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Deriving Depictive from Descriptive Knowledge Representations Using Constraint Satisfaction Techniques

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Deriving Depictive from Descriptive Knowledge Representations Using Constraint Satisfaction Techniques Deriving Depictive from Descriptive Knowledge Representations using Constraint Satisfaction Techniques by Cynthia A. Yendt-Lunn A thesis submitted to the Depaflment of Computing und Information Science in conformity with the requirements for the degree of Muster of Science Queen's University, Kingston, Ontario. Canada May. 1997 Copyright O 1997, Cynthia A. Yendt-Lunn National Library Bibliothèque nationale I*(of Canada du Canada Acquisitions and Acquisitions et Bibliographic Services services bibliographiques 395 Wellington Street 395. rue Wellington Ottawa ON K1A ON4 ûüawa ON K1A ON4 Canada Canada Ywr t& Vme mfemme Our Norie njfBrenCû The author has granted a non- L'auteur a accordé une licence non exclusive licence allowing the exclusive permettant à la National Library of Canada to Bibliothèque nationale du Canada de reproduce, loan, distribute or sell reproduire, prêter, distribuer ou copies of this thesis in rnicrofom, vendre des copies de cette thèse sous paper or electronic formats. la forme de microfiche/nlm, de reproduction sur papier ou sur format électronique. The author retains ownership of the L'auteur conserve la propriété du copyright in this thesis. Neither the droit d'auteur qui protège cette thèse. thesis nor substantid extracts fkom it Ni la thèse ni des extraits substantiels may be printed or othenivise de celle-ci ne doivent être imprimés reproduced without the author's ou autrement reproduits sans son permission. autorisation. Abstract Evidence suggests the use of both descriptive and depictive representations in the human mind. Cognitive studies indicate the use of depictive representations in activities such as recognition, communication and problern solving. It is suggested that tasks such as spatial reasoning involve depictive representations and that in such tasks a depictive representation is more efficient, informative and intuitive to use than a description. Traditional computational knowledge representation schemes are Iinguistically based. The work herein describes a scheme for computationally deriving a depic- tive representation of knowledge from a description. In particular it proposes and demonstrates the appropriateness of using wnstraint satisfaction techniques to accomplish this task- The implementation focuses on the spatial domain. In the spatial domain, knowledge is descnbed using propositions involving relational information. For example, "Vancouver is west of Toronton. Such propositions represent constraints between objects in space. Constraint propagation techniques are used to propa- gate the information in the descriptive representation and wmpute a depictive representation by mapping the description to a corresponding depiction. The implemented depictive representation is model-based and facilitates model-based reasoning [Glasgow, 941. Acknowledg ments A special thank you to Mn. C. Jenkins, who so many years ago, as a high school guidance wunsellor, saw potential and set me on this road. Thank you to al1 the professors who taught and guided me, especially Janice Glasgow, Mike Jenkins, Pat Martin and Brian Butler - a special thank you for your comments and for being on my cornmittee. A gracious thank you to Janice Glasgow for being my supervisor - your guid- ance and patience are very much appreciated. And to lrene LaFleche for al1 your assistance and for ahnrays making us smile - you are very special and 1 know you've touched many students lives and made their stay a pleasant one. You will ahvays be fondly remembered by so many. And thank you to Ann Grbavec, my office mate who became a dear friend. Thank you al1 for your kindness and sup- port. Thank you to many others in and around the Department who made this a fun place to be. There are many friends who l'II never forget. Thank you to my farnily, especially Phillip, Greg, Morn & Dad, for your inspira- tion, love, support, patience and understanding. Thank you also to the rest of our farnily and friends who were negleded many tintes over the years while I was studying. Thank you for understanding and still being there - now it's done and I'm coming to visit ! II Table of Contents Abstract ...................... .... ............................................................................... i Acknowiedgments .................................................................................................. ii ... Table OF Contents..... ......,......-.- ............................................................................ III .* List of Tables ........................................................................................................ vil .*. List of Figures..................................................................................................... viii Chapter 1 Introduction................................................................................................ 1 1.1 Thesis Approach and Organization ...................................................... 3 Chapter 2 Contributions from Psychology ........................................................ 5 2.1 Cognitive Theories of Image Representation ....................................... 5 2.1 -1 Propositional Theories ................................................................ 6 2.1.2 Propositional Representations..................................................... 7 2.2 Depictive Theories................................................................................ 9 2.3 Multiple Representations .............. ................................................. 10 2.4 Interaction between Descriptive & Depictive lmage Representations 1 1 2.5 Summary of Contributions From Psychology .................................... 17 Chapter 3 Knowledge Representation ............................................................. 20 iii 3.1 Descriptive Representations .............................................................. 2 1 3.1 .1 Logic-Based Representations........... .... ............................. 21 3.1 -2Structural Representations ................................................... 23 3.1 .2.1 Semantic Nets ................................................................... 23 3.1 .2.2 Frames & Scripts ............................................................... 24 3.1.3 Descriptive Conclusion .............................................................. 26 3.2 Depictive Representations ................................................................. 26 3.2.1 Occupancy and Sym bolic Arrays ............................................. 27 3.2.2 Image Representation System: S-percepts & A-percepts ............................................................ 28 3.2.3 Funt's Retina .............................................................................. 29 3.2.4 RTONs (Relative Topological and Orientation Node) ................ 30 3.2.5 Depictive Conclusion ................................................................. 30 3.3 Constraint Networks ........................................................................... 31 3.4 Summary and Conclusions ................................................................ 32 Chapter 4 Constraint Satisfaction Techniques............................................. 35 Representing Constraint Knowiedge................................................. 37 Constraint Propagation.................................................................... 38 Consistency Checking ..................................................................... 39 BacMracking ...................................................................................... 40 Constraint Relaxation ......................................................................... 40 Surnmary .............................. .... ........................................................... 41 Chapter 5 Contributions from Temporal and Spatial Reasoning Research ........................... 42 5.1 Representation of Spatial and Temporal Relations............................ 43 5.1 -1 Temporal Relations ................ .. .......................................... 43 5.1.2 Spatial Relations ........................................................................ 43 5.1.3 Relational Knowledge Represented As Constraint Networks ................................... 45 5.2 Reasoning and Problem-Solving..................................................... 46 5.2.1 A Constraint Propagation and Reasoning Tool: The composition Table ............................................................. 46 5.2.2 Constraint Propagation................................................................ 48 5.2.2.1 Guesgen and HertzbergasTwo Constraint Propagation Approaches for Building a Constraint Network ................. 49 5.3 Summary. Conclusions and Related Work ......................................... 49 Chapter 6 Descriptions to Depictions: A Derivation Scheme ................ 51 6.1 The Objective ..................................................................................... 52 6.2 The Descriptive Representation w ..................................................... 53 6.3 The Depictive Representation W........................................................ 54 6.4 The Derivation Process .F: w + W ................................................. 64 6.4.1 The Intemediate Constraint Network Representation .CN ..... 65 6.4.2.1 The lmplementation of Fw+ =,,, . w + CN ..................... ..... 72 6.4.3 Fm,*: CN j W ....................................................................... 78 6.5 Summary .........................................................................................
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