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- All Subjects: artificial intelligence
- Member of: ASU Electronic Theses and Dissertations
- Member of: Barrett, The Honors College Thesis/Creative Project Collection
- Resource Type: Text
Description
The rapid expansion of artificial intelligence has propelled significant growth in the GPU market. In the evolving data center landscape, Company X faces challenges due to its lag in entering the GPU market, which jeopardizes its competitive advantage against industry players like Nvidia and AMD. To address these issues, our thesis aims to analyze market dynamics between CPUs and GPUs-whether they present distinct markets or compete against each other. We seek to guide Company X in maximizing profitability and sustaining its pivotal role in the semiconductor industry amidst the AI revolution. Specifically, we discuss optimizing their GPU offering, Falcon Shores, towards specific markets and doubling down on the production of CPUs.
ContributorsMostaghimi, Dunya (Author) / Kujawa, Brennan (Co-author) / Ulreich-Power, Cameron (Co-author) / Livesay, Thomas (Co-author) / Simonson, Mark (Thesis director) / Hertzel, Mike (Committee member) / Barrett, The Honors College (Contributor) / Department of Economics (Contributor)
Created2024-05
ContributorsShankar, Kruthy (Author) / Arquiza, Jose (Thesis director) / Sobrado, Michael (Committee member) / Barrett, The Honors College (Contributor) / Harrington Bioengineering Program (Contributor)
Created2024-05
ContributorsShankar, Kruthy (Author) / Arquiza, Jose (Thesis director) / Sobrado, Michael (Committee member) / Barrett, The Honors College (Contributor) / Harrington Bioengineering Program (Contributor)
Created2024-05
Description
Multi-agent reinforcement learning (MARL) plays a pivotal role in artificial intelligence by facilitating the learning process in complex environments inhabited by multiple entities. This thesis explores the integration of learning high-level knowledge through reward machines (RMs) with MARL to effectively manage non-Markovian reward functions in non-cooperative stochastic games. Reward machines offer a sophisticated way to model the temporal structure of rewards, thereby providing an enhanced representation of agent decision-making processes. A novel algorithm JIRP-SG is introduced, enabling agents to concurrently learn RMs and optimize their best response policies while navigating the intricate temporal dependencies present in non-cooperative settings. This approach employs automata learning to iteratively acquire RMs and utilizes the Lemke-Howson method to update the Q-functions, aiming for a Nash equilibrium. It is demonstrated that the method introduced reliably converges to accurately encode the reward functions and achieve the optimal best response policy for each agent over time. The effectiveness of the proposed approach is validated through case studies, including a Pacman Game scenario and a Factory Assembly scenario, illustrating its superior performance compared to baseline methods. Additionally, the impact of batch size on learning performance is examined, revealing that a diligent agent employing smaller batches can surpass the performance of an agent using larger batches, which fails to summarize experiences as effectively.
ContributorsKim, Hyohun (Author) / Xu, Zhe ZX (Thesis advisor) / Lee, Hyunglae HL (Committee member) / Berman, Spring SB (Committee member) / Arizona State University (Publisher)
Created2024
Description
Research in building agents by employing Large Language Models (LLMs) for computer control is expanding, aiming to create agents that can efficiently automate complex or repetitive computational tasks. Prior works showcased the potential of Large Language Models (LLMs) with in-context learning (ICL). However, they suffered from limited context length and poor generalization of the underlying models, which led to poor performance in long-horizon tasks, handling multiple applications and working across multiple domains. While initial work focused on extending the coding capabilities of LLMs to work with APIs to accomplish tasks, a new body of work focused on Graphical User Interface (GUI) manipulation has shown strong success in web and mobile application automation. In this work, I introduce LUCI: Large Language Model-assisted User Control Interface, a hierarchical, modular, and efficient framework to extend the capabilities of LLMs to automate GUIs. LUCI utilizes the reasoning capabilities of LLMs to decompose tasks into sub-tasks and recursively solve them. A key innovation is the application-centric approach which creates sub-tasks by first selecting the applications needed to solve the prompt. The GUI application is decomposed into a novel compressed Information-Action-Field (IAF) representation based on the underlying syntax tree. Furthermore, LUCI follows a modular structure allowing it to be extended to new platforms without any additional training as the underlying reasoning works on my IAF representations. These innovations alongside the `ensemble of LLMs' structure allow LUCI to outperform previous supervised learning (SL), reinforcement learning (RL), and LLM approaches on Miniwob++, overcoming challenges such as limited context length, exemplar memory requirements, and human intervention for task adaptability. LUCI shows a 20% improvement over the state-of-the-art (SOTA) in GUI automation on the Mind2Web benchmark. When tested in a realistic setting with over 22 commonly used applications, LUCI achieves an 80% success rate in undertaking tasks that use a subset of these applications. I also note an over 70% success rate on unseen applications, which is a less than 5% drop as compared to the fine-tuned applications.
ContributorsLAGUDU, GUNA SEKHAR SAI HARSHA (Author) / Shrivastava, Aviral (Thesis advisor) / Ramapuram Matavalam, Amarsagar Reddy (Committee member) / Chhabria, Vidya (Committee member) / Arizona State University (Publisher)
Created2024