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STUDIES in KNOWLEDGE REPRESENTATION Modeling Change - the Frame Problem Pictures and Words

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STUDIES in KNOWLEDGE REPRESENTATION Modeling Change - the Frame Problem Pictures and Words Lars-Erik Janlert STUDIES IN KNOWLEDGE REPRESENTATION Modeling change - the frame problem Pictures and words ZD MM / r ' UNIVERSITY OF UMEÄ INSTITUTE OF INFORMATION PROCESSING A 1 STUDIES IN KNOWLEDGE REPRESENTATION Modeling change - the frame problem Pictures and words Report UMINF-127.85, ISSN 0348-0542 Lars-Erik Janlert October 1985 Institute of Information Processing University of Umeå, S-901 87 UMEÅ Sweden Akademisk avhandling som med tillstånd av Rektorsämbetet vid Umeå universi­ tet framläggs till offentlig granskning för avläggande av filosofie doktor­ sexamen onsdagen den 13 november 1985 kl 10.00 i hörsal F, Humanisthuset, Umeå universitet. ABSTRACT In two studies, the author attempts to develop a general symbol theoretical approach to knowledge representation. The first study, Modeling change - the frame problem, critically exam­ ines the - so far unsuccessful - attempts to solve the notorious frame problem. By discussing and analyzing a number of related problems - the prediction problem, the revision problem, the qualification problem, and the book-keeping problem - the frame problem is distinguished as the prob­ lem of finding a representational form permitting a changing, complex world to be efficiently and adequately represented. This form, it is argued, is dictated by the metaphysics of the problem world, the fundamental form of the symbol system we humans use in rightly characterizing the world. In the second study, Pictures and words, the symbol theoretical approach is made more explicit. The subject Is the distinction between pictorial (non-linguistic, non-propositional, analogical, "direct") representation and verbal (linguistic, propositional) representation, and the further implications of this distinction. The study focuses on pic­ torial representation, which has received little attention compared to ver­ bal representation. Observations, ideas, and theories in AI, cognitive psychology, and philosophy are critically examined. The general conclusion is that there is as yet no cogent and mature theory of pictorial represen­ tation that gives good support to computer applications. The philosophical symbol theory of Nelson Goodman is found to be the most thoroughly developed and most congenial with the aims and methods of AI. Goodman's theory of pictorial representation, however, in effect excludes computers from the use of pictures. In the final chapter, an attempt is made to develop Goodman's analysis of pictures further turning it into a theory useful to AI. The theory outlined builds on Goodman's concept of exemplif­ ication. The key idea is that a picture is a model of a description that has the depicted object as its standard model. One consequence Is that pictorial and verbal forms of representation are seen less as competing alternatives than as complementary forms of representation mutually sup­ porting and depending on each other. ? Mii'iiìiiìiii !• *!*Ï*IiÏ!Hi?Î* Tf^ »«» im w é/ogg/p*" STUDIES IN KNOWLEDGE REPRESENTATION MODELING CHANGE - THE FRAME PROBLEM PICTURES AND WORDS Lars-Erik Janlert October 1985 i ii STUDIES IN KNOWLEDGE REPRESENTATION Modeling change - the frame problem Pictures and words Report UMINF-127.85, ISSN 0348-0542 Lars-Erik Janlert October 1985 Institute of Information Processing University of Umeå, S-901 87 UMEÅ Sweden Keywords: analogical, artificial intelligence, digital, exem­ plification, frame problem, Goodman, knowledge representation, metaphysics, model, pictorial representation, symbol theory. ABSTRACT In two st udies, the author attempts to develop a general symbol theoretical approach to knowledge representation. The first study, Modeling change - the frame problem, critically examines the - so far unsuccessful - attempts to solve the notorious frame problem. By discussing and analyzing a number of related problems - the prediction problem, the revision problem, the qualification problem, and the book­ keeping problem - the frame problem is distinguished as the problem of finding a representational form permitting a chang­ ing, complex world to be efficiently and adequately represented. This form, it is argued, is dictated by t he meta­ physics of the problem world, the fundamental form of the sym­ bol system we humans use in rightly characterizing the world. In the second study, Pictures and words, the symbol theoretical approach is made more explicit. The subject is the distinction between pictorial (non-linguistic, non- propositional, analogical, "direct") representation and verbal (linguistic, propositional) representation, and the further implications of this distinction. The study focuses on pic­ torial representation, which has received little attention com­ pared to verbal representation. Observations, ideas, and theories in AI, cognitive psychology, and philosophy are criti­ cally examined. The general conclusion is that there is as yet no cogent and mature theory of pictorial representation that gives good support to computer applications. The philosophical symbol theory of Nelson Goodman is found to be the most thoroughly developed and most congenial with the aims and methods of AI. Goodman's theory of pictorial representation, however, In effect excludes computers from the use of pictures. In the final chapter, an attempt is made to develop Goodman's analysis of pictures further turning it into a theory useful to AI. The theory outlined builds on Goodman's concept of exem­ plification. The key idea is that a picture is a model of a description that has the depicted object as its standard model. One consequence is that pictorial and verbal forms of represen­ tation are seen less as competing alternatives than as comple­ mentary forms of representation mutually supporting and depend­ ing on each other. iii ACKNOWLEDGMENTS First and foremost, I thank my friend Bo Dahlbom for his constant readiness to read, comment, and discuss what I write. His comments are always interesting, often unexpected, and I v alue them highly. I thank my thesis supervisors, Axel Ruhe and Per-Âke Wedin, for their support and encouragement, and their successful efforts to provide me with favorable work­ ing conditions. I am grateful to Martin Edman and Eva Ejerhed for their long and strong support, their good advice and helpful criticism. I am grateful to Aaron Sloman for his generous and stimulating comments on a draft version of the sec­ tion about himself in the pictures and words paper. There are a few persons whose appreciation of my paper on the frame problem gave me the courage neces­ sary to go on with my work: Daniel Dennett, John Haugeland, Patrick Hayes, Zenon Pylyshyn. Several others gave me valuable reactions on a draft version of the frame paper. Among them: Peter Gär- denfors, Kenneth Kahn, Staffan Löf, Ingemar Widegren. I have received financial support from: The Faculty of Mathematics and Natural Sciences, University of Umeå; The Swedish Humanities and Social Sciences Research Council (project 'Modeling time'); The Bank of Sweden Tercentenary Foundation (project 'Natural commmunication with computers'). Many persons have helped me in various practical matters. Thank youl My family - Gunilla, Magnus and Niklas - has endured this ordeal undaunted. Well donel TABLE OF CONTENTS Acknowledgments iv INTRODUCTION AND SUMMARY vii Knowledge representation vii Modeling change - the frame problem ix Pictures and words xii Concluding remarks xiv MODELING CHANGE - THE FRAME PROBLEM 1 Part Is DEFINING THE PROBLEM 3 Representation and problem solving 4 The non-deductive approach 6 The deductive approach 8 The original frame problem 11 The general frame problem 13 The prediction problem 15 The general book-keeping problem 21 The qualification problem 23 Part 2: SURVEY OF SUGGESTED SOLUTIONS 25 Frames 25 Causal connection 28 Consistency 30 STRIPS 32 PLANNER 37 Unless 41 TMS 45 Some related ideas 50 Part 3: DISCUSSION AND CONCLUSIONS 53 Metaphysics 5 3 The situation concept 57 Explicit, Primitive, and Primary 58 Intrinsic representations ..62 Acknowledgments 65 References 67 v PICTURES AND WORDS 71 I. INTRODUCTION 73 An illustration 73 Pictures in computers 80 II. ARTIFICIAL INTELLIGENCE - THOUGHTS 89 Aaron Sloman 90 Patrick Hayes 104 III. PSYCHOLOGY 113 Daniel C. Dennett 114 Jerry A. Fodor 118 Zenon Y7. Pylys hyn 126 Stephen E. Palmer 129 John R. Anderson 138 IV. PHILOSOPHY 145 Congruence theories 147 History based theories 159 Nelson Goodman 164 Analog 182 V. ARTIFICIAL INTELLIGENCE - EXPERIMENTS 191 The geometry machine 191 WHISPER 204 VI. PICTURES AS MODELS - OUTLINE OF A THEORY 215 Application. 218 Exemplification 224 A picture of pictures 236 Uses of words and pictures 242 Prospect 254 References 255 vi INTRODUCTION AND SUMMARY A cornerstone of artifical intelligence (AI) and cog­ nitive science is the assumption that intelligent systems depend on having and manipulating internal models or representations of their task environment, their problem world (or just world, for short), "Environment" is here to be taken in a broad and abstract sense: a problem world may be rather remote from everyday experiences, it may be a toy world, or an imaginary world. A system which is able to per­ form some form of action in its problem world, is clearly to some extent part of its own environment, which means that also parts of the system itself must be represented. Knowledge representation The study of such representations is the subject of knowledge representation (KR) - a subfield of AI with close connections to cognitive science. There is no general agreement on why it is called "knowledge"
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