• Active visual category learning 

    Vijayanarasimhan, Sudheendra (2011-05)
    Visual recognition research develops algorithms and representations to autonomously recognize visual entities such as objects, actions, and attributes. The traditional protocol involves manually collecting training image ...
  • Automatic interpretation of loosely encoded knowledge 

    Fan, James Junmin (2006)
    Knowledge is critical for a variety of artificial intelligence problems. A key challenge in using knowledge-based systems is how to align one's encoding with the idiosyncrasies in the existing knowledge base. We call ...
  • Autonomous intersection management 

    Dresner, Kurt Mauro (2009-12)
    Artificial intelligence research is ushering in an era of sophisticated, mass-market transportation technology. While computers can fly a passenger jet better than a human pilot, people still face the dangerous yet ...
  • Autonomous qualitative learning of distinctions and actions in a developing agent 

    Mugan, Jonathan William (2010-08)
    How can an agent bootstrap up from a pixel-level representation to autonomously learn high-level states and actions using only domain general knowledge? This thesis attacks a piece of this problem and assumes that an agent ...
  • Cognitive Science in technology 

    Cabrera, Victoria Marrujo (2010-12)
    Cognitive Science is an interdisciplinary field that ties together the curricula of liberal arts and technical fields of study. However, it is de-emphasized in technical undergraduate studies such as Engineering. Cognitive ...
  • Creating and utilizing symbolic representations of spatial knowledge using mobile robots 

    Beeson, Patrick Foil, 1977- (2008-08)
    A map is a description of an environment allowing an agent--a human, or in our case a mobile robot--to plan and perform effective actions. From a single location, an agent’s sensors can not observe the whole structure of ...
  • Evolving multimodal behavior through modular multiobjective neuroevolution 

    Schrum, Jacob Benoid (2014-05)
    Intelligent organisms do not simply perform one task, but exhibit multiple distinct modes of behavior. For instance, humans can swim, climb, write, solve problems, and play sports. To be fully autonomous and robust, it ...
  • General-purpose optimization through information maximization 

    Lockett, Alan Justin (2012-05)
    The primary goal of artificial intelligence research is to develop a machine capable of learning to solve disparate real-world tasks autonomously, without relying on specialized problem-specific inputs. This ...
  • Improving the accuracy and scalability of discriminative learning methods for Markov logic networks 

    Huynh, Tuyen Ngoc (2011-05)
    Many real-world problems involve data that both have complex structures and uncertainty. Statistical relational learning (SRL) is an emerging area of research that addresses the problem of learning from these noisy ...
  • Inducing grammars from linguistic universals and realistic amounts of supervision 

    Garrette, Daniel Hunter (2015-05-20)
    The best performing NLP models to date are learned from large volumes of manually-annotated data. For tasks like part-of-speech tagging and grammatical parsing, high performance can be achieved with plentiful supervised ...
  • Knowledge integration in machine reading 

    Kim, Doo Soon (2011-08)
    Machine reading is the artificial-intelligence task of automatically reading a corpus of texts and, from the contents, building a knowledge base that supports automated reasoning and question answering. Success at this task ...
  • Learning language from ambiguous perceptual context 

    Chen, David Lieh-Chiang (2012-05)
    Building a computer system that can understand human languages has been one of the long-standing goals of artificial intelligence. Currently, most state-of-the-art natural language processing (NLP) systems use statistical ...
  • Learning with Markov logic networks : transfer learning, structure learning, and an application to Web query disambiguation 

    Mihalkova, Lilyana Simeonova (2009-08)
    Traditionally, machine learning algorithms assume that training data is provided as a set of independent instances, each of which can be described as a feature vector. In contrast, many domains of interest are inherently ...
  • A modular language for describing actions 

    Ren, Wanwan (2009-12)
    This dissertation is about the design of a modular language for describing actions. The modular action description language, MAD, is based on the action language C+. In this new language, the possibility of "importing" a ...
  • Ontology as a means for systematic biology 

    Tirmizi, Syed Hamid Ali (2011-05)
    Biologists use ontologies as a method to organize and publish their acquired knowledge. Computer scientists have shown the value of ontologies as a means for knowledge discovery. This dissertation makes a number of ...
  • PipeSynth : automated topological and parametric design of fluid networks 

    Patterson, William Rey (2010-12)
    PipeSynth is a design automation approach that combines various optimization research and artificial intelligence methods for synthesizing fluid networks. Starting with only the port locations, PipeSynth generates and ...
  • A probabilistic architecture for algorithm portfolios 

    Silverthorn, Bryan Connor (2012-05)
    Heuristic algorithms for logical reasoning are increasingly successful on computationally difficult problems such as satisfiability, and these solvers enable applications from circuit verification to software synthesis. ...
  • Robust color-based vision for mobile robots 

    Lee, Juhyun (2011-12)
    An intelligent agent operating in the real world needs to be fully aware of the surrounding environment to make the best decision possible at any given point of time. There are many forms of input devices for a robot ...
  • Sample efficient multiagent learning in the presence of Markovian agents 

    Chakraborty, Doran (2012-12)
    The problem of multiagent learning (or MAL) is concerned with the study of how agents can learn and adapt in the presence of other agents that are simultaneously adapting. The problem is often studied in the stylized ...