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i
LEARNING TO DETECT AND ADAPT TO
UNPREDICTED CHANGES
by
Nadeesha Oliver Ranasinghe
A Dissertation Presented to the
FACULTY OF THE USC GRADUATE SCHOOL
UNIVERSITY OF SOUTHERN CALIFORNIA
In Partial Fulfillment of the
Requirements for the Degree
DOCTOR OF PHILOSOPHY
(COMPUTER SCIENCE)
December 2012
Copyright 2012 Nadeesha Oliver Ranasinghe
Object Description
| Title | Learning to detect and adapt to unpredicted changes |
| Author | Ranasinghe, Nadeesha Oliver |
| Author email | nadeeshr@usc.edu;nadran@yahoo.com |
| Degree | Doctor of Philosophy |
| Document type | Dissertation |
| Degree program | Computer Science |
| School | Viterbi School of Engineering |
| Date defended/completed | 2012-08-14 |
| Date submitted | 2012-09-15 |
| Date approved | 2012-09-15 |
| Restricted until | 2012-09-15 |
| Date published | 2012-09-15 |
| Advisor (committee chair) | Shen, Wei-Min |
| Advisor (committee member) |
Nevatia, Ramakant Safonov, Michael |
| Abstract | To survive in the real world, a robot must be able to intelligently react to unpredicted and possibly simultaneous changes to its self (such as its sensors, actions, and goals) and dynamic situations/configurations in the environment. Typically there is a great deal of human knowledge required to transfer essential control details to the robot, which precisely describe how to operate its actuators based on environmental conditions detected by sensors. Despite the best preventative efforts, unpredicted changes such as hardware failure are unavoidable. Hence, an autonomous robot must detect and adapt to unpredicted changes in an unsupervised manner. ❧ This dissertation presents an integrated technique called Surprise-Based Learning (SBL) to address this challenge. The main idea is to have a robot perform both learning and representation in parallel by constructing and maintaining a predictive model which explains the interactions between the robot and the environment. A robot using SBL engages in a life-long cyclic learning process consisting of ""prediction, action, observation, analysis (of surprise) and adaptation"". In particular, the robot always predicts the consequences of its actions, detects surprises whenever there is a significant discrepancy between the prediction and observed reality, analyzes the surprises for its causes (correlations) and uses critical knowledge extracted from the analysis to adapt itself to unpredicted situations. ❧ SBL provides four new contributions to robotic learning. The first contribution is a novel method for structure learning capable of learning accurate enough models of interactions in an environment in an unsupervised manner. The second contribution is learning directly from uninterpreted sensors and actions with the aid of a few comparison operators. The third contribution is detecting and adapting to simultaneous unpredicted changes in sensors, actions, goals and the environment. The fourth contribution is detecting and reasoning with unpredicted interference over a short period of time. ❧ Experiments on both simulation and real robots have shown that SBL can learn accurate models of interactions and successfully adapt to unpredicted changes in the robot’s actions, sensors, goals and the environment’s configuration while navigating in different environments. Experiments on surveillance videos have shown that SBL can detect interference, and recover some information that was hidden from sensors, in the presence of noise and gaps in the data stream. |
| Keyword | learning; surprise; predictive modelling; developmental learning; robotics; artificial intelligence; structure learning; uninterpreted sensors; adapting to change; unpredicted changes; unpredictable changes; interference; autonomous navigation; autonomous surveillance |
| Language | English |
| Part of collection | University of Southern California dissertations and theses |
| Publisher (of the original version) | University of Southern California |
| Place of publication (of the original version) | Los Angeles, California |
| Publisher (of the digital version) | University of Southern California. Libraries |
| Provenance | Electronically uploaded by the author |
| Type | texts |
| Legacy record ID | usctheses-m |
| Rights | Ranasinghe, Nadeesha Oliver |
| Access conditions | The author retains rights to his/her dissertation, thesis or other graduate work according to U.S. copyright law. Electronic access is being provided by the USC Libraries in agreement with the author, as the original true and official version of the work, but does not grant the reader permission to use the work if the desired use is covered by copyright. It is the author, as rights holder, who must provide use permission if such use is covered by copyright. The original signature page accompanying the original submission of the work to the USC Libraries is retained by the USC Libraries and a copy of it may be obtained by authorized requesters contacting the repository e-mail address given. |
| Repository name | University of Southern California Digital Library |
| Repository address | USC Digital Library, University of Southern California, University Park Campus MC 7002, 106 University Village, Los Angeles, California 90089-7002, USA |
| Repository email | cisadmin@usc.edu |
| Archival file | uscthesesreloadpub_Volume4/etd-Ranasinghe-1199.pdf |
Description
| Title | Page 1 |
| Full text | i LEARNING TO DETECT AND ADAPT TO UNPREDICTED CHANGES by Nadeesha Oliver Ranasinghe A Dissertation Presented to the FACULTY OF THE USC GRADUATE SCHOOL UNIVERSITY OF SOUTHERN CALIFORNIA In Partial Fulfillment of the Requirements for the Degree DOCTOR OF PHILOSOPHY (COMPUTER SCIENCE) December 2012 Copyright 2012 Nadeesha Oliver Ranasinghe |
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