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Articles 1 - 9 of 9
Full-Text Articles in Artificial Intelligence and Robotics
Informed Search For Learning Causal Structure, Brian J. Taylor
Informed Search For Learning Causal Structure, Brian J. Taylor
Doctoral Dissertations
Over the past twenty-five years, a large number of algorithms have been developed to learn the structure of causal graphical models. Many of these algorithms learn causal structures by analyzing the implications of observed conditional independence among variables that describe characteristics of the domain being analyzed. They do so by applying inference rules, data analysis operations such as the conditional independence tests, each of which can eliminate large parts of the space of possible causal structures. Results show that the sequence of inference rules used by PC, a widely applied algorithm for constraint-based learning of causal models, is effective but …
Exploiting Social Media Sources For Search, Fusion And Evaluation, Chia-Jung Lee
Exploiting Social Media Sources For Search, Fusion And Evaluation, Chia-Jung Lee
Doctoral Dissertations
The web contains heterogeneous information that is generated with different characteristics and is presented via different media. Social media, as one of the largest content carriers, has generated information from millions of users worldwide, creating material rapidly in all types of forms such as comments, images, tags, videos and ratings, etc. In social applications, the formation of online communities contributes to conversations of substantially broader aspects, as well as unfiltered opinions about subjects that are rarely covered in public media. Information accrued on social platforms, therefore, presents a unique opportunity to augment web sources such as Wikipedia or news pages, …
Safe Reinforcement Learning, Philip S. Thomas
Safe Reinforcement Learning, Philip S. Thomas
Doctoral Dissertations
This dissertation proposes and presents solutions to two new problems that fall within the broad scope of reinforcement learning (RL) research. The first problem, high confidence off-policy evaluation (HCOPE), requires an algorithm to use historical data from one or more behavior policies to compute a high confidence lower bound on the performance of an evaluation policy. This allows us to, for the first time, provide the user of any RL algorithm with confidence that a newly proposed policy (which has never actually been used) will perform well. The second problem is to construct what we call a safe reinforcement learning …
General Program Synthesis From Examples Using Genetic Programming With Parent Selection Based On Random Lexicographic Orderings Of Test Cases, Thomas Helmuth
General Program Synthesis From Examples Using Genetic Programming With Parent Selection Based On Random Lexicographic Orderings Of Test Cases, Thomas Helmuth
Doctoral Dissertations
Software developers routinely create tests before writing code, to ensure that their programs fulfill their requirements. Instead of having human programmers write the code to meet these tests, automatic program synthesis systems can create programs to meet specifications without human intervention, only requiring examples of desired behavior. In the long-term, we envision using genetic programming to synthesize large pieces of software. This dissertation takes steps toward this goal by investigating the ability of genetic programming to solve introductory computer science programming problems. We present a suite of 29 benchmark problems intended to test general program synthesis systems, which we systematically …
Exploiting Concepts In Videos For Video Event Detection, Ethem Can
Exploiting Concepts In Videos For Video Event Detection, Ethem Can
Doctoral Dissertations
Video event detection is the task of searching videos for events of interest to a user where an event is a complex activity which is localized in time and space. The video event detection problem has gained more importance as the amount of online video is increasing by more than 300 hours every minute on Youtube alone. In this thesis, we tackle three major video event detection problems: video event detection with exemplars (VED-ex), where a large number of example videos are associated with queries; video event detection with few exemplars (VED-ex_few), in which only a small number of example …
Epistemological Databases For Probabilistic Knowledge Base Construction, Michael Louis Wick
Epistemological Databases For Probabilistic Knowledge Base Construction, Michael Louis Wick
Doctoral Dissertations
Knowledge bases (KB) facilitate real world decision making by providing access to structured relational information that enables pattern discovery and semantic queries. Although there is a large amount of data available for populating a KB; the data must first be gathered and assembled. Traditionally, this integration is performed automatically by storing the output of an information extraction pipeline directly into a database as if this prediction were the ``truth.'' However, the resulting KB is often not reliable because (a) errors accumulate in the integration pipeline, and (b) they persist in the KB even after new information arrives that could rectify …
Long Range Motion Estimation And Applications, Laura Sevilla-Lara
Long Range Motion Estimation And Applications, Laura Sevilla-Lara
Doctoral Dissertations
Finding correspondences between images underlies many computer vision problems, such as op- tical flow, tracking, stereovision and alignment. Finding these correspondences involves formulating a matching function and optimizing it. This optimization process is often gradient descent, which avoids exhaustive search, but relies on the assumption of being in the basin of attraction of the right local minimum. This is often the case when the displacement is small, and current methods obtain very accurate results for small motions. However, when the motion is large and the matching function is abrupt this assumption is less likely to be true. One traditional way …
Learning With Joint Inference And Latent Linguistic Structure In Graphical Models, Jason Narad
Learning With Joint Inference And Latent Linguistic Structure In Graphical Models, Jason Narad
Doctoral Dissertations
Constructing end-to-end NLP systems requires the processing of many types of linguistic information prior to solving the desired end task. A common approach to this problem is to construct a pipeline, one component for each task, with each system's output becoming input for the next. This approach poses two problems. First, errors propagate, and, much like the childhood game of "telephone", combining systems in this manner can lead to unintelligible outcomes. Second, each component task requires annotated training data to act as supervision for training the model. These annotations are often expensive and time-consuming to produce, may differ from each …
Learning Parameterized Skills, Bruno Castro Da Silva
Learning Parameterized Skills, Bruno Castro Da Silva
Doctoral Dissertations
One of the defining characteristics of human intelligence is the ability to acquire and refine skills. Skills are behaviors for solving problems that an agent encounters often—sometimes in different contexts and situations—throughout its lifetime. Identifying important problems that recur and retaining their solutions as skills allows agents to more rapidly solve novel problems by adjusting and combining their existing skills. In this thesis we introduce a general framework for learning reusable parameterized skills. Reusable skills are parameterized procedures that—given a description of a problem to be solved—produce appropriate behaviors or policies. They can be sequentially and hierarchically combined with other …