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Filtering by: Creator Tadepalli, Prasad Remove constraint Creator: Tadepalli, Prasad Academic Affiliation Computer Science Remove constraint Academic Affiliation: Computer Science

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  • 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:
    I would like to take this opportunity to thank Dr. Prasad Tadepalli for all of his support, ideas

  • 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:
    Reinforcement Learning Abstract approved: Prasad Tadepalli Reinforcement Learning (RL) is the study of agents

  • 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:
    : Prasad Tadepalli Arti�cial Intelligence (AI) planning techniques have been central to automating a

  • 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:
    Improved Learning of Boolean Functions. Abstract approved: Prasad Tadepalli Supervised learning

  • 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:
    : ______________________________________________________________ Prasad Tadepalli Learning easily understandable decision rules from examples is one of

  • Empirical speedup learning of decomposition rules for planning

    Creator:
    Roncagliolo, Silvana
    Abstract:
    One current research goal of Artificial Intelligence and Machine Learning is to build learning systems that robustly improve their planning performance with experience [Tade91]. This work concentrates on learning decomposition rules, i.e., learning rules that guide the planning process by determining the order in which operators are to be applied...
    Resource Type:
    Capstone Project
    Full Text:
    for the degree of Master of Science Major Professor: Dr. Prasad Tadepalli Computer Science

  • Active learning with committees : an approach to efficient learning in text categorization using linear threshold algorithms

    Creator:
    Liere, Ray
    Abstract:
    We developed and investigated machine learning methods that require minimal preprocessing of the input data, use few training examples, run fast, and still obtain high levels of accuracy. Most approaches to designing machine learning programs are based on the supervised learning paradigm – training examples are chosen randomly and given...
    Resource Type:
    Dissertation
    Full Text:
    : Prasad Tadepalli We dev eloped and investigated machine learning methods that require minimal

  • A study of model-based average reward reinforcement learning

    Creator:
    Ok, DoKyeong
    Abstract:
    Reinforcement Learning (RL) is the study of learning agents that improve their performance from rewards and punishments. Most reinforcement learning methods optimize the discounted total reward received by an agent, while, in many domains, the natural criterion is to optimize the average reward per time step. In this thesis, we...
    Resource Type:
    Dissertation
    Full Text:
    approved. Prasad Tadepalli Reinforcement Learning (RL) is the study of learning agents that improve

  • 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:
    Department of Computer Science Oregon State University Major Professor: Dr. Prasad Tadepalli Minor

  • Implementation of experimental test beds for Real Time diagnosis

    Creator:
    Krishnamurthy, Ravi
    Abstract:
    Experimental test beds to study Real Time agents are common topic in AI. A principal contribution of this paper is to develop a framework for such experimental test beds. Design and implementation of the test bed and the extension of the framework for different equipment and agents are discussed. Results...
    Resource Type:
    Capstone Project
    Full Text:
    . Prasad Tadepalli Dr. Timothy Budd Dept. Of Computer Science Oregon State University Corvallis