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Filtering by: Committee Member Tadepalli, Prasad Remove constraint Committee Member: Tadepalli, Prasad Language English [eng] Remove constraint Language: English [eng] Subject Markov processes Remove constraint Subject: Markov processes

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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
Simulator-Defined MDP Planning with Applications in Natural Resource Management

Creator:

Alkaee Taleghan, Majid

Abstract:

This work is inspired by problems in natural resource management centered on the challenge of invasive species. Computing optimal management policies for maintaining ecosystem sustainable is challenging. Many ecosystem management problems can be formulated as MDP (Markov Decision Process) planning problems. In a simulator-defined MDP, the Markovian dynamics and rewards...

Resource Type:

Dissertation
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
Data Collection in Sensor Networks via the Novel Fast Markov Decision Process Framework

Creator:

Duong, Thai

Abstract:

We investigate the data collection problem in sensor networks. The network consists of a number of stationary sensors deployed at different sites for sensing and storing data locally. A mobile element moves from sites to sites to collect data from the sensors periodically. There are different costs associated with the...

Resource Type:

Masters Thesis
Adiabatic Markov Decision Process : convergence of value iteration algorithm

Creator:

Duong, Thai

Abstract:

Markov Decision Process (MDP) is a well-known framework for devising the optimal decision making strategies under uncertainty. Typically, the decision maker assumes a stationary environment which is characterized by a time-invariant transition probability matrix. However, in many real-world scenarios, this assumption is not justified, thus the optimal strategy might not...

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Masters Thesis

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Academic Affiliation

Electrical Engineering and Computer Science5

Advisor

Dietterich, Thomas G.2
Fern, Alan P.2
Nguyen, Thinh P.2

Commencement Year

Commencement Year range begin – Commencement Year range end

Current results range from 2013 to 2017

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Committee Member

Tadepalli, Prasad[remove]5
Kovchegov, Yevgeniy2
Raich, Raviv2
Batten, Belinda A.1
Bose, Bella1
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Creator

Duong, Thai2
Alkaee Taleghan, Majid1
Hostetler, Jesse A.1
Pinto, Jervis1

Date

Date range begin – Date range end

Current results range from 2013 to 2017

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Decade

2010-20195

Degree Field

Computer Science4
Electrical and Computer Engineering1

Degree Level

Doctoral3
Master's2

Degree Name

Doctor of Philosophy (Ph.D.)3
Master of Science (M.S.)2

Language

English [eng][remove]5

License

All rights reserved5

Non-Academic Affiliation

Oregon State University. Graduate School5

Peer Reviewed

No5

Resource Type

Dissertation3
Masters Thesis2

Rights Statement

In Copyright5

Subject

Markov processes[remove]5
Artificial intelligence1
Computer algorithms1
Computer programming1
Data transmission systems1
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