51,944 research outputs found
Hybrid Influence Diagrams Using Mixtures of Truncated Exponentials
Mixtures of truncated exponentials (MTE) potentials are an alternative to
discretization for representing continuous chance variables in influence
diagrams. Also, MTE potentials can be used to approximate utility functions.
This paper introduces MTE influence diagrams, which can represent decision
problems without restrictions on the relationships between continuous and
discrete chance variables, without limitations on the distributions of
continuous chance variables, and without limitations on the nature of the
utility functions. In MTE influence diagrams, all probability distributions and
the joint utility function (or its multiplicative factors) are represented by
MTE potentials and decision nodes are assumed to have discrete state spaces.
MTE influence diagrams are solved by variable elimination using a fusion
algorithm.Comment: Appears in Proceedings of the Twentieth Conference on Uncertainty in
Artificial Intelligence (UAI2004
Solving Hybrid Influence Diagrams with Deterministic Variables
We describe a framework and an algorithm for solving hybrid influence
diagrams with discrete, continuous, and deterministic chance variables, and
discrete and continuous decision variables. A continuous chance variable in an
influence diagram is said to be deterministic if its conditional distributions
have zero variances. The solution algorithm is an extension of Shenoy's fusion
algorithm for discrete influence diagrams. We describe an extended
Shenoy-Shafer architecture for propagation of discrete, continuous, and utility
potentials in hybrid influence diagrams that include deterministic chance
variables. The algorithm and framework are illustrated by solving two small
examples.Comment: Appears in Proceedings of the Twenty-Sixth Conference on Uncertainty
in Artificial Intelligence (UAI2010
IDEAL: A Software Package for Analysis of Influence Diagrams
IDEAL (Influence Diagram Evaluation and Analysis in Lisp) is a software
environment for creation and evaluation of belief networks and influence
diagrams. IDEAL is primarily a research tool and provides an implementation of
many of the latest developments in belief network and influence diagram
evaluation in a unified framework. This paper describes IDEAL and some lessons
learned during its development.Comment: Appears in Proceedings of the Sixth Conference on Uncertainty in
Artificial Intelligence (UAI1990
Solving Multistage Influence Diagrams using Branch-and-Bound Search
A branch-and-bound approach to solving influ- ence diagrams has been
previously proposed in the literature, but appears to have never been
implemented and evaluated - apparently due to the difficulties of computing
effective bounds for the branch-and-bound search. In this paper, we describe
how to efficiently compute effective bounds, and we develop a practical
implementa- tion of depth-first branch-and-bound search for influence diagram
evaluation that outperforms existing methods for solving influence diagrams
with multiple stages.Comment: Appears in Proceedings of the Twenty-Sixth Conference on Uncertainty
in Artificial Intelligence (UAI2010
Using Potential Influence Diagrams for Probabilistic Inference and Decision Making
The potential influence diagram is a generalization of the standard
"conditional" influence diagram, a directed network representation for
probabilistic inference and decision analysis [Ndilikilikesha, 1991]. It allows
efficient inference calculations corresponding exactly to those on undirected
graphs. In this paper, we explore the relationship between potential and
conditional influence diagrams and provide insight into the properties of the
potential influence diagram. In particular, we show how to convert a potential
influence diagram into a conditional influence diagram, and how to view the
potential influence diagram operations in terms of the conditional influence
diagram.Comment: Appears in Proceedings of the Ninth Conference on Uncertainty in
Artificial Intelligence (UAI1993
Information/Relevance Influence Diagrams
In this paper we extend the influence diagram (ID) representation for
decisions under uncertainty. In the standard ID, arrows into a decision node
are only informational; they do not represent constraints on what the decision
maker can do. We can represent such constraints only indirectly, using arrows
to the children of the decision and sometimes adding more variables to the
influence diagram, thus making the ID more complicated. Users of influence
diagrams often want to represent constraints by arrows into decision nodes. We
represent constraints on decisions by allowing relevance arrows into decision
nodes. We call the resulting representation information/relevance influence
diagrams (IRIDs). Information/relevance influence diagrams allow for direct
representation and specification of constrained decisions. We use a combination
of stochastic dynamic programming and Gibbs sampling to solve IRIDs. This
method is especially useful when exact methods for solving IDs fail.Comment: Appears in Proceedings of the Eleventh Conference on Uncertainty in
Artificial Intelligence (UAI1995
Solving Asymmetric Decision Problems with Influence Diagrams
While influence diagrams have many advantages as a representation framework
for Bayesian decision problems, they have a serious drawback in handling
asymmetric decision problems. To be represented in an influence diagram, an
asymmetric decision problem must be symmetrized. A considerable amount of
unnecessary computation may be involved when a symmetrized influence diagram is
evaluated by conventional algorithms. In this paper we present an approach for
avoiding such unnecessary computation in influence diagram evaluation.Comment: Appears in Proceedings of the Tenth Conference on Uncertainty in
Artificial Intelligence (UAI1994
From influence diagrams to multi-operator cluster DAGs
There exist several architectures to solve influence diagrams using local
computations, such as the Shenoy-Shafer, the HUGIN, or the Lazy Propagation
architectures. They all extend usual variable elimination algorithms thanks to
the use of so-called 'potentials'. In this paper, we introduce a new
architecture, called the Multi-operator Cluster DAG architecture, which can
produce decompositions with an improved constrained induced-width, and
therefore induce potentially exponential gains. Its principle is to benefit
from the composite nature of influence diagrams, instead of using uniform
potentials, in order to better analyze the problem structure.Comment: Appears in Proceedings of the Twenty-Second Conference on Uncertainty
in Artificial Intelligence (UAI2006
Representing and Solving Asymmetric Bayesian Decision Problems
This paper deals with the representation and solution of asymmetric Bayesian
decision problems. We present a formal framework, termed asymmetric influence
diagrams, that is based on the influence diagram and allows an efficient
representation of asymmetric decision problems. As opposed to existing
frameworks, the asymmetric influece diagram primarily encodes asymmetry at the
qualitative level and it can therefore be read directly from the model. We give
an algorithm for solving asymmetric influence diagrams. The algorithm initially
decomposes the asymmetric decision problem into a structure of symmetric
subproblems organized as a tree. A solution to the decision problem can then be
found by propagating from the leaves toward the root using existing evaluation
methods to solve the sub-problems.Comment: Appears in Proceedings of the Sixteenth Conference on Uncertainty in
Artificial Intelligence (UAI2000
From Influence Diagrams to Junction Trees
We present an approach to the solution of decision problems formulated as
influence diagrams. This approach involves a special triangulation of the
underlying graph, the construction of a junction tree with special properties,
and a message passing algorithm operating on the junction tree for computation
of expected utilities and optimal decision policies.Comment: Appears in Proceedings of the Tenth Conference on Uncertainty in
Artificial Intelligence (UAI1994
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