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Skip to Search Results- 19Planning
- 5Reinforcement Learning
- 4Heuristic Search
- 3Artificial Intelligence
- 2Scheduling
- 1Abstraction
- 1Asadi Atui, Kavosh
- 1Brown, Jennifer A.
- 1Faid, Julian TW
- 1Fan, Gaojian
- 1Jabbari Arfaee, Shahab
- 1Kumar,Chandan
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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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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...
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Enhancements to Methods for Planning and Scheduling Fabrication Projects Utilizing Multiskilled Labour Resources
DownloadSpring 2023
In prefabrication and off-site construction, various multiskilled work crews need to be assembled to work on different workstations to process custom-designed work units, each having specific requirements for material handling, assembly connections, welding, etc. However, the frequent labour...
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Fall 2013
Scaffolds are temporary structures that are built to support workers and materials and facilitate direct work on construction sites. A considerable amount of man power resources are consumed by industrial construction scaffolding, which makes effective planning and estimation of the same very...
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Spring 2016
This thesis proposes, analyzes and tests different exploration-based techniques in Greedy Best-First Search (GBFS) for satisficing planning. First, we show the potential of exploration-based techniques by combining GBFS and random walk exploration locally. We then conduct deep analysis on how...
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Fall 2022
This thesis investigates a new approach to model-based reinforcement learning using background planning: mixing (approximate) dynamic programming updates and model-free updates, similar to the Dyna architecture. Background planning with learned models is often worse than model-free alternatives,...
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Integral Urbanism: Investigating the Materiality and Spatiality of the University of Alberta Quadrangle
DownloadFall 2015
The university quadrangle is a space that exists on the majority of North American campuses, yet detailed investigation into the creation, existence and perpetuation of the quadrangle has been minimal. Considering how universities look to distinguish themselves from one another in search of the...
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Spring 2014
Coach learning is a key component for developing quality coaches. While researchers have identified many ways that coaches learn, there is little agreement as to how coaches learn best. As a way of examining these discrepancies found in the research, this study’s aim was to explore how Canadian...
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Spring 2016
In model-based reinforcement learning a model is learned which is then used to find good actions. What model to learn? We investigate these questions in the context of two different approaches to model-based reinforcement learning. We also investigate how one should learn and plan when the reward...