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Autonomous Agents and the Concept of Concepts Autonomous Agents and the Concept of Concepts Autonomous Agents and the Concept of Concepts Paul Davidsson Doctoral dissertation submitted in partial ful®llment of the requirements for the degree of PhD in Computer Science. Department of Computer Science, Lund University First edition c 1996 by Paul Davidsson Department of Computer Science Lund University Box 118 S±221 00 Lund Sweden [email protected] http://www.dna.lth.se ISBN 91±628±2035±4 CODEN LUTEDX/(TECS±1006)/1±221/(1996) PrintedinSwedenbyKFSiLundAB Preface Although this thesis is written in the form of a monograph, some of the results presented have already been published in various articles and reports. These are listed below and will in the text be referenced using the associated Roman numerals as follows: I. E. Astor, P. Davidsson, B. Ekdahl, and R. Gustavsson. ªAnticipatory Planningº, In Advance Proceedings of the First European Workshop on Planning, 1991. II. P. Davidsson, ªConcept Acquisition by Autonomous Agents: Cognitive Model- ing versus the Engineering Approachº, Lund University Cognitive Studies 12, ISSN 1101±8453, Lund University, Sweden, 1992. III. P. Davidsson, ªA Framework for Organization and Representation of Concept Knowledge in Autonomous Agentsº, In Scandinavian Conference of Arti®cial Intelligence ± 93, pages 183±192, IOS Press, 1993. IV. P. Davidsson, ªToward a General Solution to the Symbol Grounding Problem: Combining Machine Learning and Computer Visionº, In AAAI Fall Symposium Series, Machine Learning in Computer Vision, pages 157±161, 1993. V. P. Davidsson, E. Astor, and B. Ekdahl, ªA Framework for Autonomous Agents Based on the Concept of Anticipatory Systemsº, In Cybernetics and Systems '94, pages 1427±1434, World Scienti®c, 1994. VI. B. Ekdahl, E. Astor, and P. Davidsson, ªTowards Anticipatory Agentsº, In In- telligent Agents Ð Theories, Architectures, and Languages, M. Wooldridge and N.R. Jennings (ed.), pages 191±202, Springer Verlag, 1995. VII. P. Davidsson, ªOn the Concept of Concept in the Context of Autonomous Agentsº, In Second World Conference on the Fundamentals of Arti®cial Intel- ligence, pages 85±96, 1995. VIII. P. Davidsson, ªLearning Characteristic Decision Treesº, In Eighth Australian Joint Conference on Arti®cial Intelligence (AI'95), World Scienti®c, 1995. ii IX. P. Davidsson, ªA Linearly Quasi-Anticipatory Autonomous Agent Architecture: Some preliminary experimentsº, In Distributed Arti®cial Intelligence (prelimi- nary title), C. Zhang and D. Lukose (ed.), To be published in the Lecture Notes in Arti®cial Intelligence series, Springer Verlag, 1996. X. P. Davidsson, ªCoin Classi®cation Using a Novel Technique for Learning Char- acteristic Decision Trees by Controlling the Degree of Generalizationº, Accepted as a long paper to the Ninth International Conference on Industrial & Engineer- ing Applications of Arti®cial Intelligence & Expert Systems (IEA/AIE-96), 1996. Acknowledgements First of all, I would like to thank my advisor Eric Astor for his indispensable help and encouragement while writing this thesis, not to mention the time he has spent making it readable. I also thank all my colleagues at the Department of Computer Science for pro- viding a pleasant research environment. In particular, Bertil Ekdahl (the third member of our small research group) for always sharing his often original ideas, Robert Pallbo (former group member) for similar reasons, Arne Andersson for stimulating discus- sions, Stefan Nilsson and Kurt Swanson for being good friends and for that they, like Roger Henriksson, Jesper Larsson, Hans Olsson and many others, have helped me solve numerous problems related to Unix, LATEX, C++, Simula and other esoteric topics. I am grateful to Rune Gustavsson at the Department of Economics and Computer Science, University of Karlskrona/Ronneby who among other things introduced me to the ®eld of autonomous agents more than ®ve years ago and to Staffan HaggÈ and Fredrik Ygge at the same department for their useful comments on my work and for making my visits to Ronneby memorable. I also want to thank everybody at the Department of Cognitive Science, led by Pe- ter Gardenfors,È for always providing open-minded discussions at their weekly coffee- seminars which have forced me to view many scienti®c problems from new perspec- tives. Special thanks to Christian Balkenius and MansÊ Holgersson for many valuable comments on earlier drafts of this thesis. Furthermore, I would like to thank Olof Tilly at the Department of Philosophy for his comments on the style and content of a large part of this thesis and Anders Holtsberg at the Department of Mathematical Statistics for kindly answering my naÈõve questions about multivariate statistics. Finally, I want to thank Tanja for love and understanding, and my mother and father for their encouragement and support. Lund, April 1996 Paul Davidsson Contents 1 Introduction 1 : : : : : : : : : : : : : : : : : : : : : : : 1.1 On the Scienti®c Method : 2 : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 1.2 Outline : 4 2 Autonomous Agents 7 : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 2.1 Introduction : 7 : : : : : : : : : : : : : : : : : : 2.1.1 Different Kinds of Agents : 7 : : : : : : : : : : : : : : : : : : : : : : : : 2.1.2 Requirements : 10 : : : : : : : : : : : : : 2.1.3 Single versus Multi-Agent Scenarios : 10 : : : : : : : : : : : : : : : : : : : : 2.1.4 Possible Applications : 11 : : : : : : : : : : : : : : : : 2.1.5 Three Perspectives on Agents : 12 : : : : : : : : : : : : : : : : : : : : : : 2.2 The Deliberative Approach : 13 : : : : : : : : : : : : : : : : : : : : : 2.2.1 Deliberative Agents : 14 : : : : : : : : : : 2.2.2 Limitations of the Deliberative Approach : 15 : : : : : : : : : : : : : : : : : : : : : : : 2.3 The Reactive Approach : 16 : : : : : : : : : : : : : : : : : : : : : 2.3.1 The Work of Brooks : 16 : : : : : : : : : : : : 2.3.2 Limitations of the Reactive Approach : 17 : : : : : : : : : : : : : : : : : : : : : 2.4 Combining the Approaches : 18 : : : : : : : : : : : : : : : : : : : : : : 2.4.1 TouringMachines : 21 : : : : : : : : : : : : : : 2.4.2 The Planner-Reactor Architecture : 22 : : : : : : : : : : : : : : : : : : : 2.4.3 The GLAIR Architecture : 23 : : : : : : : : : : : : : : : : : : : : : : : : : : 2.4.4 ATLANTIS : 24 : : : : : : : : : : : : : : : : : : : : 2.4.5 The ERE Architecture : 25 : : : : : : : : : : : : : : : : : : : 2.4.6 The DYNA Architecture : 26 : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 2.5 Conclusions : 27 3 Anticipatory Autonomous Agents 29 : : : : : : : : : : : : : : : : : : : : : : : : : 3.1 Anticipatory Systems : 30 : : : : : : : : : : : : : : : : : : : : : : : : : 3.2 Anticipatory Agents : 32 iv CONTENTS : : : : : : : : : : : : : : : : 3.3 A Basic Class of Anticipatory Agents : 33 : : : : : : : : : : : : : : : : : : : : 3.3.1 Agent Implementation : 34 : : : : : : : : : : : : : : : : : : : : : : : : : 3.3.2 The Testbed : 36 : : : : : : : : : : : : : : : : : : 3.3.3 Single Agent Simulations : 37 : : : : : : : : : : : : : : : : : : : 3.3.4 Multi-Agent Simulations : 41 : : : : : : : : : : : : : : : : 3.3.5 Discussion of the Experiments : 46 : : : : : : : : : : : : : : : : : : : : : : : : : : : 3.4 Related Research : 48 : : : : : : : : : : : : : : : 3.4.1 Anticipation in Control Systems : 49 : : : : : : : : : : : : : 3.4.2 Anticipation in Biological Systems : 49 : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 3.5 Conclusions : 49 4 World Modeling 51 : : : : : : : : : : : : : : : : : : : : : : : 4.1 What is a World Model? : 51 : : : : : : : : : : : : : : : : : : : : : : : : : 4.1.1 Terminology : 52 : : : : : : : : : : : : : : : 4.1.2 Explicit and Implicit Knowledge : 52 : : : : : : : : : 4.2 Is Explicit Knowledge about the World Necessary? : 52 : : : : : : : : : : : : : 4.2.1 On the Proper Level of Abstraction : 53 4.2.2 Is It Possible to Acquire Explicit Knowledge about the World? 53 : : : : : : : : : : : : : : : : : : : : : : 4.3 World Modeling within AI : 54 : : : : : : : : : : : : : : : : : : : : : : : 4.3.1 Computer Vision : 54 : : : : : : : : : : : : : : : : : : : : : : 4.3.2 Machine Learning : 56 : : : : : : : 4.3.3 Computer Vision ± Machine Learning Relation : 57 : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 4.4 Conclusions : 59 5 Concepts and Categories 61 : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 5.1 Terminology : 61 : : : : : : : : : : : : : : : : 5.2 What does it mean to have a concept? : 63 : : : : : : : : 5.2.1 Entity Theories versus Dispositional Theories : 64 : : : : : : : : : : : : : : : : : : : : : : 5.3 The Problem of Universals : 65 : : : : : : : : : : 5.4 The Meaning of (Symbols Designating) Concepts : 65 : : : : : : : : : : : : : : : : : : : : : : 5.4.1 Logical Semantics : 66 : : : : : : 5.4.2 The Meaning of Symbols in Autonomous Agents : 68 : : : : : : : 5.4.3 Theories of Meaning and Theories of Universals : 68 : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 5.5 Conclusions : 69 6 The Functions of Concepts 71 : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 6.1 Introduction : 71 : : : : : : : : : : : : : : : : : : 6.2 The Functions of Human Concepts : 74 : : : : : : : 6.3 Functions of Concepts in Arti®cial Autonomous Agents : 75 : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 6.4 Conclusions : 77 CONTENTS v 7 The Nature of Categories 79 : : : : : : : : : : : : : : : : : : 7.1 The Nature of Human Categories : 79 : : : : : : : : : : : : : : : : : : : : : : : : : : 7.1.1 Properties : 80 : :
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