | Daniel Lowd, Pedro Domingos | Learning Arithmetic Circuits | Appears in Proceedings of the Twenty-Fourth Conference on Uncertainty
in Artificial Intelligence (UAI2008) | null | null | UAI-P-2008-PG-383-392 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Graphical models are usually learned without regard to the cost of doing
inference with them. As a result, even if a good model is learned, it may
perform poorly at prediction, because it requires approximate inference. We
propose an alternative: learning models with a score function that directly
penalizes the cost of inference. Specifically, we learn arithmetic circuits
with a penalty on the number of edges in the circuit (in which the cost of
inference is linear). Our algorithm is equivalent to learning a Bayesian
network with context-specific independence by greedily splitting conditional
distributions, at each step scoring the candidates by compiling the resulting
network into an arithmetic circuit, and using its size as the penalty. We show
how this can be done efficiently, without compiling a circuit from scratch for
each candidate. Experiments on several real-world domains show that our
algorithm is able to learn tractable models with very large treewidth, and
yields more accurate predictions than a standard context-specific Bayesian
network learner, in far less time.
| [
{
"version": "v1",
"created": "Wed, 13 Jun 2012 15:38:26 GMT"
}
] | 1,339,977,600,000 | [
[
"Lowd",
"Daniel",
""
],
[
"Domingos",
"Pedro",
""
]
] |
1206.3272 | Gregory Lawrence | Gregory Lawrence, Stuart Russell | Improving Gradient Estimation by Incorporating Sensor Data | Appears in Proceedings of the Twenty-Fourth Conference on Uncertainty
in Artificial Intelligence (UAI2008) | null | null | UAI-P-2008-PG-375-382 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | An efficient policy search algorithm should estimate the local gradient of
the objective function, with respect to the policy parameters, from as few
trials as possible. Whereas most policy search methods estimate this gradient
by observing the rewards obtained during policy trials, we show, both
theoretically and empirically, that taking into account the sensor data as well
gives better gradient estimates and hence faster learning. The reason is that
rewards obtained during policy execution vary from trial to trial due to noise
in the environment; sensor data, which correlates with the noise, can be used
to partially correct for this variation, resulting in an estimatorwith lower
variance.
| [
{
"version": "v1",
"created": "Wed, 13 Jun 2012 15:38:50 GMT"
}
] | 1,339,977,600,000 | [
[
"Lawrence",
"Gregory",
""
],
[
"Russell",
"Stuart",
""
]
] |
1206.3276 | Ulf Nielsen | Ulf Nielsen, Jean-Philippe Pellet, Andr\'e Elisseeff | Explanation Trees for Causal Bayesian Networks | Appears in Proceedings of the Twenty-Fourth Conference on Uncertainty
in Artificial Intelligence (UAI2008) | null | null | UAI-P-2008-PG-427-434 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Bayesian networks can be used to extract explanations about the observed
state of a subset of variables. In this paper, we explicate the desiderata of
an explanation and confront them with the concept of explanation proposed by
existing methods. The necessity of taking into account causal approaches when a
causal graph is available is discussed. We then introduce causal explanation
trees, based on the construction of explanation trees using the measure of
causal information ow (Ay and Polani, 2006). This approach is compared to
several other methods on known networks.
| [
{
"version": "v1",
"created": "Wed, 13 Jun 2012 15:41:30 GMT"
}
] | 1,339,977,600,000 | [
[
"Nielsen",
"Ulf",
""
],
[
"Pellet",
"Jean-Philippe",
""
],
[
"Elisseeff",
"André",
""
]
] |
1206.3281 | Stephane Ross | Stephane Ross, Joelle Pineau | Model-Based Bayesian Reinforcement Learning in Large Structured Domains | Appears in Proceedings of the Twenty-Fourth Conference on Uncertainty
in Artificial Intelligence (UAI2008) | null | null | UAI-P-2008-PG-476-483 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Model-based Bayesian reinforcement learning has generated significant
interest in the AI community as it provides an elegant solution to the optimal
exploration-exploitation tradeoff in classical reinforcement learning.
Unfortunately, the applicability of this type of approach has been limited to
small domains due to the high complexity of reasoning about the joint posterior
over model parameters. In this paper, we consider the use of factored
representations combined with online planning techniques, to improve
scalability of these methods. The main contribution of this paper is a Bayesian
framework for learning the structure and parameters of a dynamical system,
while also simultaneously planning a (near-)optimal sequence of actions.
| [
{
"version": "v1",
"created": "Wed, 13 Jun 2012 15:43:32 GMT"
}
] | 1,339,977,600,000 | [
[
"Ross",
"Stephane",
""
],
[
"Pineau",
"Joelle",
""
]
] |
1206.3282 | Sebastian Riedel | Sebastian Riedel | Improving the Accuracy and Efficiency of MAP Inference for Markov Logic | Appears in Proceedings of the Twenty-Fourth Conference on Uncertainty
in Artificial Intelligence (UAI2008) | null | null | UAI-P-2008-PG-468-475 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this work we present Cutting Plane Inference (CPI), a Maximum A Posteriori
(MAP) inference method for Statistical Relational Learning. Framed in terms of
Markov Logic and inspired by the Cutting Plane Method, it can be seen as a meta
algorithm that instantiates small parts of a large and complex Markov Network
and then solves these using a conventional MAP method. We evaluate CPI on two
tasks, Semantic Role Labelling and Joint Entity Resolution, while plugging in
two different MAP inference methods: the current method of choice for MAP
inference in Markov Logic, MaxWalkSAT, and Integer Linear Programming. We
observe that when used with CPI both methods are significantly faster than when
used alone. In addition, CPI improves the accuracy of MaxWalkSAT and maintains
the exactness of Integer Linear Programming.
| [
{
"version": "v1",
"created": "Wed, 13 Jun 2012 15:43:49 GMT"
}
] | 1,339,977,600,000 | [
[
"Riedel",
"Sebastian",
""
]
] |
1206.3283 | Yan Radovilsky | Yan Radovilsky, Solomon Eyal Shimony | Observation Subset Selection as Local Compilation of Performance
Profiles | Appears in Proceedings of the Twenty-Fourth Conference on Uncertainty
in Artificial Intelligence (UAI2008) | null | null | UAI-P-2008-PG-460-467 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Deciding what to sense is a crucial task, made harder by dependencies and by
a nonadditive utility function. We develop approximation algorithms for
selecting an optimal set of measurements, under a dependency structure modeled
by a tree-shaped Bayesian network (BN). Our approach is a generalization of
composing anytime algorithm represented by conditional performance profiles.
This is done by relaxing the input monotonicity assumption, and extending the
local compilation technique to more general classes of performance profiles
(PPs). We apply the extended scheme to selecting a subset of measurements for
choosing a maximum expectation variable in a binary valued BN, and for
minimizing the worst variance in a Gaussian BN.
| [
{
"version": "v1",
"created": "Wed, 13 Jun 2012 15:44:14 GMT"
}
] | 1,339,977,600,000 | [
[
"Radovilsky",
"Yan",
""
],
[
"Shimony",
"Solomon Eyal",
""
]
] |
1206.3284 | Lars Otten | Lars Otten, Rina Dechter | Bounding Search Space Size via (Hyper)tree Decompositions | Appears in Proceedings of the Twenty-Fourth Conference on Uncertainty
in Artificial Intelligence (UAI2008) | null | null | UAI-P-2008-PG-452-459 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper develops a measure for bounding the performance of AND/OR search
algorithms for solving a variety of queries over graphical models. We show how
drawing a connection to the recent notion of hypertree decompositions allows to
exploit determinism in the problem specification and produce tighter bounds. We
demonstrate on a variety of practical problem instances that we are often able
to improve upon existing bounds by several orders of magnitude.
| [
{
"version": "v1",
"created": "Wed, 13 Jun 2012 15:44:34 GMT"
}
] | 1,339,977,600,000 | [
[
"Otten",
"Lars",
""
],
[
"Dechter",
"Rina",
""
]
] |
1206.3286 | Matthew Streeter | Matthew Streeter, Stephen F. Smith | New Techniques for Algorithm Portfolio Design | Appears in Proceedings of the Twenty-Fourth Conference on Uncertainty
in Artificial Intelligence (UAI2008) | null | null | UAI-P-2008-PG-519-527 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We present and evaluate new techniques for designing algorithm portfolios. In
our view, the problem has both a scheduling aspect and a machine learning
aspect. Prior work has largely addressed one of the two aspects in isolation.
Building on recent work on the scheduling aspect of the problem, we present a
technique that addresses both aspects simultaneously and has attractive
theoretical guarantees. Experimentally, we show that this technique can be used
to improve the performance of state-of-the-art algorithms for Boolean
satisfiability, zero-one integer programming, and A.I. planning.
| [
{
"version": "v1",
"created": "Wed, 13 Jun 2012 15:45:20 GMT"
}
] | 1,339,977,600,000 | [
[
"Streeter",
"Matthew",
""
],
[
"Smith",
"Stephen F.",
""
]
] |
1206.3289 | Tomas Singliar | Tomas Singliar, Denver Dash | Efficient inference in persistent Dynamic Bayesian Networks | Appears in Proceedings of the Twenty-Fourth Conference on Uncertainty
in Artificial Intelligence (UAI2008) | null | null | UAI-P-2008-PG-494-502 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Numerous temporal inference tasks such as fault monitoring and anomaly
detection exhibit a persistence property: for example, if something breaks, it
stays broken until an intervention. When modeled as a Dynamic Bayesian Network,
persistence adds dependencies between adjacent time slices, often making exact
inference over time intractable using standard inference algorithms. However,
we show that persistence implies a regular structure that can be exploited for
efficient inference. We present three successively more general classes of
models: persistent causal chains (PCCs), persistent causal trees (PCTs) and
persistent polytrees (PPTs), and the corresponding exact inference algorithms
that exploit persistence. We show that analytic asymptotic bounds for our
algorithms compare favorably to junction tree inference; and we demonstrate
empirically that we can perform exact smoothing on the order of 100 times
faster than the approximate Boyen-Koller method on randomly generated instances
of persistent tree models. We also show how to handle non-persistent variables
and how persistence can be exploited effectively for approximate filtering.
| [
{
"version": "v1",
"created": "Wed, 13 Jun 2012 15:46:24 GMT"
}
] | 1,339,977,600,000 | [
[
"Singliar",
"Tomas",
""
],
[
"Dash",
"Denver",
""
]
] |
1206.3291 | Marc Toussaint | Marc Toussaint, Laurent Charlin, Pascal Poupart | Hierarchical POMDP Controller Optimization by Likelihood Maximization | Appears in Proceedings of the Twenty-Fourth Conference on Uncertainty
in Artificial Intelligence (UAI2008) | null | null | UAI-P-2008-PG-562-570 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Planning can often be simpli ed by decomposing the task into smaller tasks
arranged hierarchically. Charlin et al. [4] recently showed that the hierarchy
discovery problem can be framed as a non-convex optimization problem. However,
the inherent computational di culty of solving such an optimization problem
makes it hard to scale to realworld problems. In another line of research,
Toussaint et al. [18] developed a method to solve planning problems by
maximumlikelihood estimation. In this paper, we show how the hierarchy
discovery problem in partially observable domains can be tackled using a
similar maximum likelihood approach. Our technique rst transforms the problem
into a dynamic Bayesian network through which a hierarchical structure can
naturally be discovered while optimizing the policy. Experimental results
demonstrate that this approach scales better than previous techniques based on
non-convex optimization.
| [
{
"version": "v1",
"created": "Wed, 13 Jun 2012 15:51:21 GMT"
}
] | 1,339,977,600,000 | [
[
"Toussaint",
"Marc",
""
],
[
"Charlin",
"Laurent",
""
],
[
"Poupart",
"Pascal",
""
]
] |
1206.3292 | Jin Tian | Jin Tian | Identifying Dynamic Sequential Plans | Appears in Proceedings of the Twenty-Fourth Conference on Uncertainty
in Artificial Intelligence (UAI2008) | null | null | UAI-P-2008-PG-554-561 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We address the problem of identifying dynamic sequential plans in the
framework of causal Bayesian networks, and show that the problem is reduced to
identifying causal effects, for which there are complete identi cation
algorithms available in the literature.
| [
{
"version": "v1",
"created": "Wed, 13 Jun 2012 15:51:48 GMT"
}
] | 1,339,977,600,000 | [
[
"Tian",
"Jin",
""
]
] |
1206.3295 | Haohai Yu | Haohai Yu, Robert A. van Engelen | Refractor Importance Sampling | Appears in Proceedings of the Twenty-Fourth Conference on Uncertainty
in Artificial Intelligence (UAI2008) | null | null | UAI-P-2008-PG-603-609 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this paper we introduce Refractor Importance Sampling (RIS), an
improvement to reduce error variance in Bayesian network importance sampling
propagation under evidential reasoning. We prove the existence of a collection
of importance functions that are close to the optimal importance function under
evidential reasoning. Based on this theoretic result we derive the RIS
algorithm. RIS approaches the optimal importance function by applying localized
arc changes to minimize the divergence between the evidence-adjusted importance
function and the optimal importance function. The validity and performance of
RIS is empirically tested with a large setof synthetic Bayesian networks and
two real-world networks.
| [
{
"version": "v1",
"created": "Wed, 13 Jun 2012 15:53:49 GMT"
}
] | 1,339,977,600,000 | [
[
"Yu",
"Haohai",
""
],
[
"van Engelen",
"Robert A.",
""
]
] |
1206.3296 | Ydo Wexler | Ydo Wexler, Christopher Meek | Inference for Multiplicative Models | Appears in Proceedings of the Twenty-Fourth Conference on Uncertainty
in Artificial Intelligence (UAI2008) | null | null | UAI-P-2008-PG-595-602 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The paper introduces a generalization for known probabilistic models such as
log-linear and graphical models, called here multiplicative models. These
models, that express probabilities via product of parameters are shown to
capture multiple forms of contextual independence between variables, including
decision graphs and noisy-OR functions. An inference algorithm for
multiplicative models is provided and its correctness is proved. The complexity
analysis of the inference algorithm uses a more refined parameter than the
tree-width of the underlying graph, and shows the computational cost does not
exceed that of the variable elimination algorithm in graphical models. The
paper ends with examples where using the new models and algorithm is
computationally beneficial.
| [
{
"version": "v1",
"created": "Wed, 13 Jun 2012 15:55:04 GMT"
}
] | 1,339,977,600,000 | [
[
"Wexler",
"Ydo",
""
],
[
"Meek",
"Christopher",
""
]
] |
1206.3318 | Vanessa Burke | Michael Bowling, Martin Zinkevich | On Local Regret | This is the longer version of the same-titled paper appearing in the
Proceedings of the Twenty-Ninth International Conference on Machine Learning
(ICML), 2012 | null | null | TR12-04 | cs.AI | http://creativecommons.org/licenses/by/3.0/ | Online learning aims to perform nearly as well as the best hypothesis in
hindsight. For some hypothesis classes, though, even finding the best
hypothesis offline is challenging. In such offline cases, local search
techniques are often employed and only local optimality guaranteed. For online
decision-making with such hypothesis classes, we introduce local regret, a
generalization of regret that aims to perform nearly as well as only nearby
hypotheses. We then present a general algorithm to minimize local regret with
arbitrary locality graphs. We also show how the graph structure can be
exploited to drastically speed learning. These algorithms are then demonstrated
on a diverse set of online problems: online disjunct learning, online Max-SAT,
and online decision tree learning.
| [
{
"version": "v1",
"created": "Thu, 14 Jun 2012 20:07:30 GMT"
}
] | 1,339,977,600,000 | [
[
"Bowling",
"Michael",
""
],
[
"Zinkevich",
"Martin",
""
]
] |
1206.3536 | Marc Maier | Marc Maier, David Jensen | Identifying Independence in Relational Models | This paper has been revised and expanded. See "Reasoning about
Independence in Probabilistic Models of Relational Data"
http://arxiv.org/abs/1302.4381 | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The rules of d-separation provide a framework for deriving conditional
independence facts from model structure. However, this theory only applies to
simple directed graphical models. We introduce relational d-separation, a
theory for deriving conditional independence in relational models. We provide a
sound, complete, and computationally efficient method for relational
d-separation, and we present empirical results that demonstrate effectiveness.
| [
{
"version": "v1",
"created": "Fri, 15 Jun 2012 18:23:56 GMT"
},
{
"version": "v2",
"created": "Wed, 3 Oct 2012 18:21:10 GMT"
},
{
"version": "v3",
"created": "Mon, 15 Apr 2013 13:42:19 GMT"
}
] | 1,366,070,400,000 | [
[
"Maier",
"Marc",
""
],
[
"Jensen",
"David",
""
]
] |
1206.3551 | Debarun Bhattacharjya | Debarun Bhattacharjya, Ross D. Shachter | Sensitivity analysis in decision circuits | Appears in Proceedings of the Twenty-Fourth Conference on Uncertainty
in Artificial Intelligence (UAI2008) | null | null | UAI-P-2008-PG-34-42 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Decision circuits have been developed to perform efficient evaluation of
influence diagrams [Bhattacharjya and Shachter, 2007], building on the advances
in arithmetic circuits for belief network inference [Darwiche,2003]. In the
process of model building and analysis, we perform sensitivity analysis to
understand how the optimal solution changes in response to changes in the
model. When sequential decision problems under uncertainty are represented as
decision circuits, we can exploit the efficient solution process embodied in
the decision circuit and the wealth of derivative information available to
compute the value of information for the uncertainties in the problem and the
effects of changes to model parameters on the value and the optimal strategy.
| [
{
"version": "v1",
"created": "Wed, 13 Jun 2012 14:18:02 GMT"
}
] | 1,339,977,600,000 | [
[
"Bhattacharjya",
"Debarun",
""
],
[
"Shachter",
"Ross D.",
""
]
] |
1206.3959 | Jeff Bilmes | Jeff Bilmes, Andrew Ng | Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial
Intelligence (2009) | null | null | null | UAI2009 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This is the Proceedings of the Twenty-Fifth Conference on Uncertainty in
Artificial Intelligence, which was held in Montreal, QC, Canada, June 18 - 21
2009.
| [
{
"version": "v1",
"created": "Wed, 13 Jun 2012 16:43:44 GMT"
},
{
"version": "v2",
"created": "Thu, 28 Aug 2014 04:27:28 GMT"
}
] | 1,409,270,400,000 | [
[
"Bilmes",
"Jeff",
""
],
[
"Ng",
"Andrew",
""
]
] |
1206.5242 | Vibhav Gogate | Vibhav Gogate, Bozhena Bidyuk, Rina Dechter | Studies in Lower Bounding Probabilities of Evidence using the Markov
Inequality | Appears in Proceedings of the Twenty-Third Conference on Uncertainty
in Artificial Intelligence (UAI2007) | null | null | UAI-P-2007-PG-141-148 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Computing the probability of evidence even with known error bounds is
NP-hard. In this paper we address this hard problem by settling on an easier
problem. We propose an approximation which provides high confidence lower
bounds on probability of evidence but does not have any guarantees in terms of
relative or absolute error. Our proposed approximation is a randomized
importance sampling scheme that uses the Markov inequality. However, a
straight-forward application of the Markov inequality may lead to poor lower
bounds. We therefore propose several heuristic measures to improve its
performance in practice. Empirical evaluation of our scheme with state-of-
the-art lower bounding schemes reveals the promise of our approach.
| [
{
"version": "v1",
"created": "Wed, 20 Jun 2012 14:53:07 GMT"
}
] | 1,340,668,800,000 | [
[
"Gogate",
"Vibhav",
""
],
[
"Bidyuk",
"Bozhena",
""
],
[
"Dechter",
"Rina",
""
]
] |
1206.5244 | Lucie Galand | Lucie Galand, Patrice Perny | Search for Choquet-optimal paths under uncertainty | Appears in Proceedings of the Twenty-Third Conference on Uncertainty
in Artificial Intelligence (UAI2007) | null | null | UAI-P-2007-PG-125-132 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Choquet expected utility (CEU) is one of the most sophisticated decision
criteria used in decision theory under uncertainty. It provides a
generalisation of expected utility enhancing both descriptive and prescriptive
possibilities. In this paper, we investigate the use of CEU for path-planning
under uncertainty with a special focus on robust solutions. We first recall the
main features of the CEU model and introduce some examples showing its
descriptive potential. Then we focus on the search for Choquet-optimal paths in
multivalued implicit graphs where costs depend on different scenarios. After
discussing complexity issues, we propose two different heuristic search
algorithms to solve the problem. Finally, numerical experiments are reported,
showing the practical efficiency of the proposed algorithms.
| [
{
"version": "v1",
"created": "Wed, 20 Jun 2012 14:53:49 GMT"
}
] | 1,340,668,800,000 | [
[
"Galand",
"Lucie",
""
],
[
"Perny",
"Patrice",
""
]
] |
1206.5249 | Ashwin Deshpande | Ashwin Deshpande, Brian Milch, Luke S. Zettlemoyer, Leslie Pack
Kaelbling | Learning Probabilistic Relational Dynamics for Multiple Tasks | Appears in Proceedings of the Twenty-Third Conference on Uncertainty
in Artificial Intelligence (UAI2007) | null | null | UAI-P-2007-PG-83-92 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The ways in which an agent's actions affect the world can often be modeled
compactly using a set of relational probabilistic planning rules. This paper
addresses the problem of learning such rule sets for multiple related tasks. We
take a hierarchical Bayesian approach, in which the system learns a prior
distribution over rule sets. We present a class of prior distributions
parameterized by a rule set prototype that is stochastically modified to
produce a task-specific rule set. We also describe a coordinate ascent
algorithm that iteratively optimizes the task-specific rule sets and the prior
distribution. Experiments using this algorithm show that transferring
information from related tasks significantly reduces the amount of training
data required to predict action effects in blocks-world domains.
| [
{
"version": "v1",
"created": "Wed, 20 Jun 2012 14:55:37 GMT"
}
] | 1,340,668,800,000 | [
[
"Deshpande",
"Ashwin",
""
],
[
"Milch",
"Brian",
""
],
[
"Zettlemoyer",
"Luke S.",
""
],
[
"Kaelbling",
"Leslie Pack",
""
]
] |
1206.5251 | Arthur Choi | Arthur Choi, Mark Chavira, Adnan Darwiche | Node Splitting: A Scheme for Generating Upper Bounds in Bayesian
Networks | Appears in Proceedings of the Twenty-Third Conference on Uncertainty
in Artificial Intelligence (UAI2007) | null | null | UAI-P-2007-PG-57-66 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We formulate in this paper the mini-bucket algorithm for approximate
inference in terms of exact inference on an approximate model produced by
splitting nodes in a Bayesian network. The new formulation leads to a number of
theoretical and practical implications. First, we show that branchand- bound
search algorithms that use minibucket bounds may operate in a drastically
reduced search space. Second, we show that the proposed formulation inspires
new minibucket heuristics and allows us to analyze existing heuristics from a
new perspective. Finally, we show that this new formulation allows mini-bucket
approximations to benefit from recent advances in exact inference, allowing one
to significantly increase the reach of these approximations.
| [
{
"version": "v1",
"created": "Wed, 20 Jun 2012 14:56:19 GMT"
}
] | 1,340,668,800,000 | [
[
"Choi",
"Arthur",
""
],
[
"Chavira",
"Mark",
""
],
[
"Darwiche",
"Adnan",
""
]
] |
1206.5255 | Darius Braziunas | Darius Braziunas, Craig Boutilier | Minimax regret based elicitation of generalized additive utilities | Appears in Proceedings of the Twenty-Third Conference on Uncertainty
in Artificial Intelligence (UAI2007) | null | null | UAI-P-2007-PG-25-32 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We describe the semantic foundations for elicitation of generalized
additively independent (GAI) utilities using the minimax regret criterion, and
propose several new query types and strategies for this purpose. Computational
feasibility is obtained by exploiting the local GAI structure in the model. Our
results provide a practical approach for implementing preference-based
constrained configuration optimization as well as effective search in
multiattribute product databases.
| [
{
"version": "v1",
"created": "Wed, 20 Jun 2012 14:57:59 GMT"
}
] | 1,340,668,800,000 | [
[
"Braziunas",
"Darius",
""
],
[
"Boutilier",
"Craig",
""
]
] |
1206.5257 | Debarun Bhattacharjya | Debarun Bhattacharjya, Ross D. Shachter | Evaluating influence diagrams with decision circuits | Appears in Proceedings of the Twenty-Third Conference on Uncertainty
in Artificial Intelligence (UAI2007) | null | null | UAI-P-2007-PG-9-16 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Although a number of related algorithms have been developed to evaluate
influence diagrams, exploiting the conditional independence in the diagram, the
exact solution has remained intractable for many important problems. In this
paper we introduce decision circuits as a means to exploit the local structure
usually found in decision problems and to improve the performance of influence
diagram analysis. This work builds on the probabilistic inference algorithms
using arithmetic circuits to represent Bayesian belief networks [Darwiche,
2003]. Once compiled, these arithmetic circuits efficiently evaluate
probabilistic queries on the belief network, and methods have been developed to
exploit both the global and local structure of the network. We show that
decision circuits can be constructed in a similar fashion and promise similar
benefits.
| [
{
"version": "v1",
"created": "Wed, 20 Jun 2012 14:59:08 GMT"
}
] | 1,340,668,800,000 | [
[
"Bhattacharjya",
"Debarun",
""
],
[
"Shachter",
"Ross D.",
""
]
] |
1206.5258 | Christopher Amato | Christopher Amato, Daniel S Bernstein, Shlomo Zilberstein | Optimizing Memory-Bounded Controllers for Decentralized POMDPs | Appears in Proceedings of the Twenty-Third Conference on Uncertainty
in Artificial Intelligence (UAI2007) | null | null | UAI-P-2007-PG-1-8 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We present a memory-bounded optimization approach for solving
infinite-horizon decentralized POMDPs. Policies for each agent are represented
by stochastic finite state controllers. We formulate the problem of optimizing
these policies as a nonlinear program, leveraging powerful existing nonlinear
optimization techniques for solving the problem. While existing solvers only
guarantee locally optimal solutions, we show that our formulation produces
higher quality controllers than the state-of-the-art approach. We also
incorporate a shared source of randomness in the form of a correlation device
to further increase solution quality with only a limited increase in space and
time. Our experimental results show that nonlinear optimization can be used to
provide high quality, concise solutions to decentralized decision problems
under uncertainty.
| [
{
"version": "v1",
"created": "Wed, 20 Jun 2012 14:59:30 GMT"
}
] | 1,340,668,800,000 | [
[
"Amato",
"Christopher",
""
],
[
"Bernstein",
"Daniel S",
""
],
[
"Zilberstein",
"Shlomo",
""
]
] |
1206.5260 | Suchi Saria | Suchi Saria, Uri Nodelman, Daphne Koller | Reasoning at the Right Time Granularity | Appears in Proceedings of the Twenty-Third Conference on Uncertainty
in Artificial Intelligence (UAI2007) | null | null | UAI-P-2007-PG-326-334 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Most real-world dynamic systems are composed of different components that
often evolve at very different rates. In traditional temporal graphical models,
such as dynamic Bayesian networks, time is modeled at a fixed granularity,
generally selected based on the rate at which the fastest component evolves.
Inference must then be performed at this fastest granularity, potentially at
significant computational cost. Continuous Time Bayesian Networks (CTBNs) avoid
time-slicing in the representation by modeling the system as evolving
continuously over time. The expectation-propagation (EP) inference algorithm of
Nodelman et al. (2005) can then vary the inference granularity over time, but
the granularity is uniform across all parts of the system, and must be selected
in advance. In this paper, we provide a new EP algorithm that utilizes a
general cluster graph architecture where clusters contain distributions that
can overlap in both space (set of variables) and time. This architecture allows
different parts of the system to be modeled at very different time
granularities, according to their current rate of evolution. We also provide an
information-theoretic criterion for dynamically re-partitioning the clusters
during inference to tune the level of approximation to the current rate of
evolution. This avoids the need to hand-select the appropriate granularity, and
allows the granularity to adapt as information is transmitted across the
network. We present experiments demonstrating that this approach can result in
significant computational savings.
| [
{
"version": "v1",
"created": "Wed, 20 Jun 2012 15:00:31 GMT"
}
] | 1,340,668,800,000 | [
[
"Saria",
"Suchi",
""
],
[
"Nodelman",
"Uri",
""
],
[
"Koller",
"Daphne",
""
]
] |
1206.5266 | Robert Mateescu | Robert Mateescu, Rina Dechter | AND/OR Multi-Valued Decision Diagrams (AOMDDs) for Weighted Graphical
Models | Appears in Proceedings of the Twenty-Third Conference on Uncertainty
in Artificial Intelligence (UAI2007) | null | null | UAI-P-2007-PG-276-284 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Compiling graphical models has recently been under intense investigation,
especially for probabilistic modeling and processing. We present here a novel
data structure for compiling weighted graphical models (in particular,
probabilistic models), called AND/OR Multi-Valued Decision Diagram (AOMDD).
This is a generalization of our previous work on constraint networks, to
weighted models. The AOMDD is based on the frameworks of AND/OR search spaces
for graphical models, and Ordered Binary Decision Diagrams (OBDD). The AOMDD is
a canonical representation of a graphical model, and its size and compilation
time are bounded exponentially by the treewidth of the graph, rather than
pathwidth as is known for OBDDs. We discuss a Variable Elimination schedule for
compilation, and present the general APPLY algorithm that combines two weighted
AOMDDs, and also present a search based method for compilation method. The
preliminary experimental evaluation is quite encouraging, showing the potential
of the AOMDD data structure.
| [
{
"version": "v1",
"created": "Wed, 20 Jun 2012 15:02:53 GMT"
}
] | 1,340,668,800,000 | [
[
"Mateescu",
"Robert",
""
],
[
"Dechter",
"Rina",
""
]
] |
1206.5268 | Radu Marinescu | Radu Marinescu, Rina Dechter | Best-First AND/OR Search for Most Probable Explanations | Appears in Proceedings of the Twenty-Third Conference on Uncertainty
in Artificial Intelligence (UAI2007) | null | null | UAI-P-2007-PG-259-266 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The paper evaluates the power of best-first search over AND/OR search spaces
for solving the Most Probable Explanation (MPE) task in Bayesian networks. The
main virtue of the AND/OR representation of the search space is its sensitivity
to the structure of the problem, which can translate into significant time
savings. In recent years depth-first AND/OR Branch-and- Bound algorithms were
shown to be very effective when exploring such search spaces, especially when
using caching. Since best-first strategies are known to be superior to
depth-first when memory is utilized, exploring the best-first control strategy
is called for. The main contribution of this paper is in showing that a recent
extension of AND/OR search algorithms from depth-first Branch-and-Bound to
best-first is indeed very effective for computing the MPE in Bayesian networks.
We demonstrate empirically the superiority of the best-first search approach on
various probabilistic networks.
| [
{
"version": "v1",
"created": "Wed, 20 Jun 2012 15:04:10 GMT"
}
] | 1,340,668,800,000 | [
[
"Marinescu",
"Radu",
""
],
[
"Dechter",
"Rina",
""
]
] |
1206.5271 | Eric Lantz | Eric Lantz, Soumya Ray, David Page | Learning Bayesian Network Structure from Correlation-Immune Data | Appears in Proceedings of the Twenty-Third Conference on Uncertainty
in Artificial Intelligence (UAI2007) | null | null | UAI-P-2007-PG-235-242 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Searching the complete space of possible Bayesian networks is intractable for
problems of interesting size, so Bayesian network structure learning
algorithms, such as the commonly used Sparse Candidate algorithm, employ
heuristics. However, these heuristics also restrict the types of relationships
that can be learned exclusively from data. They are unable to learn
relationships that exhibit "correlation-immunity", such as parity. To learn
Bayesian networks in the presence of correlation-immune relationships, we
extend the Sparse Candidate algorithm with a technique called "skewing". This
technique uses the observation that relationships that are correlation-immune
under a specific input distribution may not be correlation-immune under
another, sufficiently different distribution. We show that by extending Sparse
Candidate with this technique we are able to discover relationships between
random variables that are approximately correlation-immune, with a
significantly lower computational cost than the alternative of considering
multiple parents of a node at a time.
| [
{
"version": "v1",
"created": "Wed, 20 Jun 2012 15:05:10 GMT"
}
] | 1,340,668,800,000 | [
[
"Lantz",
"Eric",
""
],
[
"Ray",
"Soumya",
""
],
[
"Page",
"David",
""
]
] |
1206.5273 | Lukas Kroc | Lukas Kroc, Ashish Sabharwal, Bart Selman | Survey Propagation Revisited | Appears in Proceedings of the Twenty-Third Conference on Uncertainty
in Artificial Intelligence (UAI2007) | null | null | UAI-P-2007-PG-217-226 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Survey propagation (SP) is an exciting new technique that has been remarkably
successful at solving very large hard combinatorial problems, such as
determining the satisfiability of Boolean formulas. In a promising attempt at
understanding the success of SP, it was recently shown that SP can be viewed as
a form of belief propagation, computing marginal probabilities over certain
objects called covers of a formula. This explanation was, however, shortly
dismissed by experiments suggesting that non-trivial covers simply do not exist
for large formulas. In this paper, we show that these experiments were
misleading: not only do covers exist for large hard random formulas, SP is
surprisingly accurate at computing marginals over these covers despite the
existence of many cycles in the formulas. This re-opens a potentially simpler
line of reasoning for understanding SP, in contrast to some alternative lines
of explanation that have been proposed assuming covers do not exist.
| [
{
"version": "v1",
"created": "Wed, 20 Jun 2012 15:05:48 GMT"
}
] | 1,340,668,800,000 | [
[
"Kroc",
"Lukas",
""
],
[
"Sabharwal",
"Ashish",
""
],
[
"Selman",
"Bart",
""
]
] |
1206.5276 | Ariel Jaimovich | Ariel Jaimovich, Ofer Meshi, Nir Friedman | Template Based Inference in Symmetric Relational Markov Random Fields | Appears in Proceedings of the Twenty-Third Conference on Uncertainty
in Artificial Intelligence (UAI2007) | null | null | UAI-P-2007-PG-191-199 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Relational Markov Random Fields are a general and flexible framework for
reasoning about the joint distribution over attributes of a large number of
interacting entities. The main computational difficulty in learning such models
is inference. Even when dealing with complete data, where one can summarize a
large domain by sufficient statistics, learning requires one to compute the
expectation of the sufficient statistics given different parameter choices. The
typical solution to this problem is to resort to approximate inference
procedures, such as loopy belief propagation. Although these procedures are
quite efficient, they still require computation that is on the order of the
number of interactions (or features) in the model. When learning a large
relational model over a complex domain, even such approximations require
unrealistic running time. In this paper we show that for a particular class of
relational MRFs, which have inherent symmetry, we can perform the inference
needed for learning procedures using a template-level belief propagation. This
procedure's running time is proportional to the size of the relational model
rather than the size of the domain. Moreover, we show that this computational
procedure is equivalent to sychronous loopy belief propagation. This enables a
dramatic speedup in inference and learning time. We use this procedure to learn
relational MRFs for capturing the joint distribution of large protein-protein
interaction networks.
| [
{
"version": "v1",
"created": "Wed, 20 Jun 2012 15:06:55 GMT"
}
] | 1,340,668,800,000 | [
[
"Jaimovich",
"Ariel",
""
],
[
"Meshi",
"Ofer",
""
],
[
"Friedman",
"Nir",
""
]
] |
1206.5284 | Fusun Yaman | Fusun Yaman, Marie desJardins | More-or-Less CP-Networks | Appears in Proceedings of the Twenty-Third Conference on Uncertainty
in Artificial Intelligence (UAI2007) | null | null | UAI-P-2007-PG-434-441 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Preferences play an important role in our everyday lives. CP-networks, or
CP-nets in short, are graphical models for representing conditional qualitative
preferences under ceteris paribus ("all else being equal") assumptions. Despite
their intuitive nature and rich representation, dominance testing with CP-nets
is computationally complex, even when the CP-nets are restricted to
binary-valued preferences. Tractable algorithms exist for binary CP-nets, but
these algorithms are incomplete for multi-valued CPnets. In this paper, we
identify a class of multivalued CP-nets, which we call more-or-less CPnets,
that have the same computational complexity as binary CP-nets. More-or-less
CP-nets exploit the monotonicity of the attribute values and use intervals to
aggregate values that induce similar preferences. We then present a search
control rule for dominance testing that effectively prunes the search space
while preserving completeness.
| [
{
"version": "v1",
"created": "Wed, 20 Jun 2012 15:15:21 GMT"
}
] | 1,340,668,800,000 | [
[
"Yaman",
"Fusun",
""
],
[
"desJardins",
"Marie",
""
]
] |
1206.5287 | Chenggang Wang | Chenggang Wang, Roni Khardon | Policy Iteration for Relational MDPs | Appears in Proceedings of the Twenty-Third Conference on Uncertainty
in Artificial Intelligence (UAI2007) | null | null | UAI-P-2007-PG-408-415 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Relational Markov Decision Processes are a useful abstraction for complex
reinforcement learning problems and stochastic planning problems. Recent work
developed representation schemes and algorithms for planning in such problems
using the value iteration algorithm. However, exact versions of more complex
algorithms, including policy iteration, have not been developed or analyzed.
The paper investigates this potential and makes several contributions. First we
observe two anomalies for relational representations showing that the value of
some policies is not well defined or cannot be calculated for restricted
representation schemes used in the literature. On the other hand, we develop a
variant of policy iteration that can get around these anomalies. The algorithm
includes an aspect of policy improvement in the process of policy evaluation
and thus differs from the original algorithm. We show that despite this
difference the algorithm converges to the optimal policy.
| [
{
"version": "v1",
"created": "Wed, 20 Jun 2012 15:16:29 GMT"
}
] | 1,340,668,800,000 | [
[
"Wang",
"Chenggang",
""
],
[
"Khardon",
"Roni",
""
]
] |
1206.5292 | Parag Singla | Parag Singla, Pedro Domingos | Markov Logic in Infinite Domains | Appears in Proceedings of the Twenty-Third Conference on Uncertainty
in Artificial Intelligence (UAI2007) | null | null | UAI-P-2007-PG-368-375 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Combining first-order logic and probability has long been a goal of AI.
Markov logic (Richardson & Domingos, 2006) accomplishes this by attaching
weights to first-order formulas and viewing them as templates for features of
Markov networks. Unfortunately, it does not have the full power of first-order
logic, because it is only defined for finite domains. This paper extends Markov
logic to infinite domains, by casting it in the framework of Gibbs measures
(Georgii, 1988). We show that a Markov logic network (MLN) admits a Gibbs
measure as long as each ground atom has a finite number of neighbors. Many
interesting cases fall in this category. We also show that an MLN admits a
unique measure if the weights of its non-unit clauses are small enough. We then
examine the structure of the set of consistent measures in the non-unique case.
Many important phenomena, including systems with phase transitions, are
represented by MLNs with non-unique measures. We relate the problem of
satisfiability in first-order logic to the properties of MLN measures, and
discuss how Markov logic relates to previous infinite models.
| [
{
"version": "v1",
"created": "Wed, 20 Jun 2012 15:18:47 GMT"
}
] | 1,340,668,800,000 | [
[
"Singla",
"Parag",
""
],
[
"Domingos",
"Pedro",
""
]
] |
1206.5294 | Ilya Shpitser | Ilya Shpitser, Judea Pearl | What Counterfactuals Can Be Tested | Appears in Proceedings of the Twenty-Third Conference on Uncertainty
in Artificial Intelligence (UAI2007) | null | null | UAI-P-2007-PG-352-359 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Counterfactual statements, e.g., "my headache would be gone had I taken an
aspirin" are central to scientific discourse, and are formally interpreted as
statements derived from "alternative worlds". However, since they invoke
hypothetical states of affairs, often incompatible with what is actually known
or observed, testing counterfactuals is fraught with conceptual and practical
difficulties. In this paper, we provide a complete characterization of
"testable counterfactuals," namely, counterfactual statements whose
probabilities can be inferred from physical experiments. We provide complete
procedures for discerning whether a given counterfactual is testable and, if
so, expressing its probability in terms of experimental data.
| [
{
"version": "v1",
"created": "Wed, 20 Jun 2012 15:19:30 GMT"
}
] | 1,340,668,800,000 | [
[
"Shpitser",
"Ilya",
""
],
[
"Pearl",
"Judea",
""
]
] |
1206.5295 | Sven Seuken | Sven Seuken, Shlomo Zilberstein | Improved Memory-Bounded Dynamic Programming for Decentralized POMDPs | Appears in Proceedings of the Twenty-Third Conference on Uncertainty
in Artificial Intelligence (UAI2007) | null | null | UAI-P-2007-PG-344-351 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Memory-Bounded Dynamic Programming (MBDP) has proved extremely effective in
solving decentralized POMDPs with large horizons. We generalize the algorithm
and improve its scalability by reducing the complexity with respect to the
number of observations from exponential to polynomial. We derive error bounds
on solution quality with respect to this new approximation and analyze the
convergence behavior. To evaluate the effectiveness of the improvements, we
introduce a new, larger benchmark problem. Experimental results show that
despite the high complexity of decentralized POMDPs, scalable solution
techniques such as MBDP perform surprisingly well.
| [
{
"version": "v1",
"created": "Wed, 20 Jun 2012 15:19:47 GMT"
}
] | 1,340,668,800,000 | [
[
"Seuken",
"Sven",
""
],
[
"Zilberstein",
"Shlomo",
""
]
] |
1206.5698 | Jesse Hoey | Marek Grzes and Jesse Hoey and Shehroz Khan and Alex Mihailidis and
Stephen Czarnuch and Dan Jackson and Andrew Monk | Relational Approach to Knowledge Engineering for POMDP-based Assistance
Systems as a Translation of a Psychological Model | null | null | 10.1016/j.ijar.2013.03.006 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Assistive systems for persons with cognitive disabilities (e.g. dementia) are
difficult to build due to the wide range of different approaches people can
take to accomplishing the same task, and the significant uncertainties that
arise from both the unpredictability of client's behaviours and from noise in
sensor readings. Partially observable Markov decision process (POMDP) models
have been used successfully as the reasoning engine behind such assistive
systems for small multi-step tasks such as hand washing. POMDP models are a
powerful, yet flexible framework for modelling assistance that can deal with
uncertainty and utility. Unfortunately, POMDPs usually require a very labour
intensive, manual procedure for their definition and construction. Our previous
work has described a knowledge driven method for automatically generating POMDP
activity recognition and context sensitive prompting systems for complex tasks.
We call the resulting POMDP a SNAP (SyNdetic Assistance Process). The
spreadsheet-like result of the analysis does not correspond to the POMDP model
directly and the translation to a formal POMDP representation is required. To
date, this translation had to be performed manually by a trained POMDP expert.
In this paper, we formalise and automate this translation process using a
probabilistic relational model (PRM) encoded in a relational database. We
demonstrate the method by eliciting three assistance tasks from non-experts. We
validate the resulting POMDP models using case-based simulations to show that
they are reasonable for the domains. We also show a complete case study of a
designer specifying one database, including an evaluation in a real-life
experiment with a human actor.
| [
{
"version": "v1",
"created": "Mon, 25 Jun 2012 14:46:15 GMT"
}
] | 1,371,772,800,000 | [
[
"Grzes",
"Marek",
""
],
[
"Hoey",
"Jesse",
""
],
[
"Khan",
"Shehroz",
""
],
[
"Mihailidis",
"Alex",
""
],
[
"Czarnuch",
"Stephen",
""
],
[
"Jackson",
"Dan",
""
],
[
"Monk",
"Andrew",
""
]
] |
1206.5833 | Guido Governatori | Guido Governatori, Francesco Olivieri, Simone Scannapieco and Matteo
Cristani | Revision of Defeasible Logic Preferences | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | There are several contexts of non-monotonic reasoning where a priority
between rules is established whose purpose is preventing conflicts.
One formalism that has been widely employed for non-monotonic reasoning is
the sceptical one known as Defeasible Logic. In Defeasible Logic the tool used
for conflict resolution is a preference relation between rules, that
establishes the priority among them.
In this paper we investigate how to modify such a preference relation in a
defeasible logic theory in order to change the conclusions of the theory
itself. We argue that the approach we adopt is applicable to legal reasoning
where users, in general, cannot change facts or rules, but can propose their
preferences about the relative strength of the rules.
We provide a comprehensive study of the possible combinatorial cases and we
identify and analyse the cases where the revision process is successful.
After this analysis, we identify three revision/update operators and study
them against the AGM postulates for belief revision operators, to discover that
only a part of these postulates are satisfied by the three operators.
| [
{
"version": "v1",
"created": "Mon, 25 Jun 2012 20:46:46 GMT"
},
{
"version": "v2",
"created": "Fri, 23 Nov 2012 11:35:20 GMT"
}
] | 1,353,888,000,000 | [
[
"Governatori",
"Guido",
""
],
[
"Olivieri",
"Francesco",
""
],
[
"Scannapieco",
"Simone",
""
],
[
"Cristani",
"Matteo",
""
]
] |
1206.5928 | Truong-Huy Nguyen | Truong-Huy Dinh Nguyen, David Hsu, Wee-Sun Lee, Tze-Yun Leong, Leslie
Pack Kaelbling, Tomas Lozano-Perez, Andrew Haydn Grant | CAPIR: Collaborative Action Planning with Intention Recognition | 6 pages, accepted for presentation at AIIDE'11 | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We apply decision theoretic techniques to construct non-player characters
that are able to assist a human player in collaborative games. The method is
based on solving Markov decision processes, which can be difficult when the
game state is described by many variables. To scale to more complex games, the
method allows decomposition of a game task into subtasks, each of which can be
modelled by a Markov decision process. Intention recognition is used to infer
the subtask that the human is currently performing, allowing the helper to
assist the human in performing the correct task. Experiments show that the
method can be effective, giving near-human level performance in helping a human
in a collaborative game.
| [
{
"version": "v1",
"created": "Tue, 26 Jun 2012 09:13:53 GMT"
}
] | 1,340,755,200,000 | [
[
"Nguyen",
"Truong-Huy Dinh",
""
],
[
"Hsu",
"David",
""
],
[
"Lee",
"Wee-Sun",
""
],
[
"Leong",
"Tze-Yun",
""
],
[
"Kaelbling",
"Leslie Pack",
""
],
[
"Lozano-Perez",
"Tomas",
""
],
[
"Grant",
"Andrew Haydn",
""
]
] |
1206.5940 | Truong-Huy Nguyen | Truong-Huy Dinh Nguyen, Wee-Sun Lee, and Tze-Yun Leong | Bootstrapping Monte Carlo Tree Search with an Imperfect Heuristic | 16 pages, accepted for presentation at ECML'12 | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We consider the problem of using a heuristic policy to improve the value
approximation by the Upper Confidence Bound applied in Trees (UCT) algorithm in
non-adversarial settings such as planning with large-state space Markov
Decision Processes. Current improvements to UCT focus on either changing the
action selection formula at the internal nodes or the rollout policy at the
leaf nodes of the search tree. In this work, we propose to add an auxiliary arm
to each of the internal nodes, and always use the heuristic policy to roll out
simulations at the auxiliary arms. The method aims to get fast convergence to
optimal values at states where the heuristic policy is optimal, while retaining
similar approximation as the original UCT in other states. We show that
bootstrapping with the proposed method in the new algorithm, UCT-Aux, performs
better compared to the original UCT algorithm and its variants in two benchmark
experiment settings. We also examine conditions under which UCT-Aux works well.
| [
{
"version": "v1",
"created": "Tue, 26 Jun 2012 09:53:59 GMT"
}
] | 1,340,755,200,000 | [
[
"Nguyen",
"Truong-Huy Dinh",
""
],
[
"Lee",
"Wee-Sun",
""
],
[
"Leong",
"Tze-Yun",
""
]
] |
1206.6817 | Arthur Choi | Arthur Choi, Adnan Darwiche | A Variational Approach for Approximating Bayesian Networks by Edge
Deletion | Appears in Proceedings of the Twenty-Second Conference on Uncertainty
in Artificial Intelligence (UAI2006) | null | null | UAI-P-2006-PG-80-89 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We consider in this paper the formulation of approximate inference in
Bayesian networks as a problem of exact inference on an approximate network
that results from deleting edges (to reduce treewidth). We have shown in
earlier work that deleting edges calls for introducing auxiliary network
parameters to compensate for lost dependencies, and proposed intuitive
conditions for determining these parameters. We have also shown that our method
corresponds to IBP when enough edges are deleted to yield a polytree, and
corresponds to some generalizations of IBP when fewer edges are deleted. In
this paper, we propose a different criteria for determining auxiliary
parameters based on optimizing the KL-divergence between the original and
approximate networks. We discuss the relationship between the two methods for
selecting parameters, shedding new light on IBP and its generalizations. We
also discuss the application of our new method to approximating inference
problems which are exponential in constrained treewidth, including MAP and
nonmyopic value of information.
| [
{
"version": "v1",
"created": "Wed, 27 Jun 2012 15:38:46 GMT"
}
] | 1,341,187,200,000 | [
[
"Choi",
"Arthur",
""
],
[
"Darwiche",
"Adnan",
""
]
] |
1206.6819 | Hei Chan | Hei Chan, Adnan Darwiche | On the Robustness of Most Probable Explanations | Appears in Proceedings of the Twenty-Second Conference on Uncertainty
in Artificial Intelligence (UAI2006) | null | null | UAI-P-2006-PG-63-71 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In Bayesian networks, a Most Probable Explanation (MPE) is a complete
variable instantiation with a highest probability given the current evidence.
In this paper, we discuss the problem of finding robustness conditions of the
MPE under single parameter changes. Specifically, we ask the question: How much
change in a single network parameter can we afford to apply while keeping the
MPE unchanged? We will describe a procedure, which is the first of its kind,
that computes this answer for each parameter in the Bayesian network variable
in time O(n exp(w)), where n is the number of network variables and w is its
treewidth.
| [
{
"version": "v1",
"created": "Wed, 27 Jun 2012 15:39:15 GMT"
}
] | 1,341,187,200,000 | [
[
"Chan",
"Hei",
""
],
[
"Darwiche",
"Adnan",
""
]
] |
1206.6822 | Bozhena Bidyuk | Bozhena Bidyuk, Rina Dechter | Cutset Sampling with Likelihood Weighting | Appears in Proceedings of the Twenty-Second Conference on Uncertainty
in Artificial Intelligence (UAI2006) | null | null | UAI-P-2006-PG-39-46 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The paper analyzes theoretically and empirically the performance of
likelihood weighting (LW) on a subset of nodes in Bayesian networks. The
proposed scheme requires fewer samples to converge due to reduction in sampling
variance. The method exploits the structure of the network to bound the
complexity of exact inference used to compute sampling distributions, similar
to Gibbs cutset sampling. Yet, the extension of the previosly proposed cutset
sampling principles to likelihood weighting is non-trivial due to differences
in the sampling processes of Gibbs sampler and LW. We demonstrate empirically
that likelihood weighting on a cutset (LWLC) is effective time-wise and has a
lower rejection rate than LW when applied to networks with many deterministic
probabilities. Finally, we show that the performance of likelihood weighting on
a cutset can be improved further by caching computed sampling distributions
and, consequently, learning 'zeros' of the target distribution.
| [
{
"version": "v1",
"created": "Wed, 27 Jun 2012 15:40:35 GMT"
}
] | 1,341,187,200,000 | [
[
"Bidyuk",
"Bozhena",
""
],
[
"Dechter",
"Rina",
""
]
] |
1206.6823 | Yaxin Bi | Yaxin Bi, Jiwen W. Guan | An Efficient Triplet-based Algorithm for Evidential Reasoning | Appears in Proceedings of the Twenty-Second Conference on Uncertainty
in Artificial Intelligence (UAI2006) | null | null | UAI-P-2006-PG-31-38 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Linear-time computational techniques have been developed for combining
evidence which is available on a number of contending hypotheses. They offer a
means of making the computation-intensive calculations involved more efficient
in certain circumstances. Unfortunately, they restrict the orthogonal sum of
evidential functions to the dichotomous structure applies only to elements and
their complements. In this paper, we present a novel evidence structure in
terms of a triplet and a set of algorithms for evidential reasoning. The merit
of this structure is that it divides a set of evidence into three subsets,
distinguishing trivial evidential elements from important ones focusing some
particular elements. It avoids the deficits of the dichotomous structure in
representing the preference of evidence and estimating the basic probability
assignment of evidence. We have established a formalism for this structure and
the general formulae for combining pieces of evidence in the form of the
triplet, which have been theoretically justified.
| [
{
"version": "v1",
"created": "Wed, 27 Jun 2012 15:40:48 GMT"
}
] | 1,341,187,200,000 | [
[
"Bi",
"Yaxin",
""
],
[
"Guan",
"Jiwen W.",
""
]
] |
1206.6827 | Chalee Asavathiratham | Chalee Asavathiratham | Linear Algebra Approach to Separable Bayesian Networks | Appears in Proceedings of the Twenty-Second Conference on Uncertainty
in Artificial Intelligence (UAI2006) | null | null | UAI-P-2006-PG-1-6 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Separable Bayesian Networks, or the Influence Model, are dynamic Bayesian
Networks in which the conditional probability distribution can be separated
into a function of only the marginal distribution of a node's neighbors,
instead of the joint distributions. In terms of modeling, separable networks
has rendered possible siginificant reduction in complexity, as the state space
is only linear in the number of variables on the network, in contrast to a
typical state space which is exponential. In this work, We describe the
connection between an arbitrary Conditional Probability Table (CPT) and
separable systems using linear algebra. We give an alternate proof on the
equivalence of sufficiency and separability. We present a computational method
for testing whether a given CPT is separable.
| [
{
"version": "v1",
"created": "Wed, 27 Jun 2012 15:41:47 GMT"
}
] | 1,341,187,200,000 | [
[
"Asavathiratham",
"Chalee",
""
]
] |
1206.6831 | Yimin Huang | Yimin Huang, Marco Valtorta | Pearl's Calculus of Intervention Is Complete | Appears in Proceedings of the Twenty-Second Conference on Uncertainty
in Artificial Intelligence (UAI2006) | null | null | UAI-P-2006-PG-217-224 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper is concerned with graphical criteria that can be used to solve the
problem of identifying casual effects from nonexperimental data in a causal
Bayesian network structure, i.e., a directed acyclic graph that represents
causal relationships. We first review Pearl's work on this topic [Pearl, 1995],
in which several useful graphical criteria are presented. Then we present a
complete algorithm [Huang and Valtorta, 2006b] for the identifiability problem.
By exploiting the completeness of this algorithm, we prove that the three basic
do-calculus rules that Pearl presents are complete, in the sense that, if a
causal effect is identifiable, there exists a sequence of applications of the
rules of the do-calculus that transforms the causal effect formula into a
formula that only includes observational quantities.
| [
{
"version": "v1",
"created": "Wed, 27 Jun 2012 16:17:19 GMT"
}
] | 1,341,187,200,000 | [
[
"Huang",
"Yimin",
""
],
[
"Valtorta",
"Marco",
""
]
] |
1206.6834 | Phan H. Giang | Phan H. Giang | A new axiomatization for likelihood gambles | Appears in Proceedings of the Twenty-Second Conference on Uncertainty
in Artificial Intelligence (UAI2006) | null | null | UAI-P-2006-PG-192-199 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper studies a new and more general axiomatization than one presented
previously for preference on likelihood gambles. Likelihood gambles describe
actions in a situation where a decision maker knows multiple probabilistic
models and a random sample generated from one of those models but does not know
prior probability of models. This new axiom system is inspired by Jensen's
axiomatization of probabilistic gambles. Our approach provides a new
perspective to the role of data in decision making under ambiguity. It avoids
one of the most controversial issue of Bayesian methodology namely the
assumption of prior probability.
| [
{
"version": "v1",
"created": "Wed, 27 Jun 2012 16:18:21 GMT"
}
] | 1,341,187,200,000 | [
[
"Giang",
"Phan H.",
""
]
] |
1206.6835 | Nir Friedman | Nir Friedman, Raz Kupferman | Dimension Reduction in Singularly Perturbed Continuous-Time Bayesian
Networks | Appears in Proceedings of the Twenty-Second Conference on Uncertainty
in Artificial Intelligence (UAI2006) | null | null | UAI-P-2006-PG-182-191 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Continuous-time Bayesian networks (CTBNs) are graphical representations of
multi-component continuous-time Markov processes as directed graphs. The edges
in the network represent direct influences among components. The joint rate
matrix of the multi-component process is specified by means of conditional rate
matrices for each component separately. This paper addresses the situation
where some of the components evolve on a time scale that is much shorter
compared to the time scale of the other components. In this paper, we prove
that in the limit where the separation of scales is infinite, the Markov
process converges (in distribution, or weakly) to a reduced, or effective
Markov process that only involves the slow components. We also demonstrate that
for reasonable separation of scale (an order of magnitude) the reduced process
is a good approximation of the marginal process over the slow components. We
provide a simple procedure for building a reduced CTBN for this effective
process, with conditional rate matrices that can be directly calculated from
the original CTBN, and discuss the implications for approximate reasoning in
large systems.
| [
{
"version": "v1",
"created": "Wed, 27 Jun 2012 16:18:35 GMT"
}
] | 1,341,187,200,000 | [
[
"Friedman",
"Nir",
""
],
[
"Kupferman",
"Raz",
""
]
] |
1206.6836 | Norman Ferns | Norman Ferns, Pablo Samuel Castro, Doina Precup, Prakash Panangaden | Methods for computing state similarity in Markov Decision Processes | Appears in Proceedings of the Twenty-Second Conference on Uncertainty
in Artificial Intelligence (UAI2006) | null | null | UAI-P-2006-PG-174-181 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | A popular approach to solving large probabilistic systems relies on
aggregating states based on a measure of similarity. Many approaches in the
literature are heuristic. A number of recent methods rely instead on metrics
based on the notion of bisimulation, or behavioral equivalence between states
(Givan et al, 2001, 2003; Ferns et al, 2004). An integral component of such
metrics is the Kantorovich metric between probability distributions. However,
while this metric enables many satisfying theoretical properties, it is costly
to compute in practice. In this paper, we use techniques from network
optimization and statistical sampling to overcome this problem. We obtain in
this manner a variety of distance functions for MDP state aggregation, which
differ in the tradeoff between time and space complexity, as well as the
quality of the aggregation. We provide an empirical evaluation of these
trade-offs.
| [
{
"version": "v1",
"created": "Wed, 27 Jun 2012 16:18:48 GMT"
}
] | 1,341,187,200,000 | [
[
"Ferns",
"Norman",
""
],
[
"Castro",
"Pablo Samuel",
""
],
[
"Precup",
"Doina",
""
],
[
"Panangaden",
"Prakash",
""
]
] |
1206.6837 | Gal Elidan | Gal Elidan, Ian McGraw, Daphne Koller | Residual Belief Propagation: Informed Scheduling for Asynchronous
Message Passing | Appears in Proceedings of the Twenty-Second Conference on Uncertainty
in Artificial Intelligence (UAI2006) | null | null | UAI-P-2006-PG-165-173 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Inference for probabilistic graphical models is still very much a practical
challenge in large domains. The commonly used and effective belief propagation
(BP) algorithm and its generalizations often do not converge when applied to
hard, real-life inference tasks. While it is widely recognized that the
scheduling of messages in these algorithms may have significant consequences,
this issue remains largely unexplored. In this work, we address the question of
how to schedule messages for asynchronous propagation so that a fixed point is
reached faster and more often. We first show that any reasonable asynchronous
BP converges to a unique fixed point under conditions similar to those that
guarantee convergence of synchronous BP. In addition, we show that the
convergence rate of a simple round-robin schedule is at least as good as that
of synchronous propagation. We then propose residual belief propagation (RBP),
a novel, easy-to-implement, asynchronous propagation algorithm that schedules
messages in an informed way, that pushes down a bound on the distance from the
fixed point. Finally, we demonstrate the superiority of RBP over
state-of-the-art methods for a variety of challenging synthetic and real-life
problems: RBP converges significantly more often than other methods; and it
significantly reduces running time until convergence, even when other methods
converge.
| [
{
"version": "v1",
"created": "Wed, 27 Jun 2012 16:19:01 GMT"
}
] | 1,341,187,200,000 | [
[
"Elidan",
"Gal",
""
],
[
"McGraw",
"Ian",
""
],
[
"Koller",
"Daphne",
""
]
] |
1206.6841 | Vanessa Didelez | Vanessa Didelez | Asymmetric separation for local independence graphs | Appears in Proceedings of the Twenty-Second Conference on Uncertainty
in Artificial Intelligence (UAI2006) | null | null | UAI-P-2006-PG-130-137 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Directed possibly cyclic graphs have been proposed by Didelez (2000) and
Nodelmann et al. (2002) in order to represent the dynamic dependencies among
stochastic processes. These dependencies are based on a generalization of
Granger-causality to continuous time, first developed by Schweder (1970) for
Markov processes, who called them local dependencies. They deserve special
attention as they are asymmetric unlike stochastic (in)dependence. In this
paper we focus on their graphical representation and develop a suitable, i.e.
asymmetric notion of separation, called delta-separation. The properties of
this graph separation as well as of local independence are investigated in
detail within a framework of asymmetric (semi)graphoids allowing a deeper
insight into what information can be read off these graphs.
| [
{
"version": "v1",
"created": "Wed, 27 Jun 2012 16:20:13 GMT"
}
] | 1,341,187,200,000 | [
[
"Didelez",
"Vanessa",
""
]
] |
1206.6844 | Cedric Pralet | Cedric Pralet, Thomas Schiex, Gerard Verfaillie | From influence diagrams to multi-operator cluster DAGs | Appears in Proceedings of the Twenty-Second Conference on Uncertainty
in Artificial Intelligence (UAI2006) | null | null | UAI-P-2006-PG-393-400 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | There exist several architectures to solve influence diagrams using local
computations, such as the Shenoy-Shafer, the HUGIN, or the Lazy Propagation
architectures. They all extend usual variable elimination algorithms thanks to
the use of so-called 'potentials'. In this paper, we introduce a new
architecture, called the Multi-operator Cluster DAG architecture, which can
produce decompositions with an improved constrained induced-width, and
therefore induce potentially exponential gains. Its principle is to benefit
from the composite nature of influence diagrams, instead of using uniform
potentials, in order to better analyze the problem structure.
| [
{
"version": "v1",
"created": "Wed, 27 Jun 2012 16:21:20 GMT"
}
] | 1,341,187,200,000 | [
[
"Pralet",
"Cedric",
""
],
[
"Schiex",
"Thomas",
""
],
[
"Verfaillie",
"Gerard",
""
]
] |
1206.6849 | Brian Milch | Brian Milch, Stuart Russell | General-Purpose MCMC Inference over Relational Structures | Appears in Proceedings of the Twenty-Second Conference on Uncertainty
in Artificial Intelligence (UAI2006) | null | null | UAI-P-2006-PG-349-358 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Tasks such as record linkage and multi-target tracking, which involve
reconstructing the set of objects that underlie some observed data, are
particularly challenging for probabilistic inference. Recent work has achieved
efficient and accurate inference on such problems using Markov chain Monte
Carlo (MCMC) techniques with customized proposal distributions. Currently,
implementing such a system requires coding MCMC state representations and
acceptance probability calculations that are specific to a particular
application. An alternative approach, which we pursue in this paper, is to use
a general-purpose probabilistic modeling language (such as BLOG) and a generic
Metropolis-Hastings MCMC algorithm that supports user-supplied proposal
distributions. Our algorithm gains flexibility by using MCMC states that are
only partial descriptions of possible worlds; we provide conditions under which
MCMC over partial worlds yields correct answers to queries. We also show how to
use a context-specific Bayes net to identify the factors in the acceptance
probability that need to be computed for a given proposed move. Experimental
results on a citation matching task show that our general-purpose MCMC engine
compares favorably with an application-specific system.
| [
{
"version": "v1",
"created": "Wed, 27 Jun 2012 16:24:15 GMT"
}
] | 1,341,187,200,000 | [
[
"Milch",
"Brian",
""
],
[
"Russell",
"Stuart",
""
]
] |
1206.6854 | Anders L. Madsen | Anders L. Madsen | Belief Update in CLG Bayesian Networks With Lazy Propagation | Appears in Proceedings of the Twenty-Second Conference on Uncertainty
in Artificial Intelligence (UAI2006) | null | null | UAI-P-2006-PG-306-313 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In recent years Bayesian networks (BNs) with a mixture of continuous and
discrete variables have received an increasing level of attention. We present
an architecture for exact belief update in Conditional Linear Gaussian BNs (CLG
BNs). The architecture is an extension of lazy propagation using operations of
Lauritzen & Jensen [6] and Cowell [2]. By decomposing clique and separator
potentials into sets of factors, the proposed architecture takes advantage of
independence and irrelevance properties induced by the structure of the graph
and the evidence. The resulting benefits are illustrated by examples. Results
of a preliminary empirical performance evaluation indicate a significant
potential of the proposed architecture.
| [
{
"version": "v1",
"created": "Wed, 27 Jun 2012 16:25:42 GMT"
}
] | 1,341,187,200,000 | [
[
"Madsen",
"Anders L.",
""
]
] |
1206.6856 | Seunghwan Lee | Seunghwan Lee | Reasoning about Uncertainty in Metric Spaces | Appears in Proceedings of the Twenty-Second Conference on Uncertainty
in Artificial Intelligence (UAI2006) | null | null | UAI-P-2006-PG-289-297 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We set up a model for reasoning about metric spaces with belief theoretic
measures. The uncertainty in these spaces stems from both probability and
metric. To represent both aspect of uncertainty, we choose an expected distance
function as a measure of uncertainty. A formal logical system is constructed
for the reasoning about expected distance. Soundness and completeness are shown
for this logic. For reasoning on product metric space with uncertainty, a new
metric is defined and shown to have good properties.
| [
{
"version": "v1",
"created": "Wed, 27 Jun 2012 16:26:12 GMT"
}
] | 1,341,187,200,000 | [
[
"Lee",
"Seunghwan",
""
]
] |
1206.6859 | Kathryn Blackmond Laskey | Kathryn Blackmond Laskey, Ning Xu, Chun-Hung Chen | Propagation of Delays in the National Airspace System | Appears in Proceedings of the Twenty-Second Conference on Uncertainty
in Artificial Intelligence (UAI2006) | null | null | UAI-P-2006-PG-265-272 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The National Airspace System (NAS) is a large and complex system with
thousands of interrelated components: administration, control centers,
airports, airlines, aircraft, passengers, etc. The complexity of the NAS
creates many difficulties in management and control. One of the most pressing
problems is flight delay. Delay creates high cost to airlines, complaints from
passengers, and difficulties for airport operations. As demand on the system
increases, the delay problem becomes more and more prominent. For this reason,
it is essential for the Federal Aviation Administration to understand the
causes of delay and to find ways to reduce delay. Major contributing factors to
delay are congestion at the origin airport, weather, increasing demand, and air
traffic management (ATM) decisions such as the Ground Delay Programs (GDP).
Delay is an inherently stochastic phenomenon. Even if all known causal factors
could be accounted for, macro-level national airspace system (NAS) delays could
not be predicted with certainty from micro-level aircraft information. This
paper presents a stochastic model that uses Bayesian Networks (BNs) to model
the relationships among different components of aircraft delay and the causal
factors that affect delays. A case study on delays of departure flights from
Chicago O'Hare international airport (ORD) to Hartsfield-Jackson Atlanta
International Airport (ATL) reveals how local and system level environmental
and human-caused factors combine to affect components of delay, and how these
components contribute to the final arrival delay at the destination airport.
| [
{
"version": "v1",
"created": "Wed, 27 Jun 2012 16:27:12 GMT"
}
] | 1,341,187,200,000 | [
[
"Laskey",
"Kathryn Blackmond",
""
],
[
"Xu",
"Ning",
""
],
[
"Chen",
"Chun-Hung",
""
]
] |
1206.6869 | Amarnag Subramanya | Amarnag Subramanya, Alvin Raj, Jeff A. Bilmes, Dieter Fox | Recognizing Activities and Spatial Context Using Wearable Sensors | Appears in Proceedings of the Twenty-Second Conference on Uncertainty
in Artificial Intelligence (UAI2006) | null | null | UAI-P-2006-PG-494-502 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We introduce a new dynamic model with the capability of recognizing both
activities that an individual is performing as well as where that ndividual is
located. Our model is novel in that it utilizes a dynamic graphical model to
jointly estimate both activity and spatial context over time based on the
simultaneous use of asynchronous observations consisting of GPS measurements,
and measurements from a small mountable sensor board. Joint inference is quite
desirable as it has the ability to improve accuracy of the model. A key goal,
however, in designing our overall system is to be able to perform accurate
inference decisions while minimizing the amount of hardware an individual must
wear. This minimization leads to greater comfort and flexibility, decreased
power requirements and therefore increased battery life, and reduced cost. We
show results indicating that our joint measurement model outperforms
measurements from either the sensor board or GPS alone, using two types of
probabilistic inference procedures, namely particle filtering and pruned exact
inference.
| [
{
"version": "v1",
"created": "Wed, 27 Jun 2012 16:29:30 GMT"
}
] | 1,341,187,200,000 | [
[
"Subramanya",
"Amarnag",
""
],
[
"Raj",
"Alvin",
""
],
[
"Bilmes",
"Jeff A.",
""
],
[
"Fox",
"Dieter",
""
]
] |
1206.6875 | Tomi Silander | Tomi Silander, Petri Myllymaki | A simple approach for finding the globally optimal Bayesian network
structure | Appears in Proceedings of the Twenty-Second Conference on Uncertainty
in Artificial Intelligence (UAI2006) | null | null | UAI-P-2006-PG-445-452 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We study the problem of learning the best Bayesian network structure with
respect to a decomposable score such as BDe, BIC or AIC. This problem is known
to be NP-hard, which means that solving it becomes quickly infeasible as the
number of variables increases. Nevertheless, in this paper we show that it is
possible to learn the best Bayesian network structure with over 30 variables,
which covers many practically interesting cases. Our algorithm is less
complicated and more efficient than the techniques presented earlier. It can be
easily parallelized, and offers a possibility for efficient exploration of the
best networks consistent with different variable orderings. In the experimental
part of the paper we compare the performance of the algorithm to the previous
state-of-the-art algorithm. Free source-code and an online-demo can be found at
http://b-course.hiit.fi/bene.
| [
{
"version": "v1",
"created": "Wed, 27 Jun 2012 16:30:42 GMT"
}
] | 1,341,187,200,000 | [
[
"Silander",
"Tomi",
""
],
[
"Myllymaki",
"Petri",
""
]
] |
1206.6879 | Scott Sanner | Scott Sanner, Craig Boutilier | Practical Linear Value-approximation Techniques for First-order MDPs | Appears in Proceedings of the Twenty-Second Conference on Uncertainty
in Artificial Intelligence (UAI2006) | null | null | UAI-P-2006-PG-409-417 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Recent work on approximate linear programming (ALP) techniques for
first-order Markov Decision Processes (FOMDPs) represents the value function
linearly w.r.t. a set of first-order basis functions and uses linear
programming techniques to determine suitable weights. This approach offers the
advantage that it does not require simplification of the first-order value
function, and allows one to solve FOMDPs independent of a specific domain
instantiation. In this paper, we address several questions to enhance the
applicability of this work: (1) Can we extend the first-order ALP framework to
approximate policy iteration to address performance deficiencies of previous
approaches? (2) Can we automatically generate basis functions and evaluate
their impact on value function quality? (3) How can we decompose intractable
problems with universally quantified rewards into tractable subproblems? We
propose answers to these questions along with a number of novel optimizations
and provide a comparative empirical evaluation on logistics problems from the
ICAPS 2004 Probabilistic Planning Competition.
| [
{
"version": "v1",
"created": "Wed, 27 Jun 2012 16:31:33 GMT"
}
] | 1,341,187,200,000 | [
[
"Sanner",
"Scott",
""
],
[
"Boutilier",
"Craig",
""
]
] |
1206.7064 | Mladen Nikolic | Milena Vujosevic-Janicic, Mladen Nikolic, Dusan Tosic, Viktor Kuncak | Software Verification and Graph Similarity for Automated Evaluation of
Students' Assignments | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this paper we promote introducing software verification and control flow
graph similarity measurement in automated evaluation of students' programs. We
present a new grading framework that merges results obtained by combination of
these two approaches with results obtained by automated testing, leading to
improved quality and precision of automated grading. These two approaches are
also useful in providing a comprehensible feedback that can help students to
improve the quality of their programs We also present our corresponding tools
that are publicly available and open source. The tools are based on LLVM
low-level intermediate code representation, so they could be applied to a
number of programming languages. Experimental evaluation of the proposed
grading framework is performed on a corpus of university students' programs
written in programming language C. Results of the experiments show that
automatically generated grades are highly correlated with manually determined
grades suggesting that the presented tools can find real-world applications in
studying and grading.
| [
{
"version": "v1",
"created": "Fri, 29 Jun 2012 16:10:20 GMT"
}
] | 1,341,187,200,000 | [
[
"Vujosevic-Janicic",
"Milena",
""
],
[
"Nikolic",
"Mladen",
""
],
[
"Tosic",
"Dusan",
""
],
[
"Kuncak",
"Viktor",
""
]
] |
1207.0117 | Rajdeep Borgohain | Rajdeep Borgohain and Sugata Sanyal | Rule Based Expert System for Cerebral Palsy Diagnosis | 4 pages, 1 figure, 1 table | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The use of Artificial Intelligence is finding prominence not only in core
computer areas, but also in cross disciplinary areas including medical
diagnosis. In this paper, we present a rule based Expert System used in
diagnosis of Cerebral Palsy. The expert system takes user input and depending
on the symptoms of the patient, diagnoses if the patient is suffering from
Cerebral Palsy. The Expert System also classifies the Cerebral Palsy as mild,
moderate or severe based on the presented symptoms.
| [
{
"version": "v1",
"created": "Sat, 30 Jun 2012 16:52:12 GMT"
}
] | 1,341,273,600,000 | [
[
"Borgohain",
"Rajdeep",
""
],
[
"Sanyal",
"Sugata",
""
]
] |
1207.0206 | Loshchilov Ilya | Ilya Loshchilov (INRIA Saclay - Ile de France), Marc Schoenauer (INRIA
Saclay - Ile de France, MSR - INRIA), Mich\`ele Sebag (INRIA Saclay - Ile de
France, LRI) | Alternative Restart Strategies for CMA-ES | null | Parallel Problem Solving From Nature (2012) | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper focuses on the restart strategy of CMA-ES on multi-modal
functions. A first alternative strategy proceeds by decreasing the initial
step-size of the mutation while doubling the population size at each restart. A
second strategy adaptively allocates the computational budget among the restart
settings in the BIPOP scheme. Both restart strategies are validated on the BBOB
benchmark; their generality is also demonstrated on an independent real-world
problem suite related to spacecraft trajectory optimization.
| [
{
"version": "v1",
"created": "Sun, 1 Jul 2012 13:50:20 GMT"
}
] | 1,341,273,600,000 | [
[
"Loshchilov",
"Ilya",
"",
"INRIA Saclay - Ile de France"
],
[
"Schoenauer",
"Marc",
"",
"INRIA\n Saclay - Ile de France, MSR - INRIA"
],
[
"Sebag",
"Michèle",
"",
"INRIA Saclay - Ile de\n France, LRI"
]
] |
1207.0262 | Shiping Wang | Shiping Wang and Qingxin Zhu and William Zhu and Fan Min | Characteristic matrix of covering and its application to boolean matrix
decomposition and axiomatization | 18-page original paper | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Covering is an important type of data structure while covering-based rough
sets provide an efficient and systematic theory to deal with covering data. In
this paper, we use boolean matrices to represent and axiomatize three types of
covering approximation operators. First, we define two types of characteristic
matrices of a covering which are essentially square boolean ones, and their
properties are studied. Through the characteristic matrices, three important
types of covering approximation operators are concisely equivalently
represented. Second, matrix representations of covering approximation operators
are used in boolean matrix decomposition. We provide a sufficient and necessary
condition for a square boolean matrix to decompose into the boolean product of
another one and its transpose. And we develop an algorithm for this boolean
matrix decomposition. Finally, based on the above results, these three types of
covering approximation operators are axiomatized using boolean matrices. In a
word, this work borrows extensively from boolean matrices and present a new
view to study covering-based rough sets.
| [
{
"version": "v1",
"created": "Mon, 2 Jul 2012 01:12:16 GMT"
},
{
"version": "v2",
"created": "Sat, 7 Jul 2012 02:56:14 GMT"
},
{
"version": "v3",
"created": "Fri, 17 Aug 2012 09:05:55 GMT"
},
{
"version": "v4",
"created": "Sun, 3 Mar 2013 05:23:31 GMT"
}
] | 1,362,441,600,000 | [
[
"Wang",
"Shiping",
""
],
[
"Zhu",
"Qingxin",
""
],
[
"Zhu",
"William",
""
],
[
"Min",
"Fan",
""
]
] |
1207.0403 | Peratham Wiriyathammabhum Mr. | Peratham Wiriyathammabhum, Boonserm Kijsirikul | Robust Principal Component Analysis Using Statistical Estimators | In Proc. of the International Joint Conference on Computer Science
and Software Engineering (JCSSE) 2009 | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Principal Component Analysis (PCA) finds a linear mapping and maximizes the
variance of the data which makes PCA sensitive to outliers and may cause wrong
eigendirection. In this paper, we propose techniques to solve this problem; we
use the data-centering method and reestimate the covariance matrix using robust
statistic techniques such as median, robust scaling which is a booster to
data-centering and Huber M-estimator which measures the presentation of
outliers and reweight them with small values. The results on several real world
data sets show that our proposed method handles outliers and gains better
results than the original PCA and provides the same accuracy with lower
computation cost than the Kernel PCA using the polynomial kernel in
classification tasks.
| [
{
"version": "v1",
"created": "Mon, 2 Jul 2012 14:30:19 GMT"
}
] | 1,341,273,600,000 | [
[
"Wiriyathammabhum",
"Peratham",
""
],
[
"Kijsirikul",
"Boonserm",
""
]
] |
1207.1230 | Qibin Zhao Dr | Qibin Zhao, Cesar F. Caiafa, Danilo P. Mandic, Zenas C. Chao, Yasuo
Nagasaka, Naotaka Fujii, Liqing Zhang and Andrzej Cichocki | Higher-Order Partial Least Squares (HOPLS): A Generalized Multi-Linear
Regression Method | null | Pattern Analysis and Machine Intelligence, IEEE Transactions on,
vol. 35, no.7, July, 2013 | 10.1109/TPAMI.2012.254. | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | A new generalized multilinear regression model, termed the Higher-Order
Partial Least Squares (HOPLS), is introduced with the aim to predict a tensor
(multiway array) $\tensor{Y}$ from a tensor $\tensor{X}$ through projecting the
data onto the latent space and performing regression on the corresponding
latent variables. HOPLS differs substantially from other regression models in
that it explains the data by a sum of orthogonal Tucker tensors, while the
number of orthogonal loadings serves as a parameter to control model complexity
and prevent overfitting. The low dimensional latent space is optimized
sequentially via a deflation operation, yielding the best joint subspace
approximation for both $\tensor{X}$ and $\tensor{Y}$. Instead of decomposing
$\tensor{X}$ and $\tensor{Y}$ individually, higher order singular value
decomposition on a newly defined generalized cross-covariance tensor is
employed to optimize the orthogonal loadings. A systematic comparison on both
synthetic data and real-world decoding of 3D movement trajectories from
electrocorticogram (ECoG) signals demonstrate the advantages of HOPLS over the
existing methods in terms of better predictive ability, suitability to handle
small sample sizes, and robustness to noise.
| [
{
"version": "v1",
"created": "Thu, 5 Jul 2012 11:41:19 GMT"
}
] | 1,390,780,800,000 | [
[
"Zhao",
"Qibin",
""
],
[
"Caiafa",
"Cesar F.",
""
],
[
"Mandic",
"Danilo P.",
""
],
[
"Chao",
"Zenas C.",
""
],
[
"Nagasaka",
"Yasuo",
""
],
[
"Fujii",
"Naotaka",
""
],
[
"Zhang",
"Liqing",
""
],
[
"Cichocki",
"Andrzej",
""
]
] |
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