On Designing Adaptive Data Structures with Adaptive Data "Sub"-Structures

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Creator: 

Bisong, Ekaba Ononse

Date: 

2019

Abstract: 

This thesis proposes the use of "Adaptive" Data-Structures (ADSs) that invoke reinforcement learning schemes from the theory of Learning Automata (LA). The ADSs work in conjunction with select re-organization rules to update themselves as they receive queries from the Environment. The result of such a process is to minimize the cost of query accesses. The Environments under consideration are those that exhibit a so-called "locality of reference", and are referred to as Non-stationary Environments (NSEs). A hierarchy of data "sub"-structures is used to design Singly-Linked Lists (SLLs) on SLLs, which contains outer and sub-list contexts. The Object Migration Automaton (OMA) family of reinforcement schemes are employed to capture the probabilistic dependence of the elements query accesses from the Environment within sub-lists. The Enhanced-OMA (EOMA), the Pursuit-EOMA (PEOMA), and the Transitivity-PEOMA (TPEOMA) are incorporated into the hierarchical SLLs. The results are currently the state-of-the-art methods for SLLs operating in NSEs.

Subject: 

Computer Science
Artificial Intelligence
Engineering

Language: 

English

Publisher: 

Carleton University

Thesis Degree Name: 

Master of Computer Science: 
M.C.S.

Thesis Degree Level: 

Master's

Thesis Degree Discipline: 

Computer Science

Parent Collection: 

Theses and Dissertations

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