Matching Items (21)
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- All Subjects: robotics
- All Subjects: Social networks
- Creators: Kambhampati, Subbarao
- Status: Published
Description
This thesis proposed a novel approach to establish the trust model in a social network scenario based on users' emails. Email is one of the most important social connections nowadays. By analyzing email exchange activities among users, a social network trust model can be established to judge the trust rate between each two users. The whole trust checking process is divided into two steps: local checking and remote checking. Local checking directly contacts the email server to calculate the trust rate based on user's own email communication history. Remote checking is a distributed computing process to get help from user's social network friends and built the trust rate together. The email-based trust model is built upon a cloud computing framework called MobiCloud. Inside MobiCloud, each user occupies a virtual machine which can directly communicate with others. Based on this feature, the distributed trust model is implemented as a combination of local analysis and remote analysis in the cloud. Experiment results show that the trust evaluation model can give accurate trust rate even in a small scale social network which does not have lots of social connections. With this trust model, the security in both social network services and email communication could be improved.
ContributorsZhong, Yunji (Author) / Huang, Dijiang (Thesis advisor) / Dasgupta, Partha (Committee member) / Syrotiuk, Violet (Committee member) / Arizona State University (Publisher)
Created2011
Description
In most social networking websites, users are allowed to perform interactive activities. One of the fundamental features that these sites provide is to connecting with users of their kind. On one hand, this activity makes online connections visible and tangible; on the other hand, it enables the exploration of our connections and the expansion of our social networks easier. The aggregation of people who share common interests forms social groups, which are fundamental parts of our social lives. Social behavioral analysis at a group level is an active research area and attracts many interests from the industry. Challenges of my work mainly arise from the scale and complexity of user generated behavioral data. The multiple types of interactions, highly dynamic nature of social networking and the volatile user behavior suggest that these data are complex and big in general. Effective and efficient approaches are required to analyze and interpret such data. My work provide effective channels to help connect the like-minded and, furthermore, understand user behavior at a group level. The contributions of this dissertation are in threefold: (1) proposing novel representation of collective tagging knowledge via tag networks; (2) proposing the new information spreader identification problem in egocentric soical networks; (3) defining group profiling as a systematic approach to understanding social groups. In sum, the research proposes novel concepts and approaches for connecting the like-minded, enables the understanding of user groups, and exposes interesting research opportunities.
ContributorsWang, Xufei (Author) / Liu, Huan (Thesis advisor) / Kambhampati, Subbarao (Committee member) / Sundaram, Hari (Committee member) / Ye, Jieping (Committee member) / Arizona State University (Publisher)
Created2013
Description
Online social networks, including Twitter, have expanded in both scale and diversity of content, which has created significant challenges to the average user. These challenges include finding relevant information on a topic and building social ties with like-minded individuals. The fundamental question addressed by this thesis is if an individual can leverage social network to search for information that is relevant to him or her. We propose to answer this question by developing computational algorithms that analyze a user's social network. The features of the social network we analyze include the network topology and member communications of a specific user's social network. Determining the "social value" of one's contacts is a valuable outcome of this research. The algorithms we developed were tested on Twitter, which is an extremely popular social network. Twitter was chosen due to its popularity and a majority of the communications artifacts on Twitter is publically available. In this work, the social network of a user refers to the "following relationship" social network. Our algorithm is not specific to Twitter, and is applicable to other social networks, where the network topology and communications are accessible. My approaches are as follows. For a user interested in using the system, I first determine the immediate social network of the user as well as the social contacts for each person in this network. Afterwards, I establish and extend the social network for each user. For each member of the social network, their tweet data are analyzed and represented by using a word distribution. To accomplish this, I use WordNet, a popular lexical database, to determine semantic similarity between two words. My mechanism of search combines both communication distance between two users and social relationships to determine the search results. Additionally, I developed a search interface, where a user can interactively query the system. I conducted preliminary user study to evaluate the quality and utility of my method and system against several baseline methods, including the default Twitter search. The experimental results from the user study indicate that my method is able to find relevant people and identify valuable contacts in one's social circle based on the query. The proposed system outperforms baseline methods in terms of standard information retrieval metrics.
ContributorsXu, Ke (Author) / Sundaram, Hari (Thesis advisor) / Ye, Jieping (Committee member) / Kelliher, Aisling (Committee member) / Arizona State University (Publisher)
Created2010
Description
Goal specification is an important aspect of designing autonomous agents. A goal does not only refer to the set of states for the agent to reach. A goal also defines restrictions on the paths the agent should follow. Temporal logics are widely used in goal specification. However, they lack the ability to represent goals in a non-deterministic domain, goals that change non-monotonically, and goals with preferences. This dissertation defines new goal specification languages by extending temporal logics to address these issues. First considered is the goal specification in non-deterministic domains, in which an agent following a policy leads to a set of paths. A logic is proposed to distinguish paths of the agent from all paths in the domain. In addition, to address the need of comparing policies for finding the best ones, a language capable of quantifying over policies is proposed. As policy structures of agents play an important role in goal specification, languages are also defined by considering different policy structures. Besides, after an agent is given an initial goal, the agent may change its expectations or the domain may change, thus goals that are previously specified may need to be further updated, revised, partially retracted, or even completely changed. Non-monotonic goal specification languages that can make these changes in an elaboration tolerant manner are needed. Two languages that rely on labeling sub-formulas and connecting multiple rules are developed to address non-monotonicity in goal specification. Also, agents may have preferential relations among sub-goals, and the preferential relations may change as agents achieve other sub-goals. By nesting a comparison operator with other temporal operators, a language with dynamic preferences is proposed. Various goals that cannot be expressed in other languages are expressed in the proposed languages. Finally, plans are given for some goals specified in the proposed languages.
ContributorsZhao, Jicheng (Author) / Baral, Chitta (Thesis advisor) / Kambhampati, Subbarao (Committee member) / Lee, Joohyung (Committee member) / Lifschitz, Vladimir (Committee member) / Liu, Huan (Committee member) / Arizona State University (Publisher)
Created2010
Description
This thesis describes a multi-robot architecture which allows teams of robots to work with humans to complete tasks. The multi-agent architecture was built using Robot Operating System and Python. This architecture was designed modularly, allowing the use of different planners and robots. The system automatically replans when robots connect or disconnect. The system was demonstrated on two real robots, a Fetch and a PeopleBot, by conducting a surveillance task on the fifth floor of the Computer Science building at Arizona State University. The next part of the system includes extensions for teaming with humans. An Android application was created to serve as the interface between the system and human teammates. This application provides a way for the system to communicate with humans in the loop. In addition, it sends location information of the human teammates to the system so that goal recognition can be performed. This goal recognition allows the generation of human-aware plans. This capability was demonstrated in a mock search and rescue scenario using the Fetch to locate a missing teammate.
ContributorsSaba, Gabriel Christer (Author) / Kambhampati, Subbarao (Thesis director) / Doupé, Adam (Committee member) / Chakraborti, Tathagata (Committee member) / Computer Science and Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2017-05
Description
Many researchers aspire to create robotics systems that assist humans in common office tasks, especially by taking over delivery and messaging tasks. For meaningful interactions to take place, a mobile robot must be able to identify the humans it interacts with and communicate successfully with them. It must also be able to successfully navigate the office environment. While mobile robots are well suited for navigating and interacting with elements inside a deterministic office environment, attempting to interact with human beings in an office environment remains a challenge due to the limits on the amount of cost-efficient compute power onboard the robot. In this work, I propose the use of remote cloud services to offload intensive interaction tasks. I detail the interactions required in an office environment and discuss the challenges faced when implementing a human-robot interaction platform in a stochastic office environment. I also experiment with cloud services for facial recognition, speech recognition, and environment navigation and discuss my results. As part of my thesis, I have implemented a human-robot interaction system utilizing cloud APIs into a mobile robot, enabling it to navigate the office environment, identify humans within the environment, and communicate with these humans.
ContributorsDSouza, Daniel Anand (Author) / Kambhampati, Subbarao (Thesis director) / Zhang, Yu (Committee member) / Computer Science and Engineering Program (Contributor) / School of Computing, Informatics, and Decision Systems Engineering (Contributor) / Barrett, The Honors College (Contributor)
Created2017-05
Description
Imitation learning is a promising methodology for teaching robots how to physically interact and collaborate with human partners. However, successful interaction requires complex coordination in time and space, i.e., knowing what to do as well as when to do it. This dissertation introduces Bayesian Interaction Primitives, a probabilistic imitation learning framework which establishes a conceptual and theoretical relationship between human-robot interaction (HRI) and simultaneous localization and mapping. In particular, it is established that HRI can be viewed through the lens of recursive filtering in time and space. In turn, this relationship allows one to leverage techniques from an existing, mature field and develop a powerful new formulation which enables multimodal spatiotemporal inference in collaborative settings involving two or more agents. Through the development of exact and approximate variations of this method, it is shown in this work that it is possible to learn complex real-world interactions in a wide variety of settings, including tasks such as handshaking, cooperative manipulation, catching, hugging, and more.
ContributorsCampbell, Joseph (Author) / Ben Amor, Heni (Thesis advisor) / Fainekos, Georgios (Thesis advisor) / Yamane, Katsu (Committee member) / Kambhampati, Subbarao (Committee member) / Arizona State University (Publisher)
Created2021
Description
In this work, the problem of multi-object tracking (MOT) is studied, particularly the challenges that arise from object occlusions. A solution based on a principled approximate dynamic programming approach called ADPTrack is presented. ADPTrack relies on existing MOT solutions and directly improves them. When matching tracks to objects at a particular frame, the proposed approach simulates executions of these existing solutions into future frames to obtain approximate track extensions, from which a comparison of past and future appearance feature information is leveraged to improve overall robustness to occlusion-based error. The proposed solution when applied to the renowned MOT17 dataset empirically demonstrates a 0.7% improvement in the association accuracy (IDF1 metric) over a state-of-the-art baseline that it builds upon while obtaining minor improvements with respect to all other metrics. Moreover, it is shown that this improvement is even more pronounced in scenarios where the camera maintains a fixed position. This implies that the proposed method is effective in addressing MOT issues pertaining to object occlusions.
ContributorsMusunuru, Pratyusha (Author) / Bertsekas, Dimitri (Thesis advisor) / Kambhampati, Subbarao (Thesis advisor) / Richa, Andrea (Committee member) / Arizona State University (Publisher)
Created2024
Description
In this work, I propose to bridge the gap between human users and adaptive control of robotic systems. The goal is to enable robots to consider user feedback and adjust their behaviors. A critical challenge with designing such systems is that users are often non-experts, with limited knowledge about the robot's hardware and dynamics. In the domain of human-robot interaction, there exist different modalities of conveying information regarding the desired behavior of the robot, most commonly used are demonstrations, and preferences. While it is challenging for non-experts to provide demonstrations of robot behavior, works that consider preferences expressed as trajectory rankings lead to users providing noisy and possibly conflicting information, leading to slow adaptation or system failures. The end user can be expected to be familiar with the dynamics and how they relate to their desired objectives through repeated interactions with the system. However, due to inadequate knowledge about the system dynamics, it is expected that the user would find it challenging to provide feedback on all dimension's of the system's behavior at all times. Thus, the key innovation of this work is to enable users to provide partial instead of completely specified preferences as with traditional methods that learn from user preferences. In particular, I consider partial preferences in the form of preferences over plant dynamic parameters, for which I propose Adaptive User Control (AUC) of robotic systems. I leverage the correlations between the observed and hidden parameter preferences to deal with incompleteness. I use a sparse Gaussian Process Latent Variable Model formulation to learn hidden variables that represent the relationships between the observed and hidden preferences over the system parameters. This model is trained using Stochastic Variational Inference with a distributed loss formulation. I evaluate AUC in a custom drone-swarm environment and several domains from DeepMind control suite. I compare AUC with the state-of-the-art preference-based reinforcement learning methods that are utilized with user preferences. Results show that AUC outperforms the baselines substantially in terms of sample and feedback complexity.
ContributorsBiswas, Upasana (Author) / Zhang, Yu (Thesis advisor) / Kambhampati, Subbarao (Committee member) / Berman, Spring (Committee member) / Liu, Lantao (Committee member) / Arizona State University (Publisher)
Created2023
Description
In today's world, robotic technology has become increasingly prevalent across various fields such as manufacturing, warehouses, delivery, and household applications. Planning is crucial for robots to solve various tasks in such difficult domains. However, most robots rely heavily on humans for world models that enable planning. Consequently, it is not only expensive to create such world models, as it requires human experts who understand the domain as well as robot limitations, these models may also be biased by human embodiment, which can be limiting for robots whose kinematics are not human-like.
This thesis answers the fundamental question: Can we learn such world models automatically? This research shows that we can learn complex world models directly from unannotated and unlabeled demonstrations containing only the configurations of the robot and the objects in the environment. The core contributions of this thesis are the first known approaches for i) task and motion planning that explicitly handle stochasticity, ii) automatically inventing neuro-symbolic state and action abstractions for deterministic and stochastic motion planning, and iii) automatically inventing relational and interpretable world models in the form of symbolic predicates and actions. This thesis also presents a thorough and rigorous empirical experimentation. With experiments in both simulated and real-world settings, this thesis has demonstrated the efficacy and robustness of automatically learned world models in overcoming challenges, generalizing beyond situations encountered during training.
ContributorsShah, Naman (Author) / Srivastava, Siddharth (Thesis advisor) / Kambhampati, Subbarao (Committee member) / Konidaris, George (Committee member) / Speranzon, Alberto (Committee member) / Zhang, Yu (Committee member) / Arizona State University (Publisher)
Created2024