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

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.

Name thesis:degree_name

Master of Applied Science (M.App.Sc.)

Level thesis:degree_level

Master's

Discipline thesis:degree_discipline

Engineering, Electrical and Computer

Grantor dc:publisher

Carleton University

Year dc:date.issued

2018

Author dc:creator

• Gunaratne, Amrik Sacha Elapata

Statement dc:rights

• Copyright © 2018 the author(s). Theses may be used for non-commercial research, educational, or related academic purposes only. Such uses include personal study, research, scholarship, and teaching. Theses may only be shared by linking to Carleton University Institutional Repository and no part may be used without proper attribution to the author. No part may be used for commercial purposes directly or indirectly via a for-profit platform; no adaptation or derivative works are permitted without consent from the copyright owner.

Language dc:language.iso

en

DOI

10.22215/etd/2018-12958

OAI identifier oai:identifier

oai:carleton.scholaris.ca:20.500.14718/40125