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Filtering by: Creator Williford, Russell S. Remove constraint Creator: Williford, Russell S. Committee Member Tadepalli, Prasad Remove constraint Committee Member: Tadepalli, Prasad

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  • Conceptual Representations within the Social and Material Contexts of an Engineering Workplace and Academic Environments

    Creator:
    Barner, Matthew S.
    Abstract:
    Situated cognition theory emphasizes the role that social and material contexts have on learning and knowledge application. Several studies of engineering workplace environments have noted differences between the social and material contexts of the workplace and those of undergraduate engineering education. No existing research has studied the social and material...
    Resource Type:
    Dissertation
    Full Text:
    AN ABSTRACT OF THE DISSERTATION OF Matthew S

  • Learning MDP action models via discrete mixture trees

    Creator:
    Wynkoop, Michael S.
    Abstract:
    This thesis addresses the problem of learning dynamic Bayesian network (DBN) models to support reinforcement learning. It focuses on learning regression tree models of the conditional probability distributions of the DBNs. Existing algorithms presume that the stochasticity in the domain can be modeled as a deterministic function with additive noise....
    Resource Type:
    Masters Thesis
    Full Text:
    OF Michael S. Wynkoop for the degree of Masters of Science in Computer Science presented on June 9

  • Automated decomposition of complex parts for manufacturing with advanced joining processes

    Creator:
    Massoni, Brandon R.
    Abstract:
    Recent advancements in joining operations and additive manufacturing now allow complex metal parts to be built up from raw materials, as opposed to being machined down from solid blocks. This not only opens up the design space, but also allows for much more efficient manufacturing. By decomposing a complex part...
    Resource Type:
    Dissertation
    Full Text:
    assembly operation. In effect, each volume decomposition operation is the reverse of assembly operation(s

  • Model-based approximation methods for reinforcement learning

    Creator:
    Wang, Xin
    Abstract:
    The thesis focuses on model-based approximation methods for reinforcement learning with large scale applications such as combinatorial optimization problems. First, the thesis proposes two new model-based methods to stablize the value–function approximation for reinforcement learning. The first one is the BFBP algorithm, a batch-like reinforcement learning process which iterates between...
    Resource Type:
    Dissertation
    Full Text:
    learn- ing algorithm. In our approach, we learn the models P (s′|s, a) and R(s′|s, a), and then use

  • Learning and Improving Policies for Probabilistic Planning Problems

    Creator:
    Issakkimuthu, Murugeswari
    Abstract:
    In this work, we study the problem of learning and improving policies for probabilistic planning problems. In the first part, we train neural network policies for probabilistic planning problems modeled as factored Markov decision problems. The objective is to train problem-specific neural networks via supervised learning to imitate the action...
    Resource Type:
    Dissertation
    Full Text:
    , Prof. N. S. Narayanaswamy and Prof. Deepak Khemani at IIT Madras, and Prof. Anselm Blumer at Tufts

  • Protein secondary structure prediction using conditional random fields and profiles

    Creator:
    Shen, Rongkun
    Abstract:
    Protein secondary structure prediction plays a pivotal role in predicting protein folding in three-dimensions. Its task is to assign each residue one of the three secondary structure classes helix, strand, or random coil. This is an instance of the problem of sequential supervised learning in machine learning. This thesis describes...
    Resource Type:
    Masters Thesis
    Full Text:
    , the results from all k trials are averaged (Russell and Norvig, 2003). In order to fairly compare

  • Monte Carlo Tree Search with Fixed and Adaptive Abstractions

    Creator:
    Hostetler, Jesse A.
    Abstract:
    Monte Carlo tree search (MCTS) is a class of online planning algorithms for Markov decision processes (MDPs) and related models that has found success in challenging applications. In the online planning approach, the agent makes a decision in the current state by performing a limited forward search over possible futures...
    Resource Type:
    Dissertation
    Full Text:
    a lazy policy, since it delays computing the result of π(s) until it is called for. The two types

  • Incorporating and Learning Behavior Constraints for Sequential Decision Making

    Creator:
    Pinto, Jervis
    Abstract:
    Writing a program that performs well in a complex environment is a challenging task. In such problems, a method of deterministic programming combined with reinforcement learning (RL) can be helpful. However, current systems either force developers to encode knowledge in very specific forms (e.g., state-action features), or assume advanced RL...
    Resource Type:
    Dissertation
    Full Text:
    Problems (MDPs) In the setting considered here, an MDP [52] is a tuple (S,A, P,R), where S is a finite set

  • Reinforcement learning for job-shop scheduling

    Creator:
    Zhang, Wei, 1960-
    Resource Type:
    Dissertation
    Full Text:
    Methods vs. AI Methods Starting in the 1950's operations researchers began advocating formal optimization

  • Reinforcement learning-based off-equilibrium incentives to approximate the VCG mechanism

    Creator:
    Moissinac, Béatrice
    Abstract:
    Auctions are used to solve resource allocation problem between many agents and many items in real-world settings. Unfortunately, in most cases, it is possible for selfish agents to manipulate the system for their own interest at the expense of the social welfare. Such manipulation can be prevented using the Vickrey-Clarke-Groves...
    Resource Type:
    Masters Thesis
    Full Text:
    defined by Russell and Norvig [43], a multiagent system is composed of several intelligent agents