Matching Items (129)
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- All Subjects: artificial intelligence
- Genre: Masters Thesis
- Creators: Arizona State University
- Creators: Liang, Jianming
- Status: Published
Description
The need for incorporating game engines into robotics tools becomes increasingly crucial as their graphics continue to become more photorealistic. This thesis presents a simulation framework, referred to as OpenUAV, that addresses cloud simulation and photorealism challenges in academic and research goals. In this work, OpenUAV is used to create a simulation of an autonomous underwater vehicle (AUV) closely following a moving autonomous surface vehicle (ASV) in an underwater coral reef environment. It incorporates the Unity3D game engine and the robotics software Gazebo to take advantage of Unity3D's perception and Gazebo's physics simulation. The software is developed as a containerized solution that is deployable on cloud and on-premise systems.
This method of utilizing Gazebo's physics and Unity3D perception is evaluated for a team of marine vehicles (an AUV and an ASV) in a coral reef environment. A coordinated navigation and localization module is presented that allows the AUV to follow the path of the ASV. A fiducial marker underneath the ASV facilitates pose estimation of the AUV, and the pose estimates are filtered using the known dynamical system model of both vehicles for better localization. This thesis also investigates different fiducial markers and their detection rates in this Unity3D underwater environment. The limitations and capabilities of this Unity3D perception and Gazebo physics approach are examined.
This method of utilizing Gazebo's physics and Unity3D perception is evaluated for a team of marine vehicles (an AUV and an ASV) in a coral reef environment. A coordinated navigation and localization module is presented that allows the AUV to follow the path of the ASV. A fiducial marker underneath the ASV facilitates pose estimation of the AUV, and the pose estimates are filtered using the known dynamical system model of both vehicles for better localization. This thesis also investigates different fiducial markers and their detection rates in this Unity3D underwater environment. The limitations and capabilities of this Unity3D perception and Gazebo physics approach are examined.
ContributorsAnand, Harish (Author) / Das, Jnaneshwar (Thesis advisor) / Yang, Yezhou (Committee member) / Berman, Spring M (Committee member) / Arizona State University (Publisher)
Created2020
Description
Referring Expression Comprehension (REC) is an important area of research in Natural Language Processing (NLP) and vision domain. It involves locating an object in an image described by a natural language referring expression. This task requires information from both Natural Language and Vision aspect. The task is compositional in nature as it requires visual reasoning as underlying process along with relationships among the objects in the image. Recent works based on modular networks have
displayed to be an effective framework for performing visual reasoning task.
Although this approach is effective, it has been established that the current benchmark datasets for referring expression comprehension suffer from bias. Recent work on CLEVR-Ref+ dataset deals with bias issues by constructing a synthetic dataset
and provides an approach for the aforementioned task which performed better than the previous state-of-the-art models as well as showing the reasoning process. This work aims to improve the performance on CLEVR-Ref+ dataset and achieve comparable interpretability. In this work, the neural module network approach with the attention map technique is employed. The neural module network is composed of the primitive operation modules which are specific to their functions and the output is generated using a separate segmentation module. From empirical results, it is clear that this approach is performing significantly better than the current State-of-theart in one aspect (Predicted programs) and achieving comparable results for another aspect (Ground truth programs)
displayed to be an effective framework for performing visual reasoning task.
Although this approach is effective, it has been established that the current benchmark datasets for referring expression comprehension suffer from bias. Recent work on CLEVR-Ref+ dataset deals with bias issues by constructing a synthetic dataset
and provides an approach for the aforementioned task which performed better than the previous state-of-the-art models as well as showing the reasoning process. This work aims to improve the performance on CLEVR-Ref+ dataset and achieve comparable interpretability. In this work, the neural module network approach with the attention map technique is employed. The neural module network is composed of the primitive operation modules which are specific to their functions and the output is generated using a separate segmentation module. From empirical results, it is clear that this approach is performing significantly better than the current State-of-theart in one aspect (Predicted programs) and achieving comparable results for another aspect (Ground truth programs)
ContributorsRathor, Kuldeep Singh (Author) / Baral, Chitta (Thesis advisor) / Yang, Yezhou (Committee member) / Simeone, Michael (Committee member) / Arizona State University (Publisher)
Created2020
Description
Introduction: A diet high in fermented, oligio-, di-, monosaccharide, and polyols
(FODMAP) has been shown to exacerbate symptoms of irritable bowel syndrome
(IBS). Previous literature has shown significant improvement in IBS symptoms after
adherence to a low FODMAP diet (LFD). However, dietary adherence to the LFD is
difficult with patients stating that information provided by healthcare providers
(HCPs) is generalized and nonspecific requiring them to search for supplementary
information to fit their needs. Notably, studies that have used a combination of
online and in-person methods for treatment have shown improved adherence to the
LFD. Objective: To determine whether a novel artificial intelligence (AI) dietary
mobile application will improve adherence to the LFD compared to a standard online
dietary intervention (CON) in populations with IBS or IBS-like symptoms over a 4-
week period. Methods: Participants were randomized into two groups: APP or CON.
The intervention group was provided access to an AI mobile application, a dietary
resource verified by registered dietitians which uses artificial intelligence to
individualize dietary guidance in real-time with the ability to scan menus and
barcodes and provide individuals with food scores based on their dietary preferences.
Primary measures included mobile app engagement, dietary adherence, and
manifestation of IBS-like symptoms. Baseline Results: A total of 58 participants
were randomized to groups. This is an ongoing study and this thesis details the
methodology and baseline characteristics of the participants at baseline and
intervention start. Validation of the application could improve the range of offerings
for lifestyle diseases treatable through dietary modification.
(FODMAP) has been shown to exacerbate symptoms of irritable bowel syndrome
(IBS). Previous literature has shown significant improvement in IBS symptoms after
adherence to a low FODMAP diet (LFD). However, dietary adherence to the LFD is
difficult with patients stating that information provided by healthcare providers
(HCPs) is generalized and nonspecific requiring them to search for supplementary
information to fit their needs. Notably, studies that have used a combination of
online and in-person methods for treatment have shown improved adherence to the
LFD. Objective: To determine whether a novel artificial intelligence (AI) dietary
mobile application will improve adherence to the LFD compared to a standard online
dietary intervention (CON) in populations with IBS or IBS-like symptoms over a 4-
week period. Methods: Participants were randomized into two groups: APP or CON.
The intervention group was provided access to an AI mobile application, a dietary
resource verified by registered dietitians which uses artificial intelligence to
individualize dietary guidance in real-time with the ability to scan menus and
barcodes and provide individuals with food scores based on their dietary preferences.
Primary measures included mobile app engagement, dietary adherence, and
manifestation of IBS-like symptoms. Baseline Results: A total of 58 participants
were randomized to groups. This is an ongoing study and this thesis details the
methodology and baseline characteristics of the participants at baseline and
intervention start. Validation of the application could improve the range of offerings
for lifestyle diseases treatable through dietary modification.
ContributorsRafferty, Aaron (Author) / Johnston, Carol (Thesis advisor) / Hall, Richard (Committee member) / Fitton, Renee (Committee member) / Arizona State University (Publisher)
Created2020
Description
Decision support systems aid the human-in-the-loop by enhancing the quality of decisions and the ease of making them in complex decision-making scenarios. In the recent years, such systems have been empowered with automated techniques for sequential decision making or planning tasks to effectively assist and cooperate with the human-in-the-loop. This has received significant recognition in the planning as well as human computer interaction communities as such systems connect the key elements of automated planning in decision support to principles of naturalistic decision making in the HCI community. A decision support system, in addition to providing planning support, must be able to provide intuitive explanations based on specific user queries for proposed decisions to its end users. Using this as motivation, I consider scenarios where the user questions the system's suggestion by providing alternatives (referred to as foils). In response, I empower existing decision support technologies to engage in an interactive explanatory dialogue with the user and provide contrastive explanations based on user-specified foils to reach a consensus on proposed decisions. Furthermore, the foils specified by the user can be indicative of the latent preferences of the user. I use this interpretation to equip existing decision support technologies with three different interaction strategies that utilize the foil to provide revised plan suggestions. Finally, as part of my Master's thesis, I present RADAR-X, an extension of RADAR, a proactive decision support system, that showcases the above mentioned capabilities. Further, I present a user-study evaluation that emphasizes the need for contrastive explanations and a computational evaluation of the mentioned interaction strategies.
ContributorsValmeekam, Karthik (Author) / Kambhampati, Subbarao (Thesis advisor) / Chiou, Erin (Committee member) / Sengupta, Sailik (Committee member) / Arizona State University (Publisher)
Created2021
Description
Understanding the limits and capabilities of an AI system is essential for safe and effective usability of modern AI systems. In the query-based AI assessment paradigm, a personalized assessment module queries a black-box AI system on behalf of a user and returns a user-interpretable model of the AI system’s capabilities. This thesis develops this paradigm to learn interpretable action models of simulator-based agents. Two types of agents are considered: the first uses high-level actions where the user’s vocabulary captures the simulator state perfectly, and the second operates on low-level actions where the user’s vocabulary captures only an abstraction of the simulator state. Methods are developed to interface the assessment module with these agents. Empirical results show that this method is capable of learning interpretable models of agents operating in a range of domains.
ContributorsMarpally, Shashank Rao (Author) / Srivastava, Siddharth (Thesis advisor) / Zhang, Yu (Committee member) / Fainekos, Georgios E (Committee member) / Arizona State University (Publisher)
Created2021
Description
Multi-robot systems show great promise in performing complex tasks in areas ranging from search and rescue to interplanetary exploration. Yet controlling and coordinating the behaviors of these robots effectively is an open research problem. This research investigates techniques to control a multi-drone system where the drones learn to act in a physics-based simulator using demonstrations from artificially generated motion data that simulate flocking behavior in biological swarms. Using these demonstrations enables faster training than approaches where the agents start learning from scratch. The Graph Neural Network (GNN) controller used for the drones learns an efficient representation of low-level interactions in the system, allowing the proposed method to scale to more agents than in training data. This work also discusses techniques to improve performance in the face of real-world challenges such as sensor noise.
ContributorsKhopkar, Parth (Author) / Ben Amor, Heni H (Thesis advisor) / Pavlic, Theodore T (Committee member) / Zhou, Siyu S (Committee member) / Arizona State University (Publisher)
Created2021
Description
This work investigates the multi-agent reinforcement learning methods that have applicability to real-world scenarios including stochastic, partially observable, and infinite horizon problems. These problems are hard due to large state and control spaces and may require some form of intelligent multi-agent behavior to achieve the target objective. The study also introduces novel rollout-based methods that provide reasonable guarantees to cost improvements and obtaining a sub-optimal solution to such problems while being amenable to distributed computation and hence a faster runtime. These methods, first introduced and developed for single-agent scenarios, are gradually extended to the multi-agent variants. They have been named multi-agent rollout methods. The problems studied in this work target one or more aspects of three major challenges of real-world problems. Spider and Fly problem deals with stochastic environments, multi-robot repair problem is an example of a partial observation Markov decision problem or POMDP, whereas the Flatland challenge is an RL benchmark that aims to solve the vehicle rescheduling problem. The study also includes comparisons to some existing methods that are used widely for such problems as POMCP, DESPOT, and MADDPG. The work also delineates and compares different behaviors arising out of our methods to other existing methods thereby positing the efficacy of our rollout-based methods in solving real-world multi-agent reinforcement learning problems. Additionally, the source code and problem environments have been released for the community to further the research in this field. The source code and the related research can be found on https://sahilbadyal.com/marl.
ContributorsBadyal, Sahil (Author) / Gil, Stephanie Dr. (Thesis advisor) / Bertsekas, Dimitri Dr. (Committee member) / Yang, Yingzhen Dr. (Committee member) / Arizona State University (Publisher)
Created2021
Description
Significance of real-world knowledge for Natural Language Understanding(NLU) is well-known for decades. With advancements in technology, challenging tasks like question-answering, text-summarizing, and machine translation are made possible with continuous efforts in the field of Natural Language Processing(NLP). Yet, knowledge integration to answer common sense questions is still a daunting task. Logical reasoning has been a resort for many of the problems in NLP and has achieved considerable results in the field, but it is difficult to resolve the ambiguities in a natural language. Co-reference resolution is one of the problems where ambiguity arises due to the semantics of the sentence. Another such problem is the cause and result statements which require causal commonsense reasoning to resolve the ambiguity. Modeling these type of problems is not a simple task with rules or logic. State-of-the-art systems addressing these problems use a trained neural network model, which claims to have overall knowledge from a huge trained corpus. These systems answer the questions by using the knowledge embedded in their trained language model. Although the language models embed the knowledge from the data, they use occurrences of words and frequency of co-existing words to solve the prevailing ambiguity. This limits the performance of language models to solve the problems in common-sense reasoning task as it generalizes the concept rather than trying to answer the problem specific to its context. For example, "The painting in Mark's living room shows an oak tree. It is to the right of a house", is a co-reference resolution problem which requires knowledge. Language models can resolve whether "it" refers to "painting" or "tree", since "house" and "tree" are two common co-occurring words so the models can resolve "tree" to be the co-reference. On the other hand, "The large ball crashed right through the table. Because it was made of Styrofoam ." to resolve for "it" which can be either "table" or "ball", is difficult for a language model as it requires more information about the problem.
In this work, I have built an end-to-end framework, which uses the automatically extracted knowledge based on the problem. This knowledge is augmented with the language models using an explicit reasoning module to resolve the ambiguity. This system is built to improve the accuracy of the language models based approaches for commonsense reasoning. This system has proved to achieve the state of the art accuracy on the Winograd Schema Challenge.
In this work, I have built an end-to-end framework, which uses the automatically extracted knowledge based on the problem. This knowledge is augmented with the language models using an explicit reasoning module to resolve the ambiguity. This system is built to improve the accuracy of the language models based approaches for commonsense reasoning. This system has proved to achieve the state of the art accuracy on the Winograd Schema Challenge.
ContributorsPrakash, Ashok (Author) / Baral, Chitta (Thesis advisor) / Devarakonda, Murthy (Committee member) / Anwar, Saadat (Committee member) / Arizona State University (Publisher)
Created2019
Description
Many real-world planning problems can be modeled as Markov Decision Processes (MDPs) which provide a framework for handling uncertainty in outcomes of action executions. A solution to such a planning problem is a policy that handles possible contingencies that could arise during execution. MDP solvers typically construct policies for a problem instance without re-using information from previously solved instances. Research in generalized planning has demonstrated the utility of constructing algorithm-like plans that reuse such information. However, using such techniques in an MDP setting has not been adequately explored.
This thesis presents a novel approach for learning generalized partial policies that can be used to solve problems with different object names and/or object quantities using very few example policies for learning. This approach uses abstraction for state representation, which allows the identification of patterns in solutions such as loops that are agnostic to problem-specific properties. This thesis also presents some theoretical results related to the uniqueness and succinctness of the policies computed using such a representation. The presented algorithm can be used as fast, yet greedy and incomplete method for policy computation while falling back to a complete policy search algorithm when needed. Extensive empirical evaluation on discrete MDP benchmarks shows that this approach generalizes effectively and is often able to solve problems much faster than existing state-of-art discrete MDP solvers. Finally, the practical applicability of this approach is demonstrated by incorporating it in an anytime stochastic task and motion planning framework to successfully construct free-standing tower structures using Keva planks.
This thesis presents a novel approach for learning generalized partial policies that can be used to solve problems with different object names and/or object quantities using very few example policies for learning. This approach uses abstraction for state representation, which allows the identification of patterns in solutions such as loops that are agnostic to problem-specific properties. This thesis also presents some theoretical results related to the uniqueness and succinctness of the policies computed using such a representation. The presented algorithm can be used as fast, yet greedy and incomplete method for policy computation while falling back to a complete policy search algorithm when needed. Extensive empirical evaluation on discrete MDP benchmarks shows that this approach generalizes effectively and is often able to solve problems much faster than existing state-of-art discrete MDP solvers. Finally, the practical applicability of this approach is demonstrated by incorporating it in an anytime stochastic task and motion planning framework to successfully construct free-standing tower structures using Keva planks.
ContributorsKala Vasudevan, Deepak (Author) / Srivastava, Siddharth (Thesis advisor) / Zhang, Yu (Committee member) / Yang, Yezhou (Committee member) / Arizona State University (Publisher)
Created2020
Description
Image segmentation is one of the most critical tasks in medical imaging, which identifies target segments (e.g., organs, tissues, lesions, etc.) from images for ease of analyzing. Among nearly all of the online segmentation challenges, deep learning has shown great promise due to the invention of U-Net, a fully automated, end-to-end neural architecture designed for segmentation tasks. Recent months have also witnessed the wide success of a framework that was directly derived from U-Net architecture, called nnU-Net (“no-new-net”). However, training nnU-Net from scratch takes weeks to converge and suffers from the unstable performance. To overcome the two limitations, instead of training from scratch, transfer learning was employed to nnU-Net by transferring generic image representation learned from massive images to specific target tasks. Although the transfer learning paradigm has proven a significant performance gain in many classification tasks, its effectiveness of segmentation tasks has yet to be sufficiently studied, especially in 3D medical image segmentation. In this thesis, first, nnU-Net was pre-trained on large-scale chest CT scans (LUNA 2016), following the self-supervised learning approach introduced in Models Genesis. Further, nnU-Net was fine-tuned on various target segmentation tasks through transfer learning. The experiments on liver/liver tumor, lung tumor segmentation tasks demonstrate a significantly improved and stabilized performance between fine-tuning and learning nnU-Net from scratch. This performance gain is attributed to the scalable, generic, robust image representation learned from the consistent and recurring anatomical structure embedded in medical images.
ContributorsBajpai, Shivam (Author) / Liang, Jianming Dr. (Thesis advisor) / Wang, Yalin Dr. (Committee member) / Venkateswara, Hemanth Kumar Demakethepalli Dr. (Committee member) / Arizona State University (Publisher)
Created2021