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You searched for +publisher:"Georgia Tech" +contributor:("Riedl, Mark"). Showing records 1 – 22 of 22 total matches.

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Georgia Tech

1. Tendulkar, Purva Milind. Computational methods for creative inspiration in thematic typography and dance.

Degree: MS, Computer Science, 2020, Georgia Tech

 As progress in technology continues, there is a need to adapt and upscale tools used in artistic and creative processes. This can either take the… (more)

Subjects/Keywords: Creativity; Human studies; Typography; Dance; Music; AI; Computer vision

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APA (6th Edition):

Tendulkar, P. M. (2020). Computational methods for creative inspiration in thematic typography and dance. (Masters Thesis). Georgia Tech. Retrieved from http://hdl.handle.net/1853/63699

Chicago Manual of Style (16th Edition):

Tendulkar, Purva Milind. “Computational methods for creative inspiration in thematic typography and dance.” 2020. Masters Thesis, Georgia Tech. Accessed November 29, 2020. http://hdl.handle.net/1853/63699.

MLA Handbook (7th Edition):

Tendulkar, Purva Milind. “Computational methods for creative inspiration in thematic typography and dance.” 2020. Web. 29 Nov 2020.

Vancouver:

Tendulkar PM. Computational methods for creative inspiration in thematic typography and dance. [Internet] [Masters thesis]. Georgia Tech; 2020. [cited 2020 Nov 29]. Available from: http://hdl.handle.net/1853/63699.

Council of Science Editors:

Tendulkar PM. Computational methods for creative inspiration in thematic typography and dance. [Masters Thesis]. Georgia Tech; 2020. Available from: http://hdl.handle.net/1853/63699


Georgia Tech

2. Jacob, Mikhail. Improvisational artificial intelligence for embodied co-creativity.

Degree: PhD, Interactive Computing, 2019, Georgia Tech

 This dissertation explores embodied agents that can improvise with people in an object-based gestural proto-narrative domain. I study the improvisational action selection problem (the challenge… (more)

Subjects/Keywords: Improvisational AI; Improvisational agents; Creative arc negotiation; Improvisational action selection problem; Computational creativity; The Robot Improv Circus; CARNIVAL architecture; Affordance-based action generation; Improvisational strategies; Creativity evaluation; Enjoyment; Creativity; Coherence; Props game

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APA (6th Edition):

Jacob, M. (2019). Improvisational artificial intelligence for embodied co-creativity. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/62270

Chicago Manual of Style (16th Edition):

Jacob, Mikhail. “Improvisational artificial intelligence for embodied co-creativity.” 2019. Doctoral Dissertation, Georgia Tech. Accessed November 29, 2020. http://hdl.handle.net/1853/62270.

MLA Handbook (7th Edition):

Jacob, Mikhail. “Improvisational artificial intelligence for embodied co-creativity.” 2019. Web. 29 Nov 2020.

Vancouver:

Jacob M. Improvisational artificial intelligence for embodied co-creativity. [Internet] [Doctoral dissertation]. Georgia Tech; 2019. [cited 2020 Nov 29]. Available from: http://hdl.handle.net/1853/62270.

Council of Science Editors:

Jacob M. Improvisational artificial intelligence for embodied co-creativity. [Doctoral Dissertation]. Georgia Tech; 2019. Available from: http://hdl.handle.net/1853/62270


Georgia Tech

3. Lu, Jiasen. Visually grounded language understanding and generation.

Degree: PhD, Computer Science, 2020, Georgia Tech

 The world around us involves multiple modalities  – we see objects, feel texture, hear sounds, smell odors and so on. In order for Artificial Intelligence… (more)

Subjects/Keywords: Computer vision; Natural language processing; Visual question answering; Multi-task learning; Deep learning

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APA (6th Edition):

Lu, J. (2020). Visually grounded language understanding and generation. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/62745

Chicago Manual of Style (16th Edition):

Lu, Jiasen. “Visually grounded language understanding and generation.” 2020. Doctoral Dissertation, Georgia Tech. Accessed November 29, 2020. http://hdl.handle.net/1853/62745.

MLA Handbook (7th Edition):

Lu, Jiasen. “Visually grounded language understanding and generation.” 2020. Web. 29 Nov 2020.

Vancouver:

Lu J. Visually grounded language understanding and generation. [Internet] [Doctoral dissertation]. Georgia Tech; 2020. [cited 2020 Nov 29]. Available from: http://hdl.handle.net/1853/62745.

Council of Science Editors:

Lu J. Visually grounded language understanding and generation. [Doctoral Dissertation]. Georgia Tech; 2020. Available from: http://hdl.handle.net/1853/62745


Georgia Tech

4. Krening, Samantha. Humans teaching intelligent agents with verbal instruction.

Degree: PhD, Aerospace Engineering, 2019, Georgia Tech

 The widespread integration of robotics into everyday life requires significant improvement in the underlying machine learning (ML) agents to make them more accessible, customizable, and… (more)

Subjects/Keywords: Robotics; Machine learning; Interactive machine learning; Human-agent interaction; Reinforcement learning; Natural language processing; Human-computer interaction; Human factors; Machine learning verification

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APA (6th Edition):

Krening, S. (2019). Humans teaching intelligent agents with verbal instruction. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/61232

Chicago Manual of Style (16th Edition):

Krening, Samantha. “Humans teaching intelligent agents with verbal instruction.” 2019. Doctoral Dissertation, Georgia Tech. Accessed November 29, 2020. http://hdl.handle.net/1853/61232.

MLA Handbook (7th Edition):

Krening, Samantha. “Humans teaching intelligent agents with verbal instruction.” 2019. Web. 29 Nov 2020.

Vancouver:

Krening S. Humans teaching intelligent agents with verbal instruction. [Internet] [Doctoral dissertation]. Georgia Tech; 2019. [cited 2020 Nov 29]. Available from: http://hdl.handle.net/1853/61232.

Council of Science Editors:

Krening S. Humans teaching intelligent agents with verbal instruction. [Doctoral Dissertation]. Georgia Tech; 2019. Available from: http://hdl.handle.net/1853/61232


Georgia Tech

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

Degree: PhD, Computer Science, 2019, Georgia Tech

 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 (6th 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 (16th Edition):

Edwards, Ashley Deloris. “Emulation and imitation via perceptual goal specifications.” 2019. Doctoral Dissertation, Georgia Tech. Accessed November 29, 2020. http://hdl.handle.net/1853/61234.

MLA Handbook (7th Edition):

Edwards, Ashley Deloris. “Emulation and imitation via perceptual goal specifications.” 2019. Web. 29 Nov 2020.

Vancouver:

Edwards AD. Emulation and imitation via perceptual goal specifications. [Internet] [Doctoral dissertation]. Georgia Tech; 2019. [cited 2020 Nov 29]. 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


Georgia Tech

6. Subramanian, Kaushik. Policy-based exploration for efficient reinforcement learning.

Degree: PhD, Interactive Computing, 2020, Georgia Tech

 Reinforcement Learning (RL) is the field of research focused on solving sequential decision-making tasks modeled as Markov Decision Processes. Researchers have shown RL to be… (more)

Subjects/Keywords: Reinforcement learning; Exploration; Interactive learning; Active learning; Monte-carlo search; Bayesian

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APA (6th Edition):

Subramanian, K. (2020). Policy-based exploration for efficient reinforcement learning. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/62831

Chicago Manual of Style (16th Edition):

Subramanian, Kaushik. “Policy-based exploration for efficient reinforcement learning.” 2020. Doctoral Dissertation, Georgia Tech. Accessed November 29, 2020. http://hdl.handle.net/1853/62831.

MLA Handbook (7th Edition):

Subramanian, Kaushik. “Policy-based exploration for efficient reinforcement learning.” 2020. Web. 29 Nov 2020.

Vancouver:

Subramanian K. Policy-based exploration for efficient reinforcement learning. [Internet] [Doctoral dissertation]. Georgia Tech; 2020. [cited 2020 Nov 29]. Available from: http://hdl.handle.net/1853/62831.

Council of Science Editors:

Subramanian K. Policy-based exploration for efficient reinforcement learning. [Doctoral Dissertation]. Georgia Tech; 2020. Available from: http://hdl.handle.net/1853/62831


Georgia Tech

7. Pettinati, Michael. Supporting healthy dyadic human relationships with power differentials using robots.

Degree: PhD, Computer Science, 2020, Georgia Tech

 Conflict is a natural part of ever-evolving human-human relationships. The way in which conflicts are handled can result in relationship growth, dissatisfaction (for one or… (more)

Subjects/Keywords: Human-human-robot interaction; Human-relationship modeling; Relationship-focused robotic system

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APA (6th Edition):

Pettinati, M. (2020). Supporting healthy dyadic human relationships with power differentials using robots. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/62835

Chicago Manual of Style (16th Edition):

Pettinati, Michael. “Supporting healthy dyadic human relationships with power differentials using robots.” 2020. Doctoral Dissertation, Georgia Tech. Accessed November 29, 2020. http://hdl.handle.net/1853/62835.

MLA Handbook (7th Edition):

Pettinati, Michael. “Supporting healthy dyadic human relationships with power differentials using robots.” 2020. Web. 29 Nov 2020.

Vancouver:

Pettinati M. Supporting healthy dyadic human relationships with power differentials using robots. [Internet] [Doctoral dissertation]. Georgia Tech; 2020. [cited 2020 Nov 29]. Available from: http://hdl.handle.net/1853/62835.

Council of Science Editors:

Pettinati M. Supporting healthy dyadic human relationships with power differentials using robots. [Doctoral Dissertation]. Georgia Tech; 2020. Available from: http://hdl.handle.net/1853/62835


Georgia Tech

8. Chandrasekaran, Arjun. Towards natural human-AI interactions in vision and language.

Degree: PhD, Interactive Computing, 2019, Georgia Tech

 Inter-human interaction is a rich form of communication. Human interactions typically leverage a good theory of mind, involve pragmatics, story-telling, humor, sarcasm, empathy, sympathy, etc.… (more)

Subjects/Keywords: AI; Neural networks; Human-AI interaction; Human-AI collaboration; Humor; Narrative; Sorytelling; Explainable AI; Interpretability; Predictability; Guesswhich; Human-in-loop evaluation

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APA (6th Edition):

Chandrasekaran, A. (2019). Towards natural human-AI interactions in vision and language. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/62323

Chicago Manual of Style (16th Edition):

Chandrasekaran, Arjun. “Towards natural human-AI interactions in vision and language.” 2019. Doctoral Dissertation, Georgia Tech. Accessed November 29, 2020. http://hdl.handle.net/1853/62323.

MLA Handbook (7th Edition):

Chandrasekaran, Arjun. “Towards natural human-AI interactions in vision and language.” 2019. Web. 29 Nov 2020.

Vancouver:

Chandrasekaran A. Towards natural human-AI interactions in vision and language. [Internet] [Doctoral dissertation]. Georgia Tech; 2019. [cited 2020 Nov 29]. Available from: http://hdl.handle.net/1853/62323.

Council of Science Editors:

Chandrasekaran A. Towards natural human-AI interactions in vision and language. [Doctoral Dissertation]. Georgia Tech; 2019. Available from: http://hdl.handle.net/1853/62323


Georgia Tech

9. Yu, Hong. A data-driven approach for personalized drama management.

Degree: PhD, Interactive Computing, 2015, Georgia Tech

 An interactive narrative is a form of digital entertainment in which players can create or influence a dramatic storyline through actions, typically by assuming the… (more)

Subjects/Keywords: Personalized drama manager; Interactive narrative; Player modeling; Prefix based collaborative filtering

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APA (6th Edition):

Yu, H. (2015). A data-driven approach for personalized drama management. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/53851

Chicago Manual of Style (16th Edition):

Yu, Hong. “A data-driven approach for personalized drama management.” 2015. Doctoral Dissertation, Georgia Tech. Accessed November 29, 2020. http://hdl.handle.net/1853/53851.

MLA Handbook (7th Edition):

Yu, Hong. “A data-driven approach for personalized drama management.” 2015. Web. 29 Nov 2020.

Vancouver:

Yu H. A data-driven approach for personalized drama management. [Internet] [Doctoral dissertation]. Georgia Tech; 2015. [cited 2020 Nov 29]. Available from: http://hdl.handle.net/1853/53851.

Council of Science Editors:

Yu H. A data-driven approach for personalized drama management. [Doctoral Dissertation]. Georgia Tech; 2015. Available from: http://hdl.handle.net/1853/53851

10. Anderson, Tory S. Thinking in stories.

Degree: MS, Literature, Media, and Communication, 2015, Georgia Tech

 This thesis considers cognitive narrative a component of intelligence that specializes in generality. In exploring the ubiquitous external (mediated) and internal (cognitive) functions of narrative… (more)

Subjects/Keywords: Narrative; Psychology; Cognitive architecture; Episodic memory; Event segmentation; Literature; Drama; Story

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APA (6th Edition):

Anderson, T. S. (2015). Thinking in stories. (Masters Thesis). Georgia Tech. Retrieved from http://hdl.handle.net/1853/53880

Chicago Manual of Style (16th Edition):

Anderson, Tory S. “Thinking in stories.” 2015. Masters Thesis, Georgia Tech. Accessed November 29, 2020. http://hdl.handle.net/1853/53880.

MLA Handbook (7th Edition):

Anderson, Tory S. “Thinking in stories.” 2015. Web. 29 Nov 2020.

Vancouver:

Anderson TS. Thinking in stories. [Internet] [Masters thesis]. Georgia Tech; 2015. [cited 2020 Nov 29]. Available from: http://hdl.handle.net/1853/53880.

Council of Science Editors:

Anderson TS. Thinking in stories. [Masters Thesis]. Georgia Tech; 2015. Available from: http://hdl.handle.net/1853/53880

11. Li, Boyang. Learning knowledge to support domain-independent narrative intelligence.

Degree: PhD, Interactive Computing, 2015, Georgia Tech

 Narrative Intelligence is the ability to craft, tell, understand, and respond appropriately to narratives. It has been proposed as a vital component of machines aiming… (more)

Subjects/Keywords: Computational narrative intelligence; Story generation; Story understanding; Computational creativity; Virtual characters

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APA (6th Edition):

Li, B. (2015). Learning knowledge to support domain-independent narrative intelligence. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/53376

Chicago Manual of Style (16th Edition):

Li, Boyang. “Learning knowledge to support domain-independent narrative intelligence.” 2015. Doctoral Dissertation, Georgia Tech. Accessed November 29, 2020. http://hdl.handle.net/1853/53376.

MLA Handbook (7th Edition):

Li, Boyang. “Learning knowledge to support domain-independent narrative intelligence.” 2015. Web. 29 Nov 2020.

Vancouver:

Li B. Learning knowledge to support domain-independent narrative intelligence. [Internet] [Doctoral dissertation]. Georgia Tech; 2015. [cited 2020 Nov 29]. Available from: http://hdl.handle.net/1853/53376.

Council of Science Editors:

Li B. Learning knowledge to support domain-independent narrative intelligence. [Doctoral Dissertation]. Georgia Tech; 2015. Available from: http://hdl.handle.net/1853/53376

12. O'Neill, Brian. A computational model of suspense for the augmentation of intelligent story generation.

Degree: PhD, Interactive Computing, 2013, Georgia Tech

 In this dissertation, I present Dramatis, a computational human behavior model of suspense based on Gerrig and Bernardo's de nition of suspense. In this model,… (more)

Subjects/Keywords: Artificial intelligence; Computational creativity; Story generation; Human behavior model; Suspense; Storytelling; Artificial intelligence Computer programs; Suspense fiction

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APA (6th Edition):

O'Neill, B. (2013). A computational model of suspense for the augmentation of intelligent story generation. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/50416

Chicago Manual of Style (16th Edition):

O'Neill, Brian. “A computational model of suspense for the augmentation of intelligent story generation.” 2013. Doctoral Dissertation, Georgia Tech. Accessed November 29, 2020. http://hdl.handle.net/1853/50416.

MLA Handbook (7th Edition):

O'Neill, Brian. “A computational model of suspense for the augmentation of intelligent story generation.” 2013. Web. 29 Nov 2020.

Vancouver:

O'Neill B. A computational model of suspense for the augmentation of intelligent story generation. [Internet] [Doctoral dissertation]. Georgia Tech; 2013. [cited 2020 Nov 29]. Available from: http://hdl.handle.net/1853/50416.

Council of Science Editors:

O'Neill B. A computational model of suspense for the augmentation of intelligent story generation. [Doctoral Dissertation]. Georgia Tech; 2013. Available from: http://hdl.handle.net/1853/50416

13. Mehta, Manish. Construction and adaptation of AI behaviors in computer games.

Degree: PhD, Computing, 2011, Georgia Tech

 Computer games are an increasingly popular application for Artificial Intelligence (AI) research, and conversely AI is an increasingly popular selling point for commercial digital games.… (more)

Subjects/Keywords: Non-playing characters; AI characters; Authoring; Introspective reasoning; Artificial intelligence; Artificial intelligence Computer programs; Computer programming; Computer games Programming

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APA (6th Edition):

Mehta, M. (2011). Construction and adaptation of AI behaviors in computer games. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/42724

Chicago Manual of Style (16th Edition):

Mehta, Manish. “Construction and adaptation of AI behaviors in computer games.” 2011. Doctoral Dissertation, Georgia Tech. Accessed November 29, 2020. http://hdl.handle.net/1853/42724.

MLA Handbook (7th Edition):

Mehta, Manish. “Construction and adaptation of AI behaviors in computer games.” 2011. Web. 29 Nov 2020.

Vancouver:

Mehta M. Construction and adaptation of AI behaviors in computer games. [Internet] [Doctoral dissertation]. Georgia Tech; 2011. [cited 2020 Nov 29]. Available from: http://hdl.handle.net/1853/42724.

Council of Science Editors:

Mehta M. Construction and adaptation of AI behaviors in computer games. [Doctoral Dissertation]. Georgia Tech; 2011. Available from: http://hdl.handle.net/1853/42724

14. Boujarwah, Fatima Abdulazeez. Facilitating the authoring of multimedia social problem solving skills instructional modules.

Degree: PhD, Computing, 2012, Georgia Tech

 Difficulties in social skills are generally considered defining characteristics of High-Functioning Autism (HFA). These difficulties interfere with the educational experiences and quality of life of… (more)

Subjects/Keywords: Social skills; Human computation; Crowdsourcing; Autism; Social skills Study and teaching; Social skills in adolescence; Autism in adolescence

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APA (6th Edition):

Boujarwah, F. A. (2012). Facilitating the authoring of multimedia social problem solving skills instructional modules. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/43644

Chicago Manual of Style (16th Edition):

Boujarwah, Fatima Abdulazeez. “Facilitating the authoring of multimedia social problem solving skills instructional modules.” 2012. Doctoral Dissertation, Georgia Tech. Accessed November 29, 2020. http://hdl.handle.net/1853/43644.

MLA Handbook (7th Edition):

Boujarwah, Fatima Abdulazeez. “Facilitating the authoring of multimedia social problem solving skills instructional modules.” 2012. Web. 29 Nov 2020.

Vancouver:

Boujarwah FA. Facilitating the authoring of multimedia social problem solving skills instructional modules. [Internet] [Doctoral dissertation]. Georgia Tech; 2012. [cited 2020 Nov 29]. Available from: http://hdl.handle.net/1853/43644.

Council of Science Editors:

Boujarwah FA. Facilitating the authoring of multimedia social problem solving skills instructional modules. [Doctoral Dissertation]. Georgia Tech; 2012. Available from: http://hdl.handle.net/1853/43644

15. Wiltgen, Bryan Joseph. Incremental design revision in biologically inspired design.

Degree: PhD, Interactive Computing, 2018, Georgia Tech

 Design is the process by which solutions get developed to solve social challenges, and its products can be seen across our world from toothbrushes to… (more)

Subjects/Keywords: Design; Conceptual design; Biologically inspired design; Artificial intelligence; Analogical mapping; Simulation; Evaluation; Incremental design revision

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APA (6th Edition):

Wiltgen, B. J. (2018). Incremental design revision in biologically inspired design. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/60805

Chicago Manual of Style (16th Edition):

Wiltgen, Bryan Joseph. “Incremental design revision in biologically inspired design.” 2018. Doctoral Dissertation, Georgia Tech. Accessed November 29, 2020. http://hdl.handle.net/1853/60805.

MLA Handbook (7th Edition):

Wiltgen, Bryan Joseph. “Incremental design revision in biologically inspired design.” 2018. Web. 29 Nov 2020.

Vancouver:

Wiltgen BJ. Incremental design revision in biologically inspired design. [Internet] [Doctoral dissertation]. Georgia Tech; 2018. [cited 2020 Nov 29]. Available from: http://hdl.handle.net/1853/60805.

Council of Science Editors:

Wiltgen BJ. Incremental design revision in biologically inspired design. [Doctoral Dissertation]. Georgia Tech; 2018. Available from: http://hdl.handle.net/1853/60805

16. Simpkins, Christopher Lee. Integrating reinforcement learning into a programming language.

Degree: PhD, Computer Science, 2017, Georgia Tech

 Reinforcement learning is a promising solution to the intelligent agent problem, namely, given the state of the world, which action should an agent take to… (more)

Subjects/Keywords: Machine learning; Reinforcement learning; Modular reinforcement learning; Programming languages; Domain specific languages; Software engineering; Artificial intelligence; Intelligent agents

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APA (6th Edition):

Simpkins, C. L. (2017). Integrating reinforcement learning into a programming language. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/58683

Chicago Manual of Style (16th Edition):

Simpkins, Christopher Lee. “Integrating reinforcement learning into a programming language.” 2017. Doctoral Dissertation, Georgia Tech. Accessed November 29, 2020. http://hdl.handle.net/1853/58683.

MLA Handbook (7th Edition):

Simpkins, Christopher Lee. “Integrating reinforcement learning into a programming language.” 2017. Web. 29 Nov 2020.

Vancouver:

Simpkins CL. Integrating reinforcement learning into a programming language. [Internet] [Doctoral dissertation]. Georgia Tech; 2017. [cited 2020 Nov 29]. Available from: http://hdl.handle.net/1853/58683.

Council of Science Editors:

Simpkins CL. Integrating reinforcement learning into a programming language. [Doctoral Dissertation]. Georgia Tech; 2017. Available from: http://hdl.handle.net/1853/58683

17. Ji, Yangfeng. Semantic representation learning for discourse processing.

Degree: PhD, Computer Science, 2016, Georgia Tech

 Discourse processing is to identify coherent relations, such as contrast and causal relation, from well-organized texts. The outcomes from discourse processing can benefit both research… (more)

Subjects/Keywords: Semantics; Representation learning; Deep learning; Discourse; Discourse processing; Sentiment analysis

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APA (6th Edition):

Ji, Y. (2016). Semantic representation learning for discourse processing. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/55636

Chicago Manual of Style (16th Edition):

Ji, Yangfeng. “Semantic representation learning for discourse processing.” 2016. Doctoral Dissertation, Georgia Tech. Accessed November 29, 2020. http://hdl.handle.net/1853/55636.

MLA Handbook (7th Edition):

Ji, Yangfeng. “Semantic representation learning for discourse processing.” 2016. Web. 29 Nov 2020.

Vancouver:

Ji Y. Semantic representation learning for discourse processing. [Internet] [Doctoral dissertation]. Georgia Tech; 2016. [cited 2020 Nov 29]. Available from: http://hdl.handle.net/1853/55636.

Council of Science Editors:

Ji Y. Semantic representation learning for discourse processing. [Doctoral Dissertation]. Georgia Tech; 2016. Available from: http://hdl.handle.net/1853/55636

18. Irani, Arya John. Utilizing negative policy information to accelerate reinforcement learning.

Degree: PhD, Interactive Computing, 2015, Georgia Tech

 A pilot study by Subramanian et al. on Markov decision problem task decomposition by humans revealed that participants break down tasks into both short-term subgoals… (more)

Subjects/Keywords:

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APA (6th Edition):

Irani, A. J. (2015). Utilizing negative policy information to accelerate reinforcement learning. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/53481

Chicago Manual of Style (16th Edition):

Irani, Arya John. “Utilizing negative policy information to accelerate reinforcement learning.” 2015. Doctoral Dissertation, Georgia Tech. Accessed November 29, 2020. http://hdl.handle.net/1853/53481.

MLA Handbook (7th Edition):

Irani, Arya John. “Utilizing negative policy information to accelerate reinforcement learning.” 2015. Web. 29 Nov 2020.

Vancouver:

Irani AJ. Utilizing negative policy information to accelerate reinforcement learning. [Internet] [Doctoral dissertation]. Georgia Tech; 2015. [cited 2020 Nov 29]. Available from: http://hdl.handle.net/1853/53481.

Council of Science Editors:

Irani AJ. Utilizing negative policy information to accelerate reinforcement learning. [Doctoral Dissertation]. Georgia Tech; 2015. Available from: http://hdl.handle.net/1853/53481

19. Davis, Nicholas M. Creative sense-making: A cognitive framework for quantifying interaction dynamics in co-creation.

Degree: PhD, Interactive Computing, 2017, Georgia Tech

 Collaboration is a powerful way to inspire and support creativity. The field of computational creativity is beginning to explore how co-creative agents might collaborate with… (more)

Subjects/Keywords: Collaboration; Creativity; Creativity support tools; Co-creation; Computational creativity

…category at the Clough Student Art Competition and Georgia Tech in 2015… 

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APA (6th Edition):

Davis, N. M. (2017). Creative sense-making: A cognitive framework for quantifying interaction dynamics in co-creation. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/58304

Chicago Manual of Style (16th Edition):

Davis, Nicholas M. “Creative sense-making: A cognitive framework for quantifying interaction dynamics in co-creation.” 2017. Doctoral Dissertation, Georgia Tech. Accessed November 29, 2020. http://hdl.handle.net/1853/58304.

MLA Handbook (7th Edition):

Davis, Nicholas M. “Creative sense-making: A cognitive framework for quantifying interaction dynamics in co-creation.” 2017. Web. 29 Nov 2020.

Vancouver:

Davis NM. Creative sense-making: A cognitive framework for quantifying interaction dynamics in co-creation. [Internet] [Doctoral dissertation]. Georgia Tech; 2017. [cited 2020 Nov 29]. Available from: http://hdl.handle.net/1853/58304.

Council of Science Editors:

Davis NM. Creative sense-making: A cognitive framework for quantifying interaction dynamics in co-creation. [Doctoral Dissertation]. Georgia Tech; 2017. Available from: http://hdl.handle.net/1853/58304

20. Zook, Alexander. Automated iterative game design.

Degree: PhD, Interactive Computing, 2016, Georgia Tech

 Computational systems to model aspects of iterative game design were proposed, encompassing: game generation, sampling behaviors in a game, analyzing game behaviors for patterns, and… (more)

Subjects/Keywords: Artificial intelligence; Machine learning; Game design; Computational creativity; Games

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

APA (6th Edition):

Zook, A. (2016). Automated iterative game design. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/56346

Chicago Manual of Style (16th Edition):

Zook, Alexander. “Automated iterative game design.” 2016. Doctoral Dissertation, Georgia Tech. Accessed November 29, 2020. http://hdl.handle.net/1853/56346.

MLA Handbook (7th Edition):

Zook, Alexander. “Automated iterative game design.” 2016. Web. 29 Nov 2020.

Vancouver:

Zook A. Automated iterative game design. [Internet] [Doctoral dissertation]. Georgia Tech; 2016. [cited 2020 Nov 29]. Available from: http://hdl.handle.net/1853/56346.

Council of Science Editors:

Zook A. Automated iterative game design. [Doctoral Dissertation]. Georgia Tech; 2016. Available from: http://hdl.handle.net/1853/56346

21. Guzdial, Matthew James. Combinational machine learning creativity.

Degree: PhD, Interactive Computing, 2019, Georgia Tech

 Computational creativity is a field focused on the study and development of behaviors in computers an observer would deem creative. Traditionally, it has relied upon… (more)

Subjects/Keywords: Machine learning; Computational creativity' Game AI; Artificial Intelligence; Procedural content generation; Combinational creativity

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

APA (6th Edition):

Guzdial, M. J. (2019). Combinational machine learning creativity. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/61790

Chicago Manual of Style (16th Edition):

Guzdial, Matthew James. “Combinational machine learning creativity.” 2019. Doctoral Dissertation, Georgia Tech. Accessed November 29, 2020. http://hdl.handle.net/1853/61790.

MLA Handbook (7th Edition):

Guzdial, Matthew James. “Combinational machine learning creativity.” 2019. Web. 29 Nov 2020.

Vancouver:

Guzdial MJ. Combinational machine learning creativity. [Internet] [Doctoral dissertation]. Georgia Tech; 2019. [cited 2020 Nov 29]. Available from: http://hdl.handle.net/1853/61790.

Council of Science Editors:

Guzdial MJ. Combinational machine learning creativity. [Doctoral Dissertation]. Georgia Tech; 2019. Available from: http://hdl.handle.net/1853/61790


Georgia Tech

22. Roberts, David L. Computational techniques for reasoning about and shaping player experiences in interactive narratives.

Degree: PhD, Interactive Computing, 2010, Georgia Tech

 Interactive narratives are marked by two characteristics: 1) a space of player interactions, some subset of which are specified as aesthetic goals for the system;… (more)

Subjects/Keywords: Interactive storytelling; Drama management; Influence; Persuasion; Markov decision processes; Interactive multimedia; Shared virtual environments; Storytelling

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

APA (6th Edition):

Roberts, D. L. (2010). Computational techniques for reasoning about and shaping player experiences in interactive narratives. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/33910

Chicago Manual of Style (16th Edition):

Roberts, David L. “Computational techniques for reasoning about and shaping player experiences in interactive narratives.” 2010. Doctoral Dissertation, Georgia Tech. Accessed November 29, 2020. http://hdl.handle.net/1853/33910.

MLA Handbook (7th Edition):

Roberts, David L. “Computational techniques for reasoning about and shaping player experiences in interactive narratives.” 2010. Web. 29 Nov 2020.

Vancouver:

Roberts DL. Computational techniques for reasoning about and shaping player experiences in interactive narratives. [Internet] [Doctoral dissertation]. Georgia Tech; 2010. [cited 2020 Nov 29]. Available from: http://hdl.handle.net/1853/33910.

Council of Science Editors:

Roberts DL. Computational techniques for reasoning about and shaping player experiences in interactive narratives. [Doctoral Dissertation]. Georgia Tech; 2010. Available from: http://hdl.handle.net/1853/33910

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