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Filtering by: Committee Member Fern, Alan Remove constraint Committee Member: Fern, Alan File Format application/pdf Remove constraint File Format: application/pdf Peer Reviewed No Remove constraint Peer Reviewed: No

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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
Planning in multi-agent partially observable domains using sparse sampling

Creator:

Donneau-Golencer, Thierry D.

Abstract:

A large number of sequential decision-making problems in uncertain environments can be modeled as Markov Decision Processes (MDPs). In such settings, an agent can observe at each time step the state of the environment and then executes an action, causing a stochastic transition to a new state of the environment...

Resource Type:

Masters Thesis
Mixing time for a 3-cycle interacting particle system : a coupling approach

Creator:

Eves, Matthew Jasper

Abstract:

This thesis examines the mixing times for one-dimensional interacting particle systems. We use the coupling method to study the mixing rates for particle systems on the circle which move according to specific permutations e.g., transpositions and 3-cycles.

Resource Type:

Masters Thesis
Effective decision-theoretic assistance through relational hierarchical models

Creator:

Natarajan, Sriraam

Abstract:

Building intelligent computer assistants has been a long-cherished goal of AI. Many intelligent assistant systems were built and fine-tuned to specific application domains. In this work, we develop a general model of assistance that combines three powerful ideas: decision theory, hierarchical task models and probabilistic relational languages. We use the...

Resource Type:

Dissertation
Applying hierarchical and adaptive control to coordinating simple robots

Creator:

Knudson, Matthew D.

Abstract:

Coordinating multiple robots to achieve a complex task requires solving two distinct control problems: the high-level control problem of ensuring that each robot aims to perform a useful task (e.g., coordination) and the low-level control problem of ensuring that each robot actually performs the correct actions to achieve its task...

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Masters Thesis

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Academic Affiliation

Electrical Engineering and Computer Science3
Mathematics1
Mechanical, Industrial, and Manufacturing Engineering1

Advisor

Tadepalli, Prasad2
Dietterich, Thomas G.1
Kovchegov, Yevgeniy1
Tumer, Kagan1

Commencement Year

Commencement Year range begin – Commencement Year range end

Current results range from 2006 to 2008

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Committee Member

Fern, Alan[remove]5
Batten, Belinda1
Bolte, John1
Burton Jr, Robert M.1
Chen, Larry Lung-Kee1
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Creator

Donneau Golencer, Thierry D.1
Eves, Matthew Jasper1
Knudson, Matthew D.1
Natarajan, Sriraam1
Shen, Rongkun1

Date

Date range begin – Date range end

Current results range from 2005 to 2008

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Decade

2000-20095

Degree Field

Computer Science2
Electrical and Computer Engineering1
Mathematics1
Mechanical Engineering1

Degree Level

Master's4
Doctoral1

Degree Name

Master of Science (M.S.)4
Doctor of Philosophy (Ph.D.)1

File Format

application/pdf[remove]5

Language

English [eng]5

License

All rights reserved5

Non-Academic Affiliation

Oregon State University. Graduate School5

Peer Reviewed

No[remove]5

Resource Type

Masters Thesis4
Dissertation1

Rights Statement

In Copyright5

Subject

Adaptive control systems -- Mathematical models1
Artificial intelligence1
Artificial intelligence -- Mathematical models1
Decision making -- Computer programs1
Decision making -- Mathematical models1
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