subject:(Imitation learning)

1. Ariki, Yuka. An Imitation Learning Framework that Explicitly Considers Robot Configurations and Robot-Environment Interactions.

Degree: Nara Institute of Science and Technology / 奈良先端科学技術大学院大学

URL: http://hdl.handle.net/10061/6036

Subjects/Keywords: Imitation Learning

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APA (6^th Edition):

Ariki, Y. (n.d.). An Imitation Learning Framework that Explicitly Considers Robot Configurations and Robot-Environment Interactions. (Thesis). Nara Institute of Science and Technology / 奈良先端科学技術大学院大学. Retrieved from http://hdl.handle.net/10061/6036

Note: this citation may be lacking information needed for this citation format:
No year of publication.
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Ariki, Yuka. “An Imitation Learning Framework that Explicitly Considers Robot Configurations and Robot-Environment Interactions.” Thesis, Nara Institute of Science and Technology / 奈良先端科学技術大学院大学. Accessed March 05, 2021. http://hdl.handle.net/10061/6036.

Note: this citation may be lacking information needed for this citation format:
No year of publication.
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Ariki, Yuka. “An Imitation Learning Framework that Explicitly Considers Robot Configurations and Robot-Environment Interactions.” Web. 05 Mar 2021.

Note: this citation may be lacking information needed for this citation format:
No year of publication.

Vancouver:

Ariki Y. An Imitation Learning Framework that Explicitly Considers Robot Configurations and Robot-Environment Interactions. [Internet] [Thesis]. Nara Institute of Science and Technology / 奈良先端科学技術大学院大学; [cited 2021 Mar 05]. Available from: http://hdl.handle.net/10061/6036.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
No year of publication.

Council of Science Editors:

Ariki Y. An Imitation Learning Framework that Explicitly Considers Robot Configurations and Robot-Environment Interactions. [Thesis]. Nara Institute of Science and Technology / 奈良先端科学技術大学院大学; Available from: http://hdl.handle.net/10061/6036

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
No year of publication.

Cornell University

2. Yu, Yue. Copy The Means Or Copy The Goal: Individual Differences And Developmental Changes In Young Children’S Imitative Behavior.

Degree: PhD, Developmental Psychology, 2015, Cornell University

URL: http://hdl.handle.net/1813/41051

► For young children, imitation serves both a learning role to gain knowledge and skills, and a social role to connect with people. Research on young… (more)

▼ For young children, imitation serves both a learning role to gain knowledge and skills, and a social role to connect with people. Research on young children's imitative behavior has presented a dilemma: On one hand infants and young children engage in goaldirected imitation, in which they selectively copy the model's goal, or actions that are necessary for achieving the goal. On the other hand they engage in faithful imitation or "overimitation", in which they also copy the exact manner of actions, or actions that are apparently unnecessary for achieving the goal. In this dissertation I approach this dilemma from an individual difference perspective. Two cohorts of children (N = 48) visited the lab on 3 occasions. Each time, children were tested on two types of imitation tasks, as well as tasks measuring other aspects of development. Parents filled out questionnaires about their children. Results from children's three visit showed stable individual differences in children's imitative behavior both within and between different types of tasks. Correlations between imitation measurements revealed two factors: one for goal-directed imitation and one for means-directed imitation. These two factors are correlated but also distinctive: goal-directed imitation is associated with children's general developmental level, Theory-of-Mind and prosocial behavior at 24 and 30 months; means-directed imitation is associated with children's executive functioning and normative reasoning at 36 months. In terms of developmental trajectory, there is a significant increase in means-directed imitation between 30 and 36 months of age, which coincide with an increase in children's normative reasoning. These results are discussed in terms of their implications for understanding children's social learning mechanisms, and also in terms of continuity in individual differences among infants and young children. Advisors/Committee Members: Kushnir,Tamar (chair), Robertson,Steven S (committee member), Booth,James (committee member), Casasola,Marianella (committee member).

Subjects/Keywords: imitation; individual differences; social learning

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APA (6^th Edition):

Yu, Y. (2015). Copy The Means Or Copy The Goal: Individual Differences And Developmental Changes In Young Children’S Imitative Behavior. (Doctoral Dissertation). Cornell University. Retrieved from http://hdl.handle.net/1813/41051

Chicago Manual of Style (16^th Edition):

Yu, Yue. “Copy The Means Or Copy The Goal: Individual Differences And Developmental Changes In Young Children’S Imitative Behavior.” 2015. Doctoral Dissertation, Cornell University. Accessed March 05, 2021. http://hdl.handle.net/1813/41051.

MLA Handbook (7^th Edition):

Yu, Yue. “Copy The Means Or Copy The Goal: Individual Differences And Developmental Changes In Young Children’S Imitative Behavior.” 2015. Web. 05 Mar 2021.

Vancouver:

Yu Y. Copy The Means Or Copy The Goal: Individual Differences And Developmental Changes In Young Children’S Imitative Behavior. [Internet] [Doctoral dissertation]. Cornell University; 2015. [cited 2021 Mar 05]. Available from: http://hdl.handle.net/1813/41051.

Council of Science Editors:

Yu Y. Copy The Means Or Copy The Goal: Individual Differences And Developmental Changes In Young Children’S Imitative Behavior. [Doctoral Dissertation]. Cornell University; 2015. Available from: http://hdl.handle.net/1813/41051

Vanderbilt University

3. Erdemir, Erdem. A Developmental Approach for Affordance and Imitation Learning Through Self-Exploration in Cognitive Robots.

Degree: PhD, Electrical Engineering, 2012, Vanderbilt University

URL: http://hdl.handle.net/1803/13236

► Cognitive robotics is one of the branches of robotics that is concerned with the design and implementation of robots that will accomplish cognitive tasks such… (more)

Subjects/Keywords: robot; cognitive; robotics; imitation learning; artificial intelligence; imitation learning

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APA (6^th Edition):

Erdemir, E. (2012). A Developmental Approach for Affordance and Imitation Learning Through Self-Exploration in Cognitive Robots. (Doctoral Dissertation). Vanderbilt University. Retrieved from http://hdl.handle.net/1803/13236

Chicago Manual of Style (16^th Edition):

Erdemir, Erdem. “A Developmental Approach for Affordance and Imitation Learning Through Self-Exploration in Cognitive Robots.” 2012. Doctoral Dissertation, Vanderbilt University. Accessed March 05, 2021. http://hdl.handle.net/1803/13236.

MLA Handbook (7^th Edition):

Erdemir, Erdem. “A Developmental Approach for Affordance and Imitation Learning Through Self-Exploration in Cognitive Robots.” 2012. Web. 05 Mar 2021.

Vancouver:

Erdemir E. A Developmental Approach for Affordance and Imitation Learning Through Self-Exploration in Cognitive Robots. [Internet] [Doctoral dissertation]. Vanderbilt University; 2012. [cited 2021 Mar 05]. Available from: http://hdl.handle.net/1803/13236.

Council of Science Editors:

Erdemir E. A Developmental Approach for Affordance and Imitation Learning Through Self-Exploration in Cognitive Robots. [Doctoral Dissertation]. Vanderbilt University; 2012. Available from: http://hdl.handle.net/1803/13236

NSYSU

4. Chung, Chi-Hsiu. Achieving Imitation-Based Learning for a Humanoid Robot by Evolutionary Computation.

Degree: Master, Information Management, 2009, NSYSU

URL: http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0729109-002840

► This thesis presents an imitation-based methodology, also a simple and easy way, for a service robot to learn the behaviors demonstrated by the user. With… (more)

Subjects/Keywords: Genetic Algorithm; Imitation Learning; Robot Learning

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APA (6^th Edition):

Chung, C. (2009). Achieving Imitation-Based Learning for a Humanoid Robot by Evolutionary Computation. (Thesis). NSYSU. Retrieved from http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0729109-002840

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Chung, Chi-Hsiu. “Achieving Imitation-Based Learning for a Humanoid Robot by Evolutionary Computation.” 2009. Thesis, NSYSU. Accessed March 05, 2021. http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0729109-002840.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Chung, Chi-Hsiu. “Achieving Imitation-Based Learning for a Humanoid Robot by Evolutionary Computation.” 2009. Web. 05 Mar 2021.

Vancouver:

Chung C. Achieving Imitation-Based Learning for a Humanoid Robot by Evolutionary Computation. [Internet] [Thesis]. NSYSU; 2009. [cited 2021 Mar 05]. Available from: http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0729109-002840.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Chung C. Achieving Imitation-Based Learning for a Humanoid Robot by Evolutionary Computation. [Thesis]. NSYSU; 2009. Available from: http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0729109-002840

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Delft University of Technology

5. Jauhri, Snehal (author). Interactive Learning in State-space: Enabling robots to learn from non-expert humans.

Degree: 2020, Delft University of Technology

URL: http://resolver.tudelft.nl/uuid:be1a04dc-1780-4683-9a7c-77434cd77fa7

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Imitation Learning is a technique that enables programming the behavior of agents through demonstration, as opposed to manually engineering behavior. However, Imitation Learning methods require… (more)

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Imitation Learning is a technique that enables programming the behavior of agents through demonstration, as opposed to manually engineering behavior. However, Imitation Learning methods require demonstration data (in the form of state-action labels) and in many scenarios, the demonstrations can be expensive to obtain or too complex for a demonstrator to execute. This lack or sub-optimality of demonstrations limits the applicability and performance of many Imitation Learning methods. Advancements in Interactive Imitation Learning techniques however, have made it easier for demonstrators to train agents and improve their performance. These techniques involve demonstrators interacting with and guiding the agent as it performs the requisite task. This guidance is typically in the form of corrections or feedback on the current actions being executed by the agent. In this thesis, a novel Interactive Learning technique is proposed that uses human corrective feedback in state-space to train and improve agent behavior. This technique is beneficial since providing guidance to the agent in terms of `changing its state' is often easier or more intuitive for the human demonstrator (as opposed to changing the actions being executed). For instance, in manipulation tasks using a robotic arm, it is easier for the demonstrator to provide state information such as the Cartesian position of the end-effector rather than low-level action information such as joint angles. Keeping such scenarios in mind, we propose our method titled: Teaching Imitative Policies in State-space (TIPS). We evaluate the performance of TIPS for various control tasks as part of the OpenAI Gym toolkit as well as for a manipulation task using a KUKA LBR iiwa robotic arm. We show that through continuous improvement via feedback, agents trained using TIPS outperform the demonstrator and in-turn outperform conventional Imitation Learning agents.

Electrical Engineer | Embedded Systems

Advisors/Committee Members: Kober, Jens (mentor), Celemin, Carlos (mentor), van Genderen, Arjan (graduation committee), Peternel, Luka (graduation committee), Delft University of Technology (degree granting institution).

Subjects/Keywords: Imitation Learning; Learning from Demonstrations; Robot Control

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Jauhri, S. (. (2020). Interactive Learning in State-space: Enabling robots to learn from non-expert humans. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:be1a04dc-1780-4683-9a7c-77434cd77fa7

Chicago Manual of Style (16^th Edition):

Jauhri, Snehal (author). “Interactive Learning in State-space: Enabling robots to learn from non-expert humans.” 2020. Masters Thesis, Delft University of Technology. Accessed March 05, 2021. http://resolver.tudelft.nl/uuid:be1a04dc-1780-4683-9a7c-77434cd77fa7.

MLA Handbook (7^th Edition):

Jauhri, Snehal (author). “Interactive Learning in State-space: Enabling robots to learn from non-expert humans.” 2020. Web. 05 Mar 2021.

Vancouver:

Jauhri S(. Interactive Learning in State-space: Enabling robots to learn from non-expert humans. [Internet] [Masters thesis]. Delft University of Technology; 2020. [cited 2021 Mar 05]. Available from: http://resolver.tudelft.nl/uuid:be1a04dc-1780-4683-9a7c-77434cd77fa7.

Council of Science Editors:

Jauhri S(. Interactive Learning in State-space: Enabling robots to learn from non-expert humans. [Masters Thesis]. Delft University of Technology; 2020. Available from: http://resolver.tudelft.nl/uuid:be1a04dc-1780-4683-9a7c-77434cd77fa7

6. Silveria, Niki T. Optimal Strategy Imitation Learning from Differential Games.

Degree: 2017, University of Nevada – Reno

URL: http://hdl.handle.net/11714/2797

► The ability of a vehicle to navigate safely through any environment relies on its driver having an accurate sense of the future positions and goals… (more)

Subjects/Keywords: autonomous vehicles; differential games; imitation learning

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APA (6^th Edition):

Silveria, N. T. (2017). Optimal Strategy Imitation Learning from Differential Games. (Thesis). University of Nevada – Reno. Retrieved from http://hdl.handle.net/11714/2797

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Silveria, Niki T. “Optimal Strategy Imitation Learning from Differential Games.” 2017. Thesis, University of Nevada – Reno. Accessed March 05, 2021. http://hdl.handle.net/11714/2797.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Silveria, Niki T. “Optimal Strategy Imitation Learning from Differential Games.” 2017. Web. 05 Mar 2021.

Vancouver:

Silveria NT. Optimal Strategy Imitation Learning from Differential Games. [Internet] [Thesis]. University of Nevada – Reno; 2017. [cited 2021 Mar 05]. Available from: http://hdl.handle.net/11714/2797.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Silveria NT. Optimal Strategy Imitation Learning from Differential Games. [Thesis]. University of Nevada – Reno; 2017. Available from: http://hdl.handle.net/11714/2797

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Georgia Tech

7. Schroecker, Yannick Karl Daniel. Manipulating state space distributions for sample-efficient imitation-learning.

Degree: PhD, Interactive Computing, 2020, Georgia Tech

URL: http://hdl.handle.net/1853/62755

► Imitation learning has emerged as one of the most effective approaches to train agents to act intelligently in unstructured and unknown domains. On its own… (more)

Subjects/Keywords: Imitation learning; Reinforcement learning; Deep learning; Machine learning; Artificial intelligence

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Schroecker, Y. K. D. (2020). Manipulating state space distributions for sample-efficient imitation-learning. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/62755

Chicago Manual of Style (16^th Edition):

Schroecker, Yannick Karl Daniel. “Manipulating state space distributions for sample-efficient imitation-learning.” 2020. Doctoral Dissertation, Georgia Tech. Accessed March 05, 2021. http://hdl.handle.net/1853/62755.

MLA Handbook (7^th Edition):

Schroecker, Yannick Karl Daniel. “Manipulating state space distributions for sample-efficient imitation-learning.” 2020. Web. 05 Mar 2021.

Vancouver:

Schroecker YKD. Manipulating state space distributions for sample-efficient imitation-learning. [Internet] [Doctoral dissertation]. Georgia Tech; 2020. [cited 2021 Mar 05]. Available from: http://hdl.handle.net/1853/62755.

Council of Science Editors:

Schroecker YKD. Manipulating state space distributions for sample-efficient imitation-learning. [Doctoral Dissertation]. Georgia Tech; 2020. Available from: http://hdl.handle.net/1853/62755

Georgia Tech

8. Cheng, Ching An. Eﬃcient and principled robot learning: Theory and algorithms.

Degree: PhD, Interactive Computing, 2020, Georgia Tech

URL: http://hdl.handle.net/1853/62733

► Roboticists have long envisioned fully-automated robots that can operate reliably in unstructured environments. This is an exciting but extremely difficult problem; in order to succeed,… (more)

Subjects/Keywords: Online learning; Control theory; Robotics; Optimization; Reinforcement learning; Imitation learning

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APA (6^th Edition):

Cheng, C. A. (2020). Eﬃcient and principled robot learning: Theory and algorithms. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/62733

Chicago Manual of Style (16^th Edition):

Cheng, Ching An. “Eﬃcient and principled robot learning: Theory and algorithms.” 2020. Doctoral Dissertation, Georgia Tech. Accessed March 05, 2021. http://hdl.handle.net/1853/62733.

MLA Handbook (7^th Edition):

Cheng, Ching An. “Eﬃcient and principled robot learning: Theory and algorithms.” 2020. Web. 05 Mar 2021.

Vancouver:

Cheng CA. Eﬃcient and principled robot learning: Theory and algorithms. [Internet] [Doctoral dissertation]. Georgia Tech; 2020. [cited 2021 Mar 05]. Available from: http://hdl.handle.net/1853/62733.

Council of Science Editors:

Cheng CA. Eﬃcient and principled robot learning: Theory and algorithms. [Doctoral Dissertation]. Georgia Tech; 2020. Available from: http://hdl.handle.net/1853/62733

9. 朴, 江. Imitation Learning Framework using Principal Component Analysis for Humanoid Robot Motion Generation : ヒューマノイドロボット動作生成のための主成分分析を用いた模倣学習フレームワーク.

Degree: 博士(工学), 2015, Hokkaido University / 北海道大学

URL: http://hdl.handle.net/2115/58948 ; http://dx.doi.org/10.14943/doctoral.k11770

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This dissertation investigates the efficient imitation learning framework based on principal component analysis to generate desired and abundant motions using few human demonstrations in the… (more)

Subjects/Keywords: Machine Learning; Imitation Learning; Principal Component Analysis; Evolutionary Process

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APA (6^th Edition):

朴, �. (2015). Imitation Learning Framework using Principal Component Analysis for Humanoid Robot Motion Generation : ヒューマノイドロボット動作生成のための主成分分析を用いた模倣学習フレームワーク. (Thesis). Hokkaido University / 北海道大学. Retrieved from http://hdl.handle.net/2115/58948 ; http://dx.doi.org/10.14943/doctoral.k11770

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

朴, 江. “Imitation Learning Framework using Principal Component Analysis for Humanoid Robot Motion Generation : ヒューマノイドロボット動作生成のための主成分分析を用いた模倣学習フレームワーク.” 2015. Thesis, Hokkaido University / 北海道大学. Accessed March 05, 2021. http://hdl.handle.net/2115/58948 ; http://dx.doi.org/10.14943/doctoral.k11770.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

朴, 江. “Imitation Learning Framework using Principal Component Analysis for Humanoid Robot Motion Generation : ヒューマノイドロボット動作生成のための主成分分析を用いた模倣学習フレームワーク.” 2015. Web. 05 Mar 2021.

Vancouver:

朴 �. Imitation Learning Framework using Principal Component Analysis for Humanoid Robot Motion Generation : ヒューマノイドロボット動作生成のための主成分分析を用いた模倣学習フレームワーク. [Internet] [Thesis]. Hokkaido University / 北海道大学; 2015. [cited 2021 Mar 05]. Available from: http://hdl.handle.net/2115/58948 ; http://dx.doi.org/10.14943/doctoral.k11770.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

朴 �. Imitation Learning Framework using Principal Component Analysis for Humanoid Robot Motion Generation : ヒューマノイドロボット動作生成のための主成分分析を用いた模倣学習フレームワーク. [Thesis]. Hokkaido University / 北海道大学; 2015. Available from: http://hdl.handle.net/2115/58948 ; http://dx.doi.org/10.14943/doctoral.k11770

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Georgia Tech

10. Ramamurthy, Arun. A reinforcement learning framework for the automation of engineering decisions in complex systems.

Degree: PhD, Aerospace Engineering, 2019, Georgia Tech

URL: http://hdl.handle.net/1853/62626

► The process of engineering design is characterized by a series of decisions that determine the performance of the final product. Engineers are faced with decisions,… (more)

▼ The process of engineering design is characterized by a series of decisions that determine the performance of the final product. Engineers are faced with decisions, such as choices pertaining to the type of model to be used, appropriate parameter settings, system architecture etc., all through the design process and these decisions are undertaken with a desired goal in mind. The decisions themselves manifest as a planned set of actions that are informed by observed behavior and domain expertise. The mathematical formalization of such a design process would resemble that of a sequential decision process. It is natural to ponder if the underlying logic behind these decisions can be abstracted into a computer program such that, when faced with a similar situation, an intelligent system can aid the ensuing design process. To satisfy this need, expert systems, capable of incorporating design expertise and domain knowledge, have been designed. The state-of-the-art for such systems view them as static entities that are configured to operate on a predefined problem using some variant of rule-based or case-based decision-making methods. The lack of a dynamic quality in the face of evolving design environments and processes necessitates frequent updates and redesign of these systems making their use infrequent in a typical engineering environment. Recent developments in the field of reinforcement learning have demonstrated significant success in their application to sequential decision-making problems. The reinforcement learning setup of an agent learning from interactions with the environment makes these class of methods a perfect alternative to the static expert systems with predefined rules. In such scenarios, expert interactions can serve as demonstrations for the learning algorithm and could help train the agent. Further, the exploratory nature of the learning algorithm leads to the possibility that the training agent would identify decision paths that outperform the ones demonstrated by an expert, thereby enabling the system to self-learn to improve the resulting design or process. The current research work implements a reinforcement learning framework that relies on the principles of life-long learning in order to assist engineering design processes. Assistance is provided in the form of recommendations of design decisions to the design engineer in the course of utilization of the design environment for a given problem. The framework implements aspects of machine learning such as imitation learning from human demonstrations in order to train intelligent agents. The life-long learning aspect of the framework enables adaptation of the trained agents to new and incoming data such that both newly explored portions of the design space and new demonstrations from design engineers are incorporated into the decision making model. The exploratory nature of reinforcement learning algorithm enables the possibility of identifying decision paths that are better that the ones demonstrated by design engineers hence enabling the… Advisors/Committee Members: Mavris, Dimitri N. (advisor), Schrage, Daniel P. (committee member), Kennedy, Graeme J. (committee member), Song, Le (committee member), Briceno, Simon I. (committee member), Villeneuve, Frederic (committee member).

Subjects/Keywords: Reinforcement learning; Artificial intelligence; Engineering design; Imitation learning; CAD2Vec

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APA (6^th Edition):

Ramamurthy, A. (2019). A reinforcement learning framework for the automation of engineering decisions in complex systems. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/62626

Chicago Manual of Style (16^th Edition):

Ramamurthy, Arun. “A reinforcement learning framework for the automation of engineering decisions in complex systems.” 2019. Doctoral Dissertation, Georgia Tech. Accessed March 05, 2021. http://hdl.handle.net/1853/62626.

MLA Handbook (7^th Edition):

Ramamurthy, Arun. “A reinforcement learning framework for the automation of engineering decisions in complex systems.” 2019. Web. 05 Mar 2021.

Vancouver:

Ramamurthy A. A reinforcement learning framework for the automation of engineering decisions in complex systems. [Internet] [Doctoral dissertation]. Georgia Tech; 2019. [cited 2021 Mar 05]. Available from: http://hdl.handle.net/1853/62626.

Council of Science Editors:

Ramamurthy A. A reinforcement learning framework for the automation of engineering decisions in complex systems. [Doctoral Dissertation]. Georgia Tech; 2019. Available from: http://hdl.handle.net/1853/62626

Georgia Tech

11. Edwards, Ashley Deloris. Emulation and imitation via perceptual goal specifications.

Degree: PhD, Computer Science, 2019, Georgia Tech

URL: http://hdl.handle.net/1853/61234

► This dissertation aims to demonstrate how perceptual goal specifications may be used as alternative representations for specifying domain-specific reward functions for reinforcement learning. The works… (more)

Subjects/Keywords: Reinforcement learning; Goal specification; Imitation learning; Reward design

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Edwards, A. D. (2019). Emulation and imitation via perceptual goal specifications. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/61234

Chicago Manual of Style (16^th Edition):

Edwards, Ashley Deloris. “Emulation and imitation via perceptual goal specifications.” 2019. Doctoral Dissertation, Georgia Tech. Accessed March 05, 2021. http://hdl.handle.net/1853/61234.

MLA Handbook (7^th Edition):

Edwards, Ashley Deloris. “Emulation and imitation via perceptual goal specifications.” 2019. Web. 05 Mar 2021.

Vancouver:

Edwards AD. Emulation and imitation via perceptual goal specifications. [Internet] [Doctoral dissertation]. Georgia Tech; 2019. [cited 2021 Mar 05]. Available from: http://hdl.handle.net/1853/61234.

Council of Science Editors:

Edwards AD. Emulation and imitation via perceptual goal specifications. [Doctoral Dissertation]. Georgia Tech; 2019. Available from: http://hdl.handle.net/1853/61234

Vanderbilt University

12. O'Doherty, Katherine Duffy. Young children’s social-category based preferences and learning biases: an examination of methodological effects.

Degree: PhD, Psychology, 2013, Vanderbilt University

URL: http://hdl.handle.net/1803/12016

► By the time children reach the preschool years, they have become skilled in two social abilities: 1) dividing their world into social groups and evaluating… (more)

▼ By the time children reach the preschool years, they have become skilled in two social abilities: 1) dividing their world into social groups and evaluating people based on group dimensions; and 2) learning from social others. Researchers have begun to explore how one ability (social categorization) might affect the other (social learning). Results of two previous studies suggest that children selectively learn from social-cultural in-group members versus out-group members. However, the research methods in these studies may have affected the results (and the conclusions drawn from these results). In this dissertation, I examined two methodological factors that might influence whether preschool children show preferences and learning biases for social-cultural in-group members: 1) whether children were presented information by one source of information at a time, or presented with conflicting information from two speakers and 2) whether children saw out-group members “live” (present in the room) versus on video. I examined monolingual 3-and 5-year-olds’ learning from and preferences for a foreign-language (Russian) versus native-language (English) speaker, to determine if children consider a speaker’s social category (what language she speaks) when deciding if she is a trustworthy source of information and if they should “like” her. In the present study, children learned how to use novel objects from both speakers equally (whether the speakers were live or on video) when their information did not conflict. Children learned a novel word from an English speaker but not a Russian speaker, even when the Russian speaker was the sole source of information. When speakers were present, and children got to “warm-up” to the speakers first, the number of children who preferred the English or Russian speaker was the same. When the speakers were on video, equal numbers of children preferred the English and Russian speakers. Only children who saw the speakers “live” and were asked preference questions immediately showed an in-group bias. Overall, children learned non-verbal information equally from a Russian and English speaker. Thus, I conclude children do not have a bias to only learn from social-cultural in-group members. Advisors/Committee Members: Amy Needham (committee member), Megan M. Saylor (committee member), Norbert Ross (committee member), Georgene L. Troseth (Committee Chair).

Subjects/Keywords: social groups; social learning; social cognition; word learning; imitation

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

O'Doherty, K. D. (2013). Young children’s social-category based preferences and learning biases: an examination of methodological effects. (Doctoral Dissertation). Vanderbilt University. Retrieved from http://hdl.handle.net/1803/12016

Chicago Manual of Style (16^th Edition):

O'Doherty, Katherine Duffy. “Young children’s social-category based preferences and learning biases: an examination of methodological effects.” 2013. Doctoral Dissertation, Vanderbilt University. Accessed March 05, 2021. http://hdl.handle.net/1803/12016.

MLA Handbook (7^th Edition):

O'Doherty, Katherine Duffy. “Young children’s social-category based preferences and learning biases: an examination of methodological effects.” 2013. Web. 05 Mar 2021.

Vancouver:

O'Doherty KD. Young children’s social-category based preferences and learning biases: an examination of methodological effects. [Internet] [Doctoral dissertation]. Vanderbilt University; 2013. [cited 2021 Mar 05]. Available from: http://hdl.handle.net/1803/12016.

Council of Science Editors:

O'Doherty KD. Young children’s social-category based preferences and learning biases: an examination of methodological effects. [Doctoral Dissertation]. Vanderbilt University; 2013. Available from: http://hdl.handle.net/1803/12016

University of Illinois – Chicago

13. Tirinzoni, Andrea. Adversarial Inverse Reinforcement Learning with Changing Dynamics.

Degree: 2017, University of Illinois – Chicago

URL: http://hdl.handle.net/10027/22081

► Most work on inverse reinforcement learning, the problem of recovering the unknown reward function being optimized by a decision-making agent, has focused on cases where… (more)

Subjects/Keywords: Machine Learning; Inverse Reinforcement Learning; Reinforcement Learning; Adversarial Prediction; Markov Decision Process; Imitation Learning

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Tirinzoni, A. (2017). Adversarial Inverse Reinforcement Learning with Changing Dynamics. (Thesis). University of Illinois – Chicago. Retrieved from http://hdl.handle.net/10027/22081

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Tirinzoni, Andrea. “Adversarial Inverse Reinforcement Learning with Changing Dynamics.” 2017. Thesis, University of Illinois – Chicago. Accessed March 05, 2021. http://hdl.handle.net/10027/22081.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Tirinzoni, Andrea. “Adversarial Inverse Reinforcement Learning with Changing Dynamics.” 2017. Web. 05 Mar 2021.

Vancouver:

Tirinzoni A. Adversarial Inverse Reinforcement Learning with Changing Dynamics. [Internet] [Thesis]. University of Illinois – Chicago; 2017. [cited 2021 Mar 05]. Available from: http://hdl.handle.net/10027/22081.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Tirinzoni A. Adversarial Inverse Reinforcement Learning with Changing Dynamics. [Thesis]. University of Illinois – Chicago; 2017. Available from: http://hdl.handle.net/10027/22081

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

14. Willim, Daniel. Developing a Self-learning Intelligent Agent in StarCraft II - Deep Reinforcement Learning with Imitation Learning .

Degree: Chalmers tekniska högskola / Institutionen för data och informationsteknik, 2019, Chalmers University of Technology

URL: http://hdl.handle.net/20.500.12380/301904

► Knowledge of machine learning is becoming more essential in many fields. This thesis explores and outlines the basics of machine learning through the complex game… (more)

Subjects/Keywords: Deep Q-Learning; Deep Q-Network; Imitation Learning; Machine Learning; PySC2; Reinforcement Learning; StarCraft II

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Willim, D. (2019). Developing a Self-learning Intelligent Agent in StarCraft II - Deep Reinforcement Learning with Imitation Learning . (Thesis). Chalmers University of Technology. Retrieved from http://hdl.handle.net/20.500.12380/301904

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Willim, Daniel. “Developing a Self-learning Intelligent Agent in StarCraft II - Deep Reinforcement Learning with Imitation Learning .” 2019. Thesis, Chalmers University of Technology. Accessed March 05, 2021. http://hdl.handle.net/20.500.12380/301904.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Willim, Daniel. “Developing a Self-learning Intelligent Agent in StarCraft II - Deep Reinforcement Learning with Imitation Learning .” 2019. Web. 05 Mar 2021.

Vancouver:

Willim D. Developing a Self-learning Intelligent Agent in StarCraft II - Deep Reinforcement Learning with Imitation Learning . [Internet] [Thesis]. Chalmers University of Technology; 2019. [cited 2021 Mar 05]. Available from: http://hdl.handle.net/20.500.12380/301904.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Willim D. Developing a Self-learning Intelligent Agent in StarCraft II - Deep Reinforcement Learning with Imitation Learning . [Thesis]. Chalmers University of Technology; 2019. Available from: http://hdl.handle.net/20.500.12380/301904

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Université Montpellier II

15. Derex, Maxime. Les mécanismes de l'évolution culturelle cumulative : Mechanisms of cumulative cultural evolution.

Degree: Docteur es, Evolution, Ecologie, Ressources génétiques, Paléontologie, 2013, Université Montpellier II

URL: http://www.theses.fr/2013MON20147

►

Le succès remarquable -écologique et démographique- de l'espèce humaine est largement attribué à notre capacité pour la culture cumulative, i.e. l'accumulation d'innovations culturelles à travers… (more)

▼

Le succès remarquable -écologique et démographique- de l'espèce humaine est largement attribué à notre capacité pour la culture cumulative, i.e. l'accumulation d'innovations culturelles à travers le temps. L'absence ou du moins la rareté de la culture cumulative chez les autres animaux a conduit à de nombreuses spéculations à propos des facteurs nécessaires à son émergence. La culture cumulative dépend étroitement de processus permettant de générer de l'information, et de mécanismes permettant à cette information d'être fidèlement transmise entre les individus. A l'aide d'une approche expérimentale basée sur l'utilisation de jeux sur ordinateur, nous montrons que la capacité d'imitation des patrons comportementaux peut grandement faciliter la fidélité de transmission des informations culturelles. De même, une grande taille de population contribue à la stabilisation des informations culturelles, particulièrement dans le cas d'informations complexes. Cependant, la culture cumulative requiert également la production d'innovations qui ne peut résulter de ces seuls facteurs. D'un point de vue théorique, les innovations sont généralement plus coûteuses à produire qu'à copier, de sorte que la sélection peut difficilement opérer au profit des innovateurs. Nos résultats nous permettent cependant d'avancer que l'émergence d'objets culturels technologiquement opaques pourrait permettre aux innovateurs de bénéficier plus largement de leurs innovations. L'émergence de l'opacité technologique pourrait ainsi constituer un pivot dans l'évolution de la culture cumulative, permettant de favoriser à la fois l'innovation et les mécanismes fidèles de transmission d'information. Les capacités à hiérarchiser et planifier ses actions étant essentiel à la production d'objets culturels technologiquement opaques, il est possible que l'absence apparente de culture cumulative chez les animaux non-humains soit due à un moindre développement de ces capacités cognitives. Finalement nous proposons que la complexité de la culture humaine repose sur quatre facteurs principaux : capacité à hiérarchiser et planifier ses actions, capacité à imiter, collaboration interindividuelle et grande taille de population.

The remarkable success – both ecological and demographic- of the human species is widely attributed to our capability for cumulative culture, i.e. the accumulation of innovations over time. The lack or at least the rarity of cumulative culture in non-human animals has led to much speculation about factors enabling its emergence. Cumulative culture strongly depends on processes allowing generating information, and mechanisms allowing information to be efficiently transmitted between individuals. Using a computer-based experimental approach, we show that process-copying ability improves the fidelity of cultural information transmission. Also, population size contributes to the stability of cultural information, especially for complex information. However, cumulative culture also requires the creation of new innovations, which cannot be the…

Advisors/Committee Members: Raymond, Michel (thesis director), Godelle, Bernard (thesis director).

Subjects/Keywords: Apprentissage social; Imitation; Évolution culturelle; Transmission sociale; Apprentissage individuel; Coopération; Social learning; Imitation; Cultural evolution; Social transmission; Individual learning; Cooperation

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Derex, M. (2013). Les mécanismes de l'évolution culturelle cumulative : Mechanisms of cumulative cultural evolution. (Doctoral Dissertation). Université Montpellier II. Retrieved from http://www.theses.fr/2013MON20147

Chicago Manual of Style (16^th Edition):

Derex, Maxime. “Les mécanismes de l'évolution culturelle cumulative : Mechanisms of cumulative cultural evolution.” 2013. Doctoral Dissertation, Université Montpellier II. Accessed March 05, 2021. http://www.theses.fr/2013MON20147.

MLA Handbook (7^th Edition):

Derex, Maxime. “Les mécanismes de l'évolution culturelle cumulative : Mechanisms of cumulative cultural evolution.” 2013. Web. 05 Mar 2021.

Vancouver:

Derex M. Les mécanismes de l'évolution culturelle cumulative : Mechanisms of cumulative cultural evolution. [Internet] [Doctoral dissertation]. Université Montpellier II; 2013. [cited 2021 Mar 05]. Available from: http://www.theses.fr/2013MON20147.

Council of Science Editors:

Derex M. Les mécanismes de l'évolution culturelle cumulative : Mechanisms of cumulative cultural evolution. [Doctoral Dissertation]. Université Montpellier II; 2013. Available from: http://www.theses.fr/2013MON20147

16. Knyazeva, Elena. Apprendre par imitation : applications à quelques problèmes d'apprentissage structuré en traitement des langues : Imitation learning : application to several structured learning tasks in natural language processing.

Degree: Docteur es, Informatique, 2018, Université Paris-Saclay (ComUE)

URL: http://www.theses.fr/2018SACLS134

►

L’apprentissage structuré est devenu omniprésent dans le traitement automatique des langues naturelles. De nombreuses applications qui font maintenant partie de notre vie telles que des… (more)

▼

L’apprentissage structuré est devenu omniprésent dans le traitement automatique des langues naturelles. De nombreuses applications qui font maintenant partie de notre vie telles que des assistants personnels, la traduction automatique, ou encore la reconnaissance vocale, reposent sur ces techniques. Les problèmes d'apprentissage structuré qu’il est nécessaire de résoudre sont de plus en plus complexes et demandent de prendre en compte de plus en plus d’informations à des niveaux linguistiques variés (morphologique, syntaxique, etc.) et reposent la question du meilleurs compromis entre la finesse de la modélisation et l’exactitude des algorithmes d’apprentissage et d’inférence. L’apprentissage par imitation propose de réaliser les procédures d’apprentissage et d’inférence de manière approchée afin de pouvoir exploiter pleinement des structures de dépendance plus riches. Cette thèse explore ce cadre d’apprentissage, en particulier l’algorithme SEARN, à la fois sur le plan théorique ainsi que ses possibilités d’application aux tâches de traitement automatique des langues, notamment aux plus complexes telles que la traduction. Concernant les aspects théoriques, nous présentons un cadre unifié pour les différentes familles d’apprentissage par imitation, qui permet de redériver de manière simple les propriétés de convergence de ces algorithmes; concernant les aspects plus appliqués, nous utilisons l’apprentissage par imitation d’une part pour explorer l’étiquetage de séquences en ordre libre; d’autre part pour étudier des stratégies de décodage en deux étapes pour la traduction automatique.

Structured learning has become ubiquitousin Natural Language Processing; a multitude ofapplications, such as personal assistants, machinetranslation and speech recognition, to name just afew, rely on such techniques. The structured learningproblems that must now be solved are becomingincreasingly more complex and require an increasingamount of information at different linguisticlevels (morphological, syntactic, etc.). It is thereforecrucial to find the best trade-off between the degreeof modelling detail and the exactitude of the inferencealgorithm. Imitation learning aims to perform approximatelearning and inference in order to better exploitricher dependency structures. In this thesis, we explorethe use of this specific learning setting, in particularusing the SEARN algorithm, both from a theoreticalperspective and in terms of the practical applicationsto Natural Language Processing tasks, especiallyto complex tasks such as machine translation.Concerning the theoretical aspects, we introduce aunified framework for different imitation learning algorithmfamilies, allowing us to review and simplifythe convergence properties of the algorithms. With regardsto the more practical application of our work, weuse imitation learning first to experiment with free ordersequence labelling and secondly to explore twostepdecoding strategies for machine translation.

Advisors/Committee Members: Yvon, François (thesis director).

Subjects/Keywords: Apprentissage structuré; Apprentissage par imitation; Traitement automatique des langues naturelles naturelles; Structured learning; Imitation learning; Natural language processing

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Knyazeva, E. (2018). Apprendre par imitation : applications à quelques problèmes d'apprentissage structuré en traitement des langues : Imitation learning : application to several structured learning tasks in natural language processing. (Doctoral Dissertation). Université Paris-Saclay (ComUE). Retrieved from http://www.theses.fr/2018SACLS134

Chicago Manual of Style (16^th Edition):

Knyazeva, Elena. “Apprendre par imitation : applications à quelques problèmes d'apprentissage structuré en traitement des langues : Imitation learning : application to several structured learning tasks in natural language processing.” 2018. Doctoral Dissertation, Université Paris-Saclay (ComUE). Accessed March 05, 2021. http://www.theses.fr/2018SACLS134.

MLA Handbook (7^th Edition):

Knyazeva, Elena. “Apprendre par imitation : applications à quelques problèmes d'apprentissage structuré en traitement des langues : Imitation learning : application to several structured learning tasks in natural language processing.” 2018. Web. 05 Mar 2021.

Vancouver:

Knyazeva E. Apprendre par imitation : applications à quelques problèmes d'apprentissage structuré en traitement des langues : Imitation learning : application to several structured learning tasks in natural language processing. [Internet] [Doctoral dissertation]. Université Paris-Saclay (ComUE); 2018. [cited 2021 Mar 05]. Available from: http://www.theses.fr/2018SACLS134.

Council of Science Editors:

Knyazeva E. Apprendre par imitation : applications à quelques problèmes d'apprentissage structuré en traitement des langues : Imitation learning : application to several structured learning tasks in natural language processing. [Doctoral Dissertation]. Université Paris-Saclay (ComUE); 2018. Available from: http://www.theses.fr/2018SACLS134

17. Munzer, Thibaut. Représentations relationnelles et apprentissage interactif pour l'apprentissage efficace du comportement coopératif : Relational representations and interactive learning for efficient cooperative behavior learning.

Degree: Docteur es, Informatique, 2017, Bordeaux

URL: http://www.theses.fr/2017BORD0574

►

Cette thèse présente de nouvelles approches permettant l’apprentissage efficace et intuitif de plans de haut niveau pour les robots collaboratifs. Plus précisément, nous étudions l’application… (more)

▼

Cette thèse présente de nouvelles approches permettant l’apprentissage efficace et intuitif de plans de haut niveau pour les robots collaboratifs. Plus précisément, nous étudions l’application d’algorithmes d’apprentissage par démonstration dans des domaines relationnels. L’utilisation de domaines relationnels pour représenter le monde permet de simplifier la représentation de comportements concurrents et collaboratifs. Nous avons commencé par développer et étudier le premier algorithme d’apprentissage par renforcement inverse pour domaines relationnels. Nous avons ensuite présenté comment utiliser le formalisme RAP pour représenter des tâches collaboratives comprenant un robot et un opérateur humain. RAP est une extension des MDP relationnels qui permet de modéliser des activités concurrentes. Utiliser RAP nous a permis de représenter à la fois l’humain et le robot dans le même processus, mais également de modéliser des activités concurrentes du robot. Sous ce formalisme, nous avons montré qu’il était possible d’apprendre le comportement d’une équipe, à la fois comme une politique et une récompense. Si des connaissances a priori sur la tâche à réaliser sont disponibles, il est possible d’utiliser le même algorithme pour apprendre uniquement les préférences de l’opérateur. Cela permet de s’adapter à l’utilisateur. Nous avons montré que l’utilisation des représentations relationnelles permet d’apprendre des comportements collaboratifs à partir de peu de démonstrations.Ces comportements sont à la fois robustes au bruit, généralisables à de nouveaux états, et transférables à de nouveaux domaines (par exemple en ajoutant des objets). Nous avons également introduit une architecture d’apprentissage interactive qui permet au système de faire moins d’erreurs tout en demandant moins d’efforts à l’opérateur humain. Le robot, en estimant sa confiance dans ses décisions, est capable de demander des instructions quand il est incertain de l’activité à réaliser. Enfin, nous avons implémenté ces approches sur un robot et montré leurs impacts potentiels dans un scenario réaliste.

This thesis presents new approaches toward efficient and intuitive high-level plan learning for cooperative robots. More specifically this work study Learning from Demonstration algorithm for relational domains. Using relational representation to model the world, simplify representing concurrentand cooperative behavior.We have first developed and studied the first algorithm for Inverse ReinforcementLearning in relational domains. We have then presented how one can use the RAP formalism to represent Cooperative Tasks involving a robot and a human operator. RAP is an extension of the Relational MDP framework that allows modeling concurrent activities. Using RAP allow us to represent both the human and the robot in the same process but also to model concurrent robot activities. Under this formalism, we have demonstrated that it is possible to learn behavior, as policy and as reward, of a cooperative team. Prior knowledge about the task can also be used to…

Advisors/Committee Members: Lopes, Manuel (thesis director).

Subjects/Keywords: Coopératif; Robotique; Apprentissage par imitation; Représentations relationnelles; Apprentissage interactif; Cooperative; Robotics; Imitation Learning; Relational Representations; Interactive Learning

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Munzer, T. (2017). Représentations relationnelles et apprentissage interactif pour l'apprentissage efficace du comportement coopératif : Relational representations and interactive learning for efficient cooperative behavior learning. (Doctoral Dissertation). Bordeaux. Retrieved from http://www.theses.fr/2017BORD0574

Chicago Manual of Style (16^th Edition):

Munzer, Thibaut. “Représentations relationnelles et apprentissage interactif pour l'apprentissage efficace du comportement coopératif : Relational representations and interactive learning for efficient cooperative behavior learning.” 2017. Doctoral Dissertation, Bordeaux. Accessed March 05, 2021. http://www.theses.fr/2017BORD0574.

MLA Handbook (7^th Edition):

Munzer, Thibaut. “Représentations relationnelles et apprentissage interactif pour l'apprentissage efficace du comportement coopératif : Relational representations and interactive learning for efficient cooperative behavior learning.” 2017. Web. 05 Mar 2021.

Vancouver:

Munzer T. Représentations relationnelles et apprentissage interactif pour l'apprentissage efficace du comportement coopératif : Relational representations and interactive learning for efficient cooperative behavior learning. [Internet] [Doctoral dissertation]. Bordeaux; 2017. [cited 2021 Mar 05]. Available from: http://www.theses.fr/2017BORD0574.

Council of Science Editors:

Munzer T. Représentations relationnelles et apprentissage interactif pour l'apprentissage efficace du comportement coopératif : Relational representations and interactive learning for efficient cooperative behavior learning. [Doctoral Dissertation]. Bordeaux; 2017. Available from: http://www.theses.fr/2017BORD0574

University of Illinois – Chicago

18. Chen, Xiangli. Robust Structured Prediction for Process Data.

Degree: 2017, University of Illinois – Chicago

URL: http://hdl.handle.net/10027/21987

► Processes involve a series of actions performed to achieve a particular result. Developing prediction models for process data is important for many real problems such… (more)

Subjects/Keywords: Structured Prediction; Optimal Control; Reinforcement Learning; Inverse Reinforcement Learning; Imitation Learning; Regression; Covariate Shift

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Chen, X. (2017). Robust Structured Prediction for Process Data. (Thesis). University of Illinois – Chicago. Retrieved from http://hdl.handle.net/10027/21987

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Chen, Xiangli. “Robust Structured Prediction for Process Data.” 2017. Thesis, University of Illinois – Chicago. Accessed March 05, 2021. http://hdl.handle.net/10027/21987.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Chen, Xiangli. “Robust Structured Prediction for Process Data.” 2017. Web. 05 Mar 2021.

Vancouver:

Chen X. Robust Structured Prediction for Process Data. [Internet] [Thesis]. University of Illinois – Chicago; 2017. [cited 2021 Mar 05]. Available from: http://hdl.handle.net/10027/21987.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Chen X. Robust Structured Prediction for Process Data. [Thesis]. University of Illinois – Chicago; 2017. Available from: http://hdl.handle.net/10027/21987

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Robert Gordon University

19. Hussein, Ahmed. Deep learning based approaches for imitation learning.

Degree: PhD, 2018, Robert Gordon University

URL: http://hdl.handle.net/10059/3117

► Imitation learning refers to an agent's ability to mimic a desired behaviour by learning from observations. The field is rapidly gaining attention due to recent… (more)

▼ Imitation learning refers to an agent's ability to mimic a desired behaviour by learning from observations. The field is rapidly gaining attention due to recent advances in computational and communication capabilities as well as rising demand for intelligent applications. The goal of imitation learning is to describe the desired behaviour by providing demonstrations rather than instructions. This enables agents to learn complex behaviours with general learning methods that require minimal task specific information. However, imitation learning faces many challenges. The objective of this thesis is to advance the state of the art in imitation learning by adopting deep learning methods to address two major challenges of learning from demonstrations. Firstly, representing the demonstrations in a manner that is adequate for learning. We propose novel Convolutional Neural Networks (CNN) based methods to automatically extract feature representations from raw visual demonstrations and learn to replicate the demonstrated behaviour. This alleviates the need for task specific feature extraction and provides a general learning process that is adequate for multiple problems. The second challenge is generalizing a policy over unseen situations in the training demonstrations. This is a common problem because demonstrations typically show the best way to perform a task and don't offer any information about recovering from suboptimal actions. Several methods are investigated to improve the agent's generalization ability based on its initial performance. Our contributions in this area are three fold. Firstly, we propose an active data aggregation method that queries the demonstrator in situations of low confidence. Secondly, we investigate combining learning from demonstrations and reinforcement learning. A deep reward shaping method is proposed that learns a potential reward function from demonstrations. Finally, memory architectures in deep neural networks are investigated to provide context to the agent when taking actions. Using recurrent neural networks addresses the dependency between the state-action sequences taken by the agent. The experiments are conducted in simulated environments on 2D and 3D navigation tasks that are learned from raw visual data, as well as a 2D soccer simulator. The proposed methods are compared to state of the art deep reinforcement learning methods. The results show that deep learning architectures can learn suitable representations from raw visual data and effectively map them to atomic actions. The proposed methods for addressing generalization show improvements over using supervised learning and reinforcement learning alone. The results are thoroughly analysed to identify the benefits of each approach and situations in which it is most suitable.

Subjects/Keywords: Imitation learning; Intelligent applications; Deep learning methods; Learning; Demonstrations; Convolutional neural networks

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Hussein, A. (2018). Deep learning based approaches for imitation learning. (Doctoral Dissertation). Robert Gordon University. Retrieved from http://hdl.handle.net/10059/3117

Chicago Manual of Style (16^th Edition):

Hussein, Ahmed. “Deep learning based approaches for imitation learning.” 2018. Doctoral Dissertation, Robert Gordon University. Accessed March 05, 2021. http://hdl.handle.net/10059/3117.

MLA Handbook (7^th Edition):

Hussein, Ahmed. “Deep learning based approaches for imitation learning.” 2018. Web. 05 Mar 2021.

Vancouver:

Hussein A. Deep learning based approaches for imitation learning. [Internet] [Doctoral dissertation]. Robert Gordon University; 2018. [cited 2021 Mar 05]. Available from: http://hdl.handle.net/10059/3117.

Council of Science Editors:

Hussein A. Deep learning based approaches for imitation learning. [Doctoral Dissertation]. Robert Gordon University; 2018. Available from: http://hdl.handle.net/10059/3117

Delft University of Technology

20. van Lohuijzen, Michiel (author). Towards incremental kinesthetic teaching of bipedal walking.

Degree: 2017, Delft University of Technology

URL: http://resolver.tudelft.nl/uuid:08c3139b-bd0b-40f5-93c2-877e1b14b24a

► The large dimensionality of walking motions is a challenge for robot learning. The human seems designated to assist in this learning process, because of their… (more)

Subjects/Keywords: Kinesthetic Teaching; Iterative Learning Control; Bipedal Walking; Learning from Demonstrations; Learning by Imitation; Robots

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

van Lohuijzen, M. (. (2017). Towards incremental kinesthetic teaching of bipedal walking. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:08c3139b-bd0b-40f5-93c2-877e1b14b24a

Chicago Manual of Style (16^th Edition):

van Lohuijzen, Michiel (author). “Towards incremental kinesthetic teaching of bipedal walking.” 2017. Masters Thesis, Delft University of Technology. Accessed March 05, 2021. http://resolver.tudelft.nl/uuid:08c3139b-bd0b-40f5-93c2-877e1b14b24a.

MLA Handbook (7^th Edition):

van Lohuijzen, Michiel (author). “Towards incremental kinesthetic teaching of bipedal walking.” 2017. Web. 05 Mar 2021.

Vancouver:

van Lohuijzen M(. Towards incremental kinesthetic teaching of bipedal walking. [Internet] [Masters thesis]. Delft University of Technology; 2017. [cited 2021 Mar 05]. Available from: http://resolver.tudelft.nl/uuid:08c3139b-bd0b-40f5-93c2-877e1b14b24a.

Council of Science Editors:

van Lohuijzen M(. Towards incremental kinesthetic teaching of bipedal walking. [Masters Thesis]. Delft University of Technology; 2017. Available from: http://resolver.tudelft.nl/uuid:08c3139b-bd0b-40f5-93c2-877e1b14b24a

Robert Gordon University

21. Hussein, Ahmed. Deep learning based approaches for imitation learning.

Degree: PhD, 2018, Robert Gordon University

URL: https://rgu-repository.worktribe.com/output/248962 ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.758181

► Imitation learning refers to an agent's ability to mimic a desired behaviour by learning from observations. The field is rapidly gaining attention due to recent… (more)

▼ Imitation learning refers to an agent's ability to mimic a desired behaviour by learning from observations. The field is rapidly gaining attention due to recent advances in computational and communication capabilities as well as rising demand for intelligent applications. The goal of imitation learning is to describe the desired behaviour by providing demonstrations rather than instructions. This enables agents to learn complex behaviours with general learning methods that require minimal task specific information. However, imitation learning faces many challenges. The objective of this thesis is to advance the state of the art in imitation learning by adopting deep learning methods to address two major challenges of learning from demonstrations. Firstly, representing the demonstrations in a manner that is adequate for learning. We propose novel Convolutional Neural Networks (CNN) based methods to automatically extract feature representations from raw visual demonstrations and learn to replicate the demonstrated behaviour. This alleviates the need for task specific feature extraction and provides a general learning process that is adequate for multiple problems. The second challenge is generalizing a policy over unseen situations in the training demonstrations. This is a common problem because demonstrations typically show the best way to perform a task and don't offer any information about recovering from suboptimal actions. Several methods are investigated to improve the agent's generalization ability based on its initial performance. Our contributions in this area are three fold. Firstly, we propose an active data aggregation method that queries the demonstrator in situations of low confidence. Secondly, we investigate combining learning from demonstrations and reinforcement learning. A deep reward shaping method is proposed that learns a potential reward function from demonstrations. Finally, memory architectures in deep neural networks are investigated to provide context to the agent when taking actions. Using recurrent neural networks addresses the dependency between the state-action sequences taken by the agent. The experiments are conducted in simulated environments on 2D and 3D navigation tasks that are learned from raw visual data, as well as a 2D soccer simulator. The proposed methods are compared to state of the art deep reinforcement learning methods. The results show that deep learning architectures can learn suitable representations from raw visual data and effectively map them to atomic actions. The proposed methods for addressing generalization show improvements over using supervised learning and reinforcement learning alone. The results are thoroughly analysed to identify the benefits of each approach and situations in which it is most suitable.

Subjects/Keywords: 006.3; Imitation learning; Intelligent applications; Deep learning methods; Learning; Demonstrations; Convolutional neural networks

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Hussein, A. (2018). Deep learning based approaches for imitation learning. (Doctoral Dissertation). Robert Gordon University. Retrieved from https://rgu-repository.worktribe.com/output/248962 ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.758181

Chicago Manual of Style (16^th Edition):

Hussein, Ahmed. “Deep learning based approaches for imitation learning.” 2018. Doctoral Dissertation, Robert Gordon University. Accessed March 05, 2021. https://rgu-repository.worktribe.com/output/248962 ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.758181.

MLA Handbook (7^th Edition):

Hussein, Ahmed. “Deep learning based approaches for imitation learning.” 2018. Web. 05 Mar 2021.

Vancouver:

Hussein A. Deep learning based approaches for imitation learning. [Internet] [Doctoral dissertation]. Robert Gordon University; 2018. [cited 2021 Mar 05]. Available from: https://rgu-repository.worktribe.com/output/248962 ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.758181.

Council of Science Editors:

Hussein A. Deep learning based approaches for imitation learning. [Doctoral Dissertation]. Robert Gordon University; 2018. Available from: https://rgu-repository.worktribe.com/output/248962 ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.758181

Rice University

22. Medini, Tharun. Enhancing Exploration in Reinforcement Learning through Multi-Step Actions.

Degree: MS, Electrical & Computer Eng., 2020, Rice University

URL: http://hdl.handle.net/1911/109644

► The paradigm of Reinforcement Learning (RL) has been plagued by slow and uncertain training owing to the poor exploration in existing techniques. This can be… (more)

Subjects/Keywords: Reinforcement Learning; Imitation Learning; Machine Learning; ATARI; DeepMind; A3C; GA3C; Actor Critic

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Medini, T. (2020). Enhancing Exploration in Reinforcement Learning through Multi-Step Actions. (Masters Thesis). Rice University. Retrieved from http://hdl.handle.net/1911/109644

Chicago Manual of Style (16^th Edition):

Medini, Tharun. “Enhancing Exploration in Reinforcement Learning through Multi-Step Actions.” 2020. Masters Thesis, Rice University. Accessed March 05, 2021. http://hdl.handle.net/1911/109644.

MLA Handbook (7^th Edition):

Medini, Tharun. “Enhancing Exploration in Reinforcement Learning through Multi-Step Actions.” 2020. Web. 05 Mar 2021.

Vancouver:

Medini T. Enhancing Exploration in Reinforcement Learning through Multi-Step Actions. [Internet] [Masters thesis]. Rice University; 2020. [cited 2021 Mar 05]. Available from: http://hdl.handle.net/1911/109644.

Council of Science Editors:

Medini T. Enhancing Exploration in Reinforcement Learning through Multi-Step Actions. [Masters Thesis]. Rice University; 2020. Available from: http://hdl.handle.net/1911/109644

NSYSU

23. Yang, Qiao-Han. Posture Imitation and Balance Learning for Humanoid Robots.

Degree: Master, Electrical Engineering, 2015, NSYSU

URL: http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-1009115-173423

► In the recent years, the research of the interaction between human and humanoid robot becomes a popular topic. As the level of difficulty of the… (more)

Subjects/Keywords: Humanoid Robot; Reinforcement Learning; Key Posture; Posture Imitation; Posture Balance

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APA (6^th Edition):

Yang, Q. (2015). Posture Imitation and Balance Learning for Humanoid Robots. (Thesis). NSYSU. Retrieved from http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-1009115-173423

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Yang, Qiao-Han. “Posture Imitation and Balance Learning for Humanoid Robots.” 2015. Thesis, NSYSU. Accessed March 05, 2021. http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-1009115-173423.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Yang, Qiao-Han. “Posture Imitation and Balance Learning for Humanoid Robots.” 2015. Web. 05 Mar 2021.

Vancouver:

Yang Q. Posture Imitation and Balance Learning for Humanoid Robots. [Internet] [Thesis]. NSYSU; 2015. [cited 2021 Mar 05]. Available from: http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-1009115-173423.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Yang Q. Posture Imitation and Balance Learning for Humanoid Robots. [Thesis]. NSYSU; 2015. Available from: http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-1009115-173423

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Universiteit Utrecht

24. Asten, T. Wisdom through reflection: the mirror neuron system and its role in learning.

Degree: 2011, Universiteit Utrecht

URL: http://dspace.library.uu.nl:8080/handle/1874/193269

► Since their discovery in monkeys some seventeen years ago, mirror neurons have been the focus of an extensive debate. These neurons are active both when… (more)

▼ Since their discovery in monkeys some seventeen years ago, mirror neurons have been the focus of an extensive debate. These neurons are active both when a monkey observes an action and when it executes the same action. Clustered in the ventral premotor cortex and inferior parietal lobule, these areas form the so-called mirror neuron system. Multiple brain imaging studies have shown activity in human brain areas homologous to mirror neuron areas in monkeys while watching and executing actions. It is therefore likely that humans also possess a mirror neuron system. This system has many connections to other brain areas and is thought to be involved in action understanding and empathy. Another possibility, which is the main topic of this thesis, is that it is also involved in certain types of learning. Mirror neurons mainly seem to code for the goals of observed actions and through connections with the limbic system they are also able to couple emotions to certain facial expressions. Indeed in both monkeys and humans they seem to be important for understanding others actions and could play a role in learning of appropriate social interactions. In humans, mirror areas show more activity than in monkeys during observation of movements themselves, next to their goals. Humans also show the ability to imitate observed actions exactly and also imitate to a much greater extent than monkeys do. Also new actions can be imitated and become integrated into the motor repertoire. The human mirror system therefore also seems to be involved in imitation learning. Finally the discovery of audiovisual mirror neurons, which respond to both vision and sound of certain actions in monkeys, also indicates this system could be involved in coupling words to certain actions. Especially in humans it probably plays a role in language learning and comprehension of language. In contrast to the direct evidence of mirror neurons in monkeys, hardly any direct evidence exists for mirror neurons in humans. In imaging studies it is not correct to simply ascribe mirror neuron activity to those brain areas that are both activated during observation and execution of action. At this moment only one preliminary study using depth electrodes shows that humans indeed possess neurons with mirror properties. Future research should shed more light on this issue and show which neurons exactly are active in humans in conditions studied so far using brain imaging techniques. The mirror neuron system indeed seems to play an important role in several types of learning. It is probably essential for imitation learning in humans. Furthermore it reduces time and costs for learning actions and goals from others by direct and automatic linking of observed actions and corresponding motor areas. In humans this is also true for action related language. However one needs to keep in mind that this system is not responsible for any type of learning on its own. It integrates information from other brain areas and in turn it is also regulated by other brain areas. It mainly is an… Advisors/Committee Members: Wezel, R.J.A. van.

Subjects/Keywords: mirror neuron system; learning; imitation; Broca’s area; brain imaging; plasticity; cognition

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APA (6^th Edition):

Asten, T. (2011). Wisdom through reflection: the mirror neuron system and its role in learning. (Masters Thesis). Universiteit Utrecht. Retrieved from http://dspace.library.uu.nl:8080/handle/1874/193269

Chicago Manual of Style (16^th Edition):

Asten, T. “Wisdom through reflection: the mirror neuron system and its role in learning.” 2011. Masters Thesis, Universiteit Utrecht. Accessed March 05, 2021. http://dspace.library.uu.nl:8080/handle/1874/193269.

MLA Handbook (7^th Edition):

Asten, T. “Wisdom through reflection: the mirror neuron system and its role in learning.” 2011. Web. 05 Mar 2021.

Vancouver:

Asten T. Wisdom through reflection: the mirror neuron system and its role in learning. [Internet] [Masters thesis]. Universiteit Utrecht; 2011. [cited 2021 Mar 05]. Available from: http://dspace.library.uu.nl:8080/handle/1874/193269.

Council of Science Editors:

Asten T. Wisdom through reflection: the mirror neuron system and its role in learning. [Masters Thesis]. Universiteit Utrecht; 2011. Available from: http://dspace.library.uu.nl:8080/handle/1874/193269

University of Arizona

25. Klauck, Kenneth Aloysius, 1936-. The effect of model reinforcement on imitation .

Degree: 1966, University of Arizona

URL: http://hdl.handle.net/10150/317946

Subjects/Keywords: Imitation.; Learning, Psychology of.

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Klauck, Kenneth Aloysius, 1. (1966). The effect of model reinforcement on imitation . (Masters Thesis). University of Arizona. Retrieved from http://hdl.handle.net/10150/317946

Chicago Manual of Style (16^th Edition):

Klauck, Kenneth Aloysius, 1936-. “The effect of model reinforcement on imitation .” 1966. Masters Thesis, University of Arizona. Accessed March 05, 2021. http://hdl.handle.net/10150/317946.

MLA Handbook (7^th Edition):

Klauck, Kenneth Aloysius, 1936-. “The effect of model reinforcement on imitation .” 1966. Web. 05 Mar 2021.

Vancouver:

Klauck, Kenneth Aloysius 1. The effect of model reinforcement on imitation . [Internet] [Masters thesis]. University of Arizona; 1966. [cited 2021 Mar 05]. Available from: http://hdl.handle.net/10150/317946.

Council of Science Editors:

Klauck, Kenneth Aloysius 1. The effect of model reinforcement on imitation . [Masters Thesis]. University of Arizona; 1966. Available from: http://hdl.handle.net/10150/317946

University of Arizona

26. Hanson, Richard W., 1944-. TEMPORAL FACTORS AND RETEST EXPECTATIONS IN AN OBSERVATIONALLY ACQUIRED SIMPLE CONCEPT .

Degree: 1972, University of Arizona

URL: http://hdl.handle.net/10150/287957

Subjects/Keywords: Concept learning.; Observation (Psychology); Imitation.

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Hanson, Richard W., 1. (1972). TEMPORAL FACTORS AND RETEST EXPECTATIONS IN AN OBSERVATIONALLY ACQUIRED SIMPLE CONCEPT . (Doctoral Dissertation). University of Arizona. Retrieved from http://hdl.handle.net/10150/287957

Chicago Manual of Style (16^th Edition):

Hanson, Richard W., 1944-. “TEMPORAL FACTORS AND RETEST EXPECTATIONS IN AN OBSERVATIONALLY ACQUIRED SIMPLE CONCEPT .” 1972. Doctoral Dissertation, University of Arizona. Accessed March 05, 2021. http://hdl.handle.net/10150/287957.

MLA Handbook (7^th Edition):

Hanson, Richard W., 1944-. “TEMPORAL FACTORS AND RETEST EXPECTATIONS IN AN OBSERVATIONALLY ACQUIRED SIMPLE CONCEPT .” 1972. Web. 05 Mar 2021.

Vancouver:

Hanson, Richard W. 1. TEMPORAL FACTORS AND RETEST EXPECTATIONS IN AN OBSERVATIONALLY ACQUIRED SIMPLE CONCEPT . [Internet] [Doctoral dissertation]. University of Arizona; 1972. [cited 2021 Mar 05]. Available from: http://hdl.handle.net/10150/287957.

Council of Science Editors:

Hanson, Richard W. 1. TEMPORAL FACTORS AND RETEST EXPECTATIONS IN AN OBSERVATIONALLY ACQUIRED SIMPLE CONCEPT . [Doctoral Dissertation]. University of Arizona; 1972. Available from: http://hdl.handle.net/10150/287957

University of Arizona

27. Jeske, Patrick John. THE EFFECTS OF MODELING, IMITATIVE PERFORMANCE, AND MODELING FEEDBACK ON HIERARCHICAL SERIATION LEARNING .

Degree: 1978, University of Arizona

URL: http://hdl.handle.net/10150/298727

Subjects/Keywords: Learning, Psychology of.; Imitation.

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APA (6^th Edition):

Jeske, P. J. (1978). THE EFFECTS OF MODELING, IMITATIVE PERFORMANCE, AND MODELING FEEDBACK ON HIERARCHICAL SERIATION LEARNING . (Doctoral Dissertation). University of Arizona. Retrieved from http://hdl.handle.net/10150/298727

Chicago Manual of Style (16^th Edition):

Jeske, Patrick John. “THE EFFECTS OF MODELING, IMITATIVE PERFORMANCE, AND MODELING FEEDBACK ON HIERARCHICAL SERIATION LEARNING .” 1978. Doctoral Dissertation, University of Arizona. Accessed March 05, 2021. http://hdl.handle.net/10150/298727.

MLA Handbook (7^th Edition):

Jeske, Patrick John. “THE EFFECTS OF MODELING, IMITATIVE PERFORMANCE, AND MODELING FEEDBACK ON HIERARCHICAL SERIATION LEARNING .” 1978. Web. 05 Mar 2021.

Vancouver:

Jeske PJ. THE EFFECTS OF MODELING, IMITATIVE PERFORMANCE, AND MODELING FEEDBACK ON HIERARCHICAL SERIATION LEARNING . [Internet] [Doctoral dissertation]. University of Arizona; 1978. [cited 2021 Mar 05]. Available from: http://hdl.handle.net/10150/298727.

Council of Science Editors:

Jeske PJ. THE EFFECTS OF MODELING, IMITATIVE PERFORMANCE, AND MODELING FEEDBACK ON HIERARCHICAL SERIATION LEARNING . [Doctoral Dissertation]. University of Arizona; 1978. Available from: http://hdl.handle.net/10150/298727

Georgia State University

28. Donohue, Meghan. Learning to Repair Transgressions: Toddlers' Social Learning of a Reparative Prosocial Act.

Degree: MA, Psychology, 2015, Georgia State University

URL: https://scholarworks.gsu.edu/psych_theses/141

► This study investigated children's social learning of prosocial behaviors in a transgressor context. Two-to three-year-olds (24-47 months, N = 54) saw videos of an… (more)

Subjects/Keywords: Prosocial behaviors; Reparation; Social learning; Imitation; Goal emulation; Transgressor context

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APA (6^th Edition):

Donohue, M. (2015). Learning to Repair Transgressions: Toddlers' Social Learning of a Reparative Prosocial Act. (Thesis). Georgia State University. Retrieved from https://scholarworks.gsu.edu/psych_theses/141

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Donohue, Meghan. “Learning to Repair Transgressions: Toddlers' Social Learning of a Reparative Prosocial Act.” 2015. Thesis, Georgia State University. Accessed March 05, 2021. https://scholarworks.gsu.edu/psych_theses/141.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Donohue, Meghan. “Learning to Repair Transgressions: Toddlers' Social Learning of a Reparative Prosocial Act.” 2015. Web. 05 Mar 2021.

Vancouver:

Donohue M. Learning to Repair Transgressions: Toddlers' Social Learning of a Reparative Prosocial Act. [Internet] [Thesis]. Georgia State University; 2015. [cited 2021 Mar 05]. Available from: https://scholarworks.gsu.edu/psych_theses/141.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Donohue M. Learning to Repair Transgressions: Toddlers' Social Learning of a Reparative Prosocial Act. [Thesis]. Georgia State University; 2015. Available from: https://scholarworks.gsu.edu/psych_theses/141

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Montana Tech

29. Tibbitts, Stephen Ted. THE EFFECTS OF EXPECTANCY AND INCONSISTENT VICARIOUS CONSEQUENCES ON THE ACQUISITION AND PERFORMANCE OF IMITATIVE RESPONSES.

Degree: PhD, 1974, Montana Tech

URL: https://scholarworks.umt.edu/etd/9863

Subjects/Keywords: Imitation.; Learning; Psychology of.

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APA (6^th Edition):

Tibbitts, S. T. (1974). THE EFFECTS OF EXPECTANCY AND INCONSISTENT VICARIOUS CONSEQUENCES ON THE ACQUISITION AND PERFORMANCE OF IMITATIVE RESPONSES. (Doctoral Dissertation). Montana Tech. Retrieved from https://scholarworks.umt.edu/etd/9863

Chicago Manual of Style (16^th Edition):

Tibbitts, Stephen Ted. “THE EFFECTS OF EXPECTANCY AND INCONSISTENT VICARIOUS CONSEQUENCES ON THE ACQUISITION AND PERFORMANCE OF IMITATIVE RESPONSES.” 1974. Doctoral Dissertation, Montana Tech. Accessed March 05, 2021. https://scholarworks.umt.edu/etd/9863.

MLA Handbook (7^th Edition):

Tibbitts, Stephen Ted. “THE EFFECTS OF EXPECTANCY AND INCONSISTENT VICARIOUS CONSEQUENCES ON THE ACQUISITION AND PERFORMANCE OF IMITATIVE RESPONSES.” 1974. Web. 05 Mar 2021.

Vancouver:

Tibbitts ST. THE EFFECTS OF EXPECTANCY AND INCONSISTENT VICARIOUS CONSEQUENCES ON THE ACQUISITION AND PERFORMANCE OF IMITATIVE RESPONSES. [Internet] [Doctoral dissertation]. Montana Tech; 1974. [cited 2021 Mar 05]. Available from: https://scholarworks.umt.edu/etd/9863.

Council of Science Editors:

Tibbitts ST. THE EFFECTS OF EXPECTANCY AND INCONSISTENT VICARIOUS CONSEQUENCES ON THE ACQUISITION AND PERFORMANCE OF IMITATIVE RESPONSES. [Doctoral Dissertation]. Montana Tech; 1974. Available from: https://scholarworks.umt.edu/etd/9863

Utah State University

30. Shea, Kerry A. Evaluation of a Computer-Based Observer-Effect Training on Mothers' Vocal Imitation of Their Infant.

Degree: PhD, Special Education and Rehabilitation, 2019, Utah State University

URL: https://digitalcommons.usu.edu/etd/7694

► Infants begin to learn important skills, such as contingency learning, social referencing, and joint attention through everyday interactions with their environment. When infants learn… (more)

Subjects/Keywords: contingency learning; caregiver responsiveness; observer effect; vocal imitation; Disability Studies

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APA (6^th Edition):

Shea, K. A. (2019). Evaluation of a Computer-Based Observer-Effect Training on Mothers' Vocal Imitation of Their Infant. (Doctoral Dissertation). Utah State University. Retrieved from https://digitalcommons.usu.edu/etd/7694

Chicago Manual of Style (16^th Edition):

Shea, Kerry A. “Evaluation of a Computer-Based Observer-Effect Training on Mothers' Vocal Imitation of Their Infant.” 2019. Doctoral Dissertation, Utah State University. Accessed March 05, 2021. https://digitalcommons.usu.edu/etd/7694.

MLA Handbook (7^th Edition):

Shea, Kerry A. “Evaluation of a Computer-Based Observer-Effect Training on Mothers' Vocal Imitation of Their Infant.” 2019. Web. 05 Mar 2021.

Vancouver:

Shea KA. Evaluation of a Computer-Based Observer-Effect Training on Mothers' Vocal Imitation of Their Infant. [Internet] [Doctoral dissertation]. Utah State University; 2019. [cited 2021 Mar 05]. Available from: https://digitalcommons.usu.edu/etd/7694.

Council of Science Editors:

Shea KA. Evaluation of a Computer-Based Observer-Effect Training on Mothers' Vocal Imitation of Their Infant. [Doctoral Dissertation]. Utah State University; 2019. Available from: https://digitalcommons.usu.edu/etd/7694

Open Access Theses and Dissertations