847 research outputs found
Markov Decision Processes with Applications in Wireless Sensor Networks: A Survey
Wireless sensor networks (WSNs) consist of autonomous and resource-limited
devices. The devices cooperate to monitor one or more physical phenomena within
an area of interest. WSNs operate as stochastic systems because of randomness
in the monitored environments. For long service time and low maintenance cost,
WSNs require adaptive and robust methods to address data exchange, topology
formulation, resource and power optimization, sensing coverage and object
detection, and security challenges. In these problems, sensor nodes are to make
optimized decisions from a set of accessible strategies to achieve design
goals. This survey reviews numerous applications of the Markov decision process
(MDP) framework, a powerful decision-making tool to develop adaptive algorithms
and protocols for WSNs. Furthermore, various solution methods are discussed and
compared to serve as a guide for using MDPs in WSNs
DoShiCo Challenge: Domain Shift in Control Prediction
Training deep neural network policies end-to-end for real-world applications
so far requires big demonstration datasets in the real world or big sets
consisting of a large variety of realistic and closely related 3D CAD models.
These real or virtual data should, moreover, have very similar characteristics
to the conditions expected at test time. These stringent requirements and the
time consuming data collection processes that they entail, are currently the
most important impediment that keeps deep reinforcement learning from being
deployed in real-world applications. Therefore, in this work we advocate an
alternative approach, where instead of avoiding any domain shift by carefully
selecting the training data, the goal is to learn a policy that can cope with
it. To this end, we propose the DoShiCo challenge: to train a model in very
basic synthetic environments, far from realistic, in a way that it can be
applied in more realistic environments as well as take the control decisions on
real-world data. In particular, we focus on the task of collision avoidance for
drones. We created a set of simulated environments that can be used as
benchmark and implemented a baseline method, exploiting depth prediction as an
auxiliary task to help overcome the domain shift. Even though the policy is
trained in very basic environments, it can learn to fly without collisions in a
very different realistic simulated environment. Of course several benchmarks
for reinforcement learning already exist - but they never include a large
domain shift. On the other hand, several benchmarks in computer vision focus on
the domain shift, but they take the form of a static datasets instead of
simulated environments. In this work we claim that it is crucial to take the
two challenges together in one benchmark.Comment: Published at SIMPAR 2018. Please visit the paper webpage for more
information, a movie and code for reproducing results:
https://kkelchte.github.io/doshic
Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks
Future wireless networks have a substantial potential in terms of supporting
a broad range of complex compelling applications both in military and civilian
fields, where the users are able to enjoy high-rate, low-latency, low-cost and
reliable information services. Achieving this ambitious goal requires new radio
techniques for adaptive learning and intelligent decision making because of the
complex heterogeneous nature of the network structures and wireless services.
Machine learning (ML) algorithms have great success in supporting big data
analytics, efficient parameter estimation and interactive decision making.
Hence, in this article, we review the thirty-year history of ML by elaborating
on supervised learning, unsupervised learning, reinforcement learning and deep
learning. Furthermore, we investigate their employment in the compelling
applications of wireless networks, including heterogeneous networks (HetNets),
cognitive radios (CR), Internet of things (IoT), machine to machine networks
(M2M), and so on. This article aims for assisting the readers in clarifying the
motivation and methodology of the various ML algorithms, so as to invoke them
for hitherto unexplored services as well as scenarios of future wireless
networks.Comment: 46 pages, 22 fig
Information and communication technology solutions for outdoor navigation in dementia
INTRODUCTION:
Information and communication technology (ICT) is potentially mature enough to empower outdoor and social activities in dementia. However, actual ICT-based devices have limited functionality and impact, mainly limited to safety. What is an ideal operational framework to enhance this field to support outdoor and social activities?
METHODS:
Review of literature and cross-disciplinary expert discussion.
RESULTS:
A situation-aware ICT requires a flexible fine-tuning by stakeholders of system usability and complexity of function, and of user safety and autonomy. It should operate by artificial intelligence/machine learning and should reflect harmonized stakeholder values, social context, and user residual cognitive functions. ICT services should be proposed at the prodromal stage of dementia and should be carefully validated within the life space of users in terms of quality of life, social activities, and costs.
DISCUSSION:
The operational framework has the potential to produce ICT and services with high clinical impact but requires substantial investment
Toward Adaptive Trust Calibration for Level 2 Driving Automation
Properly calibrated human trust is essential for successful interaction
between humans and automation. However, while human trust calibration can be
improved by increased automation transparency, too much transparency can
overwhelm human workload. To address this tradeoff, we present a probabilistic
framework using a partially observable Markov decision process (POMDP) for
modeling the coupled trust-workload dynamics of human behavior in an
action-automation context. We specifically consider hands-off Level 2 driving
automation in a city environment involving multiple intersections where the
human chooses whether or not to rely on the automation. We consider automation
reliability, automation transparency, and scene complexity, along with human
reliance and eye-gaze behavior, to model the dynamics of human trust and
workload. We demonstrate that our model framework can appropriately vary
automation transparency based on real-time human trust and workload belief
estimates to achieve trust calibration.Comment: 10 pages, 8 figure
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