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Skip to Search Results- 82Artificial Intelligence
- 29Reinforcement learning
- 22Machine Learning
- 12Reinforcement Learning
- 9Planning
- 7Computer Games
- 4Müller, Martin
- 3Mueller, Martin
- 2Bowling, Michael
- 2Johanson, Michael
- 2Mahmood, Ashique
- 1Adams, Cathy
- 71Graduate and Postdoctoral Studies (GPS), Faculty of
- 71Graduate and Postdoctoral Studies (GPS), Faculty of /Theses and Dissertations
- 20Computing Science, Department of
- 20Computing Science, Department of/Technical Reports (Computing Science)
- 4Toolkit for Grant Success
- 4WISEST Summer Research Program
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Spring 2012
Mirian HosseinAbadi, MahdiehSadat
In this thesis we propose a computational model of animal behavior in spatial navigation, based on reinforcement learning ideas. In the field of computer science and specifically artificial intelligence, replay refers to retrieving and reprocessing the experiences that are stored in an abstract...
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Fall 2015
Brammadesam Manavalan, Yathirajan
Displaying believable emotional reactions in virtual characters is required in applications ranging from virtual-reality trainers to video games. Manual scripting is the most frequently used method and enables an arbitrarily high fidelity of the emotions displayed. However, scripting is labor...
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2021-07-10
Adams, Cathy, Lemermeyer, Gilllian
The Alberta Teachers’ Association (ATA), the Kule Institute for Advanced Study (KIAS) and the Faculty of Education, University of Alberta engaged in a partnership to organize a research and policy scoping initiative that would report on the expected impact of artificial intelligence (AI) in...
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2010
Lin, Guohui, Shi, Xiaoyu, Hu, Yu, Zeng, Dahua, Zaiane, Osmar
Technical report TR10-04. This paper describes a novel and fast placement algorithm for field programmable gate array (FPGA) design space exploration. The proposed algorithm generates the placement based on the topological similarity between two configurations (netlists) in the design space....
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Action Elimination and Plan Neighborhood Graph Search: Two Algorithms for Plan Improvement - Extended Version
Download2010
Nakhost, Hootan, Müller, Martin
Technical report TR10-01. Compared to optimal planners, satisficing planners can solve much harder problems but may produce overly costly and long plans. Plan quality for satisficing planners has become increasingly important. The most recent planning competition IPC-2008 used the cost of the...
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Spring 2021
Learning about many things can provide numerous benefits to a reinforcement learning system. For example, learning many auxiliary value functions, in addition to optimizing the environmental reward, appears to improve both exploration and representation learning. The question we tackle in this...
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Adaptive and Autonomous Switching: Shared Control of Powered Prosthetic Arms Using Reinforcement Learning
DownloadFall 2016
Powered prosthetic arms with numerous controllable functions (i.e., grip patterns or movable joints) can be challenging to operate. Gated control---a common control method for myoelectric arms and other human-machine interfaces---allows users to select a function by switching through a static...
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2013
Sturtevant, Nathan R., Valenzano, Richard, Schaeffer, Jonathan
While greedy best-first search (GBFS) is a popular algorithm for solving automated planning tasks, it can exhibit poor performance if the heuristic in use mistakenly identifies a region of the search space as promising. In such cases, the way the algorithm greedily trusts the heuristic can cause...
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Spring 2020
Reinforcement Learning is a formalism for learning by trial and error. Unfortunately, trial and error can take a long time to find a solution if the agent does not efficiently explore the behaviours available to it. Moreover, how an agent ought to explore depends on the task that the agent is...
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An Empirical Study on Learning and Improving the Search Objective for Unsupervised Paraphrasing
DownloadSpring 2022
Research in unsupervised text generation has been gaining attention over the years. One recent approach is local search towards a heuristically defined objective, which specifies language fluency, semantic meanings, and other task-specific attributes. Search in the sentence space is realized by...