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1207.1350 | Daniel Bryce | Daniel Bryce, Subbarao Kambhampati | Cost Sensitive Reachability Heuristics for Handling State Uncertainty | Appears in Proceedings of the Twenty-First Conference on Uncertainty
in Artificial Intelligence (UAI2005) | null | null | UAI-P-2005-PG-60-68 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | While POMDPs provide a general platform for non-deterministic conditional
planning under a variety of quality metrics they have limited scalability. On
the other hand, non-deterministic conditional planners scale very well, but
many lack the ability to optimize plan quality metrics. We present a novel
generalization of planning graph based heuristics that helps conditional
planners both scale and generate high quality plans when using actions with
nonuniform costs. We make empirical comparisons with two state of the art
planners to show the benefit of our techniques.
| [
{
"version": "v1",
"created": "Wed, 4 Jul 2012 12:11:17 GMT"
}
] | 1,341,792,000,000 | [
[
"Bryce",
"Daniel",
""
],
[
"Kambhampati",
"Subbarao",
""
]
] |
1207.1351 | Peter de Waal | Peter de Waal, Linda C. van der Gaag | Stable Independence in Perfect Maps | Appears in Proceedings of the Twenty-First Conference on Uncertainty
in Artificial Intelligence (UAI2005) | null | null | UAI-P-2005-PG-161-168 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | With the aid of the concept of stable independence we can construct, in an
efficient way, a compact representation of a semi-graphoid independence
relation. We show that this representation provides a new necessary condition
for the existence of a directed perfect map for the relation. The test for this
condition is based to a large extent on the transitivity property of a special
form of d-separation. The complexity of the test is linear in the size of the
representation. The test, moreover, brings the additional benefit that it can
be used to guide the early stages of network construction.
| [
{
"version": "v1",
"created": "Wed, 4 Jul 2012 12:12:05 GMT"
}
] | 1,341,792,000,000 | [
[
"de Waal",
"Peter",
""
],
[
"van der Gaag",
"Linda C.",
""
]
] |
1207.1353 | Kristian Kersting | Kristian Kersting, Tapani Raiko | 'Say EM' for Selecting Probabilistic Models for Logical Sequences | Appears in Proceedings of the Twenty-First Conference on Uncertainty
in Artificial Intelligence (UAI2005) | null | null | UAI-P-2005-PG-300-307 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Many real world sequences such as protein secondary structures or shell logs
exhibit a rich internal structures. Traditional probabilistic models of
sequences, however, consider sequences of flat symbols only. Logical hidden
Markov models have been proposed as one solution. They deal with logical
sequences, i.e., sequences over an alphabet of logical atoms. This comes at the
expense of a more complex model selection problem. Indeed, different
abstraction levels have to be explored. In this paper, we propose a novel
method for selecting logical hidden Markov models from data called SAGEM. SAGEM
combines generalized expectation maximization, which optimizes parameters, with
structure search for model selection using inductive logic programming
refinement operators. We provide convergence and experimental results that show
SAGEM's effectiveness.
| [
{
"version": "v1",
"created": "Wed, 4 Jul 2012 12:13:04 GMT"
}
] | 1,341,792,000,000 | [
[
"Kersting",
"Kristian",
""
],
[
"Raiko",
"Tapani",
""
]
] |
1207.1354 | Kathryn Blackmond Laskey | Kathryn Blackmond Laskey, Paulo da Costa | Of Starships and Klingons: Bayesian Logic for the 23rd Century | Appears in Proceedings of the Twenty-First Conference on Uncertainty
in Artificial Intelligence (UAI2005) | null | null | UAI-P-2005-PG-346-353 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Intelligent systems in an open world must reason about many interacting
entities related to each other in diverse ways and having uncertain features
and relationships. Traditional probabilistic languages lack the expressive
power to handle relational domains. Classical first-order logic is sufficiently
expressive, but lacks a coherent plausible reasoning capability. Recent years
have seen the emergence of a variety of approaches to integrating first-order
logic, probability, and machine learning. This paper presents Multi-entity
Bayesian networks (MEBN), a formal system that integrates First Order Logic
(FOL) with Bayesian probability theory. MEBN extends ordinary Bayesian networks
to allow representation of graphical models with repeated sub-structures, and
can express a probability distribution over models of any consistent, finitely
axiomatizable first-order theory. We present the logic using an example
inspired by the Paramount Series StarTrek.
| [
{
"version": "v1",
"created": "Wed, 4 Jul 2012 12:13:29 GMT"
}
] | 1,341,792,000,000 | [
[
"Laskey",
"Kathryn Blackmond",
""
],
[
"da Costa",
"Paulo",
""
]
] |
1207.1355 | Anders L. Madsen | Anders L. Madsen | A Differential Semantics of Lazy AR Propagation | Appears in Proceedings of the Twenty-First Conference on Uncertainty
in Artificial Intelligence (UAI2005) | null | null | UAI-P-2005-PG-364-371 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this paper we present a differential semantics of Lazy AR Propagation
(LARP) in discrete Bayesian networks. We describe how both single and multi
dimensional partial derivatives of the evidence may easily be calculated from a
junction tree in LARP equilibrium. We show that the simplicity of the
calculations stems from the nature of LARP. Based on the differential semantics
we describe how variable propagation in the LARP architecture may give access
to additional partial derivatives. The cautious LARP (cLARP) scheme is derived
to produce a flexible cLARP equilibrium that offers additional opportunities
for calculating single and multidimensional partial derivatives of the evidence
and subsets of the evidence from a single propagation. The results of an
empirical evaluation illustrates how the access to a largely increased number
of partial derivatives comes at a low computational cost.
| [
{
"version": "v1",
"created": "Wed, 4 Jul 2012 12:13:36 GMT"
}
] | 1,341,792,000,000 | [
[
"Madsen",
"Anders L.",
""
]
] |
1207.1356 | Yun Peng | Yun Peng, Zhongli Ding | Modifying Bayesian Networks by Probability Constraints | Appears in Proceedings of the Twenty-First Conference on Uncertainty
in Artificial Intelligence (UAI2005) | null | null | UAI-P-2005-PG-459-466 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper deals with the following problem: modify a Bayesian network to
satisfy a given set of probability constraints by only change its conditional
probability tables, and the probability distribution of the resulting network
should be as close as possible to that of the original network. We propose to
solve this problem by extending IPFP (iterative proportional fitting procedure)
to probability distributions represented by Bayesian networks. The resulting
algorithm E-IPFP is further developed to D-IPFP, which reduces the
computational cost by decomposing a global EIPFP into a set of smaller local
E-IPFP problems. Limited analysis is provided, including the convergence proofs
of the two algorithms. Computer experiments were conducted to validate the
algorithms. The results are consistent with the theoretical analysis.
| [
{
"version": "v1",
"created": "Wed, 4 Jul 2012 12:14:18 GMT"
}
] | 1,341,792,000,000 | [
[
"Peng",
"Yun",
""
],
[
"Ding",
"Zhongli",
""
]
] |
1207.1357 | Silja Renooij | Silja Renooij, Linda C. van der Gaag | Exploiting Evidence-dependent Sensitivity Bounds | Appears in Proceedings of the Twenty-First Conference on Uncertainty
in Artificial Intelligence (UAI2005) | null | null | UAI-P-2005-PG-485-492 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Studying the effects of one-way variation of any number of parameters on any
number of output probabilities quickly becomes infeasible in practice,
especially if various evidence profiles are to be taken into consideration. To
provide for identifying the parameters that have a potentially large effect
prior to actually performing the analysis, we need properties of sensitivity
functions that are independent of the network under study, of the available
evidence, or of both. In this paper, we study properties that depend upon just
the probability of the entered evidence. We demonstrate that these properties
provide for establishing an upper bound on the sensitivity value for a
parameter; they further provide for establishing the region in which the vertex
of the sensitivity function resides, thereby serving to identify parameters
with a low sensitivity value that may still have a large impact on the
probability of interest for relatively small parameter variations.
| [
{
"version": "v1",
"created": "Wed, 4 Jul 2012 12:14:29 GMT"
}
] | 1,341,792,000,000 | [
[
"Renooij",
"Silja",
""
],
[
"van der Gaag",
"Linda C.",
""
]
] |
1207.1359 | Daniel Szer | Daniel Szer, Francois Charpillet, Shlomo Zilberstein | MAA*: A Heuristic Search Algorithm for Solving Decentralized POMDPs | Appears in Proceedings of the Twenty-First Conference on Uncertainty
in Artificial Intelligence (UAI2005) | null | null | UAI-P-2005-PG-576-583 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We present multi-agent A* (MAA*), the first complete and optimal heuristic
search algorithm for solving decentralized partially-observable Markov decision
problems (DEC-POMDPs) with finite horizon. The algorithm is suitable for
computing optimal plans for a cooperative group of agents that operate in a
stochastic environment such as multirobot coordination, network traffic
control, `or distributed resource allocation. Solving such problems efiectively
is a major challenge in the area of planning under uncertainty. Our solution is
based on a synthesis of classical heuristic search and decentralized control
theory. Experimental results show that MAA* has significant advantages. We
introduce an anytime variant of MAA* and conclude with a discussion of
promising extensions such as an approach to solving infinite horizon problems.
| [
{
"version": "v1",
"created": "Wed, 4 Jul 2012 12:15:09 GMT"
}
] | 1,341,792,000,000 | [
[
"Szer",
"Daniel",
""
],
[
"Charpillet",
"Francois",
""
],
[
"Zilberstein",
"Shlomo",
""
]
] |
1207.1363 | Leila Amgoud | Leila Amgoud | A unified setting for inference and decision: An argumentation-based
approach | Appears in Proceedings of the Twenty-First Conference on Uncertainty
in Artificial Intelligence (UAI2005) | null | null | UAI-P-2005-PG-26-33 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Inferring from inconsistency and making decisions are two problems which have
always been treated separately by researchers in Artificial Intelligence.
Consequently, different models have been proposed for each category. Different
argumentation systems [2, 7, 10, 11] have been developed for handling
inconsistency in knowledge bases. Recently, other argumentation systems [3, 4,
8] have been defined for making decisions under uncertainty. The aim of this
paper is to present a general argumentation framework in which both inferring
from inconsistency and decision making are captured. The proposed framework can
be used for decision under uncertainty, multiple criteria decision, rule-based
decision and finally case-based decision. Moreover, works on classical decision
suppose that the information about environment is coherent, and this no longer
required by this general framework.
| [
{
"version": "v1",
"created": "Wed, 4 Jul 2012 16:02:56 GMT"
}
] | 1,341,792,000,000 | [
[
"Amgoud",
"Leila",
""
]
] |
1207.1369 | Barry Cobb | Barry Cobb, Prakash P. Shenoy | Hybrid Bayesian Networks with Linear Deterministic Variables | Appears in Proceedings of the Twenty-First Conference on Uncertainty
in Artificial Intelligence (UAI2005) | null | null | UAI-P-2005-PG-136-144 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | When a hybrid Bayesian network has conditionally deterministic variables with
continuous parents, the joint density function for the continuous variables
does not exist. Conditional linear Gaussian distributions can handle such cases
when the continuous variables have a multi-variate normal distribution and the
discrete variables do not have continuous parents. In this paper, operations
required for performing inference with conditionally deterministic variables in
hybrid Bayesian networks are developed. These methods allow inference in
networks with deterministic variables where continuous variables may be
non-Gaussian, and their density functions can be approximated by mixtures of
truncated exponentials. There are no constraints on the placement of continuous
and discrete nodes in the network.
| [
{
"version": "v1",
"created": "Wed, 4 Jul 2012 16:05:39 GMT"
}
] | 1,341,792,000,000 | [
[
"Cobb",
"Barry",
""
],
[
"Shenoy",
"Prakash P.",
""
]
] |
1207.1370 | Arthur Choi | Arthur Choi, Hei Chan, Adnan Darwiche | On Bayesian Network Approximation by Edge Deletion | Appears in Proceedings of the Twenty-First Conference on Uncertainty
in Artificial Intelligence (UAI2005) | null | null | UAI-P-2005-PG-128-135 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We consider the problem of deleting edges from a Bayesian network for the
purpose of simplifying models in probabilistic inference. In particular, we
propose a new method for deleting network edges, which is based on the evidence
at hand. We provide some interesting bounds on the KL-divergence between
original and approximate networks, which highlight the impact of given evidence
on the quality of approximation and shed some light on good and bad candidates
for edge deletion. We finally demonstrate empirically the promise of the
proposed edge deletion technique as a basis for approximate inference.
| [
{
"version": "v1",
"created": "Wed, 4 Jul 2012 16:05:54 GMT"
}
] | 1,341,792,000,000 | [
[
"Choi",
"Arthur",
""
],
[
"Chan",
"Hei",
""
],
[
"Darwiche",
"Adnan",
""
]
] |
1207.1372 | Mark Chavira | Mark Chavira, David Allen, Adnan Darwiche | Exploiting Evidence in Probabilistic Inference | Appears in Proceedings of the Twenty-First Conference on Uncertainty
in Artificial Intelligence (UAI2005) | null | null | UAI-P-2005-PG-112-119 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We define the notion of compiling a Bayesian network with evidence and
provide a specific approach for evidence-based compilation, which makes use of
logical processing. The approach is practical and advantageous in a number of
application areas-including maximum likelihood estimation, sensitivity
analysis, and MAP computations-and we provide specific empirical results in the
domain of genetic linkage analysis. We also show that the approach is
applicable for networks that do not contain determinism, and show that it
empirically subsumes the performance of the quickscore algorithm when applied
to noisy-or networks.
| [
{
"version": "v1",
"created": "Wed, 4 Jul 2012 16:06:25 GMT"
}
] | 1,341,792,000,000 | [
[
"Chavira",
"Mark",
""
],
[
"Allen",
"David",
""
],
[
"Darwiche",
"Adnan",
""
]
] |
1207.1374 | Jennifer Carlson | Jennifer Carlson, Robin R. Murphy | Use of Dempster-Shafer Conflict Metric to Detect Interpretation
Inconsistency | Appears in Proceedings of the Twenty-First Conference on Uncertainty
in Artificial Intelligence (UAI2005) | null | null | UAI-P-2005-PG-94-104 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | A model of the world built from sensor data may be incorrect even if the
sensors are functioning correctly. Possible causes include the use of
inappropriate sensors (e.g. a laser looking through glass walls), sensor
inaccuracies accumulate (e.g. localization errors), the a priori models are
wrong, or the internal representation does not match the world (e.g. a static
occupancy grid used with dynamically moving objects). We are interested in the
case where the constructed model of the world is flawed, but there is no access
to the ground truth that would allow the system to see the discrepancy, such as
a robot entering an unknown environment. This paper considers the problem of
determining when something is wrong using only the sensor data used to
construct the world model. It proposes 11 interpretation inconsistency
indicators based on the Dempster-Shafer conflict metric, Con, and evaluates
these indicators according to three criteria: ability to distinguish true
inconsistency from sensor noise (classification), estimate the magnitude of
discrepancies (estimation), and determine the source(s) (if any) of sensing
problems in the environment (isolation). The evaluation is conducted using data
from a mobile robot with sonar and laser range sensors navigating indoor
environments under controlled conditions. The evaluation shows that the Gambino
indicator performed best in terms of estimation (at best 0.77 correlation),
isolation, and classification of the sensing situation as degraded (7% false
negative rate) or normal (0% false positive rate).
| [
{
"version": "v1",
"created": "Wed, 4 Jul 2012 16:07:41 GMT"
}
] | 1,341,792,000,000 | [
[
"Carlson",
"Jennifer",
""
],
[
"Murphy",
"Robin R.",
""
]
] |
1207.1375 | Peter Carbonetto | Peter Carbonetto, Jacek Kisynski, Nando de Freitas, David L Poole | Nonparametric Bayesian Logic | Appears in Proceedings of the Twenty-First Conference on Uncertainty
in Artificial Intelligence (UAI2005) | null | null | UAI-P-2005-PG-85-93 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The Bayesian Logic (BLOG) language was recently developed for defining
first-order probability models over worlds with unknown numbers of objects. It
handles important problems in AI, including data association and population
estimation. This paper extends BLOG by adopting generative processes over
function spaces - known as nonparametrics in the Bayesian literature. We
introduce syntax for reasoning about arbitrary collections of objects, and
their properties, in an intuitive manner. By exploiting exchangeability,
distributions over unknown objects and their attributes are cast as Dirichlet
processes, which resolve difficulties in model selection and inference caused
by varying numbers of objects. We demonstrate these concepts with application
to citation matching.
| [
{
"version": "v1",
"created": "Wed, 4 Jul 2012 16:08:11 GMT"
}
] | 1,341,792,000,000 | [
[
"Carbonetto",
"Peter",
""
],
[
"Kisynski",
"Jacek",
""
],
[
"de Freitas",
"Nando",
""
],
[
"Poole",
"David L",
""
]
] |
1207.1377 | Jakub Brzostowski | Jakub Brzostowski, Ryszard Kowalczyk | Efficient algorithm for estimation of qualitative expected utility in
possibilistic case-based reasoning | Appears in Proceedings of the Twenty-First Conference on Uncertainty
in Artificial Intelligence (UAI2005) | null | null | UAI-P-2005-PG-69-76 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We propose an efficient algorithm for estimation of possibility based
qualitative expected utility. It is useful for decision making mechanisms where
each possible decision is assigned a multi-attribute possibility distribution.
The computational complexity of ordinary methods calculating the expected
utility based on discretization is growing exponentially with the number of
attributes, and may become infeasible with a high number of these attributes.
We present series of theorems and lemmas proving the correctness of our
algorithm that exibits a linear computational complexity. Our algorithm has
been applied in the context of selecting the most prospective partners in
multi-party multi-attribute negotiation, and can also be used in making
decisions about potential offers during the negotiation as other similar
problems.
| [
{
"version": "v1",
"created": "Wed, 4 Jul 2012 16:08:46 GMT"
}
] | 1,341,792,000,000 | [
[
"Brzostowski",
"Jakub",
""
],
[
"Kowalczyk",
"Ryszard",
""
]
] |
1207.1378 | Changsung Kang | Changsung Kang, Jin Tian | Local Markov Property for Models Satisfying Composition Axiom | Appears in Proceedings of the Twenty-First Conference on Uncertainty
in Artificial Intelligence (UAI2005) | null | null | UAI-P-2005-PG-284-291 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The local Markov condition for a DAG to be an independence map of a
probability distribution is well known. For DAGs with latent variables,
represented as bi-directed edges in the graph, the local Markov property may
invoke exponential number of conditional independencies. This paper shows that
the number of conditional independence relations required may be reduced if the
probability distributions satisfy the composition axiom. In certain types of
graphs, only linear number of conditional independencies are required. The
result has applications in testing linear structural equation models with
correlated errors.
| [
{
"version": "v1",
"created": "Wed, 4 Jul 2012 16:09:46 GMT"
}
] | 1,341,792,000,000 | [
[
"Kang",
"Changsung",
""
],
[
"Tian",
"Jin",
""
]
] |
1207.1381 | Rafay Hammid | Rafay Hammid, Siddhartha Maddi, Amos Johnson, Aaron Bobick, Irfan
Essa, Charles Lee Isbell | Unsupervised Activity Discovery and Characterization From Event-Streams | Appears in Proceedings of the Twenty-First Conference on Uncertainty
in Artificial Intelligence (UAI2005) | null | null | UAI-P-2005-PG-251-258 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We present a framework to discover and characterize different classes of
everyday activities from event-streams. We begin by representing activities as
bags of event n-grams. This allows us to analyze the global structural
information of activities, using their local event statistics. We demonstrate
how maximal cliques in an undirected edge-weighted graph of activities, can be
used for activity-class discovery in an unsupervised manner. We show how
modeling an activity as a variable length Markov process, can be used to
discover recurrent event-motifs to characterize the discovered
activity-classes. We present results over extensive data-sets, collected from
multiple active environments, to show the competence and generalizability of
our proposed framework.
| [
{
"version": "v1",
"created": "Wed, 4 Jul 2012 16:10:35 GMT"
}
] | 1,341,792,000,000 | [
[
"Hammid",
"Rafay",
""
],
[
"Maddi",
"Siddhartha",
""
],
[
"Johnson",
"Amos",
""
],
[
"Bobick",
"Aaron",
""
],
[
"Essa",
"Irfan",
""
],
[
"Isbell",
"Charles Lee",
""
]
] |
1207.1384 | Vibhav Gogate | Vibhav Gogate, Rina Dechter, Bozhena Bidyuk, Craig Rindt, James Marca | Modeling Transportation Routines using Hybrid Dynamic Mixed Networks | Appears in Proceedings of the Twenty-First Conference on Uncertainty
in Artificial Intelligence (UAI2005) | null | null | UAI-P-2005-PG-217-224 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper describes a general framework called Hybrid Dynamic Mixed Networks
(HDMNs) which are Hybrid Dynamic Bayesian Networks that allow representation of
discrete deterministic information in the form of constraints. We propose
approximate inference algorithms that integrate and adjust well known
algorithmic principles such as Generalized Belief Propagation,
Rao-Blackwellised Particle Filtering and Constraint Propagation to address the
complexity of modeling and reasoning in HDMNs. We use this framework to model a
person's travel activity over time and to predict destination and routes given
the current location. We present a preliminary empirical evaluation
demonstrating the effectiveness of our modeling framework and algorithms using
several variants of the activity model.
| [
{
"version": "v1",
"created": "Wed, 4 Jul 2012 16:12:38 GMT"
}
] | 1,341,792,000,000 | [
[
"Gogate",
"Vibhav",
""
],
[
"Dechter",
"Rina",
""
],
[
"Bidyuk",
"Bozhena",
""
],
[
"Rindt",
"Craig",
""
],
[
"Marca",
"James",
""
]
] |
1207.1385 | Vibhav Gogate | Vibhav Gogate, Rina Dechter | Approximate Inference Algorithms for Hybrid Bayesian Networks with
Discrete Constraints | Appears in Proceedings of the Twenty-First Conference on Uncertainty
in Artificial Intelligence (UAI2005) | null | null | UAI-P-2005-PG-209-216 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this paper, we consider Hybrid Mixed Networks (HMN) which are Hybrid
Bayesian Networks that allow discrete deterministic information to be modeled
explicitly in the form of constraints. We present two approximate inference
algorithms for HMNs that integrate and adjust well known algorithmic principles
such as Generalized Belief Propagation, Rao-Blackwellised Importance Sampling
and Constraint Propagation to address the complexity of modeling and reasoning
in HMNs. We demonstrate the performance of our approximate inference algorithms
on randomly generated HMNs.
| [
{
"version": "v1",
"created": "Wed, 4 Jul 2012 16:12:59 GMT"
}
] | 1,341,792,000,000 | [
[
"Gogate",
"Vibhav",
""
],
[
"Dechter",
"Rina",
""
]
] |
1207.1386 | Norman Ferns | Norman Ferns, Prakash Panangaden, Doina Precup | Metrics for Markov Decision Processes with Infinite State Spaces | Appears in Proceedings of the Twenty-First Conference on Uncertainty
in Artificial Intelligence (UAI2005) | null | null | UAI-P-2005-PG-201-208 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We present metrics for measuring state similarity in Markov decision
processes (MDPs) with infinitely many states, including MDPs with continuous
state spaces. Such metrics provide a stable quantitative analogue of the notion
of bisimulation for MDPs, and are suitable for use in MDP approximation. We
show that the optimal value function associated with a discounted infinite
horizon planning task varies continuously with respect to our metric distances.
| [
{
"version": "v1",
"created": "Wed, 4 Jul 2012 16:13:21 GMT"
}
] | 1,341,792,000,000 | [
[
"Ferns",
"Norman",
""
],
[
"Panangaden",
"Prakash",
""
],
[
"Precup",
"Doina",
""
]
] |
1207.1391 | Yaxin Liu | Yaxin Liu, Sven Koenig | Existence and Finiteness Conditions for Risk-Sensitive Planning: Results
and Conjectures | Appears in Proceedings of the Twenty-First Conference on Uncertainty
in Artificial Intelligence (UAI2005) | null | null | UAI-P-2005-PG-354-363 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Decision-theoretic planning with risk-sensitive planning objectives is
important for building autonomous agents or decision-support systems for
real-world applications. However, this line of research has been largely
ignored in the artificial intelligence and operations research communities
since planning with risk-sensitive planning objectives is more complicated than
planning with risk-neutral planning objectives. To remedy this situation, we
derive conditions that guarantee that the optimal expected utilities of the
total plan-execution reward exist and are finite for fully observable Markov
decision process models with non-linear utility functions. In case of Markov
decision process models with both positive and negative rewards, most of our
results hold for stationary policies only, but we conjecture that they can be
generalized to non stationary policies.
| [
{
"version": "v1",
"created": "Wed, 4 Jul 2012 16:15:31 GMT"
}
] | 1,341,792,000,000 | [
[
"Liu",
"Yaxin",
""
],
[
"Koenig",
"Sven",
""
]
] |
1207.1394 | Andreas Krause | Andreas Krause, Carlos E. Guestrin | Near-optimal Nonmyopic Value of Information in Graphical Models | Appears in Proceedings of the Twenty-First Conference on Uncertainty
in Artificial Intelligence (UAI2005) | null | null | UAI-P-2005-PG-324-331 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | A fundamental issue in real-world systems, such as sensor networks, is the
selection of observations which most effectively reduce uncertainty. More
specifically, we address the long standing problem of nonmyopically selecting
the most informative subset of variables in a graphical model. We present the
first efficient randomized algorithm providing a constant factor
(1-1/e-epsilon) approximation guarantee for any epsilon > 0 with high
confidence. The algorithm leverages the theory of submodular functions, in
combination with a polynomial bound on sample complexity. We furthermore prove
that no polynomial time algorithm can provide a constant factor approximation
better than (1 - 1/e) unless P = NP. Finally, we provide extensive evidence of
the effectiveness of our method on two complex real-world datasets.
| [
{
"version": "v1",
"created": "Wed, 4 Jul 2012 16:16:25 GMT"
}
] | 1,341,792,000,000 | [
[
"Krause",
"Andreas",
""
],
[
"Guestrin",
"Carlos E.",
""
]
] |
1207.1397 | Guilin Qi | Guilin Qi, Weiru Liu, David A. Bell | A Revision-Based Approach to Resolving Conflicting Information | Appears in Proceedings of the Twenty-First Conference on Uncertainty
in Artificial Intelligence (UAI2005) | null | null | UAI-P-2005-PG-477-484 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this paper, we propose a revision-based approach for conflict resolution
by generalizing the Disjunctive Maxi-Adjustment (DMA) approach (Benferhat et
al. 2004). Revision operators can be classified into two different families:
the model-based ones and the formula-based ones. So the revision-based approach
has two different versions according to which family of revision operators is
chosen. Two particular revision operators are considered, one is the Dalal's
revision operator, which is a model-based revision operator, and the other is
the cardinality-maximal based revision operator, which is a formulabased
revision operator. When the Dalal's revision operator is chosen, the
revision-based approach is independent of the syntactic form in each stratum
and it captures some notion of minimal change. When the cardinalitymaximal
based revision operator is chosen, the revision-based approach is equivalent to
the DMA approach. We also show that both approaches are computationally easier
than the DMA approach.
| [
{
"version": "v1",
"created": "Wed, 4 Jul 2012 16:18:06 GMT"
}
] | 1,341,792,000,000 | [
[
"Qi",
"Guilin",
""
],
[
"Liu",
"Weiru",
""
],
[
"Bell",
"David A.",
""
]
] |
1207.1398 | Avi Pfeffer | Avi Pfeffer, Terry Tai | Asynchronous Dynamic Bayesian Networks | Appears in Proceedings of the Twenty-First Conference on Uncertainty
in Artificial Intelligence (UAI2005) | null | null | UAI-P-2005-PG-467-476 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Systems such as sensor networks and teams of autonomous robots consist of
multiple autonomous entities that interact with each other in a distributed,
asynchronous manner. These entities need to keep track of the state of the
system as it evolves. Asynchronous systems lead to special challenges for
monitoring, as nodes must update their beliefs independently of each other and
no central coordination is possible. Furthermore, the state of the system
continues to change as beliefs are being updated. Previous approaches to
developing distributed asynchronous probabilistic reasoning systems have used
static models. We present an approach using dynamic models, that take into
account the way the system changes state over time. Our approach, which is
based on belief propagation, is fully distributed and asynchronous, and allows
the world to keep on changing as messages are being sent around. Experimental
results show that our approach compares favorably to the factored frontier
algorithm.
| [
{
"version": "v1",
"created": "Wed, 4 Jul 2012 16:18:25 GMT"
}
] | 1,341,792,000,000 | [
[
"Pfeffer",
"Avi",
""
],
[
"Tai",
"Terry",
""
]
] |
1207.1401 | Uri Nodelman | Uri Nodelman, Daphne Koller, Christian R. Shelton | Expectation Propagation for Continuous Time Bayesian Networks | Appears in Proceedings of the Twenty-First Conference on Uncertainty
in Artificial Intelligence (UAI2005) | null | null | UAI-P-2005-PG-431-440 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Continuous time Bayesian networks (CTBNs) describe structured stochastic
processes with finitely many states that evolve over continuous time. A CTBN is
a directed (possibly cyclic) dependency graph over a set of variables, each of
which represents a finite state continuous time Markov process whose transition
model is a function of its parents. As shown previously, exact inference in
CTBNs is intractable. We address the problem of approximate inference, allowing
for general queries conditioned on evidence over continuous time intervals and
at discrete time points. We show how CTBNs can be parameterized within the
exponential family, and use that insight to develop a message passing scheme in
cluster graphs and allows us to apply expectation propagation to CTBNs. The
clusters in our cluster graph do not contain distributions over the cluster
variables at individual time points, but distributions over trajectories of the
variables throughout a duration. Thus, unlike discrete time temporal models
such as dynamic Bayesian networks, we can adapt the time granularity at which
we reason for different variables and in different conditions.
| [
{
"version": "v1",
"created": "Wed, 4 Jul 2012 16:19:23 GMT"
}
] | 1,341,792,000,000 | [
[
"Nodelman",
"Uri",
""
],
[
"Koller",
"Daphne",
""
],
[
"Shelton",
"Christian R.",
""
]
] |
1207.1402 | Uri Nodelman | Uri Nodelman, Christian R. Shelton, Daphne Koller | Expectation Maximization and Complex Duration Distributions for
Continuous Time Bayesian Networks | Appears in Proceedings of the Twenty-First Conference on Uncertainty
in Artificial Intelligence (UAI2005) | null | null | UAI-P-2005-PG-421-430 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Continuous time Bayesian networks (CTBNs) describe structured stochastic
processes with finitely many states that evolve over continuous time. A CTBN is
a directed (possibly cyclic) dependency graph over a set of variables, each of
which represents a finite state continuous time Markov process whose transition
model is a function of its parents. We address the problem of learning the
parameters and structure of a CTBN from partially observed data. We show how to
apply expectation maximization (EM) and structural expectation maximization
(SEM) to CTBNs. The availability of the EM algorithm allows us to extend the
representation of CTBNs to allow a much richer class of transition durations
distributions, known as phase distributions. This class is a highly expressive
semi-parametric representation, which can approximate any duration distribution
arbitrarily closely. This extension to the CTBN framework addresses one of the
main limitations of both CTBNs and DBNs - the restriction to exponentially /
geometrically distributed duration. We present experimental results on a real
data set of people's life spans, showing that our algorithm learns reasonable
models - structure and parameters - from partially observed data, and, with the
use of phase distributions, achieves better performance than DBNs.
| [
{
"version": "v1",
"created": "Wed, 4 Jul 2012 16:19:39 GMT"
}
] | 1,341,792,000,000 | [
[
"Nodelman",
"Uri",
""
],
[
"Shelton",
"Christian R.",
""
],
[
"Koller",
"Daphne",
""
]
] |
1207.1405 | Joris Mooij | Joris Mooij, Hilbert Kappen | Sufficient conditions for convergence of Loopy Belief Propagation | Appears in Proceedings of the Twenty-First Conference on Uncertainty
in Artificial Intelligence (UAI2005) | null | null | UAI-P-2005-PG-396-403 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We derive novel sufficient conditions for convergence of Loopy Belief
Propagation (also known as the Sum-Product algorithm) to a unique fixed point.
Our results improve upon previously known conditions. For binary variables with
(anti-)ferromagnetic interactions, our conditions seem to be sharp.
| [
{
"version": "v1",
"created": "Wed, 4 Jul 2012 16:20:27 GMT"
}
] | 1,341,792,000,000 | [
[
"Mooij",
"Joris",
""
],
[
"Kappen",
"Hilbert",
""
]
] |
1207.1407 | Robert Mateescu | Robert Mateescu, Rina Dechter | The Relationship Between AND/OR Search and Variable Elimination | Appears in Proceedings of the Twenty-First Conference on Uncertainty
in Artificial Intelligence (UAI2005) | null | null | UAI-P-2005-PG-380-387 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this paper we compare search and inference in graphical models through the
new framework of AND/OR search. Specifically, we compare Variable Elimination
(VE) and memoryintensive AND/OR Search (AO) and place algorithms such as
graph-based backjumping and no-good and good learning, as well as Recursive
Conditioning [7] and Value Elimination [2] within the AND/OR search framework.
| [
{
"version": "v1",
"created": "Wed, 4 Jul 2012 16:21:01 GMT"
}
] | 1,341,792,000,000 | [
[
"Mateescu",
"Robert",
""
],
[
"Dechter",
"Rina",
""
]
] |
1207.1408 | Sridhar Mahadevan | Sridhar Mahadevan | Representation Policy Iteration | Appears in Proceedings of the Twenty-First Conference on Uncertainty
in Artificial Intelligence (UAI2005) | null | null | UAI-P-2005-PG-372-379 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper addresses a fundamental issue central to approximation methods for
solving large Markov decision processes (MDPs): how to automatically learn the
underlying representation for value function approximation? A novel
theoretically rigorous framework is proposed that automatically generates
geometrically customized orthonormal sets of basis functions, which can be used
with any approximate MDP solver like least squares policy iteration (LSPI). The
key innovation is a coordinate-free representation of value functions, using
the theory of smooth functions on a Riemannian manifold. Hodge theory yields a
constructive method for generating basis functions for approximating value
functions based on the eigenfunctions of the self-adjoint (Laplace-Beltrami)
operator on manifolds. In effect, this approach performs a global Fourier
analysis on the state space graph to approximate value functions, where the
basis functions reflect the largescale topology of the underlying state space.
A new class of algorithms called Representation Policy Iteration (RPI) are
presented that automatically learn both basis functions and approximately
optimal policies. Illustrative experiments compare the performance of RPI with
that of LSPI using two handcoded basis functions (RBF and polynomial state
encodings).
| [
{
"version": "v1",
"created": "Wed, 4 Jul 2012 16:21:20 GMT"
}
] | 1,341,792,000,000 | [
[
"Mahadevan",
"Sridhar",
""
]
] |
1207.1412 | Trey Smith | Trey Smith, Reid Simmons | Point-Based POMDP Algorithms: Improved Analysis and Implementation | Appears in Proceedings of the Twenty-First Conference on Uncertainty
in Artificial Intelligence (UAI2005) | null | null | UAI-P-2005-PG-542-549 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Existing complexity bounds for point-based POMDP value iteration algorithms
focus either on the curse of dimensionality or the curse of history. We derive
a new bound that relies on both and uses the concept of discounted
reachability; our conclusions may help guide future algorithm design. We also
discuss recent improvements to our (point-based) heuristic search value
iteration algorithm. Our new implementation calculates tighter initial bounds,
avoids solving linear programs, and makes more effective use of sparsity.
| [
{
"version": "v1",
"created": "Wed, 4 Jul 2012 16:23:13 GMT"
}
] | 1,341,792,000,000 | [
[
"Smith",
"Trey",
""
],
[
"Simmons",
"Reid",
""
]
] |
1207.1415 | Scott Sanner | Scott Sanner, Craig Boutilier | Approximate Linear Programming for First-order MDPs | Appears in Proceedings of the Twenty-First Conference on Uncertainty
in Artificial Intelligence (UAI2005) | null | null | UAI-P-2005-PG-509-517 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We introduce a new approximate solution technique for first-order Markov
decision processes (FOMDPs). Representing the value function linearly w.r.t. a
set of first-order basis functions, we compute suitable weights by casting the
corresponding optimization as a first-order linear program and show how
off-the-shelf theorem prover and LP software can be effectively used. This
technique allows one to solve FOMDPs independent of a specific domain
instantiation; furthermore, it allows one to determine bounds on approximation
error that apply equally to all domain instantiations. We apply this solution
technique to the task of elevator scheduling with a rich feature space and
multi-criteria additive reward, and demonstrate that it outperforms a number of
intuitive, heuristicallyguided policies.
| [
{
"version": "v1",
"created": "Wed, 4 Jul 2012 16:24:08 GMT"
}
] | 1,341,792,000,000 | [
[
"Sanner",
"Scott",
""
],
[
"Boutilier",
"Craig",
""
]
] |
1207.1416 | Matthew Rudary | Matthew Rudary, Satinder Singh, David Wingate | Predictive Linear-Gaussian Models of Stochastic Dynamical Systems | Appears in Proceedings of the Twenty-First Conference on Uncertainty
in Artificial Intelligence (UAI2005) | null | null | UAI-P-2005-PG-501-508 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Models of dynamical systems based on predictive state representations (PSRs)
are defined strictly in terms of observable quantities, in contrast with
traditional models (such as Hidden Markov Models) that use latent variables or
statespace representations. In addition, PSRs have an effectively infinite
memory, allowing them to model some systems that finite memory-based models
cannot. Thus far, PSR models have primarily been developed for domains with
discrete observations. Here, we develop the Predictive Linear-Gaussian (PLG)
model, a class of PSR models for domains with continuous observations. We show
that PLG models subsume Linear Dynamical System models (also called Kalman
filter models or state-space models) while using fewer parameters. We also
introduce an algorithm to estimate PLG parameters from data, and contrast it
with standard Expectation Maximization (EM) algorithms used to estimate Kalman
filter parameters. We show that our algorithm is a consistent estimation
procedure and present preliminary empirical results suggesting that our
algorithm outperforms EM, particularly as the model dimension increases.
| [
{
"version": "v1",
"created": "Wed, 4 Jul 2012 16:24:39 GMT"
}
] | 1,341,792,000,000 | [
[
"Rudary",
"Matthew",
""
],
[
"Singh",
"Satinder",
""
],
[
"Wingate",
"David",
""
]
] |
1207.1418 | Alice X. Zheng | Alice X. Zheng, Irina Rish, Alina Beygelzimer | Efficient Test Selection in Active Diagnosis via Entropy Approximation | Appears in Proceedings of the Twenty-First Conference on Uncertainty
in Artificial Intelligence (UAI2005) | null | null | UAI-P-2005-PG-675-682 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We consider the problem of diagnosing faults in a system represented by a
Bayesian network, where diagnosis corresponds to recovering the most likely
state of unobserved nodes given the outcomes of tests (observed nodes). Finding
an optimal subset of tests in this setting is intractable in general. We show
that it is difficult even to compute the next most-informative test using
greedy test selection, as it involves several entropy terms whose exact
computation is intractable. We propose an approximate approach that utilizes
the loopy belief propagation infrastructure to simultaneously compute
approximations of marginal and conditional entropies on multiple subsets of
nodes. We apply our method to fault diagnosis in computer networks, and show
the algorithm to be very effective on realistic Internet-like topologies. We
also provide theoretical justification for the greedy test selection approach,
along with some performance guarantees.
| [
{
"version": "v1",
"created": "Wed, 4 Jul 2012 16:26:12 GMT"
}
] | 1,341,792,000,000 | [
[
"Zheng",
"Alice X.",
""
],
[
"Rish",
"Irina",
""
],
[
"Beygelzimer",
"Alina",
""
]
] |
1207.1422 | Changhe Yuan | Changhe Yuan, Marek J. Druzdzel | Importance Sampling in Bayesian Networks: An Influence-Based
Approximation Strategy for Importance Functions | Appears in Proceedings of the Twenty-First Conference on Uncertainty
in Artificial Intelligence (UAI2005) | null | null | UAI-P-2005-PG-650-657 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | One of the main problems of importance sampling in Bayesian networks is
representation of the importance function, which should ideally be as close as
possible to the posterior joint distribution. Typically, we represent an
importance function as a factorization, i.e., product of conditional
probability tables (CPTs). Given diagnostic evidence, we do not have explicit
forms for the CPTs in the networks. We first derive the exact form for the CPTs
of the optimal importance function. Since the calculation is hard, we usually
only use their approximations. We review several popular strategies and point
out their limitations. Based on an analysis of the influence of evidence, we
propose a method for approximating the exact form of importance function by
explicitly modeling the most important additional dependence relations
introduced by evidence. Our experimental results show that the new
approximation strategy offers an immediate improvement in the quality of the
importance function.
| [
{
"version": "v1",
"created": "Wed, 4 Jul 2012 16:28:23 GMT"
}
] | 1,341,792,000,000 | [
[
"Yuan",
"Changhe",
""
],
[
"Druzdzel",
"Marek J.",
""
]
] |
1207.1426 | Max Welling | Max Welling, Thomas P. Minka, Yee Whye Teh | Structured Region Graphs: Morphing EP into GBP | Appears in Proceedings of the Twenty-First Conference on Uncertainty
in Artificial Intelligence (UAI2005) | null | null | UAI-P-2005-PG-609-614 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | GBP and EP are two successful algorithms for approximate probabilistic
inference, which are based on different approximation strategies. An open
problem in both algorithms has been how to choose an appropriate approximation
structure. We introduce 'structured region graphs', a formalism which marries
these two strategies, reveals a deep connection between them, and suggests how
to choose good approximation structures. In this formalism, each region has an
internal structure which defines an exponential family, whose sufficient
statistics must be matched by the parent region. Reduction operators on these
structures allow conversion between EP and GBP free energies. Thus it is
revealed that all EP approximations on discrete variables are special cases of
GBP, and conversely that some wellknown GBP approximations, such as overlapping
squares, are special cases of EP. Furthermore, region graphs derived from EP
have a number of good structural properties, including maxent-normality and
overall counting number of one. The result is a convenient framework for
producing high-quality approximations with a user-adjustable level of
complexity
| [
{
"version": "v1",
"created": "Wed, 4 Jul 2012 16:29:26 GMT"
}
] | 1,341,792,000,000 | [
[
"Welling",
"Max",
""
],
[
"Minka",
"Thomas P.",
""
],
[
"Teh",
"Yee Whye",
""
]
] |
1207.1427 | Segev Wasserkrug | Segev Wasserkrug, Avigdor Gal, Opher Etzion | A Model for Reasoning with Uncertain Rules in Event Composition Systems | Appears in Proceedings of the Twenty-First Conference on Uncertainty
in Artificial Intelligence (UAI2005) | null | null | UAI-P-2005-PG-599-608 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In recent years, there has been an increased need for the use of active
systems - systems required to act automatically based on events, or changes in
the environment. Such systems span many areas, from active databases to
applications that drive the core business processes of today's enterprises.
However, in many cases, the events to which the system must respond are not
generated by monitoring tools, but must be inferred from other events based on
complex temporal predicates. In addition, in many applications, such inference
is inherently uncertain. In this paper, we introduce a formal framework for
knowledge representation and reasoning enabling such event inference. Based on
probability theory, we define the representation of the associated uncertainty.
In addition, we formally define the probability space, and show how the
relevant probabilities can be calculated by dynamically constructing a Bayesian
network. To the best of our knowledge, this is the first work that enables
taking such uncertainty into account in the context of active systems.
herefore, our contribution is twofold: We formally define the representation
and semantics of event composition for probabilistic settings, and show how to
apply these extensions to the quantification of the occurrence probability of
events. These results enable any active system to handle such uncertainty.
| [
{
"version": "v1",
"created": "Wed, 4 Jul 2012 16:29:47 GMT"
}
] | 1,341,792,000,000 | [
[
"Wasserkrug",
"Segev",
""
],
[
"Gal",
"Avigdor",
""
],
[
"Etzion",
"Opher",
""
]
] |
1207.1501 | Gol Kim | Gol Kim, Fei Ye | Super-Mixed Multiple Attribute Group Decision Making Method Based on
Hybrid Fuzzy Grey Relation Approach Degree | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The feature of our method different from other fuzzy grey relation method for
supermixed multiple attribute group decision-making is that all of the
subjective and objective weights are obtained by interval grey number and that
the group decisionmaking is performed based on the relative approach degree of
grey TOPSIS, the relative approach degree of grey incidence and the relative
membership degree of grey incidence using 4-dimensional Euclidean distance. The
weighted Borda method is used to obtain final rank by using the results of four
methods. An example shows the applicability of the proposed approach.
| [
{
"version": "v1",
"created": "Fri, 6 Jul 2012 01:26:39 GMT"
}
] | 1,342,051,200,000 | [
[
"Kim",
"Gol",
""
],
[
"Ye",
"Fei",
""
]
] |
1207.1534 | Gol Kim | Gol Kim, Yunchol Jong, Sifeng Liu | Generalized Hybrid Grey Relation Method for Multiple Attribute Mixed
Type Decision Making | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The multiple attribute mixed type decision making is performed by four
methods, that is, the relative approach degree of grey TOPSIS method, the
relative approach degree of grey incidence, the relative membership degree of
grey incidence and the grey relation relative approach degree method using the
maximum entropy estimation, respectively. In these decision making methods, the
grey incidence degree in four-dimensional Euclidean space is used. The final
arrangement result is obtained by weighted Borda method. An example illustrates
the applicability of the proposed approach.
| [
{
"version": "v1",
"created": "Fri, 6 Jul 2012 06:44:08 GMT"
}
] | 1,342,051,200,000 | [
[
"Kim",
"Gol",
""
],
[
"Jong",
"Yunchol",
""
],
[
"Liu",
"Sifeng",
""
]
] |
1207.1811 | George Katsirelos | George Katsirelos and Nina Narodytska and Toby Walsh | The SeqBin Constraint Revisited | Longer version of paper accepted at CP 2012 | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We revisit the SeqBin constraint. This meta-constraint subsumes a number of
important global constraints like Change, Smooth and IncreasingNValue. We show
that the previously proposed filtering algorithm for SeqBin has two drawbacks
even under strong restrictions: it does not detect bounds disentailment and it
is not idempotent. We identify the cause for these problems, and propose a new
propagator that overcomes both issues. Our algorithm is based on a connection
to the problem of finding a path of a given cost in a restricted $n$-partite
graph. Our propagator enforces domain consistency in O(nd^2) and, for special
cases of SeqBin that include Change, Smooth and IncreasingNValue, in O(nd)
time.
| [
{
"version": "v1",
"created": "Sat, 7 Jul 2012 16:21:53 GMT"
}
] | 1,426,809,600,000 | [
[
"Katsirelos",
"George",
""
],
[
"Narodytska",
"Nina",
""
],
[
"Walsh",
"Toby",
""
]
] |
1207.2373 | Mohamed Achraf Ben Mohamed Mr. | Mohamed Achraf Ben Mohamed, Dhaou El Ghoul, Mohamed Amine Nahdi,
Mourad Mars and Mounir Zrigui | Arabic CALL system based on pedagogically indexed text | The 2011 International Conference on Artificial Intelligence
(ICAI'11), 2011, WORLDCOMP'11, Las Vegas, Nevada, USA | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This article introduces the benefits of using computer as a tool for foreign
language teaching and learning. It describes the effect of using Natural
Language Processing (NLP) tools for learning Arabic. The technique explored in
this particular case is the employment of pedagogically indexed corpora. This
text-based method provides the teacher the advantage of building activities
based on texts adapted to a particular pedagogical situation. This paper also
presents ARAC: a Platform dedicated to language educators allowing them to
create activities within their own pedagogical area of interest.
| [
{
"version": "v1",
"created": "Tue, 10 Jul 2012 14:34:24 GMT"
}
] | 1,341,964,800,000 | [
[
"Mohamed",
"Mohamed Achraf Ben",
""
],
[
"Ghoul",
"Dhaou El",
""
],
[
"Nahdi",
"Mohamed Amine",
""
],
[
"Mars",
"Mourad",
""
],
[
"Zrigui",
"Mounir",
""
]
] |
1207.2459 | Mohamed Ali Mahjoub | Fradj Ben Lamine (sage), Karim Kalti (sage), Mohamed Ali Mahjoub
(SAGE) | Etude de Mod\`eles \`a base de r\'eseaux Bay\'esiens pour l'aide au
diagnostic de tumeurs c\'er\'ebrales | Journ\'ees francophones d'ing\'enierie des connaissances, France
(2012) | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This article describes different models based on Bayesian networks RB
modeling expertise in the diagnosis of brain tumors. Indeed, they are well
adapted to the representation of the uncertainty in the process of diagnosis of
these tumors. In our work, we first tested several structures derived from the
Bayesian network reasoning performed by doctors on the one hand and structures
generated automatically on the other. This step aims to find the best structure
that increases diagnostic accuracy. The machine learning algorithms relate
MWST-EM algorithms, SEM and SEM + T. To estimate the parameters of the Bayesian
network from a database incomplete, we have proposed an extension of the EM
algorithm by adding a priori knowledge in the form of the thresholds calculated
by the first phase of the algorithm RBE . The very encouraging results obtained
are discussed at the end of the paper
| [
{
"version": "v1",
"created": "Tue, 10 Jul 2012 19:58:05 GMT"
}
] | 1,341,964,800,000 | [
[
"Lamine",
"Fradj Ben",
"",
"sage"
],
[
"Kalti",
"Karim",
"",
"sage"
],
[
"Mahjoub",
"Mohamed Ali",
"",
"SAGE"
]
] |
1207.2592 | Gol Kim | Gol Kim (Center of Natural Science, University of Sciences, Pyongyang,
DPR Korea) | Novel Grey Interval Weight Determining and Hybrid Grey Interval Relation
Method in Multiple Attribute Decision-Making | arXiv admin note: substantial text overlap with arXiv:1207.1501 | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper proposes a grey interval relation TOPSIS for the decision making
in which all of the attribute weights and attribute values are given by the
interval grey numbers. The feature of our method different from other grey
relation decision-making is that all of the subjective and objective weights
are obtained by interval grey number and that decisionmaking is performed based
on the relative approach degree of grey TOPSIS, the relative approach degree of
grey incidence and the relative membership degree of grey incidence using
2-dimensional Euclidean distance. The weighted Borda method is used for
combining the results of three methods. An example shows the applicability of
the proposed approach.
| [
{
"version": "v1",
"created": "Wed, 11 Jul 2012 10:54:15 GMT"
}
] | 1,342,051,200,000 | [
[
"Kim",
"Gol",
"",
"Center of Natural Science, University of Sciences, Pyongyang,\n DPR Korea"
]
] |
1207.3270 | Anastasios Skarlatidis | Anastasios Skarlatidis, Georgios Paliouras, Alexander Artikis, George
A. Vouros | Probabilistic Event Calculus for Event Recognition | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Symbolic event recognition systems have been successfully applied to a
variety of application domains, extracting useful information in the form of
events, allowing experts or other systems to monitor and respond when
significant events are recognised. In a typical event recognition application,
however, these systems often have to deal with a significant amount of
uncertainty. In this paper, we address the issue of uncertainty in logic-based
event recognition by extending the Event Calculus with probabilistic reasoning.
Markov Logic Networks are a natural candidate for our logic-based formalism.
However, the temporal semantics of the Event Calculus introduce a number of
challenges for the proposed model. We show how and under what assumptions we
can overcome these problems. Additionally, we study how probabilistic modelling
changes the behaviour of the formalism, affecting its key property, the inertia
of fluents. Furthermore, we demonstrate the advantages of the probabilistic
Event Calculus through examples and experiments in the domain of activity
recognition, using a publicly available dataset for video surveillance.
| [
{
"version": "v1",
"created": "Fri, 13 Jul 2012 14:57:35 GMT"
},
{
"version": "v2",
"created": "Thu, 15 Aug 2013 11:13:05 GMT"
}
] | 1,376,611,200,000 | [
[
"Skarlatidis",
"Anastasios",
""
],
[
"Paliouras",
"Georgios",
""
],
[
"Artikis",
"Alexander",
""
],
[
"Vouros",
"George A.",
""
]
] |
1207.3543 | Reza Keyvan | Mohammadreza Keyvanpour and Fereshteh Azizani | Classification of Approaches and Challenges of Frequent Subgraphs Mining
in Biological Networks | null | International Journal of Advanced Engineering Sciences and
Technologies, Vol No. 4, Issue No. 2, 014 - 017, 2011 | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Understanding the structure and dynamics of biological networks is one of the
important challenges in system biology. In addition, increasing amount of
experimental data in biological networks necessitate the use of efficient
methods to analyze these huge amounts of data. Such methods require to
recognize common patterns to analyze data. As biological networks can be
modeled by graphs, the problem of common patterns recognition is equivalent
with frequent sub graph mining in a set of graphs. In this paper, at first the
challenges of frequent subgrpahs mining in biological networks are introduced
and the existing approaches are classified for each challenge. then the
algorithms are analyzed on the basis of the type of the approach they apply for
each of the challenges.
| [
{
"version": "v1",
"created": "Sun, 15 Jul 2012 21:11:44 GMT"
}
] | 1,342,483,200,000 | [
[
"Keyvanpour",
"Mohammadreza",
""
],
[
"Azizani",
"Fereshteh",
""
]
] |
1207.3855 | Gol Kim | Gol Kim, Yunchol Jong, Sifeng Liu, Choe Rim Shong | Hybrid Grey Interval Relation Decision-Making in Artistic Talent
Evaluation of Player | arXiv admin note: substantial text overlap with arXiv:1207.2592 | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper proposes a grey interval relation TOPSIS method for the decision
making in which all of the attribute weights and attribute values are given by
the interval grey numbers. In this paper, all of the subjective and objective
weights are obtained by interval grey number and decision-making is based on
four methods such as the relative approach degree of grey TOPSIS, the relative
approach degree of grey incidence and the relative approach degree method using
the maximum entropy estimation using 2-dimensional Euclidean distance. A
multiple attribute decision-making example for evaluation of artistic talent of
Kayagum (stringed Korean harp) players is given to show practicability of the
proposed approach.
| [
{
"version": "v1",
"created": "Tue, 17 Jul 2012 01:36:39 GMT"
}
] | 1,342,569,600,000 | [
[
"Kim",
"Gol",
""
],
[
"Jong",
"Yunchol",
""
],
[
"Liu",
"Sifeng",
""
],
[
"Shong",
"Choe Rim",
""
]
] |
1207.3863 | Nitin Yadav | Nitin Yadav and Sebastian Sardina | Qualitative Approximate Behavior Composition | null | In Proceedings of the European Conference on Logics in Artificial
Intelligence (JELIA), volume 7519 of LNCS, pages 450-462, 2012 | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The behavior composition problem involves automatically building a controller
that is able to realize a desired, but unavailable, target system (e.g., a
house surveillance) by suitably coordinating a set of available components
(e.g., video cameras, blinds, lamps, a vacuum cleaner, phones, etc.) Previous
work has almost exclusively aimed at bringing about the desired component in
its totality, which is highly unsatisfactory for unsolvable problems. In this
work, we develop an approach for approximate behavior composition without
departing from the classical setting, thus making the problem applicable to a
much wider range of cases. Based on the notion of simulation, we characterize
what a maximal controller and the "closest" implementable target module
(optimal approximation) are, and show how these can be computed using ATL model
checking technology for a special case. We show the uniqueness of optimal
approximations, and prove their soundness and completeness with respect to
their imported controllers.
| [
{
"version": "v1",
"created": "Tue, 17 Jul 2012 02:56:11 GMT"
}
] | 1,467,676,800,000 | [
[
"Yadav",
"Nitin",
""
],
[
"Sardina",
"Sebastian",
""
]
] |
1207.3874 | Nitin Yadav | Nitin Yadav and Sebastian Sardina | Reasoning about Agent Programs using ATL-like Logics | null | In Proceedings of the European Conference on Logics in Artificial
Intelligence (JELIA), volume 7519 of LNCS, pages 437-449, 2012 | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We propose a variant of Alternating-time Temporal Logic (ATL) grounded in the
agents' operational know-how, as defined by their libraries of abstract plans.
Inspired by ATLES, a variant itself of ATL, it is possible in our logic to
explicitly refer to "rational" strategies for agents developed under the
Belief-Desire-Intention agent programming paradigm. This allows us to express
and verify properties of BDI systems using ATL-type logical frameworks.
| [
{
"version": "v1",
"created": "Tue, 17 Jul 2012 04:10:28 GMT"
}
] | 1,467,676,800,000 | [
[
"Yadav",
"Nitin",
""
],
[
"Sardina",
"Sebastian",
""
]
] |
1207.4107 | Charles Gretton | Charles Gretton, Sylvie Thiebaux | Exploiting First-Order Regression in Inductive Policy Selection | Appears in Proceedings of the Twentieth Conference on Uncertainty in
Artificial Intelligence (UAI2004) | null | null | UAI-P-2004-PG-217-225 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We consider the problem of computing optimal generalised policies for
relational Markov decision processes. We describe an approach combining some of
the benefits of purely inductive techniques with those of symbolic dynamic
programming methods. The latter reason about the optimal value function using
first-order decision theoretic regression and formula rewriting, while the
former, when provided with a suitable hypotheses language, are capable of
generalising value functions or policies for small instances. Our idea is to
use reasoning and in particular classical first-order regression to
automatically generate a hypotheses language dedicated to the domain at hand,
which is then used as input by an inductive solver. This approach avoids the
more complex reasoning of symbolic dynamic programming while focusing the
inductive solver's attention on concepts that are specifically relevant to the
optimal value function for the domain considered.
| [
{
"version": "v1",
"created": "Wed, 11 Jul 2012 14:40:15 GMT"
}
] | 1,342,656,000,000 | [
[
"Gretton",
"Charles",
""
],
[
"Thiebaux",
"Sylvie",
""
]
] |
1207.4111 | Phan Giang | Phan H. Giang, Sathyakama Sandilya | Decision Making for Symbolic Probability | Appears in Proceedings of the Twentieth Conference on Uncertainty in
Artificial Intelligence (UAI2004) | null | null | UAI-P-2004-PG-185-192 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper proposes a decision theory for a symbolic generalization of
probability theory (SP). Darwiche and Ginsberg [2,3] proposed SP to relax the
requirement of using numbers for uncertainty while preserving desirable
patterns of Bayesian reasoning. SP represents uncertainty by symbolic supports
that are ordered partially rather than completely as in the case of standard
probability. We show that a preference relation on acts that satisfies a number
of intuitive postulates is represented by a utility function whose domain is a
set of pairs of supports. We argue that a subjective interpretation is as
useful and appropriate for SP as it is for numerical probability. It is useful
because the subjective interpretation provides a basis for uncertainty
elicitation. It is appropriate because we can provide a decision theory that
explains how preference on acts is based on support comparison.
| [
{
"version": "v1",
"created": "Wed, 11 Jul 2012 14:42:08 GMT"
},
{
"version": "v2",
"created": "Tue, 31 Jul 2012 17:17:25 GMT"
}
] | 1,343,779,200,000 | [
[
"Giang",
"Phan H.",
""
],
[
"Sandilya",
"Sathyakama",
""
]
] |
1207.4114 | Norman Ferns | Norman Ferns, Prakash Panangaden, Doina Precup | Metrics for Finite Markov Decision Processes | Appears in Proceedings of the Twentieth Conference on Uncertainty in
Artificial Intelligence (UAI2004) | null | null | UAI-P-2004-PG-162-169 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We present metrics for measuring the similarity of states in a finite Markov
decision process (MDP). The formulation of our metrics is based on the notion
of bisimulation for MDPs, with an aim towards solving discounted infinite
horizon reinforcement learning tasks. Such metrics can be used to aggregate
states, as well as to better structure other value function approximators
(e.g., memory-based or nearest-neighbor approximators). We provide bounds that
relate our metric distances to the optimal values of states in the given MDP.
| [
{
"version": "v1",
"created": "Wed, 11 Jul 2012 14:43:04 GMT"
}
] | 1,342,656,000,000 | [
[
"Ferns",
"Norman",
""
],
[
"Panangaden",
"Prakash",
""
],
[
"Precup",
"Doina",
""
]
] |
1207.4115 | Zhengzhu Feng | Zhengzhu Feng, Richard Dearden, Nicolas Meuleau, Richard Washington | Dynamic Programming for Structured Continuous Markov Decision Problems | Appears in Proceedings of the Twentieth Conference on Uncertainty in
Artificial Intelligence (UAI2004) | null | null | UAI-P-2004-PG-154-161 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We describe an approach for exploiting structure in Markov Decision Processes
with continuous state variables. At each step of the dynamic programming, the
state space is dynamically partitioned into regions where the value function is
the same throughout the region. We first describe the algorithm for piecewise
constant representations. We then extend it to piecewise linear
representations, using techniques from POMDPs to represent and reason about
linear surfaces efficiently. We show that for complex, structured problems, our
approach exploits the natural structure so that optimal solutions can be
computed efficiently.
| [
{
"version": "v1",
"created": "Wed, 11 Jul 2012 14:43:21 GMT"
}
] | 1,342,656,000,000 | [
[
"Feng",
"Zhengzhu",
""
],
[
"Dearden",
"Richard",
""
],
[
"Meuleau",
"Nicolas",
""
],
[
"Washington",
"Richard",
""
]
] |
1207.4116 | Zhengzhu Feng | Zhengzhu Feng, Shlomo Zilberstein | Region-Based Incremental Pruning for POMDPs | Appears in Proceedings of the Twentieth Conference on Uncertainty in
Artificial Intelligence (UAI2004) | null | null | UAI-P-2004-PG-146-153 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We present a major improvement to the incremental pruning algorithm for
solving partially observable Markov decision processes. Our technique targets
the cross-sum step of the dynamic programming (DP) update, a key source of
complexity in POMDP algorithms. Instead of reasoning about the whole belief
space when pruning the cross-sums, our algorithm divides the belief space into
smaller regions and performs independent pruning in each region. We evaluate
the benefits of the new technique both analytically and experimentally, and
show that it produces very significant performance gains. The results
contribute to the scalability of POMDP algorithms to domains that cannot be
handled by the best existing techniques.
| [
{
"version": "v1",
"created": "Wed, 11 Jul 2012 14:43:38 GMT"
}
] | 1,342,656,000,000 | [
[
"Feng",
"Zhengzhu",
""
],
[
"Zilberstein",
"Shlomo",
""
]
] |
1207.4117 | Didier Dubois | Didier Dubois, Helene Fargier | A Unified framework for order-of-magnitude confidence relations | Appears in Proceedings of the Twentieth Conference on Uncertainty in
Artificial Intelligence (UAI2004) | null | null | UAI-P-2004-PG-138-145 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The aim of this work is to provide a unified framework for ordinal
representations of uncertainty lying at the crosswords between possibility and
probability theories. Such confidence relations between events are commonly
found in monotonic reasoning, inconsistency management, or qualitative decision
theory. They start either from probability theory, making it more qualitative,
or from possibility theory, making it more expressive. We show these two trends
converge to a class of genuine probability theories. We provide
characterization results for these useful tools that preserve the qualitative
nature of possibility rankings, while enjoying the power of expressivity of
additive representations.
| [
{
"version": "v1",
"created": "Wed, 11 Jul 2012 14:44:05 GMT"
},
{
"version": "v2",
"created": "Mon, 6 Aug 2012 17:54:10 GMT"
}
] | 1,344,297,600,000 | [
[
"Dubois",
"Didier",
""
],
[
"Fargier",
"Helene",
""
]
] |
1207.4119 | Rina Dechter | Rina Dechter, Robert Mateescu | Mixtures of Deterministic-Probabilistic Networks and their AND/OR Search
Space | Appears in Proceedings of the Twentieth Conference on Uncertainty in
Artificial Intelligence (UAI2004) | null | null | UAI-P-2004-PG-120-129 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The paper introduces mixed networks, a new framework for expressing and
reasoning with probabilistic and deterministic information. The framework
combines belief networks with constraint networks, defining the semantics and
graphical representation. We also introduce the AND/OR search space for
graphical models, and develop a new linear space search algorithm. This
provides the basis for understanding the benefits of processing the constraint
information separately, resulting in the pruning of the search space. When the
constraint part is tractable or has a small number of solutions, using the
mixed representation can be exponentially more effective than using pure belief
networks which odel constraints as conditional probability tables.
| [
{
"version": "v1",
"created": "Wed, 11 Jul 2012 14:44:42 GMT"
}
] | 1,342,656,000,000 | [
[
"Dechter",
"Rina",
""
],
[
"Mateescu",
"Robert",
""
]
] |
1207.4120 | Peter de Waal | Peter de Waal, Linda C. van der Gaag | Stable Independance and Complexity of Representation | Appears in Proceedings of the Twentieth Conference on Uncertainty in
Artificial Intelligence (UAI2004) | null | null | UAI-P-2004-PG-112-119 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The representation of independence relations generally builds upon the
well-known semigraphoid axioms of independence. Recently, a representation has
been proposed that captures a set of dominant statements of an independence
relation from which any other statement can be generated by means of the
axioms; the cardinality of this set is taken to indicate the complexity of the
relation. Building upon the idea of dominance, we introduce the concept of
stability to provide for a more compact representation of independence. We give
an associated algorithm for establishing such a representation.We show that,
with our concept of stability, many independence relations are found to be of
lower complexity than with existing representations.
| [
{
"version": "v1",
"created": "Wed, 11 Jul 2012 14:45:20 GMT"
}
] | 1,342,656,000,000 | [
[
"de Waal",
"Peter",
""
],
[
"van der Gaag",
"Linda C.",
""
]
] |
1207.4121 | Fabio Gagliardi Cozman | Fabio Gagliardi Cozman, Cassio Polpo de Campos, Jaime Ide, Jose Carlos
Ferreira da Rocha | Propositional and Relational Bayesian Networks Associated with Imprecise
and Qualitative Probabilistic Assesments | Appears in Proceedings of the Twentieth Conference on Uncertainty in
Artificial Intelligence (UAI2004) | null | null | UAI-P-2004-PG-104-111 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper investigates a representation language with flexibility inspired
by probabilistic logic and compactness inspired by relational Bayesian
networks. The goal is to handle propositional and first-order constructs
together with precise, imprecise, indeterminate and qualitative probabilistic
assessments. The paper shows how this can be achieved through the theory of
credal networks. New exact and approximate inference algorithms based on
multilinear programming and iterated/loopy propagation of interval
probabilities are presented; their superior performance, compared to existing
ones, is shown empirically.
| [
{
"version": "v1",
"created": "Wed, 11 Jul 2012 14:45:39 GMT"
}
] | 1,342,656,000,000 | [
[
"Cozman",
"Fabio Gagliardi",
""
],
[
"de Campos",
"Cassio Polpo",
""
],
[
"Ide",
"Jaime",
""
],
[
"da Rocha",
"Jose Carlos Ferreira",
""
]
] |
1207.4123 | Carlos Chesnevar | Carlos Chesnevar, Guillermo Simari, Teresa Alsinet, Lluis Godo | A Logic Programming Framework for Possibilistic Argumentation with Vague
Knowledge | Appears in Proceedings of the Twentieth Conference on Uncertainty in
Artificial Intelligence (UAI2004) | null | null | UAI-P-2004-PG-76-84 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Defeasible argumentation frameworks have evolved to become a sound setting to
formalize commonsense, qualitative reasoning from incomplete and potentially
inconsistent knowledge. Defeasible Logic Programming (DeLP) is a defeasible
argumentation formalism based on an extension of logic programming. Although
DeLP has been successfully integrated in a number of different real-world
applications, DeLP cannot deal with explicit uncertainty, nor with vague
knowledge, as defeasibility is directly encoded in the object language. This
paper introduces P-DeLP, a new logic programming language that extends original
DeLP capabilities for qualitative reasoning by incorporating the treatment of
possibilistic uncertainty and fuzzy knowledge. Such features will be formalized
on the basis of PGL, a possibilistic logic based on Godel fuzzy logic.
| [
{
"version": "v1",
"created": "Wed, 11 Jul 2012 14:46:10 GMT"
}
] | 1,342,656,000,000 | [
[
"Chesnevar",
"Carlos",
""
],
[
"Simari",
"Guillermo",
""
],
[
"Alsinet",
"Teresa",
""
],
[
"Godo",
"Lluis",
""
]
] |
1207.4124 | Hei Chan | Hei Chan, Adnan Darwiche | Sensitivity Analysis in Bayesian Networks: From Single to Multiple
Parameters | Appears in Proceedings of the Twentieth Conference on Uncertainty in
Artificial Intelligence (UAI2004) | null | null | UAI-P-2004-PG-67-75 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Previous work on sensitivity analysis in Bayesian networks has focused on
single parameters, where the goal is to understand the sensitivity of queries
to single parameter changes, and to identify single parameter changes that
would enforce a certain query constraint. In this paper, we expand the work to
multiple parameters which may be in the CPT of a single variable, or the CPTs
of multiple variables. Not only do we identify the solution space of multiple
parameter changes that would be needed to enforce a query constraint, but we
also show how to find the optimal solution, that is, the one which disturbs the
current probability distribution the least (with respect to a specific measure
of disturbance). We characterize the computational complexity of our new
techniques and discuss their applications to developing and debugging Bayesian
networks, and to the problem of reasoning about the value (reliability) of new
information.
| [
{
"version": "v1",
"created": "Wed, 11 Jul 2012 14:46:27 GMT"
}
] | 1,342,656,000,000 | [
[
"Chan",
"Hei",
""
],
[
"Darwiche",
"Adnan",
""
]
] |
1207.4126 | Ronen I. Brafman | Ronen I. Brafman, Carmel Domshlak, Tanya Kogan | Compact Value-Function Representations for Qualitative Preferences | Appears in Proceedings of the Twentieth Conference on Uncertainty in
Artificial Intelligence (UAI2004) | null | null | UAI-P-2004-PG-51-59 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We consider the challenge of preference elicitation in systems that help
users discover the most desirable item(s) within a given database. Past work on
preference elicitation focused on structured models that provide a factored
representation of users' preferences. Such models require less information to
construct and support efficient reasoning algorithms. This paper makes two
substantial contributions to this area: (1) Strong representation theorems for
factored value functions. (2) A methodology that utilizes our representation
results to address the problem of optimal item selection.
| [
{
"version": "v1",
"created": "Wed, 11 Jul 2012 14:47:08 GMT"
}
] | 1,342,656,000,000 | [
[
"Brafman",
"Ronen I.",
""
],
[
"Domshlak",
"Carmel",
""
],
[
"Kogan",
"Tanya",
""
]
] |
1207.4130 | Leila Amgoud | Leila Amgoud, Henri Prade | Using arguments for making decisions: A possibilistic logic approach | Appears in Proceedings of the Twentieth Conference on Uncertainty in
Artificial Intelligence (UAI2004) | null | null | UAI-P-2004-PG-10-17 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Humans currently use arguments for explaining choices which are already made,
or for evaluating potential choices. Each potential choice has usually pros and
cons of various strengths. In spite of the usefulness of arguments in a
decision making process, there have been few formal proposals handling this
idea if we except works by Fox and Parsons and by Bonet and Geffner. In this
paper we propose a possibilistic logic framework where arguments are built from
an uncertain knowledge base and a set of prioritized goals. The proposed
approach can compute two kinds of decisions by distinguishing between
pessimistic and optimistic attitudes. When the available, maybe uncertain,
knowledge is consistent, as well as the set of prioritized goals (which have to
be fulfilled as far as possible), the method for evaluating decisions on the
basis of arguments agrees with the possibility theory-based approach to
decision-making under uncertainty. Taking advantage of its relation with formal
approaches to defeasible argumentation, the proposed framework can be
generalized in case of partially inconsistent knowledge, or goal bases.
| [
{
"version": "v1",
"created": "Wed, 11 Jul 2012 14:48:33 GMT"
}
] | 1,342,656,000,000 | [
[
"Amgoud",
"Leila",
""
],
[
"Prade",
"Henri",
""
]
] |
1207.4135 | David A. McAllester | David A. McAllester, Michael Collins, Fernando Pereira | Case-Factor Diagrams for Structured Probabilistic Modeling | Appears in Proceedings of the Twentieth Conference on Uncertainty in
Artificial Intelligence (UAI2004) | null | null | UAI-P-2004-PG-382-391 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We introduce a probabilistic formalism subsuming Markov random fields of
bounded tree width and probabilistic context free grammars. Our models are
based on a representation of Boolean formulas that we call case-factor diagrams
(CFDs). CFDs are similar to binary decision diagrams (BDDs) but are concise for
circuits of bounded tree width (unlike BDDs) and can concisely represent the
set of parse trees over a given string undera given context free grammar (also
unlike BDDs). A probabilistic model consists of aCFD defining a feasible set of
Boolean assignments and a weight (or cost) for each individual Boolean
variable. We give an insideoutside algorithm for simultaneously computing the
marginal of each Boolean variable, and a Viterbi algorithm for finding the
mininum cost variable assignment. Both algorithms run in time proportional to
the size of the CFD.
| [
{
"version": "v1",
"created": "Wed, 11 Jul 2012 14:52:02 GMT"
}
] | 1,342,656,000,000 | [
[
"McAllester",
"David A.",
""
],
[
"Collins",
"Michael",
""
],
[
"Pereira",
"Fernando",
""
]
] |
1207.4136 | Yongyi Mao | Yongyi Mao, Frank Kschischang, Brendan J. Frey | Convolutional Factor Graphs as Probabilistic Models | Appears in Proceedings of the Twentieth Conference on Uncertainty in
Artificial Intelligence (UAI2004) | null | null | UAI-P-2004-PG-374-381 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Based on a recent development in the area of error control coding, we
introduce the notion of convolutional factor graphs (CFGs) as a new class of
probabilistic graphical models. In this context, the conventional factor graphs
are referred to as multiplicative factor graphs (MFGs). This paper shows that
CFGs are natural models for probability functions when summation of independent
latent random variables is involved. In particular, CFGs capture a large class
of linear models, where the linearity is in the sense that the observed
variables are obtained as a linear ransformation of the latent variables taking
arbitrary distributions. We use Gaussian models and independent factor models
as examples to emonstrate the use of CFGs. The requirement of a linear
transformation between latent variables (with certain independence restriction)
and the bserved variables, to an extent, limits the modelling flexibility of
CFGs. This structural restriction however provides a powerful analytic tool to
the framework of CFGs; that is, upon taking the Fourier transform of the
function represented by the CFG, the resulting function is represented by a FG
with identical structure. This Fourier transform duality allows inference
problems on a CFG to be solved on the corresponding dual MFG.
| [
{
"version": "v1",
"created": "Wed, 11 Jul 2012 14:52:18 GMT"
}
] | 1,342,656,000,000 | [
[
"Mao",
"Yongyi",
""
],
[
"Kschischang",
"Frank",
""
],
[
"Frey",
"Brendan J.",
""
]
] |
1207.4137 | Anders L. Madsen | Anders L. Madsen | An Empirical Evaluation of Possible Variations of Lazy Propagation | Appears in Proceedings of the Twentieth Conference on Uncertainty in
Artificial Intelligence (UAI2004) | null | null | UAI-P-2004-PG-366-373 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | As real-world Bayesian networks continue to grow larger and more complex, it
is important to investigate the possibilities for improving the performance of
existing algorithms of probabilistic inference. Motivated by examples, we
investigate the dependency of the performance of Lazy propagation on the
message computation algorithm. We show how Symbolic Probabilistic Inference
(SPI) and Arc-Reversal (AR) can be used for computation of clique to clique
messages in the addition to the traditional use of Variable Elimination (VE).
In addition, the paper resents the results of an empirical evaluation of the
performance of Lazy propagation using VE, SPI, and AR as the message
computation algorithm. The results of the empirical evaluation show that for
most networks, the performance of inference did not depend on the choice of
message computation algorithm, but for some randomly generated networks the
choice had an impact on both space and time performance. In the cases where the
choice had an impact, AR produced the best results.
| [
{
"version": "v1",
"created": "Wed, 11 Jul 2012 14:52:35 GMT"
}
] | 1,342,656,000,000 | [
[
"Madsen",
"Anders L.",
""
]
] |
1207.4150 | Carlos E. Guestrin | Carlos E. Guestrin, Milos Hauskrecht, Branislav Kveton | Solving Factored MDPs with Continuous and Discrete Variables | Appears in Proceedings of the Twentieth Conference on Uncertainty in
Artificial Intelligence (UAI2004) | null | null | UAI-P-2004-PG-235-242 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Although many real-world stochastic planning problems are more naturally
formulated by hybrid models with both discrete and continuous variables,
current state-of-the-art methods cannot adequately address these problems. We
present the first framework that can exploit problem structure for modeling and
solving hybrid problems efficiently. We formulate these problems as hybrid
Markov decision processes (MDPs with continuous and discrete state and action
variables), which we assume can be represented in a factored way using a hybrid
dynamic Bayesian network (hybrid DBN). This formulation also allows us to apply
our methods to collaborative multiagent settings. We present a new linear
program approximation method that exploits the structure of the hybrid MDP and
lets us compute approximate value functions more efficiently. In particular, we
describe a new factored discretization of continuous variables that avoids the
exponential blow-up of traditional approaches. We provide theoretical bounds on
the quality of such an approximation and on its scale-up potential. We support
our theoretical arguments with experiments on a set of control problems with up
to 28-dimensional continuous state space and 22-dimensional action space.
| [
{
"version": "v1",
"created": "Wed, 11 Jul 2012 14:57:04 GMT"
}
] | 1,342,656,000,000 | [
[
"Guestrin",
"Carlos E.",
""
],
[
"Hauskrecht",
"Milos",
""
],
[
"Kveton",
"Branislav",
""
]
] |
1207.4153 | Changhe Yuan | Changhe Yuan, Tsai-Ching Lu, Marek J. Druzdzel | Annealed MAP | Appears in Proceedings of the Twentieth Conference on Uncertainty in
Artificial Intelligence (UAI2004) | null | null | UAI-P-2004-PG-628-635 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Maximum a Posteriori assignment (MAP) is the problem of finding the most
probable instantiation of a set of variables given the partial evidence on the
other variables in a Bayesian network. MAP has been shown to be a NP-hard
problem [22], even for constrained networks, such as polytrees [18]. Hence,
previous approaches often fail to yield any results for MAP problems in large
complex Bayesian networks. To address this problem, we propose AnnealedMAP
algorithm, a simulated annealing-based MAP algorithm. The AnnealedMAP algorithm
simulates a non-homogeneous Markov chain whose invariant function is a
probability density that concentrates itself on the modes of the target
density. We tested this algorithm on several real Bayesian networks. The
results show that, while maintaining good quality of the MAP solutions, the
AnnealedMAP algorithm is also able to solve many problems that are beyond the
reach of previous approaches.
| [
{
"version": "v1",
"created": "Wed, 11 Jul 2012 14:59:29 GMT"
}
] | 1,342,656,000,000 | [
[
"Yuan",
"Changhe",
""
],
[
"Lu",
"Tsai-Ching",
""
],
[
"Druzdzel",
"Marek J.",
""
]
] |
1207.4160 | Linda C. van der Gaag | Linda C. van der Gaag, Hans L. Bodlaender, Ad Feelders | Monotonicity in Bayesian Networks | Appears in Proceedings of the Twentieth Conference on Uncertainty in
Artificial Intelligence (UAI2004) | null | null | UAI-P-2004-PG-569-576 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | For many real-life Bayesian networks, common knowledge dictates that the
output established for the main variable of interest increases with higher
values for the observable variables. We define two concepts of monotonicity to
capture this type of knowledge. We say that a network is isotone in
distribution if the probability distribution computed for the output variable
given specific observations is stochastically dominated by any such
distribution given higher-ordered observations; a network is isotone in mode if
a probability distribution given higher observations has a higher mode. We show
that establishing whether a network exhibits any of these properties of
monotonicity is coNPPP-complete in general, and remains coNP-complete for
polytrees. We present an approximate algorithm for deciding whether a network
is monotone in distribution and illustrate its application to a real-life
network in oncology.
| [
{
"version": "v1",
"created": "Wed, 11 Jul 2012 15:02:16 GMT"
}
] | 1,342,656,000,000 | [
[
"van der Gaag",
"Linda C.",
""
],
[
"Bodlaender",
"Hans L.",
""
],
[
"Feelders",
"Ad",
""
]
] |
1207.4166 | Trey Smith | Trey Smith, Reid Simmons | Heuristic Search Value Iteration for POMDPs | Appears in Proceedings of the Twentieth Conference on Uncertainty in
Artificial Intelligence (UAI2004) | null | null | UAI-P-2004-PG-520-527 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We present a novel POMDP planning algorithm called heuristic search value
iteration (HSVI).HSVI is an anytime algorithm that returns a policy and a
provable bound on its regret with respect to the optimal policy. HSVI gets its
power by combining two well-known techniques: attention-focusing search
heuristics and piecewise linear convex representations of the value function.
HSVI's soundness and convergence have been proven. On some benchmark problems
from the literature, HSVI displays speedups of greater than 100 with respect to
other state-of-the-art POMDP value iteration algorithms. We also apply HSVI to
a new rover exploration problem 10 times larger than most POMDP problems in the
literature.
| [
{
"version": "v1",
"created": "Wed, 11 Jul 2012 15:04:47 GMT"
}
] | 1,342,656,000,000 | [
[
"Smith",
"Trey",
""
],
[
"Simmons",
"Reid",
""
]
] |
1207.4168 | Lenhart Schubert | Lenhart Schubert | A New Characterization of Probabilities in Bayesian Networks | Appears in Proceedings of the Twentieth Conference on Uncertainty in
Artificial Intelligence (UAI2004) | null | null | UAI-P-2004-PG-495-503 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We characterize probabilities in Bayesian networks in terms of algebraic
expressions called quasi-probabilities. These are arrived at by casting
Bayesian networks as noisy AND-OR-NOT networks, and viewing the subnetworks
that lead to a node as arguments for or against a node. Quasi-probabilities are
in a sense the "natural" algebra of Bayesian networks: we can easily compute
the marginal quasi-probability of any node recursively, in a compact form; and
we can obtain the joint quasi-probability of any set of nodes by multiplying
their marginals (using an idempotent product operator). Quasi-probabilities are
easily manipulated to improve the efficiency of probabilistic inference. They
also turn out to be representable as square-wave pulse trains, and joint and
marginal distributions can be computed by multiplication and complementation of
pulse trains.
| [
{
"version": "v1",
"created": "Wed, 11 Jul 2012 15:05:36 GMT"
}
] | 1,342,656,000,000 | [
[
"Schubert",
"Lenhart",
""
]
] |
1207.4170 | Silja Renooij | Silja Renooij, Linda C. van der Gaag | Evidence-invariant Sensitivity Bounds | Appears in Proceedings of the Twentieth Conference on Uncertainty in
Artificial Intelligence (UAI2004) | null | null | UAI-P-2004-PG-479-486 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The sensitivities revealed by a sensitivity analysis of a probabilistic
network typically depend on the entered evidence. For a real-life network
therefore, the analysis is performed a number of times, with different
evidence. Although efficient algorithms for sensitivity analysis exist, a
complete analysis is often infeasible because of the large range of possible
combinations of observations. In this paper we present a method for studying
sensitivities that are invariant to the evidence entered. Our method builds
upon the idea of establishing bounds between which a parameter can be varied
without ever inducing a change in the most likely value of a variable of
interest.
| [
{
"version": "v1",
"created": "Wed, 11 Jul 2012 15:06:14 GMT"
}
] | 1,342,656,000,000 | [
[
"Renooij",
"Silja",
""
],
[
"van der Gaag",
"Linda C.",
""
]
] |
1207.4175 | Alon Orlitsky | Alon Orlitsky, Narayana Santhanam, Krishnamurthy Viswanathan, Junan
Zhang | On Modeling Profiles instead of Values | Appears in Proceedings of the Twentieth Conference on Uncertainty in
Artificial Intelligence (UAI2004) | null | null | UAI-P-2004-PG-426-435 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We consider the problem of estimating the distribution underlying an observed
sample of data. Instead of maximum likelihood, which maximizes the probability
of the ob served values, we propose a different estimate, the high-profile
distribution, which maximizes the probability of the observed profile the
number of symbols appearing any given number of times. We determine the
high-profile distribution of several data samples, establish some of its
general properties, and show that when the number of distinct symbols observed
is small compared to the data size, the high-profile and maximum-likelihood
distributions are roughly the same, but when the number of symbols is large,
the distributions differ, and high-profile better explains the data.
| [
{
"version": "v1",
"created": "Wed, 11 Jul 2012 15:10:15 GMT"
}
] | 1,342,656,000,000 | [
[
"Orlitsky",
"Alon",
""
],
[
"Santhanam",
"Narayana",
""
],
[
"Viswanathan",
"Krishnamurthy",
""
],
[
"Zhang",
"Junan",
""
]
] |
1207.4176 | Valentina Bayer-Zubek | Valentina Bayer-Zubek | Learning Diagnostic Policies from Examples by Systematic Search | Appears in Proceedings of the Twentieth Conference on Uncertainty in
Artificial Intelligence (UAI2004) | null | null | UAI-P-2004-PG-27-34 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | A diagnostic policy specifies what test to perform next, based on the results
of previous tests, and when to stop and make a diagnosis. Cost-sensitive
diagnostic policies perform tradeoffs between (a) the cost of tests and (b) the
cost of misdiagnoses. An optimal diagnostic policy minimizes the expected total
cost. We formalize this diagnosis process as a Markov Decision Process (MDP).
We investigate two types of algorithms for solving this MDP: systematic search
based on AO* algorithm and greedy search (particularly the Value of Information
method). We investigate the issue of learning the MDP probabilities from
examples, but only as they are relevant to the search for good policies. We do
not learn nor assume a Bayesian network for the diagnosis process. Regularizers
are developed to control overfitting and speed up the search. This research is
the first that integrates overfitting prevention into systematic search. The
paper has two contributions: it discusses the factors that make systematic
search feasible for diagnosis, and it shows experimentally, on benchmark data
sets, that systematic search methods produce better diagnostic policies than
greedy methods.
| [
{
"version": "v1",
"created": "Thu, 12 Jul 2012 19:44:39 GMT"
}
] | 1,342,656,000,000 | [
[
"Bayer-Zubek",
"Valentina",
""
]
] |
1207.4177 | Barry Cobb | Barry Cobb, Prakash P. Shenoy | Hybrid Influence Diagrams Using Mixtures of Truncated Exponentials | Appears in Proceedings of the Twentieth Conference on Uncertainty in
Artificial Intelligence (UAI2004) | null | null | UAI-P-2004-PG-85-93 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Mixtures of truncated exponentials (MTE) potentials are an alternative to
discretization for representing continuous chance variables in influence
diagrams. Also, MTE potentials can be used to approximate utility functions.
This paper introduces MTE influence diagrams, which can represent decision
problems without restrictions on the relationships between continuous and
discrete chance variables, without limitations on the distributions of
continuous chance variables, and without limitations on the nature of the
utility functions. In MTE influence diagrams, all probability distributions and
the joint utility function (or its multiplicative factors) are represented by
MTE potentials and decision nodes are assumed to have discrete state spaces.
MTE influence diagrams are solved by variable elimination using a fusion
algorithm.
| [
{
"version": "v1",
"created": "Thu, 12 Jul 2012 19:45:33 GMT"
}
] | 1,342,656,000,000 | [
[
"Cobb",
"Barry",
""
],
[
"Shenoy",
"Prakash P.",
""
]
] |
1207.4432 | Vesna Marinkovi\'c | Vesna Marinkovic, Predrag Janicic | Towards Understanding Triangle Construction Problems | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Straightedge and compass construction problems are one of the oldest and most
challenging problems in elementary mathematics. The central challenge, for a
human or for a computer program, in solving construction problems is a huge
search space. In this paper we analyze one family of triangle construction
problems, aiming at detecting a small core of the underlying geometry
knowledge. The analysis leads to a small set of needed definitions, lemmas and
primitive construction steps, and consequently, to a simple algorithm for
automated solving of problems from this family. The same approach can be
applied to other families of construction problems.
| [
{
"version": "v1",
"created": "Wed, 18 Jul 2012 18:17:14 GMT"
}
] | 1,342,656,000,000 | [
[
"Marinkovic",
"Vesna",
""
],
[
"Janicic",
"Predrag",
""
]
] |
1207.4708 | Marc G. Bellemare | Marc G. Bellemare, Yavar Naddaf, Joel Veness, Michael Bowling | The Arcade Learning Environment: An Evaluation Platform for General
Agents | null | Journal of Artificial Intelligence Research 47, pages 253-279 | 10.1613/jair.3912 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this article we introduce the Arcade Learning Environment (ALE): both a
challenge problem and a platform and methodology for evaluating the development
of general, domain-independent AI technology. ALE provides an interface to
hundreds of Atari 2600 game environments, each one different, interesting, and
designed to be a challenge for human players. ALE presents significant research
challenges for reinforcement learning, model learning, model-based planning,
imitation learning, transfer learning, and intrinsic motivation. Most
importantly, it provides a rigorous testbed for evaluating and comparing
approaches to these problems. We illustrate the promise of ALE by developing
and benchmarking domain-independent agents designed using well-established AI
techniques for both reinforcement learning and planning. In doing so, we also
propose an evaluation methodology made possible by ALE, reporting empirical
results on over 55 different games. All of the software, including the
benchmark agents, is publicly available.
| [
{
"version": "v1",
"created": "Thu, 19 Jul 2012 15:33:25 GMT"
},
{
"version": "v2",
"created": "Fri, 21 Jun 2013 18:07:06 GMT"
}
] | 1,372,032,000,000 | [
[
"Bellemare",
"Marc G.",
""
],
[
"Naddaf",
"Yavar",
""
],
[
"Veness",
"Joel",
""
],
[
"Bowling",
"Michael",
""
]
] |
1207.4813 | Ram\'on Pino P\'erez | Jos\'e Luis Chac\'on and Ram\'on Pino P\'erez | Exploring the rationality of some syntactic merging operators (extended
version) | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Most merging operators are defined by semantics methods which have very high
computational complexity. In order to have operators with a lower computational
complexity, some merging operators defined in a syntactical way have be
proposed. In this work we define some syntactical merging operators and
exploring its rationality properties. To do that we constrain the belief bases
to be sets of formulas very close to logic programs and the underlying logic is
defined through forward chaining rule (Modus Ponens). We propose two types of
operators: arbitration operators when the inputs are only two bases and fusion
with integrity constraints operators. We introduce a set of postulates inspired
of postulates LS, proposed by Liberatore and Shaerf and then we analyzed the
first class of operators through these postulates. We also introduce a set of
postulates inspired of postulates KP, proposed by Konieczny and Pino P\'erez
and then we analyzed the second class of operators through these postulates.
| [
{
"version": "v1",
"created": "Thu, 19 Jul 2012 21:30:17 GMT"
}
] | 1,343,001,600,000 | [
[
"Chacón",
"José Luis",
""
],
[
"Pérez",
"Ramón Pino",
""
]
] |
1207.5152 | Fatih Korkmaz | Fatih Korkmaz, M. Faruk Cakir, Yilmaz Korkmaz, Ismail Topaloglu | Stator flux optimization on direct torque control with fuzzy logic | 8 pages, 8 figures, Itca 2012 conference | null | 10.5121/csit.2012.2355 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The Direct Torque Control (DTC) is well known as an effective control
technique for high performance drives in a wide variety of industrial
applications and conventional DTC technique uses two constant reference value:
torque and stator flux. In this paper, fuzzy logic based stator flux
optimization technique for DTC drives that has been proposed. The proposed
fuzzy logic based stator flux optimizer self-regulates the stator flux
reference using induction motor load situation without need of any motor
parameters. Simulation studies have been carried out with Matlab/Simulink to
compare the proposed system behaviors at vary load conditions. Simulation
results show that the performance of the proposed DTC technique has been
improved and especially at low-load conditions torque ripple are greatly
reduced with respect to the conventional DTC.
| [
{
"version": "v1",
"created": "Sat, 21 Jul 2012 15:08:06 GMT"
}
] | 1,354,579,200,000 | [
[
"Korkmaz",
"Fatih",
""
],
[
"Cakir",
"M. Faruk",
""
],
[
"Korkmaz",
"Yilmaz",
""
],
[
"Topaloglu",
"Ismail",
""
]
] |
1207.5879 | David Tolpin | Nicholas Hay and Stuart Russell, David Tolpin and Solomon Eyal Shimony | Selecting Computations: Theory and Applications | 10 pages, UAI 2012 | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Sequential decision problems are often approximately solvable by simulating
possible future action sequences. {\em Metalevel} decision procedures have been
developed for selecting {\em which} action sequences to simulate, based on
estimating the expected improvement in decision quality that would result from
any particular simulation; an example is the recent work on using bandit
algorithms to control Monte Carlo tree search in the game of Go. In this paper
we develop a theoretical basis for metalevel decisions in the statistical
framework of Bayesian {\em selection problems}, arguing (as others have done)
that this is more appropriate than the bandit framework. We derive a number of
basic results applicable to Monte Carlo selection problems, including the first
finite sampling bounds for optimal policies in certain cases; we also provide a
simple counterexample to the intuitive conjecture that an optimal policy will
necessarily reach a decision in all cases. We then derive heuristic
approximations in both Bayesian and distribution-free settings and demonstrate
their superiority to bandit-based heuristics in one-shot decision problems and
in Go.
| [
{
"version": "v1",
"created": "Wed, 25 Jul 2012 03:31:08 GMT"
}
] | 1,343,260,800,000 | [
[
"Hay",
"Nicholas",
""
],
[
"Russell",
"Stuart",
""
],
[
"Tolpin",
"David",
""
],
[
"Shimony",
"Solomon Eyal",
""
]
] |
1207.5926 | Bart Demoen | Bart Demoen and Maria Garcia de la Banda | Redundant Sudoku Rules | 14 pages, 161 figures, to appear in TPLP | Theory and Practice of Logic Programming 14 (2014) 363-377 | 10.1017/S1471068412000361 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The rules of Sudoku are often specified using twenty seven
\texttt{all\_different} constraints, referred to as the {\em big} \mrules.
Using graphical proofs and exploratory logic programming, the following main
and new result is obtained: many subsets of six of these big \mrules are
redundant (i.e., they are entailed by the remaining twenty one \mrules), and
six is maximal (i.e., removing more than six \mrules is not possible while
maintaining equivalence). The corresponding result for binary inequality
constraints, referred to as the {\em small} \mrules, is stated as a conjecture.
| [
{
"version": "v1",
"created": "Wed, 25 Jul 2012 08:57:20 GMT"
}
] | 1,582,070,400,000 | [
[
"Demoen",
"Bart",
""
],
[
"de la Banda",
"Maria Garcia",
""
]
] |
1207.6514 | Steven Prestwich | Steven Prestwich | Earthquake Scenario Reduction by Symmetry Reasoning | Abstract presented at EURO conference, Vilnius, Lithuania, 2012 | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | A recently identified problem is that of finding an optimal investment plan
for a transportation network, given that a disaster such as an earthquake may
destroy links in the network. The aim is to strengthen key links to preserve
the expected network connectivity. A network based on the Istanbul highway
system has thirty links and therefore a billion scenarios, but it has been
estimated that sampling a million scenarios gives reasonable accuracy. In this
paper we use symmetry reasoning to reduce the number of scenarios to a much
smaller number, making sampling unnecessary. This result can be used to
facilitate metaheuristic and exact approaches to the problem.
| [
{
"version": "v1",
"created": "Fri, 27 Jul 2012 11:14:23 GMT"
}
] | 1,343,606,400,000 | [
[
"Prestwich",
"Steven",
""
]
] |
1207.6713 | Hankz Hankui Zhuo | Hankz Hankui Zhuo, Subbarao Kambhampati, and Tuan Nguyen | Model-Lite Case-Based Planning | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | There is increasing awareness in the planning community that depending on
complete models impedes the applicability of planning technology in many real
world domains where the burden of specifying complete domain models is too
high. In this paper, we consider a novel solution for this challenge that
combines generative planning on incomplete domain models with a library of plan
cases that are known to be correct. While this was arguably the original
motivation for case-based planning, most existing case-based planners assume
(and depend on) from-scratch planners that work on complete domain models. In
contrast, our approach views the plan generated with respect to the incomplete
model as a "skeletal plan" and augments it with directed mining of plan
fragments from library cases. We will present the details of our approach and
present an empirical evaluation of our method in comparison to a
state-of-the-art case-based planner that depends on complete domain models.
| [
{
"version": "v1",
"created": "Sat, 28 Jul 2012 17:00:01 GMT"
}
] | 1,343,692,800,000 | [
[
"Zhuo",
"Hankz Hankui",
""
],
[
"Kambhampati",
"Subbarao",
""
],
[
"Nguyen",
"Tuan",
""
]
] |
1208.1743 | Amir Noori | Amir Noori, Mohammad Bagher Menhaj, Masoud Shafiee | Hybrid systems modeling for gas transmission network | This paper has been withdrawn by the author due to a crucial citation
error in introduction section | The 4th IFAC Conference on Management and Control of Production
and Logistics, 2007 | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Gas Transmission Networks are large-scale complex systems, and corresponding
design and control problems are challenging. In this paper, we consider the
problem of control and management of these systems in crisis situations. We
present these networks by a hybrid systems framework that provides required
analysis models. Further, we discuss decision-making using computational
discrete and hybrid optimization methods. In particular, several reinforcement
learning methods are employed to explore decision space and achieve the best
policy in a specific crisis situation. Simulations are presented to illustrate
the efficiency of the method.
| [
{
"version": "v1",
"created": "Wed, 8 Aug 2012 19:24:08 GMT"
},
{
"version": "v2",
"created": "Sat, 22 Sep 2012 20:03:41 GMT"
},
{
"version": "v3",
"created": "Tue, 25 Sep 2012 16:00:02 GMT"
},
{
"version": "v4",
"created": "Fri, 28 Sep 2012 07:07:55 GMT"
}
] | 1,349,049,600,000 | [
[
"Noori",
"Amir",
""
],
[
"Menhaj",
"Mohammad Bagher",
""
],
[
"Shafiee",
"Masoud",
""
]
] |
1208.1955 | Fahimeh Farahbod | Fahimeh Farahbod and Mahdi Eftekhari | Comparison of different T-norm operators in classification problems | 6 pages, 1 figure, 4 tables; International Journal of Fuzzy Logic
Systems (IJFLS) Vol.2, No.3, July 2012 | null | 10.5121/ijfls.2012.2303 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Fuzzy rule based classification systems are one of the most popular fuzzy
modeling systems used in pattern classification problems. This paper
investigates the effect of applying nine different T-norms in fuzzy rule based
classification systems. In the recent researches, fuzzy versions of confidence
and support merits from the field of data mining have been widely used for both
rules selecting and weighting in the construction of fuzzy rule based
classification systems. For calculating these merits the product has been
usually used as a T-norm. In this paper different T-norms have been used for
calculating the confidence and support measures. Therefore, the calculations in
rule selection and rule weighting steps (in the process of constructing the
fuzzy rule based classification systems) are modified by employing these
T-norms. Consequently, these changes in calculation results in altering the
overall accuracy of rule based classification systems. Experimental results
obtained on some well-known data sets show that the best performance is
produced by employing the Aczel-Alsina operator in terms of the classification
accuracy, the second best operator is Dubois-Prade and the third best operator
is Dombi. In experiments, we have used 12 data sets with numerical attributes
from the University of California, Irvine machine learning repository (UCI).
| [
{
"version": "v1",
"created": "Thu, 9 Aug 2012 15:41:27 GMT"
}
] | 1,344,556,800,000 | [
[
"Farahbod",
"Fahimeh",
""
],
[
"Eftekhari",
"Mahdi",
""
]
] |
1208.2102 | Abdul Kareem | Abdul Kareem, Mohammad Fazle Azeem | A Novel Fuzzy Logic Based Adaptive Supertwisting Sliding Mode Control
Algorithm for Dynamic Uncertain Systems | 14 pages | International Journal of Artificial Intelligence & Applications
(IJAIA), Vol.3, No.4, July 2012, 21-34 | 10.5121/ijaia.2012.3402 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper presents a novel fuzzy logic based Adaptive Super-twisting Sliding
Mode Controller for the control of dynamic uncertain systems. The proposed
controller combines the advantages of Second order Sliding Mode Control, Fuzzy
Logic Control and Adaptive Control. The reaching conditions, stability and
robustness of the system with the proposed controller are guaranteed. In
addition, the proposed controller is well suited for simple design and
implementation. The effectiveness of the proposed controller over the first
order Sliding Mode Fuzzy Logic controller is illustrated by Matlab based
simulations performed on a DC-DC Buck converter. Based on this comparison, the
proposed controller is shown to obtain the desired transient response without
causing chattering and error under steady-state conditions. The proposed
controller is able to give robust performance in terms of rejection to input
voltage variations and load variations.
| [
{
"version": "v1",
"created": "Fri, 10 Aug 2012 07:19:48 GMT"
}
] | 1,344,816,000,000 | [
[
"Kareem",
"Abdul",
""
],
[
"Azeem",
"Mohammad Fazle",
""
]
] |
1208.2199 | Mohammad Havaei | Mohammad Havaei, Nandivada Krishna Prasad, and Velleshala Sudheer | Elimination of ISI Using Improved LMS Based Decision Feedback Equalizer | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper deals with the implementation of Least Mean Square (LMS) algorithm
in Decision Feedback Equalizer (DFE) for removal of Inter Symbol Interference
(ISI) at the receiver. The channel disrupts the transmitted signal by spreading
it in time. Although, the LMS algorithm is robust and reliable, it is slow in
convergence. In order to increase the speed of convergence, modifications have
been made in the algorithm where the weights get updated depending on the
severity of disturbance.
| [
{
"version": "v1",
"created": "Fri, 10 Aug 2012 15:05:38 GMT"
}
] | 1,344,816,000,000 | [
[
"Havaei",
"Mohammad",
""
],
[
"Prasad",
"Nandivada Krishna",
""
],
[
"Sudheer",
"Velleshala",
""
]
] |
1208.2566 | Sebastian Ordyniak | Christer Baeckstroem, Yue Chen, Peter Jonsson, Sebastian Ordyniak,
Stefan Szeider | The Complexity of Planning Revisited - A Parameterized Analysis | (author's self-archived copy) | Proc. AAAI'12 (AAAI Press 2012) pp. 1735-1741 | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The early classifications of the computational complexity of planning under
various restrictions in STRIPS (Bylander) and SAS+ (Baeckstroem and Nebel) have
influenced following research in planning in many ways. We go back and
reanalyse their subclasses, but this time using the more modern tool of
parameterized complexity analysis. This provides new results that together with
the old results give a more detailed picture of the complexity landscape. We
demonstrate separation results not possible with standard complexity theory,
which contributes to explaining why certain cases of planning have seemed
simpler in practice than theory has predicted. In particular, we show that
certain restrictions of practical interest are tractable in the parameterized
sense of the term, and that a simple heuristic is sufficient to make a
well-known partial-order planner exploit this fact.
| [
{
"version": "v1",
"created": "Mon, 13 Aug 2012 12:40:44 GMT"
}
] | 1,344,902,400,000 | [
[
"Baeckstroem",
"Christer",
""
],
[
"Chen",
"Yue",
""
],
[
"Jonsson",
"Peter",
""
],
[
"Ordyniak",
"Sebastian",
""
],
[
"Szeider",
"Stefan",
""
]
] |
1208.3015 | Andreas Schutt | Andreas Schutt and Thibaut Feydy and Peter J. Stuckey | Explaining Time-Table-Edge-Finding Propagation for the Cumulative
Resource Constraint | 22 pages, 3 figures, 11 tables, 2 algorithms | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Cumulative resource constraints can model scarce resources in scheduling
problems or a dimension in packing and cutting problems. In order to
efficiently solve such problems with a constraint programming solver, it is
important to have strong and fast propagators for cumulative resource
constraints. One such propagator is the recently developed
time-table-edge-finding propagator, which considers the current resource
profile during the edge-finding propagation. Recently, lazy clause generation
solvers, i.e. constraint programming solvers incorporating nogood learning,
have proved to be excellent at solving scheduling and cutting problems. For
such solvers, concise and accurate explanations of the reasons for propagation
are essential for strong nogood learning. In this paper, we develop the first
explaining version of time-table-edge-finding propagation and show preliminary
results on resource-constrained project scheduling problems from various
standard benchmark suites. On the standard benchmark suite PSPLib, we were able
to close one open instance and to improve the lower bound of about 60% of the
remaining open instances. Moreover, 6 of those instances were closed.
| [
{
"version": "v1",
"created": "Wed, 15 Aug 2012 02:17:55 GMT"
},
{
"version": "v2",
"created": "Mon, 10 Sep 2012 05:52:51 GMT"
}
] | 1,347,321,600,000 | [
[
"Schutt",
"Andreas",
""
],
[
"Feydy",
"Thibaut",
""
],
[
"Stuckey",
"Peter J.",
""
]
] |
1208.3148 | Ernesto Jim\'enez-Ruiz | Christian Meilicke (1), Ondrej Sv\'ab-Zamazal (2), C\'assia Trojahn
(3), Ernesto Jim\'enez-Ruiz (4), Jos\'e-Luis Aguirre (3), Heiner
Stuckenschmidt (1), Bernardo Cuenca Grau (4) ((1) University of Mannheim, (2)
University of Economics Prague, (3) INRIA and LIG Grenoble, (4) University of
Oxford) | Evaluating Ontology Matching Systems on Large, Multilingual and
Real-world Test Cases | Technical Report of the OAEI 2011.5 Evaluation Campaign | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In the field of ontology matching, the most systematic evaluation of matching
systems is established by the Ontology Alignment Evaluation Initiative (OAEI),
which is an annual campaign for evaluating ontology matching systems organized
by different groups of researchers. In this paper, we report on the results of
an intermediary OAEI campaign called OAEI 2011.5. The evaluations of this
campaign are divided in five tracks. Three of these tracks are new or have been
improved compared to previous OAEI campaigns. Overall, we evaluated 18 matching
systems. We discuss lessons learned, in terms of scalability, multilingual
issues and the ability do deal with real world cases from different domains.
| [
{
"version": "v1",
"created": "Wed, 15 Aug 2012 15:46:31 GMT"
}
] | 1,345,075,200,000 | [
[
"Meilicke",
"Christian",
""
],
[
"Sváb-Zamazal",
"Ondrej",
""
],
[
"Trojahn",
"Cássia",
""
],
[
"Jiménez-Ruiz",
"Ernesto",
""
],
[
"Aguirre",
"José-Luis",
""
],
[
"Stuckenschmidt",
"Heiner",
""
],
[
"Grau",
"Bernardo Cuenca",
""
]
] |
1208.3802 | Nisheeth Joshi | Archana Vashisth, Iti Mathur, Nisheeth Joshi | OntoAna: Domain Ontology for Human Anatomy | Proceedings of 5th CSI National Conference on Education and Research.
Organized by Lingayay University, Faridabad. Sponsored by Computer Society of
India and IEEE Delhi Chapter. Proceedings published by Lingayay University
Press | null | null | null | cs.AI | http://creativecommons.org/licenses/by/3.0/ | Today, we can find many search engines which provide us with information
which is more operational in nature. None of the search engines provide domain
specific information. This becomes very troublesome to a novice user who wishes
to have information in a particular domain. In this paper, we have developed an
ontology which can be used by a domain specific search engine. We have
developed an ontology on human anatomy, which captures information regarding
cardiovascular system, digestive system, skeleton and nervous system. This
information can be used by people working in medical and health care domain.
| [
{
"version": "v1",
"created": "Sun, 19 Aug 2012 02:44:42 GMT"
}
] | 1,345,507,200,000 | [
[
"Vashisth",
"Archana",
""
],
[
"Mathur",
"Iti",
""
],
[
"Joshi",
"Nisheeth",
""
]
] |
1208.3809 | Nima Taghipour | Nima Taghipour, Daan Fierens, Guy Van den Broeck, Jesse Davis, Hendrik
Blockeel | Lifted Variable Elimination: A Novel Operator and Completeness Results | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Various methods for lifted probabilistic inference have been proposed, but
our understanding of these methods and the relationships between them is still
limited, compared to their propositional counterparts. The only existing
theoretical characterization of lifting is for weighted first-order model
counting (WFOMC), which was shown to be complete domain-lifted for the class of
2-logvar models. This paper makes two contributions to lifted variable
elimination (LVE). First, we introduce a novel inference operator called group
inversion. Second, we prove that LVE augmented with this operator is complete
in the same sense as WFOMC.
| [
{
"version": "v1",
"created": "Sun, 19 Aug 2012 04:55:25 GMT"
},
{
"version": "v2",
"created": "Fri, 24 Aug 2012 18:10:37 GMT"
}
] | 1,346,025,600,000 | [
[
"Taghipour",
"Nima",
""
],
[
"Fierens",
"Daan",
""
],
[
"Broeck",
"Guy Van den",
""
],
[
"Davis",
"Jesse",
""
],
[
"Blockeel",
"Hendrik",
""
]
] |
1208.4942 | Cm Pintea | Camelia-M. Pintea | A Unifying Survey of Reinforced, Sensitive and Stigmergic Agent-Based
Approaches for E-GTSP | 9 pages, 2 figures | Informatica. 26(3) (2015). 509-522 | 10.15388/Informatica.2015.61 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The Generalized Traveling Salesman Problem (GTSP) is one of the NP-hard
combinatorial optimization problems. A variant of GTSP is E-GTSP where E,
meaning equality, has the constraint: exactly one node from a cluster of a
graph partition is visited. The main objective of the E-GTSP is to find a
minimum cost tour passing through exactly one node from each cluster of an
undirected graph. Agent-based approaches involving are successfully used
nowadays for solving real life complex problems. The aim of the current paper
is to illustrate some variants of agent-based algorithms including ant-based
models with specific properties for solving E-GTSP.
| [
{
"version": "v1",
"created": "Fri, 24 Aug 2012 10:23:37 GMT"
},
{
"version": "v2",
"created": "Thu, 13 Feb 2014 11:02:59 GMT"
}
] | 1,615,852,800,000 | [
[
"Pintea",
"Camelia-M.",
""
]
] |
1208.4945 | Cm Pintea | Camelia-M. Pintea, Gloria Cerasela Crisan, Mihai Manea | Parallel ACO with a Ring Neighborhood for Dynamic TSP | 8 pages, 1 figure; accepted J. Information Technology Research | J Information Technology Research 5(4): 1-13 (2012) | 10.4018/jitr.2012100101 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The current paper introduces a new parallel computing technique based on ant
colony optimization for a dynamic routing problem. In the dynamic traveling
salesman problem the distances between cities as travel times are no longer
fixed. The new technique uses a parallel model for a problem variant that
allows a slight movement of nodes within their Neighborhoods. The algorithm is
tested with success on several large data sets.
| [
{
"version": "v1",
"created": "Fri, 24 Aug 2012 10:38:49 GMT"
},
{
"version": "v2",
"created": "Wed, 10 Oct 2012 13:14:13 GMT"
}
] | 1,595,894,400,000 | [
[
"Pintea",
"Camelia-M.",
""
],
[
"Crisan",
"Gloria Cerasela",
""
],
[
"Manea",
"Mihai",
""
]
] |
1208.5154 | David McAllester | David McAllester, Petri Myllymaki | Proceedings of the Twenty-Fourth Conference on Uncertainty in Artificial
Intelligence (2008) | null | null | null | UAI2008 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This is the Proceedings of the Twenty-Fourth Conference on Uncertainty in
Artificial Intelligence, which was held in Helsinki, Finland, July 9 - 12 2008.
| [
{
"version": "v1",
"created": "Sat, 25 Aug 2012 18:22:17 GMT"
},
{
"version": "v2",
"created": "Thu, 28 Aug 2014 04:25:59 GMT"
}
] | 1,409,270,400,000 | [
[
"McAllester",
"David",
""
],
[
"Myllymaki",
"Petri",
""
]
] |
1208.5155 | Ronald Parr | Ronald Parr, Linda S. van der Gaag | Proceedings of the Twenty-Third Conference on Uncertainty in Artificial
Intelligence (2007) | null | null | null | UAI2007 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This is the Proceedings of the Twenty-Third Conference on Uncertainty in
Artificial Intelligence, which was held in Vancouver, British Columbia, July 19
- 22 2007.
| [
{
"version": "v1",
"created": "Sat, 25 Aug 2012 18:26:04 GMT"
},
{
"version": "v2",
"created": "Thu, 28 Aug 2014 04:24:43 GMT"
}
] | 1,409,270,400,000 | [
[
"Parr",
"Ronald",
""
],
[
"van der Gaag",
"Linda S.",
""
]
] |
1208.5159 | Fahiem Bacchus | Fahiem Bacchus, Tommi Jaakkola | Proceedings of the Twenty-First Conference on Uncertainty in Artificial
Intelligence (2005) | null | null | null | UAI2005 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This is the Proceedings of the Twenty-First Conference on Uncertainty in
Artificial Intelligence, which was held in Edinburgh, Scotland July 26 - 29
2005.
| [
{
"version": "v1",
"created": "Sat, 25 Aug 2012 18:44:38 GMT"
},
{
"version": "v2",
"created": "Thu, 28 Aug 2014 04:22:02 GMT"
}
] | 1,409,270,400,000 | [
[
"Bacchus",
"Fahiem",
""
],
[
"Jaakkola",
"Tommi",
""
]
] |
1208.5160 | Rina Dechter | Rina Dechter, Thomas S. Richardson | Proceedings of the Twenty-Second Conference on Uncertainty in Artificial
Intelligence (2006) | null | null | null | UAI2006 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This is the Proceedings of the Twenty-Second Conference on Uncertainty in
Artificial Intelligence, which was held in Cambridge, MA, July 13 - 16 2006.
| [
{
"version": "v1",
"created": "Sat, 25 Aug 2012 18:45:05 GMT"
},
{
"version": "v2",
"created": "Thu, 28 Aug 2014 04:23:23 GMT"
}
] | 1,409,270,400,000 | [
[
"Dechter",
"Rina",
""
],
[
"Richardson",
"Thomas S.",
""
]
] |
1208.5161 | Max Chickering | Max Chickering, Joseph Halpern | Proceedings of the Twentieth Conference on Uncertainty in Artificial
Intelligence (2004) | null | null | null | UAI2004 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This is the Proceedings of the Twentieth Conference on Uncertainty in
Artificial Intelligence, which was held in Banff, Canada, July 7 - 11 2004.
| [
{
"version": "v1",
"created": "Sat, 25 Aug 2012 18:48:34 GMT"
},
{
"version": "v2",
"created": "Thu, 28 Aug 2014 04:20:22 GMT"
}
] | 1,409,270,400,000 | [
[
"Chickering",
"Max",
""
],
[
"Halpern",
"Joseph",
""
]
] |
1208.5373 | Cm Pintea | Camelia-M. Pintea, D. Dumitrescu | Distributed Pharaoh System for Network Routing | 4 pages, 4 figures | Automat. Comput. Appl. Math. 16(1-2) (2007) 27-34 | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this paper it is introduced a biobjective ant algorithm for constructing
low cost routing networks. The new algorithm is called the Distributed Pharaoh
System (DPS). DPS is based on AntNet algorithm. The algorithm is using Pharaoh
Ant System (PAS) with an extra-exploration phase and a 'no-entry' condition in
order to improve the solutions for the Low Cost Network Routing problem.
Additionally it is used a cost model for overlay network construction that
includes network traffic demands. The Pharaoh ants (Monomorium pharaonis)
includes negative pheromones with signals concentrated at decision points where
trails fork. The negative pheromones may complement positive pheromone or could
help ants to escape from an unnecessarily long route to food that is being
reinforced by attractive signals. Numerical experiments were made for a random
10-node network. The average node degree of the network tested was 4.0. The
results are encouraging. The algorithm converges to the shortest path while
converging on a low cost overlay routing network topology.
| [
{
"version": "v1",
"created": "Mon, 27 Aug 2012 12:35:29 GMT"
}
] | 1,346,112,000,000 | [
[
"Pintea",
"Camelia-M.",
""
],
[
"Dumitrescu",
"D.",
""
]
] |
1208.5554 | Cm Pintea | Gabriela Czibula, Gloria Cerasela Crisan, Camelia-M. Pintea,
Istvan-Gergely Czibula | Soft Computing approaches on the Bandwidth Problem | 6 pages, 1 figure; accepted to Informatica | INFORMATICA 24(2):169-180 (2013) | 10.15388/Informatica.2013.390 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The Matrix Bandwidth Minimization Problem (MBMP) seeks for a simultaneous
reordering of the rows and the columns of a square matrix such that the nonzero
entries are collected within a band of small width close to the main diagonal.
The MBMP is a NP-complete problem, with applications in many scientific
domains, linear systems, artificial intelligence, and real-life situations in
industry, logistics, information recovery. The complex problems are hard to
solve, that is why any attempt to improve their solutions is beneficent.
Genetic algorithms and ant-based systems are Soft Computing methods used in
this paper in order to solve some MBMP instances. Our approach is based on a
learning agent-based model involving a local search procedure. The algorithm is
compared with the classical Cuthill-McKee algorithm, and with a hybrid genetic
algorithm, using several instances from Matrix Market collection. Computational
experiments confirm a good performance of the proposed algorithms for the
considered set of MBMP instances. On Soft Computing basis, we also propose a
new theoretical Reinforcement Learning model for solving the MBMP problem.
| [
{
"version": "v1",
"created": "Tue, 28 Aug 2012 04:26:35 GMT"
}
] | 1,595,894,400,000 | [
[
"Czibula",
"Gabriela",
""
],
[
"Crisan",
"Gloria Cerasela",
""
],
[
"Pintea",
"Camelia-M.",
""
],
[
"Czibula",
"Istvan-Gergely",
""
]
] |
1209.0852 | Ehsan Saboori Mr. | Ehsan Saboori, Shafigh Parsazad, Yasaman Sanatkhani | Automatic firewall rules generator for anomaly detection systems with
Apriori algorithm | 4 Pages | 2010 3rd International Conference on Advanced Computer Theory and
Engineering (ICACTE), vol.6, no., pp.V6-57-V6-60, 2010 | 10.1109/ICACTE.2010.5579365 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Network intrusion detection systems have become a crucial issue for computer
systems security infrastructures. Different methods and algorithms are
developed and proposed in recent years to improve intrusion detection systems.
The most important issue in current systems is that they are poor at detecting
novel anomaly attacks. These kinds of attacks refer to any action that
significantly deviates from the normal behaviour which is considered intrusion.
This paper proposed a model to improve this problem based on data mining
techniques. Apriori algorithm is used to predict novel attacks and generate
real-time rules for firewall. Apriori algorithm extracts interesting
correlation relationships among large set of data items. This paper illustrates
how to use Apriori algorithm in intrusion detection systems to cerate a
automatic firewall rules generator to detect novel anomaly attack. Apriori is
the best-known algorithm to mine association rules. This is an innovative way
to find association rules on large scale.
| [
{
"version": "v1",
"created": "Wed, 5 Sep 2012 02:58:13 GMT"
}
] | 1,346,889,600,000 | [
[
"Saboori",
"Ehsan",
""
],
[
"Parsazad",
"Shafigh",
""
],
[
"Sanatkhani",
"Yasaman",
""
]
] |
1209.0880 | Christian Blum | Christian Blum and Verena Schmid and Lukas Baumgartner | On Solving the Oriented Two-Dimensional Bin Packing Problem under Free
Guillotine Cutting: Exploiting the Power of Probabilistic Solution
Construction | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Two-dimensional bin packing problems are highly relevant combinatorial
optimization problems. They find a large number of applications, for example,
in the context of transportation or warehousing, and for the cutting of
different materials such as glass, wood or metal. In this work we deal with the
oriented two-dimensional bin packing problem under free guillotine cutting. In
this specific problem a set of oriented rectangular items is given which must
be packed into a minimum number of bins of equal size. The first algorithm
proposed in this work is a randomized multi-start version of a constructive
one-pass heuristic from the literature. Additionally we propose the use of this
randomized one-pass heuristic within an evolutionary algorithm. The results of
the two proposed algorithms are compared to the best approaches from the
literature. In particular the evolutionary algorithm compares very favorably to
current state-of-the-art approaches. The optimal solution for 4 previously
unsolved instances could be found.
| [
{
"version": "v1",
"created": "Wed, 5 Sep 2012 07:38:58 GMT"
}
] | 1,346,889,600,000 | [
[
"Blum",
"Christian",
""
],
[
"Schmid",
"Verena",
""
],
[
"Baumgartner",
"Lukas",
""
]
] |
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