Matching Items (14)
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- All Subjects: artificial intelligence
- Creators: Ben Amor, Hani
- Status: Published
Description
This project did a deep dive on AI, business applications for AI and then my team and I built an AI model to better understand shipping patterns and inefficiencies of different porting regions.
ContributorsFreudenberger, Evan Martin (Author) / Wiedmer, Robert (Thesis director) / Duarte, Brett (Committee member) / Thunderbird School of Global Management (Contributor) / Department of Supply Chain Management (Contributor) / Department of Finance (Contributor) / Barrett, The Honors College (Contributor)
Created2021-05
Description
The focus of this research paper is understanding the impacts of human factors on the technology innovations in automobiles and the direction our society is headed. There will be an assessment of our current state and the possible solutions to combat the issue of creating technology advancements for automobiles that cater towards the human factors. There will be an introduction on the history of the first automobile invented to provide an understanding of the what the first automobile consisted of and will continue discussing the technological innovations that were implemented due to human factors. Diving into the types of technological innovations such as the ignition system, car radio, the power steering system, and self-driving, it will show the progression of the technological advancements that was implemented in relation to the human factors that was prominent among society. From there, it is important to understand what human factors and the concept of human factor engineering are. It will provide a better understanding of why humans have created technology in relation to the human factors. Then, there will be an introduction of the mobile phone industry history/timeline as a comparison to show the impacts of how human factors have had on the development of the technology in mobile phones and how heavily it catered towards human factors. There will be a discussion of the 3 key human factors that have been catered towards the development and implementation of technology in automobiles. They are selecting the path that requires the least cognitive effort, overestimating the performance of technology, and reducing the attention due to an automated system being put into place. Lastly, is understanding that if we create or implement technology such as self-driving, it should not solely be for comfort and ease of use, but for the overall efficient use of transportation in the future. This way humans would not rely heavily too much on the technology and limit the effect that human factors have on us.
ContributorsParham, Gi-onli (Author) / Keane, Katy (Thesis director) / Collins, Gregory (Committee member) / Department of Supply Chain Management (Contributor) / Barrett, The Honors College (Contributor)
Created2020-05
Description
This thesis aims to improve neural control policies for self-driving cars. State-of-the-art navigation software for self-driving cars is based on deep neural networks, where the network is trained on a dataset of past driving experience in various situations. With previous methods, the car can only make decisions based on short-term memory. To address this problem, we proposed that using a Neural Turing Machine (NTM) framework adds long-term memory to the system. We evaluated this approach by using it to master a palindrome task. The network was able to infer how to create a palindrome with 100% accuracy. Since the NTM structure proves useful, we aim to use it in the given scenarios to improve the navigation safety and accuracy of a simulated autonomous car.
ContributorsMartin, Sarah (Author) / Ben Amor, Hani (Thesis director) / Fainekos, Georgios (Committee member) / Barrett, The Honors College (Contributor)
Created2018-05
Description
Trajectory forecasting is used in many fields such as vehicle future trajectory prediction, stock market price prediction, human motion prediction and so on. Also, robots having the capability to reason about human behavior is an important aspect in human robot interaction. In trajectory prediction with regards to human motion prediction, implicit learning and reproduction of human behavior is the major challenge. This work tries to compare some of the recent advances taking a phenomenological approach to trajectory prediction. \par The work is expected to mainly target on generating future events or trajectories based on the previous data observed across many time intervals. In particular, this work presents and compares machine learning models to generate various human handwriting trajectories. Although the behavior of every individual is unique, it is still possible to broadly generalize and learn the underlying human behavior from the current observations to predict future human writing trajectories. This enables the machine or the robot to generate future handwriting trajectories given an initial trajectory from the individual thus helping the person to fill up the rest of the letter or curve. This work tests and compares the performance of Conditional Variational Autoencoders and Sinusoidal Representation Network models on handwriting trajectory prediction and reconstruction.
ContributorsKota, Venkata Anil (Author) / Ben Amor, Hani (Thesis advisor) / Venkateswara, Hemanth Kumar Demakethepalli (Committee member) / Redkar, Sangram (Committee member) / Arizona State University (Publisher)
Created2021
Description
Automated driving systems (ADS) have come a long way since their inception. It is clear that these systems rely heavily on stochastic deep learning techniques for perception, planning, and prediction, as it is impossible to construct every possible driving scenario to generate driving policies. Moreover, these systems need to be trained and validated extensively on typical and abnormal driving situations before they can be trusted with human life. However, most publicly available driving datasets only consist of typical driving behaviors. On the other hand, there is a plethora of videos available on the internet that capture abnormal driving scenarios, but they are unusable for ADS training or testing as they lack important information such as camera calibration parameters, and annotated vehicle trajectories. This thesis proposes a new toolbox, DeepCrashTest-V2, that is capable of reconstructing high-quality simulations from monocular dashcam videos found on the internet. The toolbox not only estimates the crucial parameters such as camera calibration, ego-motion, and surrounding road user trajectories but also creates a virtual world in Car Learning to Act (CARLA) using data from OpenStreetMaps to simulate the estimated trajectories. The toolbox is open-source and is made available in the form of a python package on GitHub at https://github.com/C-Aniruddh/deepcrashtest_v2.
ContributorsChandratre, Aniruddh Vinay (Author) / Fainekos, Georgios (Thesis advisor) / Ben Amor, Hani (Thesis advisor) / Pedrielli, Giulia (Committee member) / Arizona State University (Publisher)
Created2022
Description
Multiple robotic arms collaboration is to control multiple robotic arms to collaborate with each other to work on the same task. During the collaboration, theagent is required to avoid all possible collisions between each part of the robotic arms.
Thus, incentivizing collaboration and preventing collisions are the two principles which
are followed by the agent during the training process. Nowadays, more and more
applications, both in industry and daily lives, require at least two arms, instead of
requiring only a single arm. A dual-arm robot satisfies much more needs of different
types of tasks, such as folding clothes at home, making a hamburger in a grill or
picking and placing a product in a warehouse.
The applications done in this paper are all about object pushing. This thesis
focuses on how to train the agent to learn pushing an object away as far as possible.
Reinforcement Learning (RL), which is a type of Machine Learning (ML), is then
utilized in this paper to train the agent to generate optimal actions. Deep Deterministic
Policy Gradient (DDPG) and Hindsight Experience Replay (HER) are the two RL
methods used in this thesis.
ContributorsLin, Steve (Author) / Ben Amor, Hani (Thesis advisor) / Redkar, Sangram (Committee member) / Zhang, Yu (Committee member) / Arizona State University (Publisher)
Created2023
Description
In this thesis work, a novel learning approach to solving the problem of controllinga quadcopter (drone) swarm is explored. To deal with large sizes, swarm control is
often achieved in a distributed fashion by combining different behaviors such that
each behavior implements some desired swarm characteristics, such as avoiding ob-
stacles and staying close to neighbors. One common approach in distributed swarm
control uses potential fields. A limitation of this approach is that the potential fields
often depend statically on a set of control parameters that are manually specified a
priori. This paper introduces Dynamic Potential Fields for flexible swarm control.
These potential fields are modulated by a set of dynamic control parameters (DCPs)
that can change under different environment situations. Since the focus is only on
these DCPs, it simplifies the learning problem and makes it feasible for practical use.
This approach uses soft actor critic (SAC) where the actor only determines how to
modify DCPs in the current situation, resulting in more flexible swarm control. In
the results, this work will show that the DCP approach allows for the drones to bet-
ter traverse environments with obstacles compared to several state-of-the-art swarm
control methods with a fixed set of control parameters. This approach also obtained
a higher safety score commonly used to assess swarm behavior. A basic reinforce-
ment learning approach is compared to demonstrate faster convergence. Finally, an
ablation study is conducted to validate the design of this approach.
ContributorsFerraro, Calvin Shores (Author) / Zhang, Yu (Thesis advisor) / Ben Amor, Hani (Committee member) / Berman, Spring (Committee member) / Arizona State University (Publisher)
Created2022
Description
This research investigates the attitude of students towards chatbots and their potential usage in finding career resources. Survey data from two sources were analyzed using descriptive statistics and correlation analysis. The first survey found that students had a neutral attitude towards chatbots, but chatbot understanding was a key factor in increasing their usage. The survey data suggested that chatbots could provide quick and convenient access to information and personalized recommendations, but their effectiveness for career resource searches may be limited. The second survey found that students who were more satisfied with the quality of resources from the career office were more likely to use chatbots. However, students who felt more prepared to explore their career options were less likely to use chatbots. These results suggest that the W. P. Carey Career Office could benefit from offering more and better resources to prepare students for exploring their career options and could explore the use of chatbots to enhance the quality of their resources and increase student satisfaction. Further research is needed to confirm these suggestions and explore other possible factors that may affect the use of chatbots and the satisfaction with career office resources.
ContributorsHuang, Hai (Author) / Kappes, Janelle (Thesis director) / Eaton, John (Committee member) / Barrett, The Honors College (Contributor) / Department of Information Systems (Contributor) / Department of Supply Chain Management (Contributor)
Created2023-05
Description
In our society, technology has found itself as the root cause of a certain level of modernization. It wasn’t long ago when people heavily depended on bank tellers to complete cash transactions at a bank. Now however, much of the bank teller’s job has been automated in the form of ATM’s and electronic kiosks at drive through lanes. Automation is the current trend, and more departments are going to experience it. To those wondering which area or department may be hit next by a wave of technological automation, the answer is quite simple: CRM.
In its raw form, CRM, which stands for Customer Relationship Management, is a “system for managing your relationships with customers” (Hubspot). Essentially, it is a software intended to help companies maintain strong relationships with their customers, customers being a critical part of the process. A good CRM system should benefit both the business and the customer. However, this is easier said than done, making the million dollar question the following: how can CRM systems be improved to truly benefit both the business and the customer? This paper will demonstrate that the answer is quite simple: automation. Through secondary research, as well as interviews conducted with various business professionals, I will demonstrate that automation and integration can make the process much more efficient and can erase a lot of errors in the process. Automation is the future of business, and this fact is not any less true in the CRM field.
ContributorsWarrier, Akshay (Author) / Riker, Elise (Thesis director) / Lee, Sanghak (Committee member) / Barrett, The Honors College (Contributor) / Department of Supply Chain Management (Contributor) / Department of Finance (Contributor) / Department of Marketing (Contributor)
Created2023-05
Description
Supply chain sustainability has become an increasingly important topic for corporations
due to consumer demands, regulatory requirements, and employee retention and productivity.
Since more and more stakeholders are beginning to care about sustainability, companies are
looking at how they can reduce their carbon footprint without it leading to higher costs. Although
sustainable supply chain operations are often associated with higher costs, new technology has
surfaced within the last decade that makes this association come into question. This paper serves as an investigation on whether or not implementation of recent technology will not only make for more sustainable supply chains, but also bring cost savings to a company. For the sake of simplicity, this paper analyzes the topic within the context of the consumer packaged goods (CPG) industry. The three categories of technology that were evaluated are artificial intelligence, Internet of Things, and data integration systems. Internship projects and/or published case studies and articles were examined to explore the relationship between the technology, supply chain sustainability, and costs. The findings of this paper indicate that recent technology offers companies innovative sustainability solutions to supply chains without sacrificing cost. This calls for CPG companies to invest in and implement technology that allows for more sustainable supply chains. Shying away from this because of cost concerns is no longer necessary.
ContributorsDixon, Logan (Author) / Printezis, Antonios (Thesis director) / Macias, Jeff (Committee member) / Barrett, The Honors College (Contributor) / Department of Finance (Contributor) / Department of Supply Chain Management (Contributor) / Dean, W.P. Carey School of Business (Contributor)
Created2024-05