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- 14Scheduling
- 5Reinforcement Learning
- 4Heuristic Search
- 3Artificial Intelligence
- 3Simulations
- 1Abdeyazdan, Zohreh
- 1Asadi Atui, Kavosh
- 1Brown, Jennifer A.
- 1Faid, Julian TW
- 1Fan, Gaojian
- 1Ho, Van H
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A Machine Learning Approach to Predict Production Time in Industrialized Building Construction
DownloadFall 2021
Industrialized building construction is an effective approach for improving the performance and management of construction projects by offering higher quality products, minimized environmental impacts, and improved schedule predictability. The industrialized building construction approach...
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Fall 2015
Transceiver duty-cycling (DC) is a popular technique to conserve energy in a wireless sensor network (WSN). In this thesis, our overall objective is to study the performance of a DC WSN. Namely, we consider the performance of a DC WSN from the point of throughput, as well as energy consumption,...
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Fall 2022
In this thesis, we investigate the empirical performance of several experience replay techniques. Efficient experience replay plays an important role in model-free reinforcement learning by improving sample efficiency through reusing past experience. However, replay-based methods were largely...
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Application-layer versus Network-layer Multicast: Networking Load, Link Stress, and Distribution Delay
DownloadFall 2017
Multicast is the task of disseminating a message from a source to a set of destinations. If supported by the network and switches, multicast can be performed at the network layer. The alternative solution is application-layer multicast (ALM) which disseminates the message through a set of unicast...
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Approximation Algorithms for Multi-processor Task Scheduling Problems on Identical Parallel Processors
DownloadFall 2013
In this thesis we present approximation algorithms for some multi-processor task scheduling problems. In a scheduling problem, there is a set of processors P that can be used to process a set of tasks T and the goal is to find a feasible scheduling of the tasks on the processors, while optimizing...
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Fall 2010
We investigate the use of machine learning to create effective heuristics for single-agent search. Our method aims to generate a sequence of heuristics from a given weak heuristic h{0} and a set of unlabeled training instances using a bootstrapping procedure. The training instances that can be...
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Fall 2013
Many important problems can be cast as state-space problems. In this dissertation we study a general paradigm for solving state-space problems which we name Cluster-and-Conquer (C&C). Algorithms that follow the C&C paradigm use the concept of equivalent states to reduce the number of states...