Practical artificial commonsense

Johnston, BG

Practical artificial commonsense

Johnston, BG

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Publication Type:: Thesis
Issue Date:: 2009

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Field	Value	Language
dc.contributor.author	Johnston, BG
dc.date.accessioned	2010-10-12T05:30:45Z
dc.date.accessioned	2012-12-15T03:53:15Z
dc.date.available	2010-10-12T05:30:45Z
dc.date.available	2012-12-15T03:53:15Z
dc.date.issued	2009
dc.identifier.uri	http://hdl.handle.net/10453/20296
dc.description	University of Technology, Sydney. Faculty of Information Technology.
dc.description.abstract	While robots and software agents have been applied with spectacular success to challenging problems in our world, these same successes often translate into spectacular failures when these systems encounter situations that their engineers never conceived. These failures stand in stark contrast to the average person who, while lacking the speed and accuracy of such machines, can draw on commonsense intuitions to effortlessly improvise novel solutions to unexpected problems. The objective of artificial commonsense is to bring some measure of this powerful mental agility and understanding to robots and software systems. In this dissertation, I offer a practical perspective on the problem of constructing systems with commonsense. Starting with philosophical underpinnings and working through formal models, object oriented design and implementation, I revisit prevailing assumptions with a pragmatic focus on the goals of constructing effective, efficient, affordable and real commonsense reasoning systems. I begin with a formal analysis—the first formal analysis—of the Symbol Grounding Problem, in which I develop an ontology of representational classes. This analysis serves as motivation for the development of a hybrid reasoning system that combines iconic and symbolic representations. I then proceed to the primary contribution of this dissertation: the development of a framework for constructing commonsense reasoning systems within constrained time and resources, from present day technology. This hybrid reasoning framework, named Comirit, integrates simulation, logical deduction and machine learning techniques into a coherent whole. It is, furthermore, an open-ended framework that allows the integration of any number of additional mechanisms. An evaluation of Comirit demonstrates the value of the framework and highlights the advantages of having developed with a practical perspective. Not only is Comirit an efficient and affordable working system (rather than pure theory) but also it is found to be more complete, elaboration tolerant and capable of autonomous independent learning when applied to standard benchmark problems of commonsense reasoning.	en
dc.format	Thesis (PhD)	en
dc.language.iso	en	en
dc.relation	https://opus.lib.uts.edu.au/bitstream/10453/20296/2/02Whole.pdf
dc.relation.replaces	http://hdl.handle.net/2100/1152
dc.rights	The author owns the copyright in this thesis including all reproduction and reuse rights for the work. The work may not be altered without the permission of the copyright owner. Attribution is essential when quoting or paraphrasing from this thesis.
dc.rights	au.edu.uts.lib/ppc
dc.rights	info:eu-repo/semantics/openAccess
dc.subject	Comirit.	en
dc.subject	Commonsense reasoning.	en
dc.subject	Artificial intelligence.	en
dc.title	Practical artificial commonsense	en
dc.type	Thesis
utslib.copyright.status	open_access

Abstract:

While robots and software agents have been applied with spectacular success to challenging problems in our world, these same successes often translate into spectacular failures when these systems encounter situations that their engineers never conceived. These failures stand in stark contrast to the average person who, while lacking the speed and accuracy of such machines, can draw on commonsense intuitions to effortlessly improvise novel solutions to unexpected problems. The objective of artificial commonsense is to bring some measure of this powerful mental agility and understanding to robots and software systems. In this dissertation, I offer a practical perspective on the problem of constructing systems with commonsense. Starting with philosophical underpinnings and working through formal models, object oriented design and implementation, I revisit prevailing assumptions with a pragmatic focus on the goals of constructing effective, efficient, affordable and real commonsense reasoning systems. I begin with a formal analysis—the first formal analysis—of the Symbol Grounding Problem, in which I develop an ontology of representational classes. This analysis serves as motivation for the development of a hybrid reasoning system that combines iconic and symbolic representations. I then proceed to the primary contribution of this dissertation: the development of a framework for constructing commonsense reasoning systems within constrained time and resources, from present day technology. This hybrid reasoning framework, named Comirit, integrates simulation, logical deduction and machine learning techniques into a coherent whole. It is, furthermore, an open-ended framework that allows the integration of any number of additional mechanisms. An evaluation of Comirit demonstrates the value of the framework and highlights the advantages of having developed with a practical perspective. Not only is Comirit an efficient and affordable working system (rather than pure theory) but also it is found to be more complete, elaboration tolerant and capable of autonomous independent learning when applied to standard benchmark problems of commonsense reasoning.

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http://hdl.handle.net/10453/20296