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Full-Text Articles in Engineering
Towards Improving Human-Robot Interaction For Social Robots, Saad Khan
Towards Improving Human-Robot Interaction For Social Robots, Saad Khan
Electronic Theses and Dissertations
Autonomous robots interacting with humans in a social setting must consider the social-cultural environment when pursuing their objectives. Thus the social robot must perceive and understand the social cultural environment in order to be able to explain and predict the actions of its human interaction partners. This dissertation contributes to the emerging field of human-robot interaction for social robots in the following ways: 1. We used the social calculus technique based on culture sanctioned social metrics (CSSMs) to quantify, analyze and predict the behavior of the robot, human soldiers and the public perception in the Market Patrol peacekeeping scenario. 2. …
On Kernel-Base Multi-Task Learning, Cong Li
On Kernel-Base Multi-Task Learning, Cong Li
Electronic Theses and Dissertations
Multi-Task Learning (MTL) has been an active research area in machine learning for two decades. By training multiple relevant tasks simultaneously with information shared across tasks, it is possible to improve the generalization performance of each task, compared to training each individual task independently. During the past decade, most MTL research has been based on the Regularization-Loss framework due to its flexibility in specifying various types of information sharing strategies, the opportunity it offers to yield a kernel-based methods and its capability in promoting sparse feature representations. However, certain limitations exist in both theoretical and practical aspects of Regularization-Loss-based MTL. …
Human Action Localization And Recognition In Unconstrained Videos, Hakan Boyraz
Human Action Localization And Recognition In Unconstrained Videos, Hakan Boyraz
Electronic Theses and Dissertations
As imaging systems become ubiquitous, the ability to recognize human actions is becoming increasingly important. Just as in the object detection and recognition literature, action recognition can be roughly divided into classification tasks, where the goal is to classify a video according to the action depicted in the video, and detection tasks, where the goal is to detect and localize a human performing a particular action. A growing literature is demonstrating the benefits of localizing discriminative sub-regions of images and videos when performing recognition tasks. In this thesis, we address the action detection and recognition problems. Action detection in video …
Exploiting Opponent Modeling For Learning In Multi-Agent Adversarial Games, Kennard R. Laviers
Exploiting Opponent Modeling For Learning In Multi-Agent Adversarial Games, Kennard R. Laviers
Electronic Theses and Dissertations
An issue with learning effective policies in multi-agent adversarial games is that the size of the search space can be prohibitively large when the actions of both teammates and opponents are considered simultaneously. Opponent modeling, predicting an opponent’s actions in advance of execution, is one approach for selecting actions in adversarial settings, but it is often performed in an ad hoc way. In this dissertation, we introduce several methods for using opponent modeling, in the form of predictions about the players’ physical movements, to learn team policies. To explore the problem of decision-making in multi-agent adversarial scenarios, we use our …
A Contextual Approach To Learning Collaborative Behavior Via Observation, Cynthia L. Johnson
A Contextual Approach To Learning Collaborative Behavior Via Observation, Cynthia L. Johnson
Electronic Theses and Dissertations
This dissertation describes a novel technique to creating a simulated team of agents through observation. Simulated human teamwork can be used for a number of purposes, such as expert examples, automated teammates for training purposes and realistic opponents in games and training simulation. Current teamwork simulations require the team member behaviors be programmed into the simulation, often requiring a great deal of time and effort. None are able to observe a team at work and replicate the teamwork behaviors. Machine learning techniques for learning by observation and learning by demonstration have proven successful at observing behavior of humans or other …
An Analysis Of Misclassification Rates For Decision Trees, Mingyu Zhong
An Analysis Of Misclassification Rates For Decision Trees, Mingyu Zhong
Electronic Theses and Dissertations
The decision tree is a well-known methodology for classification and regression. In this dissertation, we focus on the minimization of the misclassification rate for decision tree classifiers. We derive the necessary equations that provide the optimal tree prediction, the estimated risk of the tree's prediction, and the reliability of the tree's risk estimation. We carry out an extensive analysis of the application of Lidstone's law of succession for the estimation of the class probabilities. In contrast to existing research, we not only compute the expected values of the risks but also calculate the corresponding reliability of the risk (measured by …