Jorrand / Sgurev Artificial Intelligence IV

Methodology, Systems, Applications
1. Auflage 2016
ISBN: 978-1-4832-9778-1
Verlag: Elsevier Science & Techn.
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, 450 Seiten, Web PDF

Presenting recent results and ongoing research in Artificial Intelligence, this book has a strong emphasis on fundamental questions in several key areas: programming languages, automated reasoning, natural language processing and computer vision.AI is at the source of major programming language design efforts. Different approaches are described, with some of their most significant results: languages combining logic and functional styles, logic and parallel, functional and parallel, logic with constraints.A central problem in AI is automated reasoning, and formal logic is, historically, at the root of research in this domain. This book presents results in automatic deduction, non-monotonic reasoning, non-standard logic, machine learning, and common-sense reasoning. Proposals for knowledge representation and knowledge engineering are described and the neural net challenger to classical symbolic AI is also defended.Finally, AI systems must be able to interact with their environment in a natural and autonomous way. Natural language processing is an important part of this. Various results are presented in discourse planning, natural language parsing, understanding and generation. The autonomy of a machine for perception of its physical environment is also an AI problem and some research in image processing and computer vision is described.

Autoren/Hrsg.

Jorrand, P.

Sgurev, V.

Inhaltsverzeichnis

1;Front Cover;1
2;Artificial Intelligence IV: Methodology, Systems, Applications;4
3;Copyright Page;5
4;Table of Contents;12
5;FOREWORD;6
6;ACKNOWLEDGEMENTS;8
7;CONFERENCE CHAIRMAN;10
8;PART: 1. AUTOMATED REASONING AND LOGICS FOR AI;18
8.1;Chapter 1. The Use of Renaming to Improve the Efficiency of Clausal Theorem Proving;20
8.1.1;Abstract;20
8.1.2;1 Introduction: renaming subformulas;20
8.1.3;2 Different renaming transformations;22
8.1.4;3 Comparison;24
8.1.5;4 Conclusion;29
8.1.6;Acknowledgements;29
8.1.7;References;29
8.2;Chapter 2. Compilation of Recursive Two-Literal Clauses into Unification Algorithms;30
8.2.1;Abstract;30
8.2.2;1 Introduction;30
8.2.3;2 Prerequisites;33
8.2.4;3 Generation of ATs from Recursive Two-Literal Clauses;36
8.2.5;4 Generation of Unification Algorithms;39
8.2.6;5 Summary;39
8.2.7;References;39
8.3;Chapter 3. An application of many-valued logic to decide propositional S5 formulae: a strategy designed for a parameterized tableaux-based Theorem Prover;40
8.3.1;Abstract;40
8.3.2;1. Introduction;40
8.3.3;2. Definitions and Notations;41
8.3.4;3. Improving the tableau procedure for the Lm-logics;42
8.3.5;4. The strategy: an algorithm for deciding S5 formulae;45
8.3.6;5. Description of a Parameterized Theorem Prover for n-valued logic and Examples;46
8.3.7;6. Conclusion and Future work;49
8.3.8;7. Bibliography;49
8.4;Chapter 4. Logics for Automated Reasoning in the Presence of Contradictions;50
8.4.1;Abstract;50
8.4.2;I INTRODUCTION;50
8.4.3;II PARACONSISTENT LOGICS;51
8.4.4;III A BASIC PARACONSISTENT LOGIC;52
8.4.5;IV TRANSITIVITY AND DISJUNCTIVE SYLLOGISM;53
8.4.6;V AN EXAMPLE OF APPLICATION;54
8.4.7;VI A WEAKLY PARACONSISTENT LOGIC;55
8.4.8;VII COMPARISON WITH REVISION AND NON-MONOTONIC LOGICS;57
8.4.9;VIII CONCLUSION;58
8.4.10;IX REFERENCES;58
8.5;Chapter 5. Logics with structured contexts;60
8.5.1;Abstract;60
8.5.2;1 Introduction;60
8.5.3;2 Context;60
8.5.4;3 Language;61
8.5.5;4 Extensions and Kripke semantics;61
8.5.6;5 Axiomatization and completeness;62
8.5.7;6 Automatic deduction;63
8.5.8;7 Special cases;63
8.5.9;8 Logic programming with contexts;64
8.5.10;9 Structured semantics for structured context;65
8.5.11;References;66
8.6;CHAPTER 6. A PROOF-THEORETIC ACCOUNT OF MODEL-PREFERENCE DEFAULT REASONING;68
8.6.1;1. INTRODUCTION;68
8.6.2;2. AN OVERVIEW OF SELMAN & KAUTZ'S SYSTEM D+;69
8.6.3;3. A PROOF SYSTEM FOR MODEL-PREFERENCE DEFAULT REASONING;70
8.6.4;4. AN EXAMPLE;74
8.6.5;5. CONCLUSION;77
8.6.6;BIBLIOGRAPHY;77
8.7;Chapter 7. Unexpected and unwanted results of circumscription;78
8.7.1;Abstract;78
8.7.2;1 Introduction;78
8.7.3;2 Predicate circumscriptions;79
8.7.4;3 Where the circumscription axiom is a finiteness axiom;81
8.7.5;4 Where minimizing P may allow to prove that P is the largest possible;82
8.7.6;5 Inconsistency of circumscription;83
8.7.7;6 Where circumscription gives only expected results;85
8.7.8;7 Conclusion;86
8.7.9;References;87
8.8;CHAPTER 8. A LOGIC FOR TRUTH MAINTENANCE REASONING;88
8.8.1;Abstract;88
8.8.2;1 Introduction;88
8.8.3;2 Motivation;89
8.8.4;3 Language;90
8.8.5;4 Inference;91
8.8.6;5 Some results about the inference relation;93
8.8.7;6 One special class of derivable formulae;95
8.8.8;7 Conclusion;97
8.8.9;References;97
8.9;Chapter 9. Querying an inconsistent database;98
8.9.1;1 Introduction;98
8.9.2;2 Hypothesis and informal presentation of our approach;100
8.9.3;3 The formalism;101
8.9.4;4 Presentation of approach;103
8.9.5;5 An example;104
8.9.6;6 Concluding remarks;107
8.9.7;References;107
8.9.8;APPENDIX;109
8.10;CHAPTER 10. AN APPROACH TO THE MODELLING OF NATURAL REASONING;110
8.10.1;1. INTRODUCTION;110
8.10.2;2. THE DETERMINANTS OF HUMAN REASONING;111
8.10.3;3. THE PROCESSES OF REASONING;113
8.10.4;4. CONCLUDING REMARKS;118
8.10.5;REFERENCES;118
8.11;CHAPTER 11. NUMBER GENERALIZATION IN LEARNING FROM EXAMPLES;120
8.11.1;1. INTRODUCTION;120
8.11.2;2. KNOWLEDGE REPRESENTATION;121
8.11.3;3. GENERALITY DEFINITION;122
8.11.4;4. GENERALIZATION RULES;125
8.11.5;5. USING EXAMPLES AND COUNTER-EXAMPLES;128
8.11.6;6. CONCLUSIONS;129
8.11.7;REFERENCES;129
9;PART 2: LANGUAGES AND COMPUTATIONAL STRUCTURES FOR AI;132
9.1;Chapter 12. About Redundant Inequalities Generated by Fourier's Algorithm;134
9.1.1;Abstract;134
9.1.2;1. Introduction;134
9.1.3;2. Preliminaries;135
9.1.4;3. Fourier's algorithm;135
9.1.5;4. A sub-system equivalent to S(En);136
9.1.6;5. Characterisation of minimal formulae;139
9.1.7;6. Detection of minimal formulae;142
9.1.8;7. Conclusion;143
9.1.9;References;144
9.2;Chapter 13. Equations over Trees and Lists with Constraints;146
9.2.1;ABSTRACT;146
9.2.2;Introduction;146
9.2.3;Trees;147
9.2.4;Terms and Constraints;148
9.2.5;Assignments;149
9.2.6;Simple Systems;149
9.2.7;Splitting Constraints;150
9.2.8;Algorithm;152
9.2.9;Conclusion;155
9.2.10;Bibliography;155
9.3;CHAPTER 14. TWO ALGORITHMS FOR CONSTRAINT SYSTEM SOLVING IN PROPOSITIONAL CALCULUS AND THEIR IMPLEMENTATION IN CONSTRAINT PROGRAMMING LANGUAGES;156
9.3.1;1. Introduction;156
9.3.2;2. An example of logic programming with boolean constraints;157
9.3.3;3. Functionalities of the Prolog III boolean module;158
9.3.4;4. A production algorithm derived from SL-Resolution;159
9.3.5;5. A production algorithm derived from semantic evaluation;162
9.3.6;6. Conclusion;165
9.3.7;REFERENCES;165
9.4;Chapter 15. Rule Based Mechanisms for Constraint Checking in Logic Programs;166
9.4.1;1. Introduction;166
9.4.2;2. A method for checking constraints from the structure of a program;167
9.4.3;3. A method for checking constraints from the rules of a program;170
9.4.4;References;175
9.5;CHAPTER 16. A COMPILING APPROACH FOR EXPLOITING AND-OR-PARALLELISM IN LOGIC PROGRAMS;176
9.5.1;Abstract;176
9.5.2;Introduction;176
9.5.3;1- Compiling clauses into an execution graph;177
9.5.4;2- The merge of the streams in the graph nodes;184
9.5.5;Conclusion;184
9.5.6;References;185
9.6;CHAPTER 17. LOGICAL INFERENCE IN A NETWORK ENVIRONMENT;186
9.6.1;1. INTRODUCTION;186
9.6.2;2. NET-CLAUSE PROGRAMMING;186
9.6.3;3. LOGIC AND NET-CLAUSE PROGRAMMING;189
9.6.4;4. DEFAULT REASONING IN NET-CLAUSES;192
9.6.5;5. CONCLUSION;194
9.6.6;REFERENCES;195
9.7;CHAPTER 18. A PRACTICABLE APPROACH TO FUNCTIONAL LOGIC PROGRAMMING;196
9.7.1;1. INTRODUCTION;196
9.7.2;2. BASIC DEFINITIONS AND NOTATIONS;197
9.7.3;3. SYNTAX AND SEMANTICS OF F-PROLOG;198
9.7.4;4. SEMANTIC UNIFICATIONS;200
9.7.5;5. TYPES AS TYPE FUNCTIONS;203
9.7.6;6. CONCLUDING REMARKS;204
9.7.7;REFERENCES;205
9.8;Chapter 19. Combining Horn Clause Logic with Rewrite Rules;206
9.8.1;Abstract;206
9.8.2;1 Introduction;206
9.8.3;2 Equality transformation DP;210
9.8.4;3 Improving DP;213
9.8.5;4 Summary;215
9.8.6;References;215
9.9;CHAPTER 20. PARALLELISM IN BACKUS-LIKE FP-SYSTEMS: AN APPROACH BASED ON THE FP2 LANGUAGE;216
9.9.1;Abstract;216
9.9.2;1. INTRODUCTION;216
9.9.3;2. DESCRIBING MEANS OF FP* AS PROCESSES OF FP2;217
9.9.4;3. DISCUSSION;224
9.9.5;REFERENCES;225
9.10;CHAPTER 21. KOHONEN FEATURE MAPS: TOWARD INVARIANT CHARACTER RECOGNITION;226
9.10.1;1. INTRODUCTION;226
9.10.2;2. KOHONEN FEATURE MAPS;227
9.10.3;3. FEATURE MAPS FOR CHARACTER RECOGNITION;228
9.10.4;4. CONCLUSION AND DESCRIPTION OF FUTURE WORK;234
9.10.5;REFERENCES;234
9.11;CHAPTER 22. OCCAM Based Neural Network Description Language;236
9.11.1;1. Introduction;236
9.11.2;2. Neural network description language W;237
9.11.3;References;243
9.12;CHAPTER 23. HYBRID CONNECTIONIST RULE-BASED SYSTEMS;244
9.12.1;1. INTRODUCTION;244
9.12.2;2. A TWO-LEVEL HYBRID CONNECTIONIST RULE-BASED MODEL OF KNOWLEDGE REPRESENTSTION;245
9.12.3;3. FIRST EXAMPLE - BIRTH PREDICTION AND TREATMENT OF AN AGRICULTURAL INSECT;248
9.12.4;4. CORE - AN HYBRID CONNECTIONIST RULE - BASED ENVIRONMENT;250
9.12.5;5. CURRENT APPLICATIONS AND UNSOLVED PROBLEMS;250
9.12.6;6. CONCLUSIONS;251
9.12.7;REFERENCES;251
10;PART 3: KNOWLEDGE REPRESENTATION, KNOWLEDGE-BASED SYSTEMS;254
10.1;CHAPTER 24. AN OBJECT-ORIENTED REPRESENTATION FRAMEWORK FOR HIERARCHICAL EVIDENTIAL REASONING;256
10.1.1;1. INTRODUCTION;256
10.1.2;2. KNOWLEDGE REPRESENTATION FRAMEWORK;257
10.1.3;3. KNOWLEDGE ACQUISITION SUPPORT WITHIN THE OBJECT-ORIENTED FRAMEWORK;259
10.1.4;4. PROPAGATION OF UNCERTAINTY;260
10.1.5;5. COPING WITH IMPRECISE MEASUREMENTS;261
10.1.6;6. CONCLUDING REMARKS;264
10.1.7;REFERENCES;265
10.2;CHAPTER 25. A SYSTEMS-BASED FRAMEWORK FOR KNOWLEDGE REPRESENTATION;266
10.2.1;1. INTRODUCTION;266
10.2.2;2. NEW FEATURES FOR KNOWLEDGE REPRESENTATION;267
10.2.3;3. SYSTEMS;269
10.2.4;4. USING SYSTEMS FOR REPRESENTING KNOWLEDGE;272
10.2.5;5. CONCLUSIONS;274
10.2.6;REFERENCES;275
10.3;CHAPTER 26. MODELING OF MEDICAL DIAGNOSTIC KNOWLEDGE AND REASONING IN DEDEX EXPERT SYSTEM;276
10.3.1;1. INTRODUCTION;276
10.3.2;2. KNOWLEDGE REPRESENTATION SCHEME;277
10.3.3;3. INFERENCE MECHANISM SCHEME;279
10.3.4;4. CONCLUSION;279
10.3.5;REFERENCES;280
10.3.6;APPENDIX A;280
10.3.7;APPENDIX B;280
10.4;CHAPTER 27. ON THE USE OF DIAGRAMS;282
10.4.1;1. INTRODUCTION;282
10.4.2;2. RELEVANT STUDIES;283
10.4.3;3. EXPERIMENTAL STUDY;284
10.4.4;4. HYPOTHESES;284
10.4.5;5. PROCEDURE;285
10.4.6;6. MEASUREMENT OF DEPENDENT VARIABLES;287
10.4.7;7. RESULTS AND DISCUSSION;287
10.4.8;8. GENERAL DISCUSSION;289
10.4.9;REFERENCES;290
10.5;CHAPTER 28. DEVELOPING A KNOWLEDGE BASED SYSTEM;292
10.5.1;INTRODUCTION;292
10.5.2;1. THE CLONING PROBLEM;293
10.5.3;2. THE COGNITIVE MODEL;295
10.5.4;3. THE COMPUTING CHOICES;298
10.5.5;4. OTHER SYSTEMS AND ACTUAL SITUATION;299
10.5.6;CONCLUSION;301
10.5.7;REFERENCES;301
10.6;CHAPTER 29. DEFEASIBLE REASONING BY USING ANALOGIES;302
10.6.1;1. INTRODUCTION;302
10.6.2;2. COMMON VIEW OF THE PROPOSED DEFEASIBLE REASONING;303
10.6.3;3. APPLICATIONS OF THE DEFEASIBLE PROCEDURE IN KNOWLEDGE ACQUISITION SYSTEM;305
10.6.4;4. PERSPECTIVES;308
10.6.5;5. CONCLUSIONS;308
10.6.6;REFERENCES;308
10.7;CHAPTER 30. SOLVING PROGRAM CONFIGURATION TASK THROUGH A KNOWLEDGE BASED SYSTEM;310
10.7.1;1. INTRODUCTION;310
10.7.2;2. AN APPROACH FOR PROGRAM CONFIGURATION TASK SOLVING;311
10.7.3;3. KNOWLEDGE REPRESENTATION;313
10.7.4;4. IMPLEMENTATION;314
10.7.5;5. EXAMPLE;316
10.7.6;6. CONCLUSIONS;319
10.7.7;REFERENCES;319
11;PART 4: NATURAL LANGUAGE PROCESSING;320
11.1;CHAPTER 31. A FORMAL SEMANTICS FOR INTERNAL LOCALIZATION : AN ESSAY ON SPATIAL COMMONSENSE KNOWLEDGE;322
11.1.1;INTRODUCTION;322
11.1.2;1. THE GENERAL SYSTEM FOR THE CHARACTERIZATION OF SPATIAL ENTITIES;322
11.1.3;2. THE INTERNAL LOCALIZATION NOUNS (ILN) AND THEIR SEMANTICS;326
11.1.4;3. SOME COMPOSITION PRINCIPLES OF SPATIAL REFERENCE;328
11.1.5;4. SOME PERSPECTIVES FOR SPACE SEMANTICS;333
11.1.6;REFERENCES;334
11.2;CHAPTER 32. WHAT'S IN A 'DET' ? Steps towards Determiner-Dependent Inferencing;336
11.2.1;1 Natural Language Determiners - Problems, Types, and Theory;336
11.2.2;2 Linguistic Universale and Semantic Constraints of NL Determiners;338
11.2.3;3 Requirements for a determiner processing system;340
11.2.4;4 Processing determiners in an integrated NL system;343
11.2.5;5 Summary;345
11.2.6;6 Bibliography;345
11.3;Chapter 33. Dialogue Modeling and Response Generation in CFID@ a robust man-machine interface system;346
11.3.1;1. INTRODUCTION;346
11.3.2;2. AN OVERVIEW OF THE SYSTEM;347
11.3.3;3. THE DIALOGUE MODEL;348
11.3.4;4. GENERATION OF RESPONSES;353
11.3.5;5. CONCLUSIONS;356
11.3.6;REFERENCES;356
11.4;CHAPTER 34. ANALOGICAL REASONING AND SENTENCE PARSING;358
11.4.1;1. INTRODUCTION;358
11.4.2;2. ANALOGICAL REASONING;358
11.4.3;3. ANALOGICAL REASONING AND SENTENCE PARSING;361
11.4.4;4. CONCLUSION;365
11.4.5;REFERENCES;366
11.5;CHAPTER 35. SPEECH ACT THEORY AND EPISTEMIC PLANNING;368
11.5.1;1 Background;368
11.5.2;2 Rational Behaviour and AI Planning Theory;370
11.5.3;3 Speech Act Theory;372
11.5.4;4 Epistemic Planning;376
11.5.5;References;377
11.6;CHAPTER 36. SOME LINGUISTIC AND CONCEPTUAL ASPECTS IN THE GENERATION OF BULGARIAN NATURAL LANGUAGE TEXTS;378
11.6.1;ABSTRACT;378
11.6.2;1. INTRODUCTION;378
11.6.3;2. KNOWLEDGE REPRESENTATION APPROACH;379
11.6.4;3. THE TWO SIDES OF GENERATION;379
11.6.5;4. PRELIMINARY FACTORS FOR STRATEGY AND TACTICS;380
11.6.6;5. LOGICAL EMPHASIS AND SENTENCE SYNTAX;381
11.6.7;6. THE GRAMMAR AND SENTENCE PRODUCTION;384
11.6.8;7. SAMPLE TEXTS;385
11.6.9;REFERENCES;386
11.7;Chapter 37. Syntactic Processing of Unknown Words;388
11.7.1;Abstract;388
11.7.2;1 On the need for processing unknown words;389
11.7.3;2 Syntactic processing: A relational view;389
11.7.4;3 A method for processing unknown words;390
11.7.5;4 An example;390
11.7.6;5 Problems;397
11.7.7;6 Conclusion;398
11.7.8;Bibliography;398
11.8;CHAPTER 38. A NETWORK PARSING SCHEME;400
11.8.1;1. INTRODUCTION;400
11.8.2;2. AN OVERVIEW OF THE NET-CLAUSE LANGUAGE;400
11.8.3;3. THE PARSING SCHEME;401
11.8.4;4. EXAMPLES;402
11.8.5;5. CONCLUSION;405
11.8.6;REFERENCES;406
11.8.7;APPENDIX 1: NET-CLAUSE CODE OF THE EXAMPLE NETWORK;407
11.8.8;APPENDIX 2: EXAMPLES IN PARSING;408
11.9;CHAPTER 39. HOW TO DEAL INTELLIGENTLY WITH THE UNEXPECTED ?;410
11.9.1;Abstract;410
11.9.2;Introduction;410
11.9.3;1. Previous work;411
11.9.4;2. System composition and architecture;412
11.9.5;3. The system's knowledge;413
11.9.6;4. The pilot and the sub-pilots;416
11.9.7;Conclusion;418
11.9.8;References;418
12;PART 5: IMAGE UNDERSTANDING AND COMPUTER VISION;420
12.1;CHAPTER 40. KNOWLEDGE-BASED INTERPRETATION OF BIOPHYSICAL IMAGES;422
12.1.1;1. INTRODUCTION;422
12.1.2;2. THE ARCHITECTURE OF THE SYSTEM ISIA;423
12.1.3;3. INFORMATION EXTRACTION AND KNOWLEDGE REPRESENTATION;425
12.1.4;4. CONCLUSIONS;428
12.1.5;REFERENCES;429
12.1.6;APPENDIX 1: EXAMPLE FOR COMPOSITE LINE AND FRAGMENTS OF ITS REPRESENTATION;430
12.2;CHAPTER 41. COMPUTER VISION AND STOCHASTIC GEOMETRY;432
12.2.1;1. INTRODUCTION;432
12.2.2;2. SYSTEM'S STRUCTURE AND RECOGNITION INVARIANT FEATURES;433
12.2.3;3. SCANNING TRAJECTORY PROPERTIES;435
12.2.4;4. DECISION PROCEDURES;436
12.2.5;5. SYSTEMS' VARIANTS;437
12.2.6;6. CONCLUSIONS;437
12.2.7;REFERENCES;438
12.3;CHAPTER 43. QUANTITATIVE ECOLOGICAL OPTICS;440
12.3.1;1. INTRODUCTION;440
12.3.2;2. GIBSON'S ECOLOGICAL OPTICS;441
12.3.3;3. MARR'S MODEL OF EARLY VISUAL PROCESSING;442
12.3.4;4. FRACTAL IMAGE MODELS;444
12.3.5;5. SIMULATED NEURAL NETWORKS;445
12.3.6;6. CONCLUSION;447
12.3.7;REFERENCES;447
13;AUTHOR INDEX;450