210,718 research outputs found
Restricted Value Iteration: Theory and Algorithms
Value iteration is a popular algorithm for finding near optimal policies for
POMDPs. It is inefficient due to the need to account for the entire belief
space, which necessitates the solution of large numbers of linear programs. In
this paper, we study value iteration restricted to belief subsets. We show
that, together with properly chosen belief subsets, restricted value iteration
yields near-optimal policies and we give a condition for determining whether a
given belief subset would bring about savings in space and time. We also apply
restricted value iteration to two interesting classes of POMDPs, namely
informative POMDPs and near-discernible POMDPs
Exploiting Causal Independence in Bayesian Network Inference
A new method is proposed for exploiting causal independencies in exact
Bayesian network inference. A Bayesian network can be viewed as representing a
factorization of a joint probability into the multiplication of a set of
conditional probabilities. We present a notion of causal independence that
enables one to further factorize the conditional probabilities into a
combination of even smaller factors and consequently obtain a finer-grain
factorization of the joint probability. The new formulation of causal
independence lets us specify the conditional probability of a variable given
its parents in terms of an associative and commutative operator, such as
``or'', ``sum'' or ``max'', on the contribution of each parent. We start with a
simple algorithm VE for Bayesian network inference that, given evidence and a
query variable, uses the factorization to find the posterior distribution of
the query. We show how this algorithm can be extended to exploit causal
independence. Empirical studies, based on the CPCS networks for medical
diagnosis, show that this method is more efficient than previous methods and
allows for inference in larger networks than previous algorithms.Comment: See http://www.jair.org/ for any accompanying file
A Model Approximation Scheme for Planning in Partially Observable Stochastic Domains
Partially observable Markov decision processes (POMDPs) are a natural model
for planning problems where effects of actions are nondeterministic and the
state of the world is not completely observable. It is difficult to solve
POMDPs exactly. This paper proposes a new approximation scheme. The basic idea
is to transform a POMDP into another one where additional information is
provided by an oracle. The oracle informs the planning agent that the current
state of the world is in a certain region. The transformed POMDP is
consequently said to be region observable. It is easier to solve than the
original POMDP. We propose to solve the transformed POMDP and use its optimal
policy to construct an approximate policy for the original POMDP. By
controlling the amount of additional information that the oracle provides, it
is possible to find a proper tradeoff between computational time and
approximation quality. In terms of algorithmic contributions, we study in
details how to exploit region observability in solving the transformed POMDP.
To facilitate the study, we also propose a new exact algorithm for general
POMDPs. The algorithm is conceptually simple and yet is significantly more
efficient than all previous exact algorithms.Comment: See http://www.jair.org/ for any accompanying file
Determination of a set of constitutive equations for an al-li alloy at SPF conditions
© 2015 The Authors.Uniaxial tensile tests of aluminium-lithium alloy AA1420wereconducted at superplastic forming conditions. The mechanical properties of this Al-Li alloy were then modelled by a set of physicallybased constitutive equations. The constitutive equations describe the isotropic work hardening,recovery and damage by dislocation density changes and grain size evolution. Based on a recent upgraded optimisation technique, the material constants for these constitutive equations were determined
Control of beam propagation in optically written waveguides beyond the paraxial approximation
Beam propagation beyond the paraxial approximation is studied in an optically
written waveguide structure. The waveguide structure that leads to
diffractionless light propagation, is imprinted on a medium consisting of a
five-level atomic vapor driven by an incoherent pump and two coherent spatially
dependent control and plane-wave fields. We first study propagation in a single
optically written waveguide, and find that the paraxial approximation does not
provide an accurate description of the probe propagation. We then employ
coherent control fields such that two parallel and one tilted Gaussian beams
produce a branched waveguide structure. The tilted beam allows selective
steering of the probe beam into different branches of the waveguide structure.
The transmission of the probe beam for a particular branch can be improved by
changing the width of the titled Gaussian control beam as well as the intensity
of the spatially dependent incoherent pump field.Comment: 10 pages, 9 figure
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