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1212.5776 | Mohammed El-Dosuky | M. A. El-Dosuky, M. Z. Rashad, T. T. Hamza and A.H. EL-Bassiouny | Improving problem solving by exploiting the concept of symmetry | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We investigate the concept of symmetry and its role in problem solving. This
paper first defines precisely the elements that constitute a "problem" and its
"solution," and gives several examples to illustrate these definitions. Given
precise definitions of problems, it is relatively straightforward to construct
a search process for finding solutions. Finally this paper attempts to exploit
the concept of symmetry in improving problem solving.
| [
{
"version": "v1",
"created": "Sun, 23 Dec 2012 09:18:59 GMT"
},
{
"version": "v2",
"created": "Mon, 4 Mar 2013 23:26:24 GMT"
}
] | 1,362,528,000,000 | [
[
"El-Dosuky",
"M. A.",
""
],
[
"Rashad",
"M. Z.",
""
],
[
"Hamza",
"T. T.",
""
],
[
"EL-Bassiouny",
"A. H.",
""
]
] |
1212.6207 | Kirill Sorudeykin Mr | Kirill A. Sorudeykin | Irrespective Priority-Based Regular Properties of High-Intensity Virtual
Environments | 4 pages, 2 figures; ISBN: 978-1-4673-2984-2 | 20th Telecommunications Forum TELFOR 2012, 2012, pp. 510-513 | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We have a lot of relation to the encoding and the Theory of Information, when
considering thinking. This is a natural process and, at once, the complex thing
we investigate. This always was a challenge - to understand how our mind works,
and we are trying to find some universal models for this. A lot of ways have
been considered so far, but we are looking for Something, we seek for
approaches. And the goal is to find a consistent, noncontradictory view, which
should at once be enough flexible in any dimensions to allow to represent
various kinds of processes and environments, matters of different nature and
diverse objects. Developing of such a model is the destination of this article.
| [
{
"version": "v1",
"created": "Fri, 21 Dec 2012 08:43:22 GMT"
}
] | 1,356,566,400,000 | [
[
"Sorudeykin",
"Kirill A.",
""
]
] |
1212.6521 | Jan Koutn\'ik | Jan Koutn\'ik, Juergen Schmidhuber, Faustino Gomez | A Frequency-Domain Encoding for Neuroevolution | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Neuroevolution has yet to scale up to complex reinforcement learning tasks
that require large networks. Networks with many inputs (e.g. raw video) imply a
very high dimensional search space if encoded directly. Indirect methods use a
more compact genotype representation that is transformed into networks of
potentially arbitrary size. In this paper, we present an indirect method where
networks are encoded by a set of Fourier coefficients which are transformed
into network weight matrices via an inverse Fourier-type transform. Because
there often exist network solutions whose weight matrices contain regularity
(i.e. adjacent weights are correlated), the number of coefficients required to
represent these networks in the frequency domain is much smaller than the
number of weights (in the same way that natural images can be compressed by
ignore high-frequency components). This "compressed" encoding is compared to
the direct approach where search is conducted in the weight space on the
high-dimensional octopus arm task. The results show that representing networks
in the frequency domain can reduce the search-space dimensionality by as much
as two orders of magnitude, both accelerating convergence and yielding more
general solutions.
| [
{
"version": "v1",
"created": "Fri, 28 Dec 2012 14:23:02 GMT"
}
] | 1,356,998,400,000 | [
[
"Koutník",
"Jan",
""
],
[
"Schmidhuber",
"Juergen",
""
],
[
"Gomez",
"Faustino",
""
]
] |
1212.6550 | Andre Martins | Andre F. T. Martins, Mario A. T. Figueiredo, Pedro M. Q. Aguiar, Noah
A. Smith, Eric P. Xing | Alternating Directions Dual Decomposition | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We propose AD3, a new algorithm for approximate maximum a posteriori (MAP)
inference on factor graphs based on the alternating directions method of
multipliers. Like dual decomposition algorithms, AD3 uses worker nodes to
iteratively solve local subproblems and a controller node to combine these
local solutions into a global update. The key characteristic of AD3 is that
each local subproblem has a quadratic regularizer, leading to a faster
consensus than subgradient-based dual decomposition, both theoretically and in
practice. We provide closed-form solutions for these AD3 subproblems for binary
pairwise factors and factors imposing first-order logic constraints. For
arbitrary factors (large or combinatorial), we introduce an active set method
which requires only an oracle for computing a local MAP configuration, making
AD3 applicable to a wide range of problems. Experiments on synthetic and
realworld problems show that AD3 compares favorably with the state-of-the-art.
| [
{
"version": "v1",
"created": "Fri, 28 Dec 2012 18:38:57 GMT"
}
] | 1,356,998,400,000 | [
[
"Martins",
"Andre F. T.",
""
],
[
"Figueiredo",
"Mario A. T.",
""
],
[
"Aguiar",
"Pedro M. Q.",
""
],
[
"Smith",
"Noah A.",
""
],
[
"Xing",
"Eric P.",
""
]
] |
1301.0216 | Jan Hrncir | Jan Hrn\v{c}\'i\v{r} and Michael Rovatsos | Applying Strategic Multiagent Planning to Real-World Travel Sharing
Problems | 7th International Workshop on Agents in Traffic and Transportation,
AAMAS, 2012 | null | null | null | cs.AI | http://creativecommons.org/licenses/by/3.0/ | Travel sharing, i.e., the problem of finding parts of routes which can be
shared by several travellers with different points of departure and
destinations, is a complex multiagent problem that requires taking into account
individual agents' preferences to come up with mutually acceptable joint plans.
In this paper, we apply state-of-the-art planning techniques to real-world
public transportation data to evaluate the feasibility of multiagent planning
techniques in this domain. The potential application value of improving travel
sharing technology has great application value due to its ability to reduce the
environmental impact of travelling while providing benefits to travellers at
the same time. We propose a three-phase algorithm that utilises performant
single-agent planners to find individual plans in a simplified domain first,
then merges them using a best-response planner which ensures resulting
solutions are individually rational, and then maps the resulting plan onto the
full temporal planning domain to schedule actual journeys. The evaluation of
our algorithm on real-world, multi-modal public transportation data for the
United Kingdom shows linear scalability both in the scenario size and in the
number of agents, where trade-offs have to be made between total cost
improvement, the percentage of feasible timetables identified for journeys, and
the prolongation of these journeys. Our system constitutes the first
implementation of strategic multiagent planning algorithms in large-scale
domains and provides insights into the engineering process of translating
general domain-independent multiagent planning algorithms to real-world
applications.
| [
{
"version": "v1",
"created": "Wed, 2 Jan 2013 12:06:59 GMT"
}
] | 1,357,171,200,000 | [
[
"Hrnčíř",
"Jan",
""
],
[
"Rovatsos",
"Michael",
""
]
] |
1301.0552 | Ionut Aron | Ionut Aron, Pascal Van Hentenryck | A constraint satisfaction approach to the robust spanning tree problem
with interval data | Appears in Proceedings of the Eighteenth Conference on Uncertainty in
Artificial Intelligence (UAI2002) | null | null | UAI-P-2002-PG-18-25 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Robust optimization is one of the fundamental approaches to deal with
uncertainty in combinatorial optimization. This paper considers the robust
spanning tree problem with interval data, which arises in a variety of
telecommunication applications. It proposes a constraint satisfaction approach
using a combinatorial lower bound, a pruning component that removes infeasible
and suboptimal edges, as well as a search strategy exploring the most uncertain
edges first. The resulting algorithm is shown to produce very dramatic
improvements over the mathematical programming approach of Yaman et al. and to
enlarge considerably the class of problems amenable to effective solutions
| [
{
"version": "v1",
"created": "Wed, 12 Dec 2012 15:55:09 GMT"
}
] | 1,357,516,800,000 | [
[
"Aron",
"Ionut",
""
],
[
"Van Hentenryck",
"Pascal",
""
]
] |
1301.0553 | Vincent Auvray | Vincent Auvray, Louis Wehenkel | On the Construction of the Inclusion Boundary Neighbourhood for Markov
Equivalence Classes of Bayesian Network Structures | Appears in Proceedings of the Eighteenth Conference on Uncertainty in
Artificial Intelligence (UAI2002) | null | null | UAI-P-2002-PG-26-35 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The problem of learning Markov equivalence classes of Bayesian network
structures may be solved by searching for the maximum of a scoring metric in a
space of these classes. This paper deals with the definition and analysis of
one such search space. We use a theoretically motivated neighbourhood, the
inclusion boundary, and represent equivalence classes by essential graphs. We
show that this search space is connected and that the score of the neighbours
can be evaluated incrementally. We devise a practical way of building this
neighbourhood for an essential graph that is purely graphical and does not
explicitely refer to the underlying independences. We find that its size can be
intractable, depending on the complexity of the essential graph of the
equivalence class. The emphasis is put on the potential use of this space with
greedy hill -climbing search
| [
{
"version": "v1",
"created": "Wed, 12 Dec 2012 15:55:13 GMT"
}
] | 1,357,516,800,000 | [
[
"Auvray",
"Vincent",
""
],
[
"Wehenkel",
"Louis",
""
]
] |
1301.0555 | Salem Benferhat | Salem Benferhat, Didier Dubois, Souhila Kaci, Henri Prade | Bipolar Possibilistic Representations | Appears in Proceedings of the Eighteenth Conference on Uncertainty in
Artificial Intelligence (UAI2002) | null | null | UAI-P-2002-PG-45-52 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Recently, it has been emphasized that the possibility theory framework allows
us to distinguish between i) what is possible because it is not ruled out by
the available knowledge, and ii) what is possible for sure. This distinction
may be useful when representing knowledge, for modelling values which are not
impossible because they are consistent with the available knowledge on the one
hand, and values guaranteed to be possible because reported from observations
on the other hand. It is also of interest when expressing preferences, to point
out values which are positively desired among those which are not rejected.
This distinction can be encoded by two types of constraints expressed in terms
of necessity measures and in terms of guaranteed possibility functions, which
induce a pair of possibility distributions at the semantic level. A consistency
condition should ensure that what is claimed to be guaranteed as possible is
indeed not impossible. The present paper investigates the representation of
this bipolar view, including the case when it is stated by means of conditional
measures, or by means of comparative context-dependent constraints. The
interest of this bipolar framework, which has been recently stressed for
expressing preferences, is also pointed out in the representation of diagnostic
knowledge.
| [
{
"version": "v1",
"created": "Wed, 12 Dec 2012 15:55:21 GMT"
}
] | 1,357,516,800,000 | [
[
"Benferhat",
"Salem",
""
],
[
"Dubois",
"Didier",
""
],
[
"Kaci",
"Souhila",
""
],
[
"Prade",
"Henri",
""
]
] |
1301.0557 | Blai Bonet | Blai Bonet, Judea Pearl | Qualitative MDPs and POMDPs: An Order-Of-Magnitude Approximation | Appears in Proceedings of the Eighteenth Conference on Uncertainty in
Artificial Intelligence (UAI2002) | null | null | UAI-P-2002-PG-61-68 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We develop a qualitative theory of Markov Decision Processes (MDPs) and
Partially Observable MDPs that can be used to model sequential decision making
tasks when only qualitative information is available. Our approach is based
upon an order-of-magnitude approximation of both probabilities and utilities,
similar to epsilon-semantics. The result is a qualitative theory that has close
ties with the standard maximum-expected-utility theory and is amenable to
general planning techniques.
| [
{
"version": "v1",
"created": "Wed, 12 Dec 2012 15:55:30 GMT"
}
] | 1,357,516,800,000 | [
[
"Bonet",
"Blai",
""
],
[
"Pearl",
"Judea",
""
]
] |
1301.0558 | Ronen I. Brafman | Ronen I. Brafman, Carmel Domshlak | Introducing Variable Importance Tradeoffs into CP-Nets | Appears in Proceedings of the Eighteenth Conference on Uncertainty in
Artificial Intelligence (UAI2002) | null | null | UAI-P-2002-PG-69-76 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The ability to make decisions and to assess potential courses of action is a
corner-stone of many AI applications, and usually this requires explicit
information about the decision-maker s preferences. IN many applications,
preference elicitation IS a serious bottleneck.The USER either does NOT have
the time, the knowledge, OR the expert support required TO specify complex
multi - attribute utility functions. IN such cases, a method that IS based ON
intuitive, yet expressive, preference statements IS required. IN this paper we
suggest the USE OF TCP - nets, an enhancement OF CP - nets, AS a tool FOR
representing, AND reasoning about qualitative preference statements.We present
AND motivate this framework, define its semantics, AND show how it can be used
TO perform constrained optimization.
| [
{
"version": "v1",
"created": "Wed, 12 Dec 2012 15:55:33 GMT"
}
] | 1,357,516,800,000 | [
[
"Brafman",
"Ronen I.",
""
],
[
"Domshlak",
"Carmel",
""
]
] |
1301.0559 | John Bresina | John Bresina, Richard Dearden, Nicolas Meuleau, Sailesh Ramkrishnan,
David Smith, Richard Washington | Planning under Continuous Time and Resource Uncertainty: A Challenge for
AI | Appears in Proceedings of the Eighteenth Conference on Uncertainty in
Artificial Intelligence (UAI2002) | null | null | UAI-P-2002-PG-77-84 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We outline a class of problems, typical of Mars rover operations, that are
problematic for current methods of planning under uncertainty. The existing
methods fail because they suffer from one or more of the following limitations:
1) they rely on very simple models of actions and time, 2) they assume that
uncertainty is manifested in discrete action outcomes, 3) they are only
practical for very small problems. For many real world problems, these
assumptions fail to hold. In particular, when planning the activities for a
Mars rover, none of the above assumptions is valid: 1) actions can be
concurrent and have differing durations, 2) there is uncertainty concerning
action durations and consumption of continuous resources like power, and 3)
typical daily plans involve on the order of a hundred actions. This class of
problems may be of particular interest to the UAI community because both
classical and decision-theoretic planning techniques may be useful in solving
it. We describe the rover problem, discuss previous work on planning under
uncertainty, and present a detailed, but very small, example illustrating some
of the difficulties of finding good plans.
| [
{
"version": "v1",
"created": "Wed, 12 Dec 2012 15:55:37 GMT"
}
] | 1,357,516,800,000 | [
[
"Bresina",
"John",
""
],
[
"Dearden",
"Richard",
""
],
[
"Meuleau",
"Nicolas",
""
],
[
"Ramkrishnan",
"Sailesh",
""
],
[
"Smith",
"David",
""
],
[
"Washington",
"Richard",
""
]
] |
1301.0560 | Carlos Brito | Carlos Brito, Judea Pearl | Generalized Instrumental Variables | Appears in Proceedings of the Eighteenth Conference on Uncertainty in
Artificial Intelligence (UAI2002) | null | null | UAI-P-2002-PG-85-93 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper concerns the assessment of direct causal effects from a
combination of: (i) non-experimental data, and (ii) qualitative domain
knowledge. Domain knowledge is encoded in the form of a directed acyclic graph
(DAG), in which all interactions are assumed linear, and some variables are
presumed to be unobserved. We provide a generalization of the well-known method
of Instrumental Variables, which allows its application to models with few
conditional independeces.
| [
{
"version": "v1",
"created": "Wed, 12 Dec 2012 15:55:41 GMT"
}
] | 1,357,516,800,000 | [
[
"Brito",
"Carlos",
""
],
[
"Pearl",
"Judea",
""
]
] |
1301.0561 | David Maxwell Chickering | David Maxwell Chickering, Christopher Meek | Finding Optimal Bayesian Networks | Appears in Proceedings of the Eighteenth Conference on Uncertainty in
Artificial Intelligence (UAI2002) | null | null | UAI-P-2002-PG-94-102 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this paper, we derive optimality results for greedy Bayesian-network
search algorithms that perform single-edge modifications at each step and use
asymptotically consistent scoring criteria. Our results extend those of Meek
(1997) and Chickering (2002), who demonstrate that in the limit of large
datasets, if the generative distribution is perfect with respect to a DAG
defined over the observable variables, such search algorithms will identify
this optimal (i.e. generative) DAG model. We relax their assumption about the
generative distribution, and assume only that this distribution satisfies the
{em composition property} over the observable variables, which is a more
realistic assumption for real domains. Under this assumption, we guarantee that
the search algorithms identify an {em inclusion-optimal} model; that is, a
model that (1) contains the generative distribution and (2) has no sub-model
that contains this distribution. In addition, we show that the composition
property is guaranteed to hold whenever the dependence relationships in the
generative distribution can be characterized by paths between singleton
elements in some generative graphical model (e.g. a DAG, a chain graph, or a
Markov network) even when the generative model includes unobserved variables,
and even when the observed data is subject to selection bias.
| [
{
"version": "v1",
"created": "Wed, 12 Dec 2012 15:55:46 GMT"
}
] | 1,357,516,800,000 | [
[
"Chickering",
"David Maxwell",
""
],
[
"Meek",
"Christopher",
""
]
] |
1301.0564 | Rina Dechter | Rina Dechter, Kalev Kask, Robert Mateescu | Iterative Join-Graph Propagation | Appears in Proceedings of the Eighteenth Conference on Uncertainty in
Artificial Intelligence (UAI2002) | null | null | UAI-P-2002-PG-128-136 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The paper presents an iterative version of join-tree clustering that applies
the message passing of join-tree clustering algorithm to join-graphs rather
than to join-trees, iteratively. It is inspired by the success of Pearl's
belief propagation algorithm as an iterative approximation scheme on one hand,
and by a recently introduced mini-clustering i. success as an anytime
approximation method, on the other. The proposed Iterative Join-graph
Propagation IJGP belongs to the class of generalized belief propagation
methods, recently proposed using analogy with algorithms in statistical
physics. Empirical evaluation of this approach on a number of problem classes
demonstrates that even the most time-efficient variant is almost always
superior to IBP and MC i, and is sometimes more accurate by as much as several
orders of magnitude.
| [
{
"version": "v1",
"created": "Wed, 12 Dec 2012 15:55:58 GMT"
}
] | 1,357,516,800,000 | [
[
"Dechter",
"Rina",
""
],
[
"Kask",
"Kalev",
""
],
[
"Mateescu",
"Robert",
""
]
] |
1301.0566 | Thomas Eiter | Thomas Eiter, Thomas Lukasiewicz | Causes and Explanations in the Structural-Model Approach: Tractable
Cases | Appears in Proceedings of the Eighteenth Conference on Uncertainty in
Artificial Intelligence (UAI2002) | null | null | UAI-P-2002-PG-146-153 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this paper, we continue our research on the algorithmic aspects of Halpern
and Pearl's causes and explanations in the structural-model approach. To this
end, we present new characterizations of weak causes for certain classes of
causal models, which show that under suitable restrictions deciding causes and
explanations is tractable. To our knowledge, these are the first explicit
tractability results for the structural-model approach.
| [
{
"version": "v1",
"created": "Wed, 12 Dec 2012 15:56:06 GMT"
}
] | 1,357,516,800,000 | [
[
"Eiter",
"Thomas",
""
],
[
"Lukasiewicz",
"Thomas",
""
]
] |
1301.0568 | Dan Geiger | Dan Geiger, Christopher Meek, Bernd Sturmfels | Factorization of Discrete Probability Distributions | Appears in Proceedings of the Eighteenth Conference on Uncertainty in
Artificial Intelligence (UAI2002) | null | null | UAI-P-2002-PG-162-169 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We formulate necessary and sufficient conditions for an arbitrary discrete
probability distribution to factor according to an undirected graphical model,
or a log-linear model, or other more general exponential models. This result
generalizes the well known Hammersley-Clifford Theorem.
| [
{
"version": "v1",
"created": "Wed, 12 Dec 2012 15:56:14 GMT"
}
] | 1,357,516,800,000 | [
[
"Geiger",
"Dan",
""
],
[
"Meek",
"Christopher",
""
],
[
"Sturmfels",
"Bernd",
""
]
] |
1301.0569 | Phan H. Giang | Phan H. Giang, Prakash P. Shenoy | Statistical Decisions Using Likelihood Information Without Prior
Probabilities | Appears in Proceedings of the Eighteenth Conference on Uncertainty in
Artificial Intelligence (UAI2002) | null | null | UAI-P-2002-PG-170-178 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper presents a decision-theoretic approach to statistical inference
that satisfies the likelihood principle (LP) without using prior information.
Unlike the Bayesian approach, which also satisfies LP, we do not assume
knowledge of the prior distribution of the unknown parameter. With respect to
information that can be obtained from an experiment, our solution is more
efficient than Wald's minimax solution.However, with respect to information
assumed to be known before the experiment, our solution demands less input than
the Bayesian solution.
| [
{
"version": "v1",
"created": "Wed, 12 Dec 2012 15:56:18 GMT"
}
] | 1,357,516,800,000 | [
[
"Giang",
"Phan H.",
""
],
[
"Shenoy",
"Prakash P.",
""
]
] |
1301.0571 | Carlos E. Guestrin | Carlos E. Guestrin, Geoffrey Gordon | Distributed Planning in Hierarchical Factored MDPs | Appears in Proceedings of the Eighteenth Conference on Uncertainty in
Artificial Intelligence (UAI2002) | null | null | UAI-P-2002-PG-197-206 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We present a principled and efficient planning algorithm for collaborative
multiagent dynamical systems. All computation, during both the planning and the
execution phases, is distributed among the agents; each agent only needs to
model and plan for a small part of the system. Each of these local subsystems
is small, but once they are combined they can represent an exponentially larger
problem. The subsystems are connected through a subsystem hierarchy.
Coordination and communication between the agents is not imposed, but derived
directly from the structure of this hierarchy. A globally consistent plan is
achieved by a message passing algorithm, where messages correspond to natural
local reward functions and are computed by local linear programs; another
message passing algorithm allows us to execute the resulting policy. When two
portions of the hierarchy share the same structure, our algorithm can reuse
plans and messages to speed up computation.
| [
{
"version": "v1",
"created": "Wed, 12 Dec 2012 15:56:27 GMT"
}
] | 1,357,516,800,000 | [
[
"Guestrin",
"Carlos E.",
""
],
[
"Gordon",
"Geoffrey",
""
]
] |
1301.0572 | Tom Heskes | Tom Heskes, Onno Zoeter | Expectation Propogation for approximate inference in dynamic Bayesian
networks | Appears in Proceedings of the Eighteenth Conference on Uncertainty in
Artificial Intelligence (UAI2002) | null | null | UAI-P-2002-PG-216-223 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We describe expectation propagation for approximate inference in dynamic
Bayesian networks as a natural extension of Pearl s exact belief
propagation.Expectation propagation IS a greedy algorithm, converges IN many
practical cases, but NOT always.We derive a DOUBLE - loop algorithm, guaranteed
TO converge TO a local minimum OF a Bethe free energy.Furthermore, we show that
stable fixed points OF (damped) expectation propagation correspond TO local
minima OF this free energy, but that the converse need NOT be the CASE .We
illustrate the algorithms BY applying them TO switching linear dynamical
systems AND discuss implications FOR approximate inference IN general Bayesian
networks.
| [
{
"version": "v1",
"created": "Wed, 12 Dec 2012 15:56:30 GMT"
}
] | 1,357,516,800,000 | [
[
"Heskes",
"Tom",
""
],
[
"Zoeter",
"Onno",
""
]
] |
1301.0574 | Finn Verner Jensen | Finn Verner Jensen, Marta Vomlelova | Unconstrained Influence Diagrams | Appears in Proceedings of the Eighteenth Conference on Uncertainty in
Artificial Intelligence (UAI2002) | null | null | UAI-P-2002-PG-234-241 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We extend the language of influence diagrams to cope with decision scenarios
where the order of decisions and observations is not determined. As the
ordering of decisions is dependent on the evidence, a step-strategy of such a
scenario is a sequence of dependent choices of the next action. A strategy is a
step-strategy together with selection functions for decision actions. The
structure of a step-strategy can be represented as a DAG with nodes labeled
with action variables. We introduce the concept of GS-DAG: a DAG incorporating
an optimal step-strategy for any instantiation. We give a method for
constructing GS-DAGs, and we show how to use a GS-DAG for determining an
optimal strategy. Finally we discuss how analysis of relevant past can be used
to reduce the size of the GS-DAG.
| [
{
"version": "v1",
"created": "Wed, 12 Dec 2012 15:56:38 GMT"
}
] | 1,357,516,800,000 | [
[
"Jensen",
"Finn Verner",
""
],
[
"Vomlelova",
"Marta",
""
]
] |
1301.0576 | Mehmet Kayaalp | Mehmet Kayaalp, Gregory F. Cooper | A Bayesian Network Scoring Metric That Is Based On Globally Uniform
Parameter Priors | Appears in Proceedings of the Eighteenth Conference on Uncertainty in
Artificial Intelligence (UAI2002) | null | null | UAI-P-2002-PG-251-258 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We introduce a new Bayesian network (BN) scoring metric called the Global
Uniform (GU) metric. This metric is based on a particular type of default
parameter prior. Such priors may be useful when a BN developer is not willing
or able to specify domain-specific parameter priors. The GU parameter prior
specifies that every prior joint probability distribution P consistent with a
BN structure S is considered to be equally likely. Distribution P is consistent
with S if P includes just the set of independence relations defined by S. We
show that the GU metric addresses some undesirable behavior of the BDeu and K2
Bayesian network scoring metrics, which also use particular forms of default
parameter priors. A closed form formula for computing GU for special classes of
BNs is derived. Efficiently computing GU for an arbitrary BN remains an open
problem.
| [
{
"version": "v1",
"created": "Wed, 12 Dec 2012 15:56:46 GMT"
}
] | 1,357,516,800,000 | [
[
"Kayaalp",
"Mehmet",
""
],
[
"Cooper",
"Gregory F.",
""
]
] |
1301.0580 | Michail Lagoudakis | Michail Lagoudakis, Ron Parr | Value Function Approximation in Zero-Sum Markov Games | Appears in Proceedings of the Eighteenth Conference on Uncertainty in
Artificial Intelligence (UAI2002) | null | null | UAI-P-2002-PG-283-292 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper investigates value function approximation in the context of
zero-sum Markov games, which can be viewed as a generalization of the Markov
decision process (MDP) framework to the two-agent case. We generalize error
bounds from MDPs to Markov games and describe generalizations of reinforcement
learning algorithms to Markov games. We present a generalization of the optimal
stopping problem to a two-player simultaneous move Markov game. For this
special problem, we provide stronger bounds and can guarantee convergence for
LSTD and temporal difference learning with linear value function approximation.
We demonstrate the viability of value function approximation for Markov games
by using the Least squares policy iteration (LSPI) algorithm to learn good
policies for a soccer domain and a flow control problem.
| [
{
"version": "v1",
"created": "Wed, 12 Dec 2012 15:57:02 GMT"
}
] | 1,357,516,800,000 | [
[
"Lagoudakis",
"Michail",
""
],
[
"Parr",
"Ron",
""
]
] |
1301.0582 | Uri Lerner | Uri Lerner, Brooks Moses, Maricia Scott, Sheila McIlraith, Daphne
Koller | Monitoring a Complez Physical System using a Hybrid Dynamic Bayes Net | Appears in Proceedings of the Eighteenth Conference on Uncertainty in
Artificial Intelligence (UAI2002) | null | null | UAI-P-2002-PG-301-310 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The Reverse Water Gas Shift system (RWGS) is a complex physical system
designed to produce oxygen from the carbon dioxide atmosphere on Mars. If sent
to Mars, it would operate without human supervision, thus requiring a reliable
automated system for monitoring and control. The RWGS presents many challenges
typical of real-world systems, including: noisy and biased sensors, nonlinear
behavior, effects that are manifested over different time granularities, and
unobservability of many important quantities. In this paper we model the RWGS
using a hybrid (discrete/continuous) Dynamic Bayesian Network (DBN), where the
state at each time slice contains 33 discrete and 184 continuous variables. We
show how the system state can be tracked using probabilistic inference over the
model. We discuss how to deal with the various challenges presented by the
RWGS, providing a suite of techniques that are likely to be useful in a wide
range of applications. In particular, we describe a general framework for
dealing with nonlinear behavior using numerical integration techniques,
extending the successful Unscented Filter. We also show how to use a
fixed-point computation to deal with effects that develop at different time
scales, specifically rapid changes occurring during slowly changing processes.
We test our model using real data collected from the RWGS, demonstrating the
feasibility of hybrid DBNs for monitoring complex real-world physical systems.
| [
{
"version": "v1",
"created": "Wed, 12 Dec 2012 15:57:11 GMT"
}
] | 1,357,516,800,000 | [
[
"Lerner",
"Uri",
""
],
[
"Moses",
"Brooks",
""
],
[
"Scott",
"Maricia",
""
],
[
"McIlraith",
"Sheila",
""
],
[
"Koller",
"Daphne",
""
]
] |
1301.0585 | Peter McBurney | Peter McBurney, Simon Parsons | Formalizing Scenario Analysis | Appears in Proceedings of the Eighteenth Conference on Uncertainty in
Artificial Intelligence (UAI2002) | null | null | UAI-P-2002-PG-327-334 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We propose a formal treatment of scenarios in the context of a dialectical
argumentation formalism for qualitative reasoning about uncertain propositions.
Our formalism extends prior work in which arguments for and against uncertain
propositions were presented and compared in interaction spaces called Agoras.
We now define the notion of a scenario in this framework and use it to define a
set of qualitative uncertainty labels for propositions across a collection of
scenarios. This work is intended to lead to a formal theory of scenarios and
scenario analysis.
| [
{
"version": "v1",
"created": "Wed, 12 Dec 2012 15:57:23 GMT"
}
] | 1,357,516,800,000 | [
[
"McBurney",
"Peter",
""
],
[
"Parsons",
"Simon",
""
]
] |
1301.0589 | Andrew Moore | Andrew Moore, Jeff Schneider | Real-valued All-Dimensions search: Low-overhead rapid searching over
subsets of attributes | Appears in Proceedings of the Eighteenth Conference on Uncertainty in
Artificial Intelligence (UAI2002) | null | null | UAI-P-2002-PG-360-369 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper is about searching the combinatorial space of contingency tables
during the inner loop of a nonlinear statistical optimization. Examples of this
operation in various data analytic communities include searching for nonlinear
combinations of attributes that contribute significantly to a regression
(Statistics), searching for items to include in a decision list (machine
learning) and association rule hunting (Data Mining).
This paper investigates a new, efficient approach to this class of problems,
called RADSEARCH (Real-valued All-Dimensions-tree Search). RADSEARCH finds the
global optimum, and this gives us the opportunity to empirically evaluate the
question: apart from algorithmic elegance what does this attention to
optimality buy us?
We compare RADSEARCH with other recent successful search algorithms such as
CN2, PRIM, APriori, OPUS and DenseMiner. Finally, we introduce RADREG, a new
regression algorithm for learning real-valued outputs based on RADSEARCHing for
high-order interactions.
| [
{
"version": "v1",
"created": "Wed, 12 Dec 2012 15:57:39 GMT"
}
] | 1,357,516,800,000 | [
[
"Moore",
"Andrew",
""
],
[
"Schneider",
"Jeff",
""
]
] |
1301.0590 | Brenda Ng | Brenda Ng, Leonid Peshkin, Avi Pfeffer | Factored Particles for Scalable Monitoring | Appears in Proceedings of the Eighteenth Conference on Uncertainty in
Artificial Intelligence (UAI2002) | null | null | UAI-P-2002-PG-370-377 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Exact monitoring in dynamic Bayesian networks is intractable, so approximate
algorithms are necessary. This paper presents a new family of approximate
monitoring algorithms that combine the best qualities of the particle filtering
and Boyen-Koller methods. Our algorithms maintain an approximate representation
the belief state in the form of sets of factored particles, that correspond to
samples of clusters of state variables. Empirical results show that our
algorithms outperform both ordinary particle filtering and the Boyen-Koller
algorithm on large systems.
| [
{
"version": "v1",
"created": "Wed, 12 Dec 2012 15:57:43 GMT"
}
] | 1,357,516,800,000 | [
[
"Ng",
"Brenda",
""
],
[
"Peshkin",
"Leonid",
""
],
[
"Pfeffer",
"Avi",
""
]
] |
1301.0591 | Uri Nodelman | Uri Nodelman, Christian R. Shelton, Daphne Koller | Continuous Time Bayesian Networks | Appears in Proceedings of the Eighteenth Conference on Uncertainty in
Artificial Intelligence (UAI2002) | null | null | UAI-P-2002-PG-378-387 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this paper we present a language for finite state continuous time Bayesian
networks (CTBNs), which describe structured stochastic processes that evolve
over continuous time. The state of the system is decomposed into a set of local
variables whose values change over time. The dynamics of the system are
described by specifying the behavior of each local variable as a function of
its parents in a directed (possibly cyclic) graph. The model specifies, at any
given point in time, the distribution over two aspects: when a local variable
changes its value and the next value it takes. These distributions are
determined by the variable s CURRENT value AND the CURRENT VALUES OF its
parents IN the graph.More formally, each variable IS modelled AS a finite state
continuous time Markov process whose transition intensities are functions OF
its parents.We present a probabilistic semantics FOR the language IN terms OF
the generative model a CTBN defines OVER sequences OF events.We list types OF
queries one might ask OF a CTBN, discuss the conceptual AND computational
difficulties associated WITH exact inference, AND provide an algorithm FOR
approximate inference which takes advantage OF the structure within the
process.
| [
{
"version": "v1",
"created": "Wed, 12 Dec 2012 15:57:47 GMT"
}
] | 1,357,516,800,000 | [
[
"Nodelman",
"Uri",
""
],
[
"Shelton",
"Christian R.",
""
],
[
"Koller",
"Daphne",
""
]
] |
1301.0592 | James D. Park | James D. Park | MAP Complexity Results and Approximation Methods | Appears in Proceedings of the Eighteenth Conference on Uncertainty in
Artificial Intelligence (UAI2002) | null | null | UAI-P-2002-PG-388-396 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | MAP is the problem of finding a most probable instantiation of a set of
nvariables in a Bayesian network, given some evidence. MAP appears to be a
significantly harder problem than the related problems of computing the
probability of evidence Pr, or MPE a special case of MAP. Because of the
complexity of MAP, and the lack of viable algorithms to approximate it,MAP
computations are generally avoided by practitioners. This paper investigates
the complexity of MAP. We show that MAP is complete for NP. We also provide
negative complexity results for elimination based algorithms. It turns out that
MAP remains hard even when MPE, and Pr are easy. We show that MAP is NPcomplete
when the networks are restricted to polytrees, and even then can not be
effectively approximated. Because there is no approximation algorithm with
guaranteed results, we investigate best effort approximations. We introduce a
generic MAP approximation framework. As one instantiation of it, we implement
local search coupled with belief propagation BP to approximate MAP. We show how
to extract approximate evidence retraction information from belief propagation
which allows us to perform efficient local search. This allows MAP
approximation even on networks that are too complex to even exactly solve the
easier problems of computing Pr or MPE. Experimental results indicate that
using BP and local search provides accurate MAP estimates in many cases.
| [
{
"version": "v1",
"created": "Wed, 12 Dec 2012 15:57:50 GMT"
}
] | 1,357,516,800,000 | [
[
"Park",
"James D.",
""
]
] |
1301.0596 | Silja Renooij | Silja Renooij, Linda C. van der Gaag | From Qualitative to Quantitative Probabilistic Networks | Appears in Proceedings of the Eighteenth Conference on Uncertainty in
Artificial Intelligence (UAI2002) | null | null | UAI-P-2002-PG-422-429 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Quantification is well known to be a major obstacle in the construction of a
probabilistic network, especially when relying on human experts for this
purpose. The construction of a qualitative probabilistic network has been
proposed as an initial step in a network s quantification, since the
qualitative network can be used TO gain preliminary insight IN the projected
networks reasoning behaviour. We extend on this idea and present a new type of
network in which both signs and numbers are specified; we further present an
associated algorithm for probabilistic inference. Building upon these
semi-qualitative networks, a probabilistic network can be quantified and
studied in a stepwise manner. As a result, modelling inadequacies can be
detected and amended at an early stage in the quantification process.
| [
{
"version": "v1",
"created": "Wed, 12 Dec 2012 15:58:05 GMT"
}
] | 1,357,516,800,000 | [
[
"Renooij",
"Silja",
""
],
[
"van der Gaag",
"Linda C.",
""
]
] |
1301.0597 | Jose Carlos Ferreira da Rocha | Jose Carlos Ferreira da Rocha, Fabio Gagliardi Cozman | Inference with Seperately Specified Sets of Probabilities in Credal
Networks | Appears in Proceedings of the Eighteenth Conference on Uncertainty in
Artificial Intelligence (UAI2002) | null | null | UAI-P-2002-PG-430-437 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We present new algorithms for inference in credal networks --- directed
acyclic graphs associated with sets of probabilities. Credal networks are here
interpreted as encoding strong independence relations among variables. We first
present a theory of credal networks based on separately specified sets of
probabilities. We also show that inference with polytrees is NP-hard in this
setting. We then introduce new techniques that reduce the computational effort
demanded by inference, particularly in polytrees, by exploring separability of
credal sets.
| [
{
"version": "v1",
"created": "Wed, 12 Dec 2012 15:58:09 GMT"
}
] | 1,357,516,800,000 | [
[
"da Rocha",
"Jose Carlos Ferreira",
""
],
[
"Cozman",
"Fabio Gagliardi",
""
]
] |
1301.0603 | Masami Takikawa | Masami Takikawa, Bruce D'Ambrosio, Ed Wright | Real-Time Inference with Large-Scale Temporal Bayes Nets | Appears in Proceedings of the Eighteenth Conference on Uncertainty in
Artificial Intelligence (UAI2002) | null | null | UAI-P-2002-PG-477-484 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | An increasing number of applications require real-time reasoning under
uncertainty with streaming input. The temporal (dynamic) Bayes net formalism
provides a powerful representational framework for such applications. However,
existing exact inference algorithms for dynamic Bayes nets do not scale to the
size of models required for real world applications which often contain
hundreds or even thousands of variables for each time slice. In addition,
existing algorithms were not developed with real-time processing in mind. We
have developed a new computational approach to support real-time exact
inference in large temporal Bayes nets. Our approach tackles scalability by
recognizing that the complexity of the inference depends on the number of
interface nodes between time slices and by exploiting the distinction between
static and dynamic nodes in order to reduce the number of interface nodes and
to factorize their joint probability distribution. We approach the real-time
issue by organizing temporal Bayes nets into static representations, and then
using the symbolic probabilistic inference algorithm to derive analytic
expressions for the static representations. The parts of these expressions that
do not change at each time step are pre-computed. The remaining parts are
compiled into efficient procedural code so that the memory and CPU resources
required by the inference are small and fixed.
| [
{
"version": "v1",
"created": "Wed, 12 Dec 2012 15:58:34 GMT"
}
] | 1,357,516,800,000 | [
[
"Takikawa",
"Masami",
""
],
[
"D'Ambrosio",
"Bruce",
""
],
[
"Wright",
"Ed",
""
]
] |
1301.0605 | Sekhar Tatikonda | Sekhar Tatikonda, Michael I. Jordan | Loopy Belief Propogation and Gibbs Measures | Appears in Proceedings of the Eighteenth Conference on Uncertainty in
Artificial Intelligence (UAI2002) | null | null | UAI-P-2002-PG-493-500 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We address the question of convergence in the loopy belief propagation (LBP)
algorithm. Specifically, we relate convergence of LBP to the existence of a
weak limit for a sequence of Gibbs measures defined on the LBP s associated
computation tree.Using tools FROM the theory OF Gibbs measures we develop
easily testable sufficient conditions FOR convergence.The failure OF
convergence OF LBP implies the existence OF multiple phases FOR the associated
Gibbs specification.These results give new insight INTO the mechanics OF the
algorithm.
| [
{
"version": "v1",
"created": "Wed, 12 Dec 2012 15:58:42 GMT"
}
] | 1,357,516,800,000 | [
[
"Tatikonda",
"Sekhar",
""
],
[
"Jordan",
"Michael I.",
""
]
] |
1301.0606 | Sylvie Thiebaux | Sylvie Thiebaux, Froduald Kabanza, John Slanley | Anytime State-Based Solution Methods for Decision Processes with
non-Markovian Rewards | Appears in Proceedings of the Eighteenth Conference on Uncertainty in
Artificial Intelligence (UAI2002) | null | null | UAI-P-2002-PG-501-510 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | A popular approach to solving a decision process with non-Markovian rewards
(NMRDP) is to exploit a compact representation of the reward function to
automatically translate the NMRDP into an equivalent Markov decision process
(MDP) amenable to our favorite MDP solution method. The contribution of this
paper is a representation of non-Markovian reward functions and a translation
into MDP aimed at making the best possible use of state-based anytime
algorithms as the solution method. By explicitly constructing and exploring
only parts of the state space, these algorithms are able to trade computation
time for policy quality, and have proven quite effective in dealing with large
MDPs. Our representation extends future linear temporal logic (FLTL) to express
rewards. Our translation has the effect of embedding model-checking in the
solution method. It results in an MDP of the minimal size achievable without
stepping outside the anytime framework, and consequently in better policies by
the deadline.
| [
{
"version": "v1",
"created": "Wed, 12 Dec 2012 15:58:46 GMT"
}
] | 1,357,516,800,000 | [
[
"Thiebaux",
"Sylvie",
""
],
[
"Kabanza",
"Froduald",
""
],
[
"Slanley",
"John",
""
]
] |
1301.0608 | Jin Tian | Jin Tian, Judea Pearl | On the Testable Implications of Causal Models with Hidden Variables | Appears in Proceedings of the Eighteenth Conference on Uncertainty in
Artificial Intelligence (UAI2002) | null | null | UAI-P-2002-PG-519-527 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The validity OF a causal model can be tested ONLY IF the model imposes
constraints ON the probability distribution that governs the generated data. IN
the presence OF unmeasured variables, causal models may impose two types OF
constraints : conditional independencies, AS READ through the d - separation
criterion, AND functional constraints, FOR which no general criterion IS
available.This paper offers a systematic way OF identifying functional
constraints AND, thus, facilitates the task OF testing causal models AS well AS
inferring such models FROM data.
| [
{
"version": "v1",
"created": "Wed, 12 Dec 2012 15:58:54 GMT"
}
] | 1,357,516,800,000 | [
[
"Tian",
"Jin",
""
],
[
"Pearl",
"Judea",
""
]
] |
1301.0609 | Jirka Vomlel | Jirka Vomlel | Exploiting Functional Dependence in Bayesian Network Inference | Appears in Proceedings of the Eighteenth Conference on Uncertainty in
Artificial Intelligence (UAI2002) | null | null | UAI-P-2002-PG-528-535 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We propose an efficient method for Bayesian network inference in models with
functional dependence. We generalize the multiplicative factorization method
originally designed by Takikawa and D Ambrosio(1999) FOR models WITH
independence OF causal influence.Using a hidden variable, we transform a
probability potential INTO a product OF two - dimensional potentials.The
multiplicative factorization yields more efficient inference. FOR example, IN
junction tree propagation it helps TO avoid large cliques. IN ORDER TO keep
potentials small, the number OF states OF the hidden variable should be
minimized.We transform this problem INTO a combinatorial problem OF minimal
base IN a particular space.We present an example OF a computerized adaptive
test, IN which the factorization method IS significantly more efficient than
previous inference methods.
| [
{
"version": "v1",
"created": "Wed, 12 Dec 2012 15:58:58 GMT"
}
] | 1,357,516,800,000 | [
[
"Vomlel",
"Jirka",
""
]
] |
1301.0611 | Peter P. Wakker | Peter P. Wakker | Decision Principles to justify Carnap's Updating Method and to Suggest
Corrections of Probability Judgments (Invited Talks) | Appears in Proceedings of the Eighteenth Conference on Uncertainty in
Artificial Intelligence (UAI2002) | null | null | UAI-P-2002-PG-544-551 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper uses decision-theoretic principles to obtain new insights into the
assessment and updating of probabilities. First, a new foundation of
Bayesianism is given. It does not require infinite atomless uncertainties as
did Savage s classical result, AND can therefore be applied TO ANY finite
Bayesian network.It neither requires linear utility AS did de Finetti s
classical result, AND r ntherefore allows FOR the empirically AND normatively
desirable risk r naversion.Finally, BY identifying AND fixing utility IN an
elementary r nmanner, our result can readily be applied TO identify methods OF
r nprobability updating.Thus, a decision - theoretic foundation IS given r nto
the computationally efficient method OF inductive reasoning r ndeveloped BY
Rudolf Carnap.Finally, recent empirical findings ON r nprobability assessments
are discussed.It leads TO suggestions FOR r ncorrecting biases IN probability
assessments, AND FOR an alternative r nto the Dempster - Shafer belief
functions that avoids the reduction TO r ndegeneracy after multiple updatings.r
n
| [
{
"version": "v1",
"created": "Wed, 12 Dec 2012 15:59:06 GMT"
}
] | 1,357,516,800,000 | [
[
"Wakker",
"Peter P.",
""
]
] |
1301.0614 | Sung Wook Yoon | Sung Wook Yoon, Alan Fern, Robert Givan | Inductive Policy Selection for First-Order MDPs | Appears in Proceedings of the Eighteenth Conference on Uncertainty in
Artificial Intelligence (UAI2002) | null | null | UAI-P-2002-PG-568-576 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We select policies for large Markov Decision Processes (MDPs) with compact
first-order representations. We find policies that generalize well as the
number of objects in the domain grows, potentially without bound. Existing
dynamic-programming approaches based on flat, propositional, or first-order
representations either are impractical here or do not naturally scale as the
number of objects grows without bound. We implement and evaluate an alternative
approach that induces first-order policies using training data constructed by
solving small problem instances using PGraphplan (Blum & Langford, 1999). Our
policies are represented as ensembles of decision lists, using a taxonomic
concept language. This approach extends the work of Martin and Geffner (2000)
to stochastic domains, ensemble learning, and a wider variety of problems.
Empirically, we find "good" policies for several stochastic first-order MDPs
that are beyond the scope of previous approaches. We also discuss the
application of this work to the relational reinforcement-learning problem.
| [
{
"version": "v1",
"created": "Wed, 12 Dec 2012 15:59:19 GMT"
}
] | 1,357,516,800,000 | [
[
"Yoon",
"Sung Wook",
""
],
[
"Fern",
"Alan",
""
],
[
"Givan",
"Robert",
""
]
] |
1301.1385 | Michael Fink | Mario Alviano and Wolfgang Faber | Translating NP-SPEC into ASP | Proceedings of Answer Set Programming and Other Computing Paradigms
(ASPOCP 2012), 5th International Workshop, September 4, 2012, Budapest,
Hungary | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | NP-SPEC is a language for specifying problems in NP in a declarative way.
Despite the fact that the semantics of the language was given by referring to
Datalog with circumscription, which is very close to ASP, so far the only
existing implementations are by means of ECLiPSe Prolog and via Boolean
satisfiability solvers. In this paper, we present translations from NP-SPEC
into various forms of ASP and analyze them. We also argue that it might be
useful to incorporate certain language constructs of NP-SPEC into mainstream
ASP.
| [
{
"version": "v1",
"created": "Tue, 8 Jan 2013 02:28:49 GMT"
}
] | 1,357,689,600,000 | [
[
"Alviano",
"Mario",
""
],
[
"Faber",
"Wolfgang",
""
]
] |
1301.1387 | Michael Fink | Marcello Balduccini and Michael Gelfond | Language ASP{f} with Arithmetic Expressions and Consistency-Restoring
Rules | Proceedings of Answer Set Programming and Other Computing Paradigms
(ASPOCP 2012), 5th International Workshop, September 4, 2012, Budapest,
Hungary | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this paper we continue the work on our extension of Answer Set Programming
by non-Herbrand functions and add to the language support for arithmetic
expressions and various inequality relations over non-Herbrand functions, as
well as consistency-restoring rules from CR-Prolog. We demonstrate the use of
this latest version of the language in the representation of important kinds of
knowledge.
| [
{
"version": "v1",
"created": "Tue, 8 Jan 2013 02:29:05 GMT"
}
] | 1,357,689,600,000 | [
[
"Balduccini",
"Marcello",
""
],
[
"Gelfond",
"Michael",
""
]
] |
1301.1388 | Michael Fink | G\"unther Charwat, Johannes Peter Wallner, and Stefan Woltran | Utilizing ASP for Generating and Visualizing Argumentation Frameworks | Proceedings of Answer Set Programming and Other Computing Paradigms
(ASPOCP 2012), 5th International Workshop, September 4, 2012, Budapest,
Hungary | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Within the area of computational models of argumentation, the
instantiation-based approach is gaining more and more attention, not at least
because meaningful input for Dung's abstract frameworks is provided in that
way. In a nutshell, the aim of instantiation-based argumentation is to form,
from a given knowledge base, a set of arguments and to identify the conflicts
between them. The resulting network is then evaluated by means of
extension-based semantics on an abstract level, i.e. on the resulting graph.
While several systems are nowadays available for the latter step, the
automation of the instantiation process itself has received less attention. In
this work, we provide a novel approach to construct and visualize an
argumentation framework from a given knowledge base. The system we propose
relies on Answer-Set Programming and follows a two-step approach. A first
program yields the logic-based arguments as its answer-sets; a second program
is then used to specify the relations between arguments based on the
answer-sets of the first program. As it turns out, this approach not only
allows for a flexible and extensible tool for instantiation-based
argumentation, but also provides a new method for answer-set visualization in
general.
| [
{
"version": "v1",
"created": "Tue, 8 Jan 2013 02:29:11 GMT"
}
] | 1,357,689,600,000 | [
[
"Charwat",
"Günther",
""
],
[
"Wallner",
"Johannes Peter",
""
],
[
"Woltran",
"Stefan",
""
]
] |
1301.1389 | Michael Fink | Sandeep Chintabathina | Planning and Scheduling in Hybrid Domains Using Answer Set Programming | Proceedings of Answer Set Programming and Other Computing Paradigms
(ASPOCP 2012), 5th International Workshop, September 4, 2012, Budapest,
Hungary | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this paper we present an Action Language-Answer Set Programming based
approach to solving planning and scheduling problems in hybrid domains -
domains that exhibit both discrete and continuous behavior. We use action
language H to represent the domain and then translate the resulting theory into
an A-Prolog program. In this way, we reduce the problem of finding solutions to
planning and scheduling problems to computing answer sets of A-Prolog programs.
We cite a planning and scheduling example from the literature and show how to
model it in H. We show how to translate the resulting H theory into an
equivalent A-Prolog program. We compute the answer sets of the resulting
program using a hybrid solver called EZCSP which loosely integrates a
constraint solver with an answer set solver. The solver allows us reason about
constraints over reals and compute solutions to complex planning and scheduling
problems. Results have shown that our approach can be applied to any planning
and scheduling problem in hybrid domains.
| [
{
"version": "v1",
"created": "Tue, 8 Jan 2013 02:29:17 GMT"
}
] | 1,357,689,600,000 | [
[
"Chintabathina",
"Sandeep",
""
]
] |
1301.1392 | Michael Fink | Martin Gebser, Torsten Grote, Roland Kaminski, Philipp Obermeier,
Orkunt Sabuncu, and Torsten Schaub | Answer Set Programming for Stream Reasoning | Proceedings of Answer Set Programming and Other Computing Paradigms
(ASPOCP 2012), 5th International Workshop, September 4, 2012, Budapest,
Hungary | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The advance of Internet and Sensor technology has brought about new
challenges evoked by the emergence of continuous data streams. Beyond rapid
data processing, application areas like ambient assisted living, robotics, or
dynamic scheduling involve complex reasoning tasks. We address such scenarios
and elaborate upon approaches to knowledge-intense stream reasoning, based on
Answer Set Programming (ASP). While traditional ASP methods are devised for
singular problem solving, we develop new techniques to formulate and process
problems dealing with emerging as well as expiring data in a seamless way.
| [
{
"version": "v1",
"created": "Tue, 8 Jan 2013 02:29:44 GMT"
}
] | 1,357,689,600,000 | [
[
"Gebser",
"Martin",
""
],
[
"Grote",
"Torsten",
""
],
[
"Kaminski",
"Roland",
""
],
[
"Obermeier",
"Philipp",
""
],
[
"Sabuncu",
"Orkunt",
""
],
[
"Schaub",
"Torsten",
""
]
] |
1301.2005 | Xiaowang Zhang | Xiaowang Zhang and Kewen Wang and Zhe Wang and Yue Ma and Guilin Qi | A Distance-based Paraconsistent Semantics for DL-Lite | 17 pages | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | DL-Lite is an important family of description logics. Recently, there is an
increasing interest in handling inconsistency in DL-Lite as the constraint
imposed by a TBox can be easily violated by assertions in ABox in DL-Lite. In
this paper, we present a distance-based paraconsistent semantics based on the
notion of feature in DL-Lite, which provides a novel way to rationally draw
meaningful conclusions even from an inconsistent knowledge base. Finally, we
investigate several important logical properties of this entailment relation
based on the new semantics and show its promising advantages in non-monotonic
reasoning for DL-Lite.
| [
{
"version": "v1",
"created": "Wed, 9 Jan 2013 23:02:34 GMT"
},
{
"version": "v2",
"created": "Wed, 23 Jan 2013 14:57:25 GMT"
},
{
"version": "v3",
"created": "Wed, 3 Jun 2015 07:03:58 GMT"
}
] | 1,433,376,000,000 | [
[
"Zhang",
"Xiaowang",
""
],
[
"Wang",
"Kewen",
""
],
[
"Wang",
"Zhe",
""
],
[
"Ma",
"Yue",
""
],
[
"Qi",
"Guilin",
""
]
] |
1301.2137 | Xiaowang Zhang | Dai Xu and Xiaowang Zhang and Zuoquan Lin | A Forgetting-based Approach to Merging Knowledge Bases | 5 pages | 2010 International Conference on Progress in Informatics and
Computing, IEEE Computer Society, vol 1, pp. 321-325 | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper presents a novel approach based on variable forgetting, which is a
useful tool in resolving contradictory by filtering some given variables, to
merging multiple knowledge bases. This paper first builds a relationship
between belief merging and variable forgetting by using dilation. Variable
forgetting is applied to capture belief merging operation. Finally, some new
merging operators are developed by modifying candidate variables to amend the
shortage of traditional merging operators. Different from model selection of
traditional merging operators, as an alternative approach, variable selection
in those new operators could provide intuitive information about an atom
variable among whole knowledge bases.
| [
{
"version": "v1",
"created": "Thu, 10 Jan 2013 14:41:52 GMT"
}
] | 1,357,862,400,000 | [
[
"Xu",
"Dai",
""
],
[
"Zhang",
"Xiaowang",
""
],
[
"Lin",
"Zuoquan",
""
]
] |
1301.2146 | Xiaowang Zhang | Xiaowang Zhang and Guohui Xiao and Zuoquan Lin | A Paraconsistent Tableau Algorithm Based on Sign Transformation in
Semantic Web | 11 pages, in Chinese; the 4th Chinese Semantic Web Symposium (CSWS
2010), Beijing, China | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In an open, constantly changing and collaborative environment like the
forthcoming Semantic Web, it is reasonable to expect that knowledge sources
will contain noise and inaccuracies. It is well known, as the logical
foundation of the Semantic Web, description logic is lack of the ability of
tolerating inconsistent or incomplete data. Recently, the ability of
paraconsistent approaches in Semantic Web is weaker in this paper, we present a
tableau algorithm based on sign transformation in Semantic Web which holds the
stronger ability of reasoning. We prove that the tableau algorithm is decidable
which hold the same function of classical tableau algorithm for consistent
knowledge bases.
| [
{
"version": "v1",
"created": "Thu, 10 Jan 2013 15:07:22 GMT"
}
] | 1,357,862,400,000 | [
[
"Zhang",
"Xiaowang",
""
],
[
"Xiao",
"Guohui",
""
],
[
"Lin",
"Zuoquan",
""
]
] |
1301.2215 | Michael Fink | Michael Fink and Yuliya Lierler | Proceedings of Answer Set Programming and Other Computing Paradigms
(ASPOCP 2012), 5th International Workshop, September 4, 2012, Budapest,
Hungary | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This volume contains the papers presented at the fifth workshop on Answer Set
Programming and Other Computing Paradigms (ASPOCP 2012) held on September 4th,
2012 in Budapest, co-located with the 28th International Conference on Logic
Programming (ICLP 2012). It thus continues a series of previous events
co-located with ICLP, aiming at facilitating the discussion about crossing the
boundaries of current ASP techniques in theory, solving, and applications, in
combination with or inspired by other computing paradigms.
| [
{
"version": "v1",
"created": "Thu, 10 Jan 2013 18:52:32 GMT"
}
] | 1,357,862,400,000 | [
[
"Fink",
"Michael",
""
],
[
"Lierler",
"Yuliya",
""
]
] |
1301.2254 | Nicos Angelopoulos | Nicos Angelopoulos, James Cussens | Markov Chain Monte Carlo using Tree-Based Priors on Model Structure | Appears in Proceedings of the Seventeenth Conference on Uncertainty
in Artificial Intelligence (UAI2001) | null | null | UAI-P-2001-PG-16-23 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We present a general framework for defining priors on model structure and
sampling from the posterior using the Metropolis-Hastings algorithm. The key
idea is that structure priors are defined via a probability tree and that the
proposal mechanism for the Metropolis-Hastings algorithm operates by traversing
this tree, thereby defining a cheaply computable acceptance probability. We
have applied this approach to Bayesian net structure learning using a number of
priors and tree traversal strategies. Our results show that these must be
chosen appropriately for this approach to be successful.
| [
{
"version": "v1",
"created": "Thu, 10 Jan 2013 16:22:27 GMT"
}
] | 1,358,121,600,000 | [
[
"Angelopoulos",
"Nicos",
""
],
[
"Cussens",
"James",
""
]
] |
1301.2255 | Salem Benferhat | Salem Benferhat, Didier Dubois, Souhila Kaci, Henri Prade | Graphical readings of possibilistic logic bases | Appears in Proceedings of the Seventeenth Conference on Uncertainty
in Artificial Intelligence (UAI2001) | null | null | UAI-P-2001-PG-24-31 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Possibility theory offers either a qualitive, or a numerical framework for
representing uncertainty, in terms of dual measures of possibility and
necessity. This leads to the existence of two kinds of possibilistic causal
graphs where the conditioning is either based on the minimum, or the product
operator. Benferhat et al. (1999) have investigated the connections between
min-based graphs and possibilistic logic bases (made of classical formulas
weighted in terms of certainty). This paper deals with a more difficult issue :
the product-based graphical representations of possibilistic bases, which
provides an easy structural reading of possibilistic bases. Moreover, this
paper also provides another reading of possibilistic bases in terms of
comparative preferences of the form "in the context p, q is preferred to not
q". This enables us to explicit preferences underlying a set of goals with
different levels of priority.
| [
{
"version": "v1",
"created": "Thu, 10 Jan 2013 16:22:32 GMT"
}
] | 1,358,121,600,000 | [
[
"Benferhat",
"Salem",
""
],
[
"Dubois",
"Didier",
""
],
[
"Kaci",
"Souhila",
""
],
[
"Prade",
"Henri",
""
]
] |
1301.2257 | Blai Bonet | Blai Bonet | A Calculus for Causal Relevance | Appears in Proceedings of the Seventeenth Conference on Uncertainty
in Artificial Intelligence (UAI2001) | null | null | UAI-P-2001-PG-40-47 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper presents a sound and completecalculus for causal relevance, based
onPearl's functional models semantics.The calculus consists of axioms and
rulesof inference for reasoning about causalrelevance relationships.We extend
the set of known axioms for causalrelevance with three new axioms, andintroduce
two new rules of inference forreasoning about specific subclasses
ofmodels.These subclasses give a more refinedcharacterization of causal models
than the one given in Halpern's axiomatizationof counterfactual
reasoning.Finally, we show how the calculus for causalrelevance can be used in
the task ofidentifying causal structure from non-observational data.
| [
{
"version": "v1",
"created": "Thu, 10 Jan 2013 16:22:40 GMT"
}
] | 1,358,121,600,000 | [
[
"Bonet",
"Blai",
""
]
] |
1301.2259 | Craig Boutilier | Craig Boutilier, Fahiem Bacchus, Ronen I. Brafman | UCP-Networks: A Directed Graphical Representation of Conditional
Utilities | Appears in Proceedings of the Seventeenth Conference on Uncertainty
in Artificial Intelligence (UAI2001) | null | null | UAI-P-2001-PG-56-64 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We propose a new directed graphical representation of utility functions,
called UCP-networks, that combines aspects of two existing graphical models:
generalized additive models and CP-networks. The network decomposes a utility
function into a number of additive factors, with the directionality of the arcs
reflecting conditional dependence of preference statements - in the underlying
(qualitative) preference ordering - under a {em ceteris paribus} (all else
being equal) interpretation. This representation is arguably natural in many
settings. Furthermore, the strong CP-semantics ensures that computation of
optimization and dominance queries is very efficient. We also demonstrate the
value of this representation in decision making. Finally, we describe an
interactive elicitation procedure that takes advantage of the linear nature of
the constraints on "`tradeoff weights" imposed by a UCP-network. This procedure
allows the network to be refined until the regret of the decision with minimax
regret (with respect to the incompletely specified utility function) falls
below a specified threshold (e.g., the cost of further questioning.
| [
{
"version": "v1",
"created": "Thu, 10 Jan 2013 16:22:49 GMT"
}
] | 1,358,121,600,000 | [
[
"Boutilier",
"Craig",
""
],
[
"Bacchus",
"Fahiem",
""
],
[
"Brafman",
"Ronen I.",
""
]
] |
1301.2260 | Jian Cheng | Jian Cheng, Marek J. Druzdzel | Confidence Inference in Bayesian Networks | Appears in Proceedings of the Seventeenth Conference on Uncertainty
in Artificial Intelligence (UAI2001) | null | null | UAI-P-2001-PG-75-82 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We present two sampling algorithms for probabilistic confidence inference in
Bayesian networks. These two algorithms (we call them AIS-BN-mu and
AIS-BN-sigma algorithms) guarantee that estimates of posterior probabilities
are with a given probability within a desired precision bound. Our algorithms
are based on recent advances in sampling algorithms for (1) estimating the mean
of bounded random variables and (2) adaptive importance sampling in Bayesian
networks. In addition to a simple stopping rule for sampling that they provide,
the AIS-BN-mu and AIS-BN-sigma algorithms are capable of guiding the learning
process in the AIS-BN algorithm. An empirical evaluation of the proposed
algorithms shows excellent performance, even for very unlikely evidence.
| [
{
"version": "v1",
"created": "Thu, 10 Jan 2013 16:22:53 GMT"
}
] | 1,358,121,600,000 | [
[
"Cheng",
"Jian",
""
],
[
"Druzdzel",
"Marek J.",
""
]
] |
1301.2263 | David Danks | David Danks, Clark Glymour | Linearity Properties of Bayes Nets with Binary Variables | Appears in Proceedings of the Seventeenth Conference on Uncertainty
in Artificial Intelligence (UAI2001) | null | null | UAI-P-2001-PG-98-104 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | It is "well known" that in linear models: (1) testable constraints on the
marginal distribution of observed variables distinguish certain cases in which
an unobserved cause jointly influences several observed variables; (2) the
technique of "instrumental variables" sometimes permits an estimation of the
influence of one variable on another even when the association between the
variables may be confounded by unobserved common causes; (3) the association
(or conditional probability distribution of one variable given another) of two
variables connected by a path or trek can be computed directly from the
parameter values associated with each edge in the path or trek; (4) the
association of two variables produced by multiple treks can be computed from
the parameters associated with each trek; and (5) the independence of two
variables conditional on a third implies the corresponding independence of the
sums of the variables over all units conditional on the sums over all units of
each of the original conditioning variables.These properties are exploited in
search procedures. It is also known that properties (2)-(5) do not hold for all
Bayes nets with binary variables. We show that (1) holds for all Bayes nets
with binary variables and (5) holds for all singly trek-connected Bayes nets of
that kind. We further show that all five properties hold for Bayes nets with
any DAG and binary variables parameterized with noisy-or and noisy-and gates.
| [
{
"version": "v1",
"created": "Thu, 10 Jan 2013 16:23:05 GMT"
}
] | 1,358,121,600,000 | [
[
"Danks",
"David",
""
],
[
"Glymour",
"Clark",
""
]
] |
1301.2265 | Rina Dechter | Rina Dechter, David Ephraim Larkin | Hybrid Processing of Beliefs and Constraints | Appears in Proceedings of the Seventeenth Conference on Uncertainty
in Artificial Intelligence (UAI2001) | null | null | UAI-P-2001-PG-112-119 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper explores algorithms for processing probabilistic and deterministic
information when the former is represented as a belief network and the latter
as a set of boolean clauses. The motivating tasks are 1. evaluating beliefs
networks having a large number of deterministic relationships and2. evaluating
probabilities of complex boolean querie over a belief network. We propose a
parameterized family of variable elimination algorithms that exploit both types
of information, and that allows varying levels of constraint propagation
inferences. The complexity of the scheme is controlled by the induced-width of
the graph {em augmented} by the dependencies introduced by the boolean
constraints. Preliminary empirical evaluation demonstrate the effect of
constraint propagation on probabilistic computation.
| [
{
"version": "v1",
"created": "Thu, 10 Jan 2013 16:23:13 GMT"
}
] | 1,358,121,600,000 | [
[
"Dechter",
"Rina",
""
],
[
"Larkin",
"David Ephraim",
""
]
] |
1301.2271 | Phan H. Giang | Phan H. Giang, Prakash P. Shenoy | A Comparison of Axiomatic Approaches to Qualitative Decision Making
Using Possibility Theory | Appears in Proceedings of the Seventeenth Conference on Uncertainty
in Artificial Intelligence (UAI2001) | null | null | UAI-P-2001-PG-162-170 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this paper we analyze two recent axiomatic approaches proposed by Dubois
et al and by Giang and Shenoy to qualitative decision making where uncertainty
is described by possibility theory. Both axiomtizations are inspired by von
Neumann and Morgenstern's system of axioms for the case of probability theory.
We show that our approach naturally unifies two axiomatic systems that
correspond respectively to pessimistic and optimistic decision criteria
proposed by Dubois et al. The simplifying unification is achieved by (i)
replacing axioms that are supposed to reflect two informational attitudes
(uncertainty aversion and uncertainty attraction) by an axiom that imposes
order on set of standard lotteries and (ii) using a binary utility scale in
which each utility level is represented by a pair of numbers.
| [
{
"version": "v1",
"created": "Thu, 10 Jan 2013 16:23:40 GMT"
}
] | 1,358,121,600,000 | [
[
"Giang",
"Phan H.",
""
],
[
"Shenoy",
"Prakash P.",
""
]
] |
1301.2272 | Steven B. Gillispie | Steven B. Gillispie, Michael D. Perlman | Enumerating Markov Equivalence Classes of Acyclic Digraph Models | Appears in Proceedings of the Seventeenth Conference on Uncertainty
in Artificial Intelligence (UAI2001) | null | null | UAI-P-2001-PG-171-177 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Graphical Markov models determined by acyclic digraphs (ADGs), also called
directed acyclic graphs (DAGs), are widely studied in statistics, computer
science (as Bayesian networks), operations research (as influence diagrams),
and many related fields. Because different ADGs may determine the same Markov
equivalence class, it long has been of interest to determine the efficiency
gained in model specification and search by working directly with Markov
equivalence classes of ADGs rather than with ADGs themselves. A computer
program was written to enumerate the equivalence classes of ADG models as
specified by Pearl & Verma's equivalence criterion. The program counted
equivalence classes for models up to and including 10 vertices. The ratio of
number of classes to ADGs appears to approach an asymptote of about 0.267.
Classes were analyzed according to number of edges and class size. By edges,
the distribution of number of classes approaches a Gaussian shape. By class
size, classes of size 1 are most common, with the proportions for larger sizes
initially decreasing but then following a more irregular pattern. The maximum
number of classes generated by any undirected graph was found to increase
approximately factorially. The program also includes a new variation of orderly
algorithm for generating undirected graphs.
| [
{
"version": "v1",
"created": "Thu, 10 Jan 2013 16:23:44 GMT"
}
] | 1,358,121,600,000 | [
[
"Gillispie",
"Steven B.",
""
],
[
"Perlman",
"Michael D.",
""
]
] |
1301.2274 | Vu A. Ha | Vu A. Ha, Peter Haddawy, John Miyamoto | Similarity Measures on Preference Structures, Part II: Utility Functions | Appears in Proceedings of the Seventeenth Conference on Uncertainty
in Artificial Intelligence (UAI2001) | null | null | UAI-P-2001-PG-186-193 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In previous work cite{Ha98:Towards} we presented a case-based approach to
eliciting and reasoning with preferences. A key issue in this approach is the
definition of similarity between user preferences. We introduced the
probabilistic distance as a measure of similarity on user preferences, and
provided an algorithm to compute the distance between two partially specified
{em value} functions. This is for the case of decision making under {em
certainty}. In this paper we address the more challenging issue of computing
the probabilistic distance in the case of decision making under{em
uncertainty}. We provide an algorithm to compute the probabilistic distance
between two partially specified {em utility} functions. We demonstrate the use
of this algorithm with a medical data set of partially specified patient
preferences,where none of the other existing distancemeasures appear definable.
Using this data set, we also demonstrate that the case-based approach to
preference elicitation isapplicable in domains with uncertainty. Finally, we
provide a comprehensive analytical comparison of the probabilistic distance
with some existing distance measures on preferences.
| [
{
"version": "v1",
"created": "Thu, 10 Jan 2013 16:23:53 GMT"
}
] | 1,358,121,600,000 | [
[
"Ha",
"Vu A.",
""
],
[
"Haddawy",
"Peter",
""
],
[
"Miyamoto",
"John",
""
]
] |
1301.2275 | Joseph Y. Halpern | Joseph Y. Halpern, Judea Pearl | Causes and Explanations: A Structural-Model Approach --- Part 1: Causes | Appears in Proceedings of the Seventeenth Conference on Uncertainty
in Artificial Intelligence (UAI2001), later extended version is
arXiv:cs/0011012 | null | null | UAI-P-2001-PG-194-202 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We propose a new definition of actual causes, using structural equations to
model counterfactuals.We show that the definitions yield a plausible and
elegant account ofcausation that handles well examples which have caused
problems forother definitions and resolves major difficulties in the
traditionalaccount. In a companion paper, we show how the definition of
causality can beused to give an elegant definition of (causal) explanation.
| [
{
"version": "v1",
"created": "Thu, 10 Jan 2013 16:23:57 GMT"
}
] | 1,358,121,600,000 | [
[
"Halpern",
"Joseph Y.",
""
],
[
"Pearl",
"Judea",
""
]
] |
1301.2279 | Eric J. Horvitz | Eric J. Horvitz, Yongshao Ruan, Carla P. Gomes, Henry Kautz, Bart
Selman, David Maxwell Chickering | A Bayesian Approach to Tackling Hard Computational Problems | Appears in Proceedings of the Seventeenth Conference on Uncertainty
in Artificial Intelligence (UAI2001) | null | null | UAI-P-2001-PG-235-244 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We are developing a general framework for using learned Bayesian models for
decision-theoretic control of search and reasoningalgorithms. We illustrate the
approach on the specific task of controlling both general and domain-specific
solvers on a hard class of structured constraint satisfaction problems. A
successful strategyfor reducing the high (and even infinite) variance in
running time typically exhibited by backtracking search algorithms is to cut
off and restart the search if a solution is not found within a certainamount of
time. Previous work on restart strategies have employed fixed cut off values.
We show how to create a dynamic cut off strategy by learning a Bayesian model
that predicts the ultimate length of a trial based on observing the early
behavior of the search algorithm. Furthermore, we describe the general
conditions under which a dynamic restart strategy can outperform the
theoretically optimal fixed strategy.
| [
{
"version": "v1",
"created": "Thu, 10 Jan 2013 16:24:15 GMT"
}
] | 1,358,121,600,000 | [
[
"Horvitz",
"Eric J.",
""
],
[
"Ruan",
"Yongshao",
""
],
[
"Gomes",
"Carla P.",
""
],
[
"Kautz",
"Henry",
""
],
[
"Selman",
"Bart",
""
],
[
"Chickering",
"David Maxwell",
""
]
] |
1301.2282 | Tomas Kocka | Tomas Kocka, Remco R. Bouckaert, Milan Studeny | On characterizing Inclusion of Bayesian Networks | Appears in Proceedings of the Seventeenth Conference on Uncertainty
in Artificial Intelligence (UAI2001) | null | null | UAI-P-2001-PG-261-268 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Every directed acyclic graph (DAG) over a finite non-empty set of variables
(= nodes) N induces an independence model over N, which is a list of
conditional independence statements over N.The inclusion problem is how to
characterize (in graphical terms) whether all independence statements in the
model induced by a DAG K are in the model induced by a second DAG L. Meek
(1997) conjectured that this inclusion holds iff there exists a sequence of
DAGs from L to K such that only certain 'legal' arrow reversal and 'legal'
arrow adding operations are performed to get the next DAG in the sequence.In
this paper we give several characterizations of inclusion of DAG models and
verify Meek's conjecture in the case that the DAGs K and L differ in at most
one adjacency. As a warming up a rigorous proof of well-known graphical
characterizations of equivalence of DAGs, which is a highly related problem, is
given.
| [
{
"version": "v1",
"created": "Thu, 10 Jan 2013 16:24:28 GMT"
}
] | 1,358,121,600,000 | [
[
"Kocka",
"Tomas",
""
],
[
"Bouckaert",
"Remco R.",
""
],
[
"Studeny",
"Milan",
""
]
] |
1301.2285 | Jerome Lang | Jerome Lang, Philippe Muller | Plausible reasoning from spatial observations | Appears in Proceedings of the Seventeenth Conference on Uncertainty
in Artificial Intelligence (UAI2001) | null | null | UAI-P-2001-PG-285-292 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This article deals with plausible reasoning from incomplete knowledge about
large-scale spatial properties. The availableinformation, consisting of a set
of pointwise observations,is extrapolated to neighbour points. We make use of
belief functions to represent the influence of the knowledge at a given point
to another point; the quantitative strength of this influence decreases when
the distance between both points increases. These influences arethen aggregated
using a variant of Dempster's rule of combination which takes into account the
relative dependence between observations.
| [
{
"version": "v1",
"created": "Thu, 10 Jan 2013 16:24:41 GMT"
}
] | 1,358,121,600,000 | [
[
"Lang",
"Jerome",
""
],
[
"Muller",
"Philippe",
""
]
] |
1301.2287 | Kathryn Blackmond Laskey | Kathryn Blackmond Laskey, Suzanne M. Mahoney, Ed Wright | Hypothesis Management in Situation-Specific Network Construction | Appears in Proceedings of the Seventeenth Conference on Uncertainty
in Artificial Intelligence (UAI2001) | null | null | UAI-P-2001-PG-301-309 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper considers the problem of knowledge-based model construction in the
presence of uncertainty about the association of domain entities to random
variables. Multi-entity Bayesian networks (MEBNs) are defined as a
representation for knowledge in domains characterized by uncertainty in the
number of relevant entities, their interrelationships, and their association
with observables. An MEBN implicitly specifies a probability distribution in
terms of a hierarchically structured collection of Bayesian network fragments
that together encode a joint probability distribution over arbitrarily many
interrelated hypotheses. Although a finite query-complete model can always be
constructed, association uncertainty typically makes exact model construction
and evaluation intractable. The objective of hypothesis management is to
balance tractability against accuracy. We describe an application to the
problem of using intelligence reports to infer the organization and activities
of groups of military vehicles. Our approach is compared to related work in the
tracking and fusion literature.
| [
{
"version": "v1",
"created": "Thu, 10 Jan 2013 16:24:50 GMT"
}
] | 1,358,121,600,000 | [
[
"Laskey",
"Kathryn Blackmond",
""
],
[
"Mahoney",
"Suzanne M.",
""
],
[
"Wright",
"Ed",
""
]
] |
1301.2288 | Uri Lerner | Uri Lerner, Ron Parr | Inference in Hybrid Networks: Theoretical Limits and Practical
Algorithms | Appears in Proceedings of the Seventeenth Conference on Uncertainty
in Artificial Intelligence (UAI2001) | null | null | UAI-P-2001-PG-310-318 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | An important subclass of hybrid Bayesian networks are those that represent
Conditional Linear Gaussian (CLG) distributions --- a distribution with a
multivariate Gaussian component for each instantiation of the discrete
variables. In this paper we explore the problem of inference in CLGs. We show
that inference in CLGs can be significantly harder than inference in Bayes
Nets. In particular, we prove that even if the CLG is restricted to an
extremely simple structure of a polytree in which every continuous node has at
most one discrete ancestor, the inference task is NP-hard.To deal with the
often prohibitive computational cost of the exact inference algorithm for CLGs,
we explore several approximate inference algorithms. These algorithms try to
find a small subset of Gaussians which are a good approximation to the full
mixture distribution. We consider two Monte Carlo approaches and a novel
approach that enumerates mixture components in order of prior probability. We
compare these methods on a variety of problems and show that our novel
algorithm is very promising for large, hybrid diagnosis problems.
| [
{
"version": "v1",
"created": "Thu, 10 Jan 2013 16:24:54 GMT"
}
] | 1,358,121,600,000 | [
[
"Lerner",
"Uri",
""
],
[
"Parr",
"Ron",
""
]
] |
1301.2289 | Uri Lerner | Uri Lerner, Eran Segal, Daphne Koller | Exact Inference in Networks with Discrete Children of Continuous Parents | Appears in Proceedings of the Seventeenth Conference on Uncertainty
in Artificial Intelligence (UAI2001) | null | null | UAI-P-2001-PG-319-328 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Many real life domains contain a mixture of discrete and continuous variables
and can be modeled as hybrid Bayesian Networks. Animportant subclass of hybrid
BNs are conditional linear Gaussian (CLG) networks, where the conditional
distribution of the continuous variables given an assignment to the discrete
variables is a multivariate Gaussian. Lauritzen's extension to the clique tree
algorithm can be used for exact inference in CLG networks. However, many
domains also include discrete variables that depend on continuous ones, and CLG
networks do not allow such dependencies to berepresented. No exact inference
algorithm has been proposed for these enhanced CLG networks. In this paper, we
generalize Lauritzen's algorithm, providing the first "exact" inference
algorithm for augmented CLG networks - networks where continuous nodes are
conditional linear Gaussians but that also allow discrete children ofcontinuous
parents. Our algorithm is exact in the sense that it computes the exact
distributions over the discrete nodes, and the exact first and second moments
of the continuous ones, up to the accuracy obtained by numerical integration
used within thealgorithm. When the discrete children are modeled with softmax
CPDs (as is the case in many real world domains) the approximation of the
continuous distributions using the first two moments is particularly accurate.
Our algorithm is simple to implement and often comparable in its complexity to
Lauritzen's algorithm. We show empirically that it achieves substantially
higher accuracy than previous approximate algorithms.
| [
{
"version": "v1",
"created": "Thu, 10 Jan 2013 16:24:58 GMT"
}
] | 1,358,121,600,000 | [
[
"Lerner",
"Uri",
""
],
[
"Segal",
"Eran",
""
],
[
"Koller",
"Daphne",
""
]
] |
1301.2290 | Thomas Lukasiewicz | Thomas Lukasiewicz | Probabilistic Logic Programming under Inheritance with Overriding | Appears in Proceedings of the Seventeenth Conference on Uncertainty
in Artificial Intelligence (UAI2001) | null | null | UAI-P-2001-PG-329-336 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We present probabilistic logic programming under inheritance with overriding.
This approach is based on new notions of entailment for reasoning with
conditional constraints, which are obtained from the classical notion of
logical entailment by adding the principle of inheritance with overriding. This
is done by using recent approaches to probabilistic default reasoning with
conditional constraints. We analyze the semantic properties of the new
entailment relations. We also present algorithms for probabilistic logic
programming under inheritance with overriding, and program transformations for
an increased efficiency.
| [
{
"version": "v1",
"created": "Thu, 10 Jan 2013 16:25:03 GMT"
}
] | 1,358,121,600,000 | [
[
"Lukasiewicz",
"Thomas",
""
]
] |
1301.2291 | Anders L. Madsen | Anders L. Madsen, Dennis Nilsson | Solving Influence Diagrams using HUGIN, Shafer-Shenoy and Lazy
Propagation | Appears in Proceedings of the Seventeenth Conference on Uncertainty
in Artificial Intelligence (UAI2001) | null | null | UAI-P-2001-PG-337-345 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this paper we compare three different architectures for the evaluation of
influence diagrams: HUGIN, Shafer-Shenoy, and Lazy Evaluation architecture. The
computational complexity of the architectures are compared on the LImited
Memory Influence Diagram (LIMID): a diagram where only the requiste information
for the computation of the optimal policies are depicted. Because the requsite
information is explicitly represented in the LIMID the evaluation can take
advantage of it, and significant savings in computational can be obtained. In
this paper we show how the obtained savings is considerably increased when the
computations performed on the LIMID is according to the Lazy Evaluation scheme.
| [
{
"version": "v1",
"created": "Thu, 10 Jan 2013 16:25:07 GMT"
}
] | 1,358,121,600,000 | [
[
"Madsen",
"Anders L.",
""
],
[
"Nilsson",
"Dennis",
""
]
] |
1301.2293 | Pedrito Maynard-Reid II | Pedrito Maynard-Reid II, Urszula Chajewska | Aggregating Learned Probabilistic Beliefs | Appears in Proceedings of the Seventeenth Conference on Uncertainty
in Artificial Intelligence (UAI2001) | null | null | UAI-P-2001-PG-354-361 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We consider the task of aggregating beliefs of severalexperts. We assume that
these beliefs are represented as probabilitydistributions. We argue that the
evaluation of any aggregationtechnique depends on the semantic context of this
task. We propose aframework, in which we assume that nature generates samples
from a`true' distribution and different experts form their beliefs based onthe
subsets of the data they have a chance to observe. Naturally, theideal
aggregate distribution would be the one learned from thecombined sample sets.
Such a formulation leads to a natural way tomeasure the accuracy of the
aggregation mechanism.We show that the well-known aggregation operator LinOP is
ideallysuited for that task. We propose a LinOP-based learning
algorithm,inspired by the techniques developed for Bayesian learning,
whichaggregates the experts' distributions represented as Bayesiannetworks. Our
preliminary experiments show that this algorithmperforms well in practice.
| [
{
"version": "v1",
"created": "Thu, 10 Jan 2013 16:25:16 GMT"
}
] | 1,358,121,600,000 | [
[
"Maynard-Reid",
"Pedrito",
"II"
],
[
"Chajewska",
"Urszula",
""
]
] |
1301.2295 | Quaid Morris | Quaid Morris | Recognition Networks for Approximate Inference in BN20 Networks | Appears in Proceedings of the Seventeenth Conference on Uncertainty
in Artificial Intelligence (UAI2001) | null | null | UAI-P-2001-PG-370-377 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We propose using recognition networks for approximate inference inBayesian
networks (BNs). A recognition network is a multilayerperception (MLP) trained
to predict posterior marginals given observedevidence in a particular BN. The
input to the MLP is a vector of thestates of the evidential nodes. The activity
of an output unit isinterpreted as a prediction of the posterior marginal of
thecorresponding variable. The MLP is trained using samples generated fromthe
corresponding BN.We evaluate a recognition network that was trained to do
inference ina large Bayesian network, similar in structure and complexity to
theQuick Medical Reference, Decision Theoretic (QMR-DT). Our networkis a
binary, two-layer, noisy-OR network containing over 4000 potentially observable
nodes and over 600 unobservable, hidden nodes. Inreal medical diagnosis, most
observables are unavailable, and there isa complex and unknown bias that
selects which ones are provided. Weincorporate a very basic type of selection
bias in our network: a knownpreference that available observables are positive
rather than negative.Even this simple bias has a significant effect on the
posterior. We compare the performance of our recognition network
tostate-of-the-art approximate inference algorithms on a large set oftest
cases. In order to evaluate the effect of our simplistic modelof the selection
bias, we evaluate algorithms using a variety ofincorrectly modeled observation
biases. Recognition networks performwell using both correct and incorrect
observation biases.
| [
{
"version": "v1",
"created": "Thu, 10 Jan 2013 16:25:25 GMT"
}
] | 1,358,121,600,000 | [
[
"Morris",
"Quaid",
""
]
] |
1301.2296 | Kevin Murphy | Kevin Murphy, Yair Weiss | The Factored Frontier Algorithm for Approximate Inference in DBNs | Appears in Proceedings of the Seventeenth Conference on Uncertainty
in Artificial Intelligence (UAI2001) | null | null | UAI-P-2001-PG-378-385 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The Factored Frontier (FF) algorithm is a simple approximate
inferencealgorithm for Dynamic Bayesian Networks (DBNs). It is very similar
tothe fully factorized version of the Boyen-Koller (BK) algorithm, butinstead
of doing an exact update at every step followed bymarginalisation (projection),
it always works with factoreddistributions. Hence it can be applied to models
for which the exactupdate step is intractable. We show that FF is equivalent to
(oneiteration of) loopy belief propagation (LBP) on the original DBN, andthat
BK is equivalent (to one iteration of) LBP on a DBN where wecluster some of the
nodes. We then show empirically that byiterating, LBP can improve on the
accuracy of both FF and BK. Wecompare these algorithms on two real-world DBNs:
the first is a modelof a water treatment plant, and the second is a coupled
HMM, used tomodel freeway traffic.
| [
{
"version": "v1",
"created": "Thu, 10 Jan 2013 16:25:29 GMT"
}
] | 1,358,121,600,000 | [
[
"Murphy",
"Kevin",
""
],
[
"Weiss",
"Yair",
""
]
] |
1301.2297 | Ann Nicholson | Ann Nicholson, Tal Boneh, Tim Wilkin, Kaye Stacey, Liz Sonenberg,
Vicki Steinle | A Case Study in Knowledge Discovery and Elicitation in an Intelligent
Tutoring Application | Appears in Proceedings of the Seventeenth Conference on Uncertainty
in Artificial Intelligence (UAI2001) | null | null | UAI-P-2001-PG-386-394 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Most successful Bayesian network (BN) applications to datehave been built
through knowledge elicitation from experts.This is difficult and time
consuming, which has lead to recentinterest in automated methods for learning
BNs from data. We present a case study in the construction of a BN in
anintelligent tutoring application, specifically decimal misconceptions.
Wedescribe the BN construction using expert elicitation and then investigate
how certainexisting automated knowledge discovery methods might support the BN
knowledge engineering process.
| [
{
"version": "v1",
"created": "Thu, 10 Jan 2013 16:25:34 GMT"
}
] | 1,358,121,600,000 | [
[
"Nicholson",
"Ann",
""
],
[
"Boneh",
"Tal",
""
],
[
"Wilkin",
"Tim",
""
],
[
"Stacey",
"Kaye",
""
],
[
"Sonenberg",
"Liz",
""
],
[
"Steinle",
"Vicki",
""
]
] |
1301.2299 | James D. Park | James D. Park, Adnan Darwiche | Approximating MAP using Local Search | Appears in Proceedings of the Seventeenth Conference on Uncertainty
in Artificial Intelligence (UAI2001) | null | null | UAI-P-2001-PG-403-410 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | MAP is the problem of finding a most probable instantiation of a set of
variables in a Bayesian network, given evidence. Unlike computing marginals,
posteriors, and MPE (a special case of MAP), the time and space complexity of
MAP is not only exponential in the network treewidth, but also in a larger
parameter known as the "constrained" treewidth. In practice, this means that
computing MAP can be orders of magnitude more expensive than
computingposteriors or MPE. Thus, practitioners generally avoid MAP
computations, resorting instead to approximating them by the most likely value
for each MAP variableseparately, or by MPE.We present a method for
approximating MAP using local search. This method has space complexity which is
exponential onlyin the treewidth, as is the complexity of each search step. We
investigate the effectiveness of different local searchmethods and several
initialization strategies and compare them to otherapproximation
schemes.Experimental results show that local search provides a much more
accurate approximation of MAP, while requiring few search steps.Practically,
this means that the complexity of local search is often exponential only in
treewidth as opposed to the constrained treewidth, making approximating MAP as
efficient as other computations.
| [
{
"version": "v1",
"created": "Thu, 10 Jan 2013 16:25:42 GMT"
}
] | 1,358,121,600,000 | [
[
"Park",
"James D.",
""
],
[
"Darwiche",
"Adnan",
""
]
] |
1301.2301 | Avi Pfeffer | Avi Pfeffer | Sufficiency, Separability and Temporal Probabilistic Models | Appears in Proceedings of the Seventeenth Conference on Uncertainty
in Artificial Intelligence (UAI2001) | null | null | UAI-P-2001-PG-421-428 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Suppose we are given the conditional probability of one variable given some
other variables.Normally the full joint distribution over the conditioning
variablesis required to determine the probability of the conditioned
variable.Under what circumstances are the marginal distributions over the
conditioning variables sufficient to determine the probability ofthe
conditioned variable?Sufficiency in this sense is equivalent to additive
separability ofthe conditional probability distribution.Such separability
structure is natural and can be exploited forefficient inference.Separability
has a natural generalization to conditional separability.Separability provides
a precise notion of weaklyinteracting subsystems in temporal probabilistic
models.Given a system that is decomposed into separable subsystems,
exactmarginal probabilities over subsystems at future points in time can
becomputed by propagating marginal subsystem probabilities, rather thancomplete
system joint probabilities.Thus, separability can make exact prediction
tractable.However, observations can break separability,so exact monitoring of
dynamic systems remains hard.
| [
{
"version": "v1",
"created": "Thu, 10 Jan 2013 16:25:51 GMT"
}
] | 1,358,121,600,000 | [
[
"Pfeffer",
"Avi",
""
]
] |
1301.2302 | Daniel Pless | Daniel Pless, George Luger | Toward General Analysis of Recursive Probability Models | Appears in Proceedings of the Seventeenth Conference on Uncertainty
in Artificial Intelligence (UAI2001) | null | null | UAI-P-2001-PG-429-436 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | There is increasing interest within the research community in the design and
use of recursive probability models. Although there still remains concern about
computational complexity costs and the fact that computing exact solutions can
be intractable for many nonrecursive models and impossible in the general case
for recursive problems, several research groups are actively developing
computational techniques for recursive stochastic languages. We have developed
an extension to the traditional lambda-calculus as a framework for families of
Turing complete stochastic languages. We have also developed a class of exact
inference algorithms based on the traditional reductions of the
lambda-calculus. We further propose that using the deBruijn notation (a
lambda-calculus notation with nameless dummies) supports effective caching in
such systems (caching being an essential component of efficient computation).
Finally, our extension to the lambda-calculus offers a foundation and general
theory for the construction of recursive stochastic modeling languages as well
as promise for effective caching and efficient approximation algorithms for
inference.
| [
{
"version": "v1",
"created": "Thu, 10 Jan 2013 16:25:55 GMT"
}
] | 1,358,121,600,000 | [
[
"Pless",
"Daniel",
""
],
[
"Luger",
"George",
""
]
] |
1301.2304 | Pascal Poupart | Pascal Poupart, Craig Boutilier | Vector-space Analysis of Belief-state Approximation for POMDPs | Appears in Proceedings of the Seventeenth Conference on Uncertainty
in Artificial Intelligence (UAI2001) | null | null | UAI-P-2001-PG-445-452 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We propose a new approach to value-directed belief state approximation for
POMDPs. The value-directed model allows one to choose approximation methods for
belief state monitoring that have a small impact on decision quality. Using a
vector space analysis of the problem, we devise two new search procedures for
selecting an approximation scheme that have much better computational
properties than existing methods. Though these provide looser error bounds, we
show empirically that they have a similar impact on decision quality in
practice, and run up to two orders of magnitude more quickly.
| [
{
"version": "v1",
"created": "Thu, 10 Jan 2013 16:26:03 GMT"
}
] | 1,358,121,600,000 | [
[
"Poupart",
"Pascal",
""
],
[
"Boutilier",
"Craig",
""
]
] |
1301.2305 | Pascal Poupart | Pascal Poupart, Luis E. Ortiz, Craig Boutilier | Value-Directed Sampling Methods for POMDPs | Appears in Proceedings of the Seventeenth Conference on Uncertainty
in Artificial Intelligence (UAI2001) | null | null | UAI-P-2001-PG-453-461 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We consider the problem of approximate belief-state monitoring using particle
filtering for the purposes of implementing a policy for a partially-observable
Markov decision process (POMDP). While particle filtering has become a
widely-used tool in AI for monitoring dynamical systems, rather scant attention
has been paid to their use in the context of decision making. Assuming the
existence of a value function, we derive error bounds on decision quality
associated with filtering using importance sampling. We also describe an
adaptive procedure that can be used to dynamically determine the number of
samples required to meet specific error bounds. Empirical evidence is offered
supporting this technique as a profitable means of directing sampling effort
where it is needed to distinguish policies.
| [
{
"version": "v1",
"created": "Thu, 10 Jan 2013 16:26:08 GMT"
}
] | 1,358,121,600,000 | [
[
"Poupart",
"Pascal",
""
],
[
"Ortiz",
"Luis E.",
""
],
[
"Boutilier",
"Craig",
""
]
] |
1301.2307 | Khashayar Rohanimanesh | Khashayar Rohanimanesh, Sridhar Mahadevan | Decision-Theoretic Planning with Concurrent Temporally Extended Actions | Appears in Proceedings of the Seventeenth Conference on Uncertainty
in Artificial Intelligence (UAI2001) | null | null | UAI-P-2001-PG-472-479 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We investigate a model for planning under uncertainty with temporallyextended
actions, where multiple actions can be taken concurrently at each decision
epoch. Our model is based on the options framework, and combines it with
factored state space models,where the set of options can be partitioned into
classes that affectdisjoint state variables. We show that the set of
decisionepochs for concurrent options defines a semi-Markov decisionprocess, if
the underlying temporally extended actions being parallelized arerestricted to
Markov options. This property allows us to use SMDPalgorithms for computing the
value function over concurrentoptions. The concurrent options model allows
overlapping execution ofoptions in order to achieve higher performance or in
order to performa complex task. We describe a simple experiment using a
navigationtask which illustrates how concurrent options results in a faster
planwhen compared to the case when only one option is taken at a time.
| [
{
"version": "v1",
"created": "Thu, 10 Jan 2013 16:26:17 GMT"
}
] | 1,358,121,600,000 | [
[
"Rohanimanesh",
"Khashayar",
""
],
[
"Mahadevan",
"Sridhar",
""
]
] |
1301.2308 | Paat Rusmevichientong | Paat Rusmevichientong, Benjamin van Roy | A Tractable POMDP for a Class of Sequencing Problems | Appears in Proceedings of the Seventeenth Conference on Uncertainty
in Artificial Intelligence (UAI2001) | null | null | UAI-P-2001-PG-480-487 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We consider a partially observable Markov decision problem (POMDP) that
models a class of sequencing problems. Although POMDPs are typically
intractable, our formulation admits tractable solution. Instead of maintaining
a value function over a high-dimensional set of belief states, we reduce the
state space to one of smaller dimension, in which grid-based dynamic
programming techniques are effective. We develop an error bound for the
resulting approximation, and discuss an application of the model to a problem
in targeted advertising.
| [
{
"version": "v1",
"created": "Thu, 10 Jan 2013 16:26:22 GMT"
}
] | 1,358,121,600,000 | [
[
"Rusmevichientong",
"Paat",
""
],
[
"van Roy",
"Benjamin",
""
]
] |
1301.2312 | Jin Tian | Jin Tian, Judea Pearl | Causal Discovery from Changes | Appears in Proceedings of the Seventeenth Conference on Uncertainty
in Artificial Intelligence (UAI2001) | null | null | UAI-P-2001-PG-512-521 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We propose a new method of discovering causal structures, based on the
detection of local, spontaneous changes in the underlying data-generating
model. We analyze the classes of structures that are equivalent relative to a
stream of distributions produced by local changes, and devise algorithms that
output graphical representations of these equivalence classes. We present
experimental results, using simulated data, and examine the errors associated
with detection of changes and recovery of structures.
| [
{
"version": "v1",
"created": "Thu, 10 Jan 2013 16:26:39 GMT"
}
] | 1,358,121,600,000 | [
[
"Tian",
"Jin",
""
],
[
"Pearl",
"Judea",
""
]
] |
1301.2313 | Tim Van Allen | Tim Van Allen, Russell Greiner, Peter Hooper | Bayesian Error-Bars for Belief Net Inference | Appears in Proceedings of the Seventeenth Conference on Uncertainty
in Artificial Intelligence (UAI2001) | null | null | UAI-P-2001-PG-522-529 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | A Bayesian Belief Network (BN) is a model of a joint distribution over a
setof n variables, with a DAG structure to represent the immediate
dependenciesbetween the variables, and a set of parameters (aka CPTables) to
represent thelocal conditional probabilities of a node, given each assignment
to itsparents. In many situations, these parameters are themselves random
variables - this may reflect the uncertainty of the domain expert, or may come
from atraining sample used to estimate the parameter values. The distribution
overthese "CPtable variables" induces a distribution over the response the
BNwill return to any "What is Pr(H | E)?" query. This paper investigates
thevariance of this response, showing first that it is asymptotically
normal,then providing its mean and asymptotical variance. We then present
aneffective general algorithm for computing this variance, which has the
samecomplexity as simply computing the (mean value of) the response itself -
ie,O(n 2^w), where n is the number of variables and w is the effective
treewidth. Finally, we provide empirical evidence that this algorithm,
whichincorporates assumptions and approximations, works effectively in
practice,given only small samples.
| [
{
"version": "v1",
"created": "Thu, 10 Jan 2013 16:26:44 GMT"
}
] | 1,358,121,600,000 | [
[
"Van Allen",
"Tim",
""
],
[
"Greiner",
"Russell",
""
],
[
"Hooper",
"Peter",
""
]
] |
1301.2314 | Linda C. van der Gaag | Linda C. van der Gaag, Silja Renooij | Analysing Sensitivity Data from Probabilistic Networks | Appears in Proceedings of the Seventeenth Conference on Uncertainty
in Artificial Intelligence (UAI2001) | null | null | UAI-P-2001-PG-530-537 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | With the advance of efficient analytical methods for sensitivity analysis
ofprobabilistic networks, the interest in the sensitivities revealed by
real-life networks is rekindled. As the amount of data resulting from a
sensitivity analysis of even a moderately-sized network is alreadyoverwhelming,
methods for extracting relevant information are called for. One such methodis
to study the derivative of the sensitivity functions yielded for a network's
parameters. We further propose to build upon the concept of admissible
deviation, that is, the extent to which a parameter can deviate from the true
value without inducing a change in the most likely outcome. We illustrate these
concepts by means of a sensitivity analysis of a real-life probabilistic
network in oncology.
| [
{
"version": "v1",
"created": "Thu, 10 Jan 2013 16:26:48 GMT"
}
] | 1,358,121,600,000 | [
[
"van der Gaag",
"Linda C.",
""
],
[
"Renooij",
"Silja",
""
]
] |
1301.2319 | Bo Zhang | Bo Zhang, Qingsheng Cai, Jianfeng Mao, Baining Guo | Planning and Acting under Uncertainty: A New Model for Spoken Dialogue
Systems | Appears in Proceedings of the Seventeenth Conference on Uncertainty
in Artificial Intelligence (UAI2001) | null | null | UAI-P-2001-PG-572-579 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Uncertainty plays a central role in spoken dialogue systems. Some stochastic
models like Markov decision process (MDP) are used to model the dialogue
manager. But the partially observable system state and user intention hinder
the natural representation of the dialogue state. MDP-based system degrades
fast when uncertainty about a user's intention increases. We propose a novel
dialogue model based on the partially observable Markov decision process
(POMDP). We use hidden system states and user intentions as the state set,
parser results and low-level information as the observation set, domain actions
and dialogue repair actions as the action set. Here the low-level information
is extracted from different input modals, including speech, keyboard, mouse,
etc., using Bayesian networks. Because of the limitation of the exact
algorithms, we focus on heuristic approximation algorithms and their
applicability in POMDP for dialogue management. We also propose two methods for
grid point selection in grid-based approximation algorithms.
| [
{
"version": "v1",
"created": "Thu, 10 Jan 2013 16:27:11 GMT"
}
] | 1,358,121,600,000 | [
[
"Zhang",
"Bo",
""
],
[
"Cai",
"Qingsheng",
""
],
[
"Mao",
"Jianfeng",
""
],
[
"Guo",
"Baining",
""
]
] |
1301.2774 | Jafar Muhammadi | Jafar Muhammadi, Hamid Reza Rabiee and Abbas Hosseini | Crowd Labeling: a survey | Under consideration for publication in Knowledge and Information
Systems | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Recently, there has been a burst in the number of research projects on human
computation via crowdsourcing. Multiple choice (or labeling) questions could be
referred to as a common type of problem which is solved by this approach. As an
application, crowd labeling is applied to find true labels for large machine
learning datasets. Since crowds are not necessarily experts, the labels they
provide are rather noisy and erroneous. This challenge is usually resolved by
collecting multiple labels for each sample, and then aggregating them to
estimate the true label. Although the mechanism leads to high-quality labels,
it is not actually cost-effective. As a result, efforts are currently made to
maximize the accuracy in estimating true labels, while fixing the number of
acquired labels.
This paper surveys methods to aggregate redundant crowd labels in order to
estimate unknown true labels. It presents a unified statistical latent model
where the differences among popular methods in the field correspond to
different choices for the parameters of the model. Afterwards, algorithms to
make inference on these models will be surveyed. Moreover, adaptive methods
which iteratively collect labels based on the previously collected labels and
estimated models will be discussed. In addition, this paper compares the
distinguished methods, and provides guidelines for future work required to
address the current open issues.
| [
{
"version": "v1",
"created": "Sun, 13 Jan 2013 14:12:53 GMT"
},
{
"version": "v2",
"created": "Tue, 8 Apr 2014 05:59:49 GMT"
},
{
"version": "v3",
"created": "Wed, 3 Sep 2014 06:37:23 GMT"
}
] | 1,409,788,800,000 | [
[
"Muhammadi",
"Jafar",
""
],
[
"Rabiee",
"Hamid Reza",
""
],
[
"Hosseini",
"Abbas",
""
]
] |
1301.3832 | Teresa Alsinet | Teresa Alsinet, Lluis Godo | A Complete Calculus for Possibilistic Logic Programming with Fuzzy
Propositional Variables | Appears in Proceedings of the Sixteenth Conference on Uncertainty in
Artificial Intelligence (UAI2000) | null | null | UAI-P-2000-PG-1-10 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this paper we present a propositional logic programming language for
reasoning under possibilistic uncertainty and representing vague knowledge.
Formulas are represented by pairs (A, c), where A is a many-valued proposition
and c is value in the unit interval [0,1] which denotes a lower bound on the
belief on A in terms of necessity measures. Belief states are modeled by
possibility distributions on the set of all many-valued interpretations. In
this framework, (i) we define a syntax and a semantics of the general
underlying uncertainty logic; (ii) we provide a modus ponens-style calculus for
a sublanguage of Horn-rules and we prove that it is complete for determining
the maximum degree of possibilistic belief with which a fuzzy propositional
variable can be entailed from a set of formulas; and finally, (iii) we show how
the computation of a partial matching between fuzzy propositional variables, in
terms of necessity measures for fuzzy sets, can be included in our logic
programming system.
| [
{
"version": "v1",
"created": "Wed, 16 Jan 2013 15:48:38 GMT"
}
] | 1,358,467,200,000 | [
[
"Alsinet",
"Teresa",
""
],
[
"Godo",
"Lluis",
""
]
] |
1301.3834 | Ann Becker | Ann Becker, Dan Geiger, Christopher Meek | Perfect Tree-Like Markovian Distributions | Appears in Proceedings of the Sixteenth Conference on Uncertainty in
Artificial Intelligence (UAI2000) | null | null | UAI-P-2000-PG-19-23 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We show that if a strictly positive joint probability distribution for a set
of binary random variables factors according to a tree, then vertex separation
represents all and only the independence relations enclosed in the
distribution. The same result is shown to hold also for multivariate strictly
positive normal distributions. Our proof uses a new property of conditional
independence that holds for these two classes of probability distributions.
| [
{
"version": "v1",
"created": "Wed, 16 Jan 2013 15:48:48 GMT"
}
] | 1,358,467,200,000 | [
[
"Becker",
"Ann",
""
],
[
"Geiger",
"Dan",
""
],
[
"Meek",
"Christopher",
""
]
] |
1301.3835 | Salem Benferhat | Salem Benferhat, Didier Dubois, Souhila Kaci, Henri Prade | A Principled Analysis of Merging Operations in Possibilistic Logic | Appears in Proceedings of the Sixteenth Conference on Uncertainty in
Artificial Intelligence (UAI2000) | null | null | UAI-P-2000-PG-24-31 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Possibilistic logic offers a qualitative framework for representing pieces of
information associated with levels of uncertainty of priority. The fusion of
multiple sources information is discussed in this setting. Different classes of
merging operators are considered including conjunctive, disjunctive,
reinforcement, adaptive and averaging operators. Then we propose to analyse
these classes in terms of postulates. This is done by first extending the
postulate for merging classical bases to the case where priorites are avaialbe.
| [
{
"version": "v1",
"created": "Wed, 16 Jan 2013 15:48:52 GMT"
}
] | 1,358,467,200,000 | [
[
"Benferhat",
"Salem",
""
],
[
"Dubois",
"Didier",
""
],
[
"Kaci",
"Souhila",
""
],
[
"Prade",
"Henri",
""
]
] |
1301.3836 | Daniel S Bernstein | Daniel S Bernstein, Shlomo Zilberstein, Neil Immerman | The Complexity of Decentralized Control of Markov Decision Processes | Appears in Proceedings of the Sixteenth Conference on Uncertainty in
Artificial Intelligence (UAI2000) | null | null | UAI-P-2000-PG-32-37 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Planning for distributed agents with partial state information is considered
from a decision- theoretic perspective. We describe generalizations of both the
MDP and POMDP models that allow for decentralized control. For even a small
number of agents, the finite-horizon problems corresponding to both of our
models are complete for nondeterministic exponential time. These complexity
results illustrate a fundamental difference between centralized and
decentralized control of Markov processes. In contrast to the MDP and POMDP
problems, the problems we consider provably do not admit polynomial-time
algorithms and most likely require doubly exponential time to solve in the
worst case. We have thus provided mathematical evidence corresponding to the
intuition that decentralized planning problems cannot easily be reduced to
centralized problems and solved exactly using established techniques.
| [
{
"version": "v1",
"created": "Wed, 16 Jan 2013 15:48:55 GMT"
}
] | 1,358,467,200,000 | [
[
"Bernstein",
"Daniel S",
""
],
[
"Zilberstein",
"Shlomo",
""
],
[
"Immerman",
"Neil",
""
]
] |
1301.3839 | Craig Boutilier | Craig Boutilier | Approximately Optimal Monitoring of Plan Preconditions | Appears in Proceedings of the Sixteenth Conference on Uncertainty in
Artificial Intelligence (UAI2000) | null | null | UAI-P-2000-PG-54-62 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Monitoring plan preconditions can allow for replanning when a precondition
fails, generally far in advance of the point in the plan where the precondition
is relevant. However, monitoring is generally costly, and some precondition
failures have a very small impact on plan quality. We formulate a model for
optimal precondition monitoring, using partially-observable Markov decisions
processes, and describe methods for solving this model efficitively, though
approximately. Specifically, we show that the single-precondition monitoring
problem is generally tractable, and the multiple-precondition monitoring
policies can be efficitively approximated using single-precondition soultions.
| [
{
"version": "v1",
"created": "Wed, 16 Jan 2013 15:49:07 GMT"
}
] | 1,358,467,200,000 | [
[
"Boutilier",
"Craig",
""
]
] |
1301.3841 | Jian Cheng | Jian Cheng, Marek J. Druzdzel | Computational Investigation of Low-Discrepancy Sequences in Simulation
Algorithms for Bayesian Networks | Appears in Proceedings of the Sixteenth Conference on Uncertainty in
Artificial Intelligence (UAI2000) | null | null | UAI-P-2000-PG-72-81 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Monte Carlo sampling has become a major vehicle for approximate inference in
Bayesian networks. In this paper, we investigate a family of related simulation
approaches, known collectively as quasi-Monte Carlo methods based on
deterministic low-discrepancy sequences. We first outline several theoretical
aspects of deterministic low-discrepancy sequences, show three examples of such
sequences, and then discuss practical issues related to applying them to belief
updating in Bayesian networks. We propose an algorithm for selecting direction
numbers for Sobol sequence. Our experimental results show that low-discrepancy
sequences (especially Sobol sequence) significantly improve the performance of
simulation algorithms in Bayesian networks compared to Monte Carlo sampling.
| [
{
"version": "v1",
"created": "Wed, 16 Jan 2013 15:49:15 GMT"
}
] | 1,358,467,200,000 | [
[
"Cheng",
"Jian",
""
],
[
"Druzdzel",
"Marek J.",
""
]
] |
1301.3842 | David Maxwell Chickering | David Maxwell Chickering, David Heckerman | A Decision Theoretic Approach to Targeted Advertising | Appears in Proceedings of the Sixteenth Conference on Uncertainty in
Artificial Intelligence (UAI2000) | null | null | UAI-P-2000-PG-82-88 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | A simple advertising strategy that can be used to help increase sales of a
product is to mail out special offers to selected potential customers. Because
there is a cost associated with sending each offer, the optimal mailing
strategy depends on both the benefit obtained from a purchase and how the offer
affects the buying behavior of the customers. In this paper, we describe two
methods for partitioning the potential customers into groups, and show how to
perform a simple cost-benefit analysis to decide which, if any, of the groups
should be targeted. In particular, we consider two decision-tree learning
algorithms. The first is an "off the shelf" algorithm used to model the
probability that groups of customers will buy the product. The second is a new
algorithm that is similar to the first, except that for each group, it
explicitly models the probability of purchase under the two mailing scenarios:
(1) the mail is sent to members of that group and (2) the mail is not sent to
members of that group. Using data from a real-world advertising experiment, we
compare the algorithms to each other and to a naive mail-to-all strategy.
| [
{
"version": "v1",
"created": "Wed, 16 Jan 2013 15:49:19 GMT"
}
] | 1,358,467,200,000 | [
[
"Chickering",
"David Maxwell",
""
],
[
"Heckerman",
"David",
""
]
] |
1301.3844 | Gregory F. Cooper | Gregory F. Cooper | A Bayesian Method for Causal Modeling and Discovery Under Selection | Appears in Proceedings of the Sixteenth Conference on Uncertainty in
Artificial Intelligence (UAI2000) | null | null | UAI-P-2000-PG-98-106 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper describes a Bayesian method for learning causal networks using
samples that were selected in a non-random manner from a population of
interest. Examples of data obtained by non-random sampling include convenience
samples and case-control data in which a fixed number of samples with and
without some condition is collected; such data are not uncommon. The paper
describes a method for combining data under selection with prior beliefs in
order to derive a posterior probability for a model of the causal processes
that are generating the data in the population of interest. The priors include
beliefs about the nature of the non-random sampling procedure. Although exact
application of the method would be computationally intractable for most
realistic datasets, efficient special-case and approximation methods are
discussed. Finally, the paper describes how to combine learning under selection
with previous methods for learning from observational and experimental data
that are obtained on random samples of the population of interest. The net
result is a Bayesian methodology that supports causal modeling and discovery
from a rich mixture of different types of data.
| [
{
"version": "v1",
"created": "Wed, 16 Jan 2013 15:49:26 GMT"
}
] | 1,358,467,200,000 | [
[
"Cooper",
"Gregory F.",
""
]
] |
1301.3845 | Fabio Gagliardi Cozman | Fabio Gagliardi Cozman | Separation Properties of Sets of Probability Measures | Appears in Proceedings of the Sixteenth Conference on Uncertainty in
Artificial Intelligence (UAI2000) | null | null | UAI-P-2000-PG-107-114 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper analyzes independence concepts for sets of probability measures
associated with directed acyclic graphs. The paper shows that epistemic
independence and the standard Markov condition violate desirable separation
properties. The adoption of a contraction condition leads to d-separation but
still fails to guarantee a belief separation property. To overcome this
unsatisfactory situation, a strong Markov condition is proposed, based on
epistemic independence. The main result is that the strong Markov condition
leads to strong independence and does enforce separation properties; this
result implies that (1) separation properties of Bayesian networks do extend to
epistemic independence and sets of probability measures, and (2) strong
independence has a clear justification based on epistemic independence and the
strong Markov condition.
| [
{
"version": "v1",
"created": "Wed, 16 Jan 2013 15:49:30 GMT"
}
] | 1,358,467,200,000 | [
[
"Cozman",
"Fabio Gagliardi",
""
]
] |
1301.3846 | James Cussens | James Cussens | Stochastic Logic Programs: Sampling, Inference and Applications | Appears in Proceedings of the Sixteenth Conference on Uncertainty in
Artificial Intelligence (UAI2000) | null | null | UAI-P-2000-PG-115-122 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Algorithms for exact and approximate inference in stochastic logic programs
(SLPs) are presented, based respectively, on variable elimination and
importance sampling. We then show how SLPs can be used to represent prior
distributions for machine learning, using (i) logic programs and (ii) Bayes net
structures as examples. Drawing on existing work in statistics, we apply the
Metropolis-Hasting algorithm to construct a Markov chain which samples from the
posterior distribution. A Prolog implementation for this is described. We also
discuss the possibility of constructing explicit representations of the
posterior.
| [
{
"version": "v1",
"created": "Wed, 16 Jan 2013 15:49:34 GMT"
}
] | 1,358,467,200,000 | [
[
"Cussens",
"James",
""
]
] |
1301.3847 | Adnan Darwiche | Adnan Darwiche | A Differential Approach to Inference in Bayesian Networks | Appears in Proceedings of the Sixteenth Conference on Uncertainty in
Artificial Intelligence (UAI2000) | null | null | UAI-P-2000-PG-123-132 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We present a new approach for inference in Bayesian networks, which is mainly
based on partial differentiation. According to this approach, one compiles a
Bayesian network into a multivariate polynomial and then computes the partial
derivatives of this polynomial with respect to each variable. We show that once
such derivatives are made available, one can compute in constant-time answers
to a large class of probabilistic queries, which are central to classical
inference, parameter estimation, model validation and sensitivity analysis. We
present a number of complexity results relating to the compilation of such
polynomials and to the computation of their partial derivatives. We argue that
the combined simplicity, comprehensiveness and computational complexity of the
presented framework is unique among existing frameworks for inference in
Bayesian networks.
| [
{
"version": "v1",
"created": "Wed, 16 Jan 2013 15:49:38 GMT"
}
] | 1,358,467,200,000 | [
[
"Darwiche",
"Adnan",
""
]
] |
1301.3848 | Adnan Darwiche | Adnan Darwiche | Any-Space Probabilistic Inference | Appears in Proceedings of the Sixteenth Conference on Uncertainty in
Artificial Intelligence (UAI2000) | null | null | UAI-P-2000-PG-133-142 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We have recently introduced an any-space algorithm for exact inference in
Bayesian networks, called Recursive Conditioning, RC, which allows one to trade
space with time at increments of X-bytes, where X is the number of bytes needed
to cache a floating point number. In this paper, we present three key
extensions of RC. First, we modify the algorithm so it applies to more general
factorization of probability distributions, including (but not limited to)
Bayesian network factorizations. Second, we present a forgetting mechanism
which reduces the space requirements of RC considerably and then compare such
requirmenets with those of variable elimination on a number of realistic
networks, showing orders of magnitude improvements in certain cases. Third, we
present a version of RC for computing maximum a posteriori hypotheses (MAP),
which turns out to be the first MAP algorithm allowing a smooth time-space
tradeoff. A key advantage of presented MAP algorithm is that it does not have
to start from scratch each time a new query is presented, but can reuse some of
its computations across multiple queries, leading to significant savings in
ceratain cases.
| [
{
"version": "v1",
"created": "Wed, 16 Jan 2013 15:49:42 GMT"
}
] | 1,358,467,200,000 | [
[
"Darwiche",
"Adnan",
""
]
] |
1301.3855 | Nir Friedman | Nir Friedman, Dan Geiger, Noam Lotner | Likelihood Computations Using Value Abstractions | Appears in Proceedings of the Sixteenth Conference on Uncertainty in
Artificial Intelligence (UAI2000) | null | null | UAI-P-2000-PG-192-200 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this paper, we use evidence-specific value abstraction for speeding
Bayesian networks inference. This is done by grouping variable values and
treating the combined values as a single entity. As we show, such abstractions
can exploit regularities in conditional probability distributions and also the
specific values of observed variables. To formally justify value abstraction,
we define the notion of safe value abstraction and devise inference algorithms
that use it to reduce the cost of inference. Our procedure is particularly
useful for learning complex networks with many hidden variables. In such cases,
repeated likelihood computations are required for EM or other parameter
optimization techniques. Since these computations are repeated with respect to
the same evidence set, our methods can provide significant speedup to the
learning procedure. We demonstrate the algorithm on genetic linkage problems
where the use of value abstraction sometimes differentiates between a feasible
and non-feasible solution.
| [
{
"version": "v1",
"created": "Wed, 16 Jan 2013 15:50:10 GMT"
}
] | 1,358,467,200,000 | [
[
"Friedman",
"Nir",
""
],
[
"Geiger",
"Dan",
""
],
[
"Lotner",
"Noam",
""
]
] |
1301.3858 | Phan H. Giang | Phan H. Giang, Prakash P. Shenoy | A Qualitative Linear Utility Theory for Spohn's Theory of Epistemic
Beliefs | Appears in Proceedings of the Sixteenth Conference on Uncertainty in
Artificial Intelligence (UAI2000) | null | null | UAI-P-2000-PG-220-229 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this paper, we formulate a qualitative "linear" utility theory for
lotteries in which uncertainty is expressed qualitatively using a Spohnian
disbelief function. We argue that a rational decision maker facing an uncertain
decision problem in which the uncertainty is expressed qualitatively should
behave so as to maximize "qualitative expected utility." Our axiomatization of
the qualitative utility is similar to the axiomatization developed by von
Neumann and Morgenstern for probabilistic lotteries. We compare our results
with other recent results in qualitative decision making.
| [
{
"version": "v1",
"created": "Wed, 16 Jan 2013 15:50:22 GMT"
}
] | 1,358,467,200,000 | [
[
"Giang",
"Phan H.",
""
],
[
"Shenoy",
"Prakash P.",
""
]
] |
1301.3859 | Peter J Gorniak | Peter J. Gorniak, David L. Poole | Building a Stochastic Dynamic Model of Application Use | Appears in Proceedings of the Sixteenth Conference on Uncertainty in
Artificial Intelligence (UAI2000) | null | null | UAI-P-2000-PG-230-237 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Many intelligent user interfaces employ application and user models to
determine the user's preferences, goals and likely future actions. Such models
require application analysis, adaptation and expansion. Building and
maintaining such models adds a substantial amount of time and labour to the
application development cycle. We present a system that observes the interface
of an unmodified application and records users' interactions with the
application. From a history of such observations we build a coarse state space
of observed interface states and actions between them. To refine the space, we
hypothesize sub-states based upon the histories that led users to a given
state. We evaluate the information gain of possible state splits, varying the
length of the histories considered in such splits. In this way, we
automatically produce a stochastic dynamic model of the application and of how
it is used. To evaluate our approach, we present models derived from real-world
application usage data.
| [
{
"version": "v1",
"created": "Wed, 16 Jan 2013 15:50:26 GMT"
}
] | 1,358,467,200,000 | [
[
"Gorniak",
"Peter J.",
""
],
[
"Poole",
"David L.",
""
]
] |
1301.3860 | Peter D Grunwald | Peter D. Grunwald | Maximum Entropy and the Glasses You Are Looking Through | Appears in Proceedings of the Sixteenth Conference on Uncertainty in
Artificial Intelligence (UAI2000) | null | null | UAI-P-2000-PG-238-246 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We give an interpretation of the Maximum Entropy (MaxEnt) Principle in
game-theoretic terms. Based on this interpretation, we make a formal
distinction between different ways of {em applying/} Maximum Entropy
distributions. MaxEnt has frequently been criticized on the grounds that it
leads to highly representation dependent results. Our distinction allows us to
avoid this problem in many cases.
| [
{
"version": "v1",
"created": "Wed, 16 Jan 2013 15:50:30 GMT"
}
] | 1,358,467,200,000 | [
[
"Grunwald",
"Peter D.",
""
]
] |
1301.3864 | Michael C. Horsch | Michael C. Horsch, Bill Havens | Probabilistic Arc Consistency: A Connection between Constraint Reasoning
and Probabilistic Reasoning | Appears in Proceedings of the Sixteenth Conference on Uncertainty in
Artificial Intelligence (UAI2000) | null | null | UAI-P-2000-PG-282-290 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We document a connection between constraint reasoning and probabilistic
reasoning. We present an algorithm, called {em probabilistic arc consistency},
which is both a generalization of a well known algorithm for arc consistency
used in constraint reasoning, and a specialization of the belief updating
algorithm for singly-connected networks. Our algorithm is exact for singly-
connected constraint problems, but can work well as an approximation for
arbitrary problems. We briefly discuss some empirical results, and related
methods.
| [
{
"version": "v1",
"created": "Wed, 16 Jan 2013 15:50:46 GMT"
}
] | 1,358,467,200,000 | [
[
"Horsch",
"Michael C.",
""
],
[
"Havens",
"Bill",
""
]
] |
1301.3866 | Radim Jirousek | Radim Jirousek | Marginalization in Composed Probabilistic Models | Appears in Proceedings of the Sixteenth Conference on Uncertainty in
Artificial Intelligence (UAI2000) | null | null | UAI-P-2000-PG-301-308 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Composition of low-dimensional distributions, whose foundations were laid in
the papaer published in the Proceeding of UAI'97 (Jirousek 1997), appeared to
be an alternative apparatus to describe multidimensional probabilistic models.
In contrast to Graphical Markov Models, which define multidomensinoal
distributions in a declarative way, this approach is rather procedural.
Ordering of low-dimensional distributions into a proper sequence fully defines
the resepctive computational procedure; therefore, a stury of different type of
generating sequences is one fo the central problems in this field. Thus, it
appears that an important role is played by special sequences that are called
perfect. Their main characterization theorems are presetned in this paper.
However, the main result of this paper is a solution to the problem of
margnialization for general sequences. The main theorem describes a way to
obtain a generating sequence that defines the model corresponding to the
marginal of the distribution defined by an arbitrary genearting sequence. From
this theorem the reader can see to what extent these comutations are local;
i.e., the sequence consists of marginal distributions whose computation must be
made by summing up over the values of the variable eliminated (the paper deals
with finite model).
| [
{
"version": "v1",
"created": "Wed, 16 Jan 2013 15:50:54 GMT"
}
] | 1,358,467,200,000 | [
[
"Jirousek",
"Radim",
""
]
] |
1301.3868 | Uffe Kj{\ae}rulff | Uffe Kj{\ae}rulff, Linda C. van der Gaag | Making Sensitivity Analysis Computationally Efficient | Appears in Proceedings of the Sixteenth Conference on Uncertainty in
Artificial Intelligence (UAI2000) | null | null | UAI-P-2000-PG-317-325 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | To investigate the robustness of the output probabilities of a Bayesian
network, a sensitivity analysis can be performed. A one-way sensitivity
analysis establishes, for each of the probability parameters of a network, a
function expressing a posterior marginal probability of interest in terms of
the parameter. Current methods for computing the coefficients in such a
function rely on a large number of network evaluations. In this paper, we
present a method that requires just a single outward propagation in a junction
tree for establishing the coefficients in the functions for all possible
parameters; in addition, an inward propagation is required for processing
evidence. Conversely, the method requires a single outward propagation for
computing the coefficients in the functions expressing all possible posterior
marginals in terms of a single parameter. We extend these results to an n-way
sensitivity analysis in which sets of parameters are studied.
| [
{
"version": "v1",
"created": "Wed, 16 Jan 2013 15:51:02 GMT"
}
] | 1,358,467,200,000 | [
[
"Kjærulff",
"Uffe",
""
],
[
"van der Gaag",
"Linda C.",
""
]
] |
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