A Comparison of Case-Based Reasoning and Probabilistic Graphical Models in the Context of Learning from Observation

It appears your Web browser is not configured to display PDF files. Download adobe Acrobat or click here to download the PDF file.

Click here to download the PDF file.

Creator: 

Gunaratne, Amrik Sacha Elapata

Date: 

2018

Abstract: 

Learning from observation is a technique whereby learning occurs through observation or experience. In this work, we compare two existing techniques of learning from observation: Probabilistic Graphical Models (PGM) and Case-Based Reasoning (CBR) with the goal of identifying a preferred approach for future improvement. We show that the Naive Bayes Classifier is better than a previously used PGM model in learning behavior in a vacuum cleaner domain and introduce a state-based retrieval technique in CBR and show that there is no once-size-fits-all approach to learn state-based behavior. We also compare the two learning techniques in fully and partially observable continuous domains, namely Cartpole V0, obstacle avoidance, and 2D RoboCup. We show that the CBR approach works best in Cartpole V0, the PGM approach works best in obstacle avoidance, and the PGM approach works best in 2D RoboCup. Ultimately, we show that the preferred technique is generally behavior and domain specific.

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

Items in CURVE are protected by copyright, with all rights reserved, unless otherwise indicated. They are made available with permission from the author(s).