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You searched for +publisher:"Georgia Tech" +contributor:("Bodner, Douglas"). Showing records 1 – 3 of 3 total matches.

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1. 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 maximize goal attainment. However, reinforcement learning algorithms are slow to converge for larger state spaces and using reinforcement learning in agent programs requires detailed knowledge of reinforcement learning algorithms. One approach to solving the curse of dimensionality in reinforcement learning is decomposition. Modular reinforcement learning, as it is called in the literature, decomposes an agent into concurrently running reinforcement learning modules that each learn a ``selfish'' solution to a subset of the original problem. For example, a bunny agent might be decomposed into a module that avoids predators and a module that finds food. Current approaches to modular reinforcement learning support decomposition but, because the reward scales of the modules must be comparable, they are not composable  – a module written for one agent cannot be reused in another agent without modifying its reward function. This dissertation makes two contributions: (1) a command arbitration algorithm for modular reinforcement learning that enables composability by decoupling the reward scales of reinforcement learning modules, and (2) a Scala-embedded domain-specific language  – AFABL (A Friendly Adaptive Behavior Language)  – that integrates modular reinforcement learning in a way that allows programmers to use reinforcement learning without knowing much about reinforcement learning algorithms. We empirically demonstrate the reward comparability problem and show that our command arbitration algorithm solves it, and we present the results of a study in which programmers used AFABL and traditional programming to write a simple agent and adapt it to a new domain, demonstrating the promise of language-integrated reinforcement learning for practical agent software engineering. Advisors/Committee Members: Isbell, Charles L. (advisor), Bodner, Douglas (committee member), Riedl, Mark (committee member), Rugaber, Spencer (committee member), Thomaz, Andrea (committee member).

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 December 04, 2020. http://hdl.handle.net/1853/58683.

MLA Handbook (7th Edition):

Simpkins, Christopher Lee. “Integrating reinforcement learning into a programming language.” 2017. Web. 04 Dec 2020.

Vancouver:

Simpkins CL. Integrating reinforcement learning into a programming language. [Internet] [Doctoral dissertation]. Georgia Tech; 2017. [cited 2020 Dec 04]. 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


Georgia Tech

2. Chauhan, Lokendra Pratap Singh. Modelling stock market performance of firms as a function of the quality and quantity of intellectual property owned.

Degree: MS, Public Policy, 2007, Georgia Tech

This thesis attempts to analyze a part of the big and complex process of how intellectual property ownership and technological innovation influence the performance of firms and their revenues. Here I analyze a firm's stock market performance as a function of the quantity and quality of intellectual property (patents) owned by the firm in context of the three US high-technology sectors, Pharmaceuticals, Semiconductors and Wireless. In these sectors, value of a firm is predominantly driven by the technologies which a firm owns. I use citation based indicators and number of claims to measure the quality of patents. This research presents empirical evidence for the hypothesis that in high-tech sectors, companies which generate better quality intellectual property perform better than average in the stock market. I also posit that firms which are producing better quality technologies (good R&D) invest more in R&D regardless of their market performance. Furthermore, though smaller firms get relatively less returns on quality and quantity of innovation, they tend to invest a bigger fraction of their total assets in R&D when they are generating high quality patents. Larger firms enjoy the super-additivity effects in terms of market performance as the same intellectual property gives better returns to them. In addition, returns to R&D are relatively higher in the pharmaceutical industry than semiconductor or wireless industries. Advisors/Committee Members: Hicks, Diana (Committee Chair), Rouse, Bill (Committee Co-Chair), Bodner, Douglas (Committee Member).

Subjects/Keywords: Intangible assets; Citations; Returns to R & D; Lagged returns; Patents; IP valuation; Patent analysis; Tobin's Q; Patent claims

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

APA (6th Edition):

Chauhan, L. P. S. (2007). Modelling stock market performance of firms as a function of the quality and quantity of intellectual property owned. (Masters Thesis). Georgia Tech. Retrieved from http://hdl.handle.net/1853/16218

Chicago Manual of Style (16th Edition):

Chauhan, Lokendra Pratap Singh. “Modelling stock market performance of firms as a function of the quality and quantity of intellectual property owned.” 2007. Masters Thesis, Georgia Tech. Accessed December 04, 2020. http://hdl.handle.net/1853/16218.

MLA Handbook (7th Edition):

Chauhan, Lokendra Pratap Singh. “Modelling stock market performance of firms as a function of the quality and quantity of intellectual property owned.” 2007. Web. 04 Dec 2020.

Vancouver:

Chauhan LPS. Modelling stock market performance of firms as a function of the quality and quantity of intellectual property owned. [Internet] [Masters thesis]. Georgia Tech; 2007. [cited 2020 Dec 04]. Available from: http://hdl.handle.net/1853/16218.

Council of Science Editors:

Chauhan LPS. Modelling stock market performance of firms as a function of the quality and quantity of intellectual property owned. [Masters Thesis]. Georgia Tech; 2007. Available from: http://hdl.handle.net/1853/16218


Georgia Tech

3. Kim, Hansoo. Reference Model Based High Fidelity Simulation Modeling for Manufacturing Systems.

Degree: PhD, Industrial and Systems Engineering, 2004, Georgia Tech

Today, discrete event simulation is the only reliable tool for detailed analysis of complex behaviors of modern manufacturing systems. However, building high fidelity simulation models is expensive. Hence, it is important to improve the simulation modeling productivity. In this research, we explore two approaches for the improvement of simulation modeling productivity. One approach is the Virtual Factory Approach, using a general-purpose model for a system to achieve various simulation objectives with a single high fidelity model through abstraction. The other approach is the Reference Model Approach, which is to build fundamental building blocks for simulation models of any system in a domain with formal descriptions and domain knowledge. In the Virtual Factory Approach, the challenge is to show the validity of the methodology. We develop a formal framework for the relationships between higher fidelity and lower fidelity models, and provide justification that the models abstracted from a higher fidelity model are interchangeable with various abstract simulation models for a target system. For the Reference Model Approach, we attempt to overcome the weak points inherited from ad-hoc modeling and develop a formal reference model and a model generation procedure for discrete part manufacturing systems, which covers most modern manufacturing systems. Advisors/Committee Members: Zhou, Chen (Committee Chair), McGinnis, Leon F. (Committee Co-Chair), Alexopoulos, Christos (Committee Member), Bodner, Douglas A. (Committee Member), Narasimhan, Sridhar (Committee Member).

Subjects/Keywords: Reference model for manufacturing; Simulation model fidelity; Automatic model generation; Modeling productivity; Simulation for manufacturing systems

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

APA (6th Edition):

Kim, H. (2004). Reference Model Based High Fidelity Simulation Modeling for Manufacturing Systems. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/5207

Chicago Manual of Style (16th Edition):

Kim, Hansoo. “Reference Model Based High Fidelity Simulation Modeling for Manufacturing Systems.” 2004. Doctoral Dissertation, Georgia Tech. Accessed December 04, 2020. http://hdl.handle.net/1853/5207.

MLA Handbook (7th Edition):

Kim, Hansoo. “Reference Model Based High Fidelity Simulation Modeling for Manufacturing Systems.” 2004. Web. 04 Dec 2020.

Vancouver:

Kim H. Reference Model Based High Fidelity Simulation Modeling for Manufacturing Systems. [Internet] [Doctoral dissertation]. Georgia Tech; 2004. [cited 2020 Dec 04]. Available from: http://hdl.handle.net/1853/5207.

Council of Science Editors:

Kim H. Reference Model Based High Fidelity Simulation Modeling for Manufacturing Systems. [Doctoral Dissertation]. Georgia Tech; 2004. Available from: http://hdl.handle.net/1853/5207

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