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Filtering by: Creator Salisbury, Morgan J. Remove constraint Creator: Salisbury, Morgan J. Advisor Tadepalli, Prasad Remove constraint Advisor: Tadepalli, Prasad

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  • Towards Narrative Understanding with Deep Neural Networks and Hidden Markov Models

    Creator:
    Orr, J. Walker
    Abstract:
    Narratives are central to communication and the human experience. For a computer system to understand a narrative, it must be able to identify the key facts or plot elements that describe what happened or how the world has changed. These element are called events;establishing a document’s events and the relationships...
    Resource Type:
    Dissertation
    Full Text:
    AN ABSTRACT OF THE DISSERTATION OF J. Walker Orr for the

  • Causal Structure Learning from Experiments and Observations

    Creator:
    Sinha, Meghamala
    Abstract:
    In this research, we address the problem of learning a single causal network structure from multiple dataset generated from different experiments. The experiments can be observational or interventional. We assume that each dataset is generated by an unknown causal network altered under different experimental conditions (interventions, manipulation or perturbation). As...
    Resource Type:
    Masters Thesis
    Full Text:
    Intervention The “Principle of Common Cause” [31] states that given two associated events Vi and V j, there

  • Constructive induction for improved learning of boolean functions

    Creator:
    Drake, Peter (Peter Dudey), 1970-
    Abstract:
    Supervised learning programs, such as decision tree learners and neural networks, often must learn Boolean functions. The concept being learned may not easily be expressed in terms of the atomic features given. Constructive induction automatically produces higher level features (combinations of the atomic features), which can improve learning performance. The...
    Resource Type:
    Masters Thesis
    Full Text:
    Conference on Machine Learning, 41- 48. San Mateo, CA: Morgan Kaufmann. Dietterich, T. and Shavlik, J., eds

  • Average-reward reinforcement learning for product delivery by multiple vehicles

    Creator:
    Tang, Hong
    Abstract:
    Real time delivery of products is the context of stochastic demands and multiple vehicles is a difficult problem as it requires the joint investigation of the problems in inventory control and vehicle routing. We model this problem in the framework of Average reward Reinforcement Learning (ARL) and present experimental results...
    Resource Type:
    Capstone Project
    Full Text:
    minimize some objective function such as the total distance traveled. 11 ~ !customers I J

  • Learning hierarchical decomposition rules for planning : an inductive logic programming approach

    Creator:
    Reddy, Chandrasekhara K.
    Abstract:
    Artificial Intelligence (AI) planning techniques have been central to automating a gamut of tasks from the mundane route planning and beer production to the ethereal image processing of space-ship images. Of all the planning techniques, hierarchical- decomposition planning has been the technique most employed in industrial-strength planners. Hierarchical-decomposition planning is...
    Resource Type:
    Dissertation
    Full Text:
    , Gi = sgi�, and for j 2 [2; n], Sj is the state resulting after applying a complete decomposition of

  • Learning classification rules by randomized iterative local search

    Creator:
    Chisholm, Michael (Michael Andrew), 1974-
    Abstract:
    Learning easily understandable decision rules from examples is one of the classic problems in machine learning. Most learning algorithms for this problem employ some variation of a greedy separate-and-conquer algorithm. In this paper, we describe a system called LERILS that learns highly accurate and comprehensible rules from examples using a...
    Resource Type:
    Masters Thesis
    Full Text:
    . Quinlan, J. R. (1993). C4.5: Programs for Machine Learning. Morgan Kaufmann, San Mateo, California. 3

  • Hierarchical average reward reinforcement learning

    Creator:
    Seri, Sandeep
    Abstract:
    Reinforcement Learning (RL) is the study of agents that learn optimal behavior by interacting with and receiving rewards and punishments from an unknown environment. RL agents typically do this by learning value functions that assign a value to each state (situation) or to each state-action pair. Recently, there has been...
    Resource Type:
    Masters Thesis
    Full Text:
    during the j" action and T3 be the time taken, when the agent started in state s and followed the policy

  • Active Learning from Examples, Queries and Explanations

    Creator:
    Hamidi-Haines, Mandana
    Abstract:
    Humans are remarkably efficient in learning by interacting with other people and observing their behavior. Children learn by watching their parents’ actions and mimic their behavior. When they are not sure about their parents demonstration, they communicate with them, ask questions, and learn from their feedback. On the other hand,...
    Resource Type:
    Dissertation
    Full Text:
    learning of hierarchical policy framework . . . . . . . . . . . . . 13 2.2 The inside, αi,j(A) and outside

  • Local search methods for job shop scheduling

    Creator:
    Qi, Yenong
    Abstract:
    Scheduling problem has both theoretical and practical interest. A great deal of research has been done in this field. In this paper we will use a local optimal search method to do job shop scheduling. This method will be compared with a constraint satisfaction method called Micro-boss. Other issues such...
    Resource Type:
    Capstone Project
    Full Text:
    01j+1, Ti be the time when machine Mi finishes all operation on its queue, EST/j and EFT1j be the

  • Scaling multiagent reinforcement learning

    Creator:
    Proper, Scott
    Abstract:
    Reinforcement learning in real-world domains suffers from three curses of dimensionality: explosions in state and action spaces, and high stochasticity or "outcome space" explosion. Multiagent domains are particularly susceptible to these problems. This thesis describes ways to mitigate these curses in several different multiagent domains, including real-time delivery of products...
    Resource Type:
    Dissertation