Contributions to Techniques for Learning Non-Reactive Behaviour from Observation

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Creator: 

Chan, Caleb King-Hin

Date: 

2016

Abstract: 

Learning from observation allows an expert to train a software agent or robot without explicitly programming the behaviour. Behaviour learned can be broken down into categories: reactive and non-reactive. Actions in reactive behaviour are based on the current environment state whereas actions in non-reactive uses both current state, and any past action or states. We analyze and compare using a common benchmark two studied approaches to learning non-reactive behaviour from observation: Dynamic Bayesian Networks (DBN) and Temporal Backtracking (TB). Our purpose is to characterize situations where one approach should be preferred over the other. We will build upon the case-based reasoning framework for non-reactive behaviour learning. Using the framework and the benchmark, we will analyze and compare three different metrics for comparing cases: run similarity, edit distance and Jaccard distance. This will allow characterization of situations where one metric should outperform the other.

Subject: 

Artificial Intelligence

Language: 

English

Publisher: 

Carleton University

Thesis Degree Name: 

Master of Applied Science: 
M.App.Sc.

Thesis Degree Level: 

Master's

Thesis Degree Discipline: 

Engineering, Electrical and Computer

Parent Collection: 

Theses and Dissertations

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