16,147 research outputs found
Bayesian emulation for optimization in multi-step portfolio decisions
We discuss the Bayesian emulation approach to computational solution of
multi-step portfolio studies in financial time series. "Bayesian emulation for
decisions" involves mapping the technical structure of a decision analysis
problem to that of Bayesian inference in a purely synthetic "emulating"
statistical model. This provides access to standard posterior analytic,
simulation and optimization methods that yield indirect solutions of the
decision problem. We develop this in time series portfolio analysis using
classes of economically and psychologically relevant multi-step ahead portfolio
utility functions. Studies with multivariate currency, commodity and stock
index time series illustrate the approach and show some of the practical
utility and benefits of the Bayesian emulation methodology.Comment: 24 pages, 7 figures, 2 table
A Game-Theoretic Approach to Energy Trading in the Smart Grid
Electric storage units constitute a key element in the emerging smart grid
system. In this paper, the interactions and energy trading decisions of a
number of geographically distributed storage units are studied using a novel
framework based on game theory. In particular, a noncooperative game is
formulated between storage units, such as PHEVs, or an array of batteries that
are trading their stored energy. Here, each storage unit's owner can decide on
the maximum amount of energy to sell in a local market so as to maximize a
utility that reflects the tradeoff between the revenues from energy trading and
the accompanying costs. Then in this energy exchange market between the storage
units and the smart grid elements, the price at which energy is traded is
determined via an auction mechanism. The game is shown to admit at least one
Nash equilibrium and a novel proposed algorithm that is guaranteed to reach
such an equilibrium point is proposed. Simulation results show that the
proposed approach yields significant performance improvements, in terms of the
average utility per storage unit, reaching up to 130.2% compared to a
conventional greedy approach.Comment: 11 pages, 11 figures, journa
Learning Vine Copula Models For Synthetic Data Generation
A vine copula model is a flexible high-dimensional dependence model which
uses only bivariate building blocks. However, the number of possible
configurations of a vine copula grows exponentially as the number of variables
increases, making model selection a major challenge in development. In this
work, we formulate a vine structure learning problem with both vector and
reinforcement learning representation. We use neural network to find the
embeddings for the best possible vine model and generate a structure.
Throughout experiments on synthetic and real-world datasets, we show that our
proposed approach fits the data better in terms of log-likelihood. Moreover, we
demonstrate that the model is able to generate high-quality samples in a
variety of applications, making it a good candidate for synthetic data
generation
A Deep Learning and Gamification Approach to Energy Conservation at Nanyang Technological University
The implementation of smart building technology in the form of smart
infrastructure applications has great potential to improve sustainability and
energy efficiency by leveraging humans-in-the-loop strategy. However, human
preference in regard to living conditions is usually unknown and heterogeneous
in its manifestation as control inputs to a building. Furthermore, the
occupants of a building typically lack the independent motivation necessary to
contribute to and play a key role in the control of smart building
infrastructure. Moreover, true human actions and their integration with
sensing/actuation platforms remains unknown to the decision maker tasked with
improving operational efficiency. By modeling user interaction as a sequential
discrete game between non-cooperative players, we introduce a gamification
approach for supporting user engagement and integration in a human-centric
cyber-physical system. We propose the design and implementation of a
large-scale network game with the goal of improving the energy efficiency of a
building through the utilization of cutting-edge Internet of Things (IoT)
sensors and cyber-physical systems sensing/actuation platforms. A benchmark
utility learning framework that employs robust estimations for classical
discrete choice models provided for the derived high dimensional imbalanced
data. To improve forecasting performance, we extend the benchmark utility
learning scheme by leveraging Deep Learning end-to-end training with Deep
bi-directional Recurrent Neural Networks. We apply the proposed methods to high
dimensional data from a social game experiment designed to encourage energy
efficient behavior among smart building occupants in Nanyang Technological
University (NTU) residential housing. Using occupant-retrieved actions for
resources such as lighting and A/C, we simulate the game defined by the
estimated utility functions.Comment: 16 double pages, shorter version submitted to Applied Energy Journa
A Knowledge Gradient Policy for Sequencing Experiments to Identify the Structure of RNA Molecules Using a Sparse Additive Belief Model
We present a sparse knowledge gradient (SpKG) algorithm for adaptively
selecting the targeted regions within a large RNA molecule to identify which
regions are most amenable to interactions with other molecules. Experimentally,
such regions can be inferred from fluorescence measurements obtained by binding
a complementary probe with fluorescence markers to the targeted regions. We use
a biophysical model which shows that the fluorescence ratio under the log scale
has a sparse linear relationship with the coefficients describing the
accessibility of each nucleotide, since not all sites are accessible (due to
the folding of the molecule). The SpKG algorithm uniquely combines the Bayesian
ranking and selection problem with the frequentist regularized
regression approach Lasso. We use this algorithm to identify the sparsity
pattern of the linear model as well as sequentially decide the best regions to
test before experimental budget is exhausted. Besides, we also develop two
other new algorithms: batch SpKG algorithm, which generates more suggestions
sequentially to run parallel experiments; and batch SpKG with a procedure which
we call length mutagenesis. It dynamically adds in new alternatives, in the
form of types of probes, are created by inserting, deleting or mutating
nucleotides within existing probes. In simulation, we demonstrate these
algorithms on the Group I intron (a mid-size RNA molecule), showing that they
efficiently learn the correct sparsity pattern, identify the most accessible
region, and outperform several other policies
Non-Monotonic Sequential Text Generation
Standard sequential generation methods assume a pre-specified generation
order, such as text generation methods which generate words from left to right.
In this work, we propose a framework for training models of text generation
that operate in non-monotonic orders; the model directly learns good orders,
without any additional annotation. Our framework operates by generating a word
at an arbitrary position, and then recursively generating words to its left and
then words to its right, yielding a binary tree. Learning is framed as
imitation learning, including a coaching method which moves from imitating an
oracle to reinforcing the policy's own preferences. Experimental results
demonstrate that using the proposed method, it is possible to learn policies
which generate text without pre-specifying a generation order, while achieving
competitive performance with conventional left-to-right generation.Comment: ICML 201
A Scheme for Continuous Input to the Tsetlin Machine with Applications to Forecasting Disease Outbreaks
In this paper, we apply a new promising tool for pattern classification,
namely, the Tsetlin Machine (TM), to the field of disease forecasting. The TM
is interpretable because it is based on manipulating expressions in
propositional logic, leveraging a large team of Tsetlin Automata (TA). Apart
from being interpretable, this approach is attractive due to its low
computational cost and its capacity to handle noise. To attack the problem of
forecasting, we introduce a preprocessing method that extends the TM so that it
can handle continuous input. Briefly stated, we convert continuous input into a
binary representation based on thresholding. The resulting extended TM is
evaluated and analyzed using an artificial dataset. The TM is further applied
to forecast dengue outbreaks of all the seventeen regions in the Philippines
using the spatio-temporal properties of the data. Experimental results show
that dengue outbreak forecasts made by the TM are more accurate than those
obtained by a Support Vector Machine (SVM), Decision Trees (DTs), and several
multi-layered Artificial Neural Networks (ANNs), both in terms of forecasting
precision and F1-score.Comment: 14 page
Interpretable Counting for Visual Question Answering
Questions that require counting a variety of objects in images remain a major
challenge in visual question answering (VQA). The most common approaches to VQA
involve either classifying answers based on fixed length representations of
both the image and question or summing fractional counts estimated from each
section of the image. In contrast, we treat counting as a sequential decision
process and force our model to make discrete choices of what to count.
Specifically, the model sequentially selects from detected objects and learns
interactions between objects that influence subsequent selections. A
distinction of our approach is its intuitive and interpretable output, as
discrete counts are automatically grounded in the image. Furthermore, our
method outperforms the state of the art architecture for VQA on multiple
metrics that evaluate counting.Comment: ICLR 201
Dyna-H: a heuristic planning reinforcement learning algorithm applied to role-playing-game strategy decision systems
In a Role-Playing Game, finding optimal trajectories is one of the most
important tasks. In fact, the strategy decision system becomes a key component
of a game engine. Determining the way in which decisions are taken (online,
batch or simulated) and the consumed resources in decision making (e.g.
execution time, memory) will influence, in mayor degree, the game performance.
When classical search algorithms such as A* can be used, they are the very
first option. Nevertheless, such methods rely on precise and complete models of
the search space, and there are many interesting scenarios where their
application is not possible. Then, model free methods for sequential decision
making under uncertainty are the best choice. In this paper, we propose a
heuristic planning strategy to incorporate the ability of heuristic-search in
path-finding into a Dyna agent. The proposed Dyna-H algorithm, as A* does,
selects branches more likely to produce outcomes than other branches. Besides,
it has the advantages of being a model-free online reinforcement learning
algorithm. The proposal was evaluated against the one-step Q-Learning and
Dyna-Q algorithms obtaining excellent experimental results: Dyna-H
significantly overcomes both methods in all experiments. We suggest also, a
functional analogy between the proposed sampling from worst trajectories
heuristic and the role of dreams (e.g. nightmares) in human behavior
Game-Theoretic Modeling of Multi-Vehicle Interactions at Uncontrolled Intersections
Motivated by the need to develop simulation tools for verification and
validation of autonomous driving systems operating in traffic consisting of
both autonomous and human-driven vehicles, we propose a framework for modeling
vehicle interactions at uncontrolled intersections. The proposed interaction
modeling approach is based on game theory with multiple concurrent
leader-follower pairs, and accounts for common traffic rules. We parameterize
the intersection layouts and geometries to model uncontrolled intersections
with various configurations, and apply the proposed approach to model the
interactive behavior of vehicles at these intersections. Based on simulation
results in various traffic scenarios, we show that the model exhibits
reasonable behavior expected in traffic, including the capability of
reproducing scenarios extracted from real-world traffic data and reasonable
performance in resolving traffic conflicts. The model is further validated
based on the level-of-service traffic quality rating system and demonstrates
manageable computational complexity compared to traditional multi-player
game-theoretic models.Comment: 18 pages, 13 figures, 1 tabl
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