Knowledge engineering: building cognitive assistants for evidence-based reasoning
Gespeichert in:
| Hauptverfasser: | , , , |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
New York, NY
Cambridge University Press
2016
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| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis Klappentext |
| Beschreibung: | Includes bibliographical references and index |
| Beschreibung: | xxiv, 455 Seiten Illustrationen, Diagramme |
| ISBN: | 9781107122567 |
Internformat
MARC
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| 245 | 1 | 0 | |a Knowledge engineering |b building cognitive assistants for evidence-based reasoning |c Gheorghe Tecuci (George Mason University), Dorin Marcu (George Mason University), Mihai Boicu (George Mason University), David A. Schum (George Mason University) |
| 264 | 1 | |a New York, NY |b Cambridge University Press |c 2016 | |
| 300 | |a xxiv, 455 Seiten |b Illustrationen, Diagramme | ||
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| 653 | 0 | |a Expert systems (Computer science) | |
| 653 | 0 | |a Knowledge, Theory of / Data processing | |
| 653 | 0 | |a Computational learning theory | |
| 653 | 0 | |a A priori / Data processing | |
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| 700 | 1 | |a Marcu, Dorin |e Verfasser |4 aut | |
| 700 | 1 | |a Boicu, Mihai |e Verfasser |4 aut | |
| 700 | 1 | |a Schum, David A. |e Verfasser |4 aut | |
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Datensatz im Suchindex
| _version_ | 1804176706256240640 |
|---|---|
| adam_text | Contents
Preface page xv
Acknowledgments xxi
About the Authors xxiii
1 Introduction..................... . 1
1.1 Understanding the World through Evidence-based
Reasoning 1
1.1.1 What Is Evidence? 1
1.1.2 Evidence, Data, and Information 1
1.1.3 Evidence and Fact 2
1.1.4 Evidence and Knowledge 2
1.1.5 Ubiquity of Evidence 5
1.2 Abductive Reasoning 5
1.2.1 From Aristotle to Peirce 5
1.2.2 Peirce and Sherlock Holmes on Abductive
Reasoning 6
1.3 Probabilistic Reasoning 9
1.3.1 Enumerative Probabilities: Obtained by Counting 9
1.3.1.1 Aleatory Probability 9
1.3.1.2 Relative Frequency and Statistics 9
1.3.2 Subjective Bayesian View of Probability 11
1.3.3 Belief Functions 13
1.3.4 Baconian Probability 16
1.3.4.1 Variative and Eliminative Inferences 16
1.3.4.2 Importance of Evidential Completeness 17
1.3.4.3 Baconian Probability of Boolean
Expressions 20
1.3.5 Fuzzy Probability 20
1.3.5.1 Fuzzy Force of Evidence 20
1.3.5.2 Fuzzy Probability of Boolean Expressions 21
1.3.5.3 On Verbal Assessments of Probabilities 22
1.3.6 A Summary of Uncertainty Methods and What
They Best Capture 23
1.4 Evidence-based Reasoning 25
1.4.1 Deduction, Induction, and Abduction 25
1.4.2 The Search for Knowledge 26
1.4.3 Evidence-based Reasoning Everywhere 27
v
Contents
1.5 Artificial Intelligence 29
1.5.1 Intelligent Agents 30
1.5.2 Mixed-Initiative Reasoning 32
1.6 Knowledge Engineering 33
1.6.1 From Expert Systems to Knowledge-based Agents
and Cognitive Assistants 33
1.6.2 An Ontology of Problem-Solving Tasks 35
1.6.2.1 Analytic Tasks 36
1.6.2.2 Synthetic Tasks 36
1.6.3 Building Knowledge-based Agents 37
1.6.3.1 How Knowledge-based Agents Are Built
and Why It Is Hard 37
1.6.3.2 Teaching as an Alternative to
Programming: Disciple Agents 39
1.6.3.3 Disciple-EBR, Disciple-CD, and
TIACRITIS 40
1.7 Obtaining Disciple-EBR 41
1.8 Review Questions 42
2 Evidence-based Reasoning: Connecting the Dots .... 46
2.1 How Easy Is It to Connect the Dots? 46
2.1.1 How Many Kinds of Dots Are There? 47
2.1.2 Which Evidential Dots Can Be Believed? 48
2.1.3 Which Evidential Dots Should Be Considered? 50
2.1.4 Which Evidential Dots Should We Try to
Connect? 50
2.1.5 How to Connect Evidential Dots to Hypotheses? 52
2.1.6 What Do Our Dot Connections Mean? 54
2.2 Sample Evidence-based Reasoning Task: Intelligence
Analysis 56
2.2.1 Evidence in Search of Hypotheses 56
2.2.2 Hypotheses in Search of Evidence 58
2.2.3 Evidentiary Testing of Hypotheses 60
2.2.4 Completing the Analysis 62
2.3 Other Evidence-based Reasoning Tasks 64
2.3.1 Cyber Insider Threat Discovery and Analysis 64
2.3.2 Analysis of Wide-Area Motion Imagery 68
2.3.3 Inquiry-based Teaching and Learning in a Science
Classroom 70
2.3.3.1 Need for Inquiry-based Teaching and
Learning 70
2.3.3.2 Illustration of Inquiry-based Teaching
and Learning 71
2.3.3.3 Other Examples of Inquiry-based
Teaching and Learning 74
2.4 Hands On: Browsing an Argumentation 76
2.5 Project Assignment 1 81
2.6 Review Questions 81
Contents
3 Methodologies and Tools for Agent Design and
Development..............................................83
3.1 A Conventional Design and Development Scenario 83
3.1.1 Conventional Design and Development Phases 83
3.1.2 Requirements Specification and Domain
Understanding 83
3.1.3 Ontology Design and Development 85
3.1.4 Development of the Problem-Solving Rules or
Methods 86
3.1.5 Verification, Validation, and Certification 87
3.2 Development Tools and Reusable Ontologies 88
3.2.1 Expert System Shells 88
3.2.2 Foundational and Utility Ontologies and Their
Reuse 89
3.2.3 Learning Agent Shells 90
3.2.4 Learning Agent Shell for Evidence-based
Reasoning 91
3.3 Agent Design and Development Using Learning
Technology 93
3.3.1 Requirements Specification and Domain
Understanding 93
3.3.2 Rapid Prototyping 93
3.3.3 Ontology Design and Development 100
3.3.4 Rule Learning and Ontology Refinement 101
3.3.5 Hierarchical Organization of the Knowledge
Repository 104
3.3.6 Learning-based Design and Development Phases 105
3.4 Hands On: Loading, Saving, and Closing Knowledge
Bases 107
3.5 Knowledge Base Guidelines 111
3.6 Project Assignment 2 111
3.7 Review Questions 112
4
Modeling the Problem-Solving Process.........................
4.1 Problem Solving through Analysis and Synthesis
4.2 Inquiry-driven Analysis and Synthesis
4.3 Inquiry-driven Analysis and Synthesis for Evidence-based
Reasoning
4.3.1 Hypothesis Reduction and Assessment Synthesis
4.3.2 Necessary and Sufficient Conditions
4.3.3 Sufficient Conditions and Scenarios
4.3.4 Indicators
4.4 Evidence-based Assessment
4.5 Hands On: Was the Cesium Stolen?
4.6 Hands On: Hypothesis Analysis and Evidence Search
and Representation
4.7 Believability Assessment
4.7.1 Tangible Evidence
4.7.2 Testimonial Evidence
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fi viii
4.7.3 Missing Evidence
4.7.4 Authoritative Record
4.7.5 Mixed Evidence and Chains of Custody
4.8 Hands On: Believability Analysis
4.9 Drill-Down Analysis, Assumption-based Reasoning,
and What-If Scenarios
4.10 Hands On: Modeling, Formalization, and Pattern
Learning
4.11 Hands On: Analysis Based on Learned Patterns
4.12 Modeling Guidelines
4.13 Project Assignment 3
4.14 Review Questions
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5 Ontologies............................
5.1 What Is an Ontology?
5.2 Concepts and Instances
5.3 Generalization Hierarchies
5.4 Object Features
5.5 Defining Features
5.6 Representation of N-ary Features
5.7 Transitivity
5.8 Inheritance
5.8.1 Default Inheritance
5.8.2 Multiple Inheritance
5.9 Concepts as Feature Values
5.10 Ontology Matching
5.11 Hands On: Browsing an Ontology
5.12 Project Assignment 4
5.13 Review Questions
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6 Ontology Design and Development......................
6.1 Design and Development Methodology
6.2 Steps in Ontology Development
6.3 Domain Understanding and Concept Elicitation
6.3.1 Tutorial Session Delivered by the Expert
6.3.2 Ad-hoc List Created by the Expert
6.3.3 Book Index
6.3.4 Unstructured Interviews with the Expert
6.3.5 Structured Interviews with the Expert
6.3.6 Protocol Analysis (Think-Aloud Technique)
6.3.7 The Card-Sort Method
6.4 Modeling-based Ontology Specification
6.5 Hands On: Developing a Hierarchy of Concepts and
Instances
6.6 Guidelines for Developing Generalization Hierarchies
6.6.1 Well-Structured Hierarchies
6.6.2 Instance or Concept?
6.6.3 Specific Instance or Generic Instance?
6.6.4 Naming Conventions
6.6.5 Automatic Support
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Contents
6.7 Hands On: Developing a Hierarchy of Features 189
6.8 Hands On: Defining Instances and Their Features 192
6.9 Guidelines for Defining Features and Values 195
6.9.1 Concept or Feature? 195
6.9.2 Concept, Instance, or Constant? 196
6.9.3 Naming of Features 196
6.9.4 Automatic Support 197
6.10 Ontology Maintenance 197
6.11 Project Assignment 5 198
6.12 Review Questions 198
7 Reasoning with Ontologies and Rules........................202
7.1 Production System Architecture 202
7.2 Complex Ontology-based Concepts 203
7.3 Reduction and Synthesis Rules and the Inference Engine 204
7.4 Reduction and Synthesis Rules for Evidence-based
Hypotheses Analysis 206
7.5 Rule and Ontology Matching 207
7.6 Partially Learned Knowledge 212
7.6.1 Partially Learned Concepts 212
7.6.2 Partially Learned Features 213
7.6.3 Partially Learned Hypotheses 214
7.6.4 Partially Learned Rules 214
7.7 Reasoning with Partially Learned Knowledge 215
7.8 Review Questions 216
8
Learning for Knowledge-based Agents.......................
8.1 Introduction to Machine Learning
8.1.1 What Is Learning?
8.1.2 Inductive Learning from Examples
8.1.3 Explanation-based Learning
8.1.4 Learning by Analogy
8.1.5 Multistrategy Learning
8.2 Concepts
8.2.1 Concepts, Examples, and Exceptions
8.2.2 Examples and Exceptions of a Partially Learned
Concept
8.3 Generalization and Specialization Rules
8.3.1 Turning Constants into Variables
8.3.2 Turning Occurrences of a Variable into Different
Variables
8.3.3 Climbing the Generalization Hierarchies
8.3.4 Dropping Conditions
8.3.5 Extending Intervals
8.3.6 Extending Ordered Sets of Intervals
8.3.7 Extending Symbolic Probabilities
8.3.8 Extending Discrete Sets
8.3.9 Using Feature Definitions
8.3.10 Using Inference Rules
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Contents
8.4 Types of Generalizations and Specializations 234
8.4.1 Definition of Generalization 234
8.4.2 Minimal Generalization 234
8.4.3 Minimal Specialization 235
8.4.4 Generalization of Two Concepts 236
8.4.5 Minimal Generalization of Two Concepts 236
8.4.6 Specialization of Two Concepts 237
8.4.7 Minimal Specialization of Two Concepts 237
8.5 Inductive Concept Learning from Examples 238
8.6 Learning with an Incomplete Representation Language 242
8.7 Formal Definition of Generalization 243
8.7.1 Formal Representation Language for Concepts 243
8.7.2 Term Generalization 245
8.7.3 Clause Generalization 245
8.7.4 BRU Generalization 246
8.7.5 Generalization of Concepts with Negations 247
8.7.6 Substitutions and the Generalization Rules 247
8.8 Review Questions 247
9 Rule Learning................................................252
9.1 Modeling, Learning, and Problem Solving 252
9.2 An Illustration of Rule Learning and Refinement 253
9.3 The Rule-Learning Problem 257
9.4 Overview of the Rule-Learning Method 258
9.5 Mixed-Initiative Example Understanding 260
9.5.1 What Is an Explanation of an Example? 260
9.5.2 Explanation Generation 262
9.6 Example Reformulation 264
9.7 Analogy-based Generalization 265
9.7.1 Analogical Problem Solving Based on
Explanation Similarity 265
9.7.2 Upper Bound Condition as a Maximally General
Analogy Criterion 266
9.7.3 Lower Bound Condition as a Minimally General
Analogy Criterion 268
9.8 Rule Generation and Analysis 270
9.9 Generalized Examples 270
9.10 Hypothesis Learning 271
9.11 Hands On: Rule and Hypotheses Learning 275
9.12 Explanation Generation Operations 279
9.12.1 Guiding Explanation Generation 279
9.12.2 Fixing Values 280
9.12.3 Explanations with Functions 280
9.12.4 Explanations with Comparisons 283
9.12.5 Hands On: Explanations with Functions and
Comparisons 285
9.13 Guidelines for Rule and Hypothesis Learning 285
9.14 Project Assignment 6 289
9.15 Review Questions 289
Contents
10 Rule Refinement.......................................294
10.1 Incremental Rule Refinement 294
10.1.1 The Rule Refinement Problem 294
10.1.2 Overview of the Rule Refinement Method 295
10.1.3 Rule Refinement with Positive Examples 296
10.1.3.1 Illustration of Rule Refinement with a
Positive Example 296
10.1.3.2 The Method of Rule Refinement with
a Positive Example 298
10.1.3.3 Summary of Rule Refinement with a
Positive Example 300
10.1.4 Rule Refinement with Negative Examples 300
10.1.4.1 Illustration of Rule Refinement with
Except-When Conditions 300
10.1.4.2 The Method of Rule Refinement with
Except-When Conditions 305
10.1.4.3 Illustration of Rule Refinement
through Condition Specialization 305
10.1.4.4 The Method of Rule Refinement
through Condition Specialization 307
10.1.4.5 Summary of Rule Refinement with a
Negative Example 308
10.2 Learning with an Evolving Ontology 309
10.2.1 The Rule Regeneration Problem 309
10.2.2 On-Demand Rule Regeneration 310
10.2.3 Illustration of the Rule Regeneration Method 312
10.2.4 The Rule Regeneration Method 316
10.3 Hypothesis Refinement 316
10.4 Characterization of Rule Learning and Refinement 317
10.5 Hands On: Rule Refinement 319
10.6 Guidelines for Rule Refinement 321
10.7 Project Assignment 7 322
10.8 Review Questions 322
11 Abstraction of Reasoning...................................329
11.1 Statement Abstraction 329
11.2 Reasoning Tree Abstraction 331
11.3 Reasoning Tree Browsing 331
11.4 Hands On: Abstraction of Reasoning 331
11.5 Abstraction Guideline 334
11.6 Project Assignment 8 335
11.7 Review Questions 335
12 Disciple Agents..........................................338
12.1 Introduction 338
12.2 Disciple-WA: Military Engineering Planning 338
12.2.1 The Workaround Planning Problem 338
12.2.2 Modeling the Workaround Planning Process 341
12.2.3 Ontology Design and Development 343
Contents
Xll
12.2.4 Rule Learning 345
12.2.5 Experimental Results 346
12.3 Disciple-COA: Course of Action Critiquing 348
12.3.1 The Course of Action Critiquing Problem 348
12.3.2 Modeling the COA Critiquing Process 351
12.3.3 Ontology Design and Development 352
12.3.4 Training the Disciple-COA Agent 355
12.3.5 Experimental Results 360
12.4 Disciple-COG: Center of Gravity Analysis 364
12.4.1 The Center of Gravity Analysis Problem 364
12.4.2 Overview of the Use of Disciple-COG 367
12.4.3 Ontology Design and Development 376
12.4.4 Script Development for Scenario Elicitation 376
12.4.5 Agent Teaching and Learning 380
12.4.6 Experimental Results 383
12.5 Disciple-VPT: Multi-Agent Collaborative Planning 387
12.5.1 Introduction 387
12.5.2 The Architecture of Disciple-VPT 388
12.5.3 The Emergency Response Planning Problem 389
12.5.4 The Disciple-VE Learning Agent Shell 390
12.5.5 Hierarchical Task Network Planning 394
12.5.6 Guidelines for HTN Planning 396
12.5.7 Integration of Planning and Inference 400
12.5.8 Teaching Disciple-VE to Perform Inference Tasks 403
12.5.9 Teaching Disciple-VE to Perform Planning Tasks 409
12.5.9.1 Why Learning Planning Rules Is
Difficult 409
12.5.9.2 Learning a Set of Correlated Planning
Rules 409
12.5.9.3 The Learning Problem and Method for
a Set of Correlated Planning Rules 413
12.5.9.4 Learning Correlated Planning Task
Reduction Rules 413
12.5.9.5 Learning Correlated Planning Task
Concretion Rules 414
12.5.9.6 Learning a Correlated Action
Concretion Rule 415
12.5.10 The Virtual Experts Library 416
12.5.11 Multidomain Collaborative Planning 420
12.5.12 Basic Virtual Planning Experts 421
12.5.13 Evaluation of Disciple-VPT 422
12.5.14 Final Remarks 422
13 Design Principles for Cognitive Assistants...................426
13.1 Learning-based Knowledge Engineering 426
13.2 Problem-Solving Paradigm for User-Agent
Collaboration 427
13.3 Multi-Agent and Multidomain Problem Solving 427
13.4 Knowledge Base Structuring for Knowledge Reuse 427
13.5 Integrated Teaching and Learning 428
Contents
13.6 Multistrategy Learning 428
13.7 Knowledge Adaptation 429
13.8 Mixed-Initiative Modeling, Learning, and Problem
Solving 429
13.9 Plausible Reasoning with Partially Learned Knowledge 430
13.10 User Tutoring in Problem Solving 430
13.11 Agent Architecture for Rapid Agent Development 430
13.12 Design Based on a Complete Agent Life Cycle 431
References 433
Appendixes 443
Summary: Knowledge Engineering Guidelines 443
Summary: Operations with Disciple-EBR 444
Summary: Hands-On Exercises 446
447
Index
This book presents a significant advancement in the theory and practice of knowledge
engineering, the discipline concerned with the development of intelligent agents that
use knowledge and reasoning to perform problem-solving and decision-making tasks. It
covers the main stages in the development of a knowledge-based agent: understanding
the application domain, modeling problem solving in that domain, developing the ontology,
learning the reasoning rules, and testing the agent. The book focuses on a special class
of agents: cognitive assistants for evidence-based reasoning that learn complex problem-
solving expertise directly from human experts, support experts and nonexperts in problem
solving and decision making, and teach their problem-solving expertise to students.
A powerful learning agent shell, Disciple-EBR, is included with the book, enabling
students, practitioners, and researchers to develop cognitive assistants rapidly in a wide
variety of domains that require evidence-based reasoning, including intelligence analysis,
cyber security, law, forensics, medicine, and education.
Gheorghe Tecuci is Professor of Computer Science and Director of the Learning Agents
Center at George Mason University, Member of the Romanian Academy, and former
Chair of Artificial Intelligence at the US Army War College.
Dorm Marcu is Research Assistant Professor in the Learning Agents Center at George
Mason University.
Mihai Boicu is Associate Professor of Information Sciences and Technology and Associate
Director of the Learning Agents Center at George Mason University.
David A. Schum is Emeritus Professor of Systems Engineering, Operations Research, and
Law, as well as Chief Scientist of the Learning Agents Center at George Mason University.
|
| any_adam_object | 1 |
| author | Tecuci, Gheorghe 1954- Marcu, Dorin Boicu, Mihai Schum, David A. |
| author_GND | (DE-588)1120151937 |
| author_facet | Tecuci, Gheorghe 1954- Marcu, Dorin Boicu, Mihai Schum, David A. |
| author_role | aut aut aut aut |
| author_sort | Tecuci, Gheorghe 1954- |
| author_variant | g t gt d m dm m b mb d a s da das |
| building | Verbundindex |
| bvnumber | BV043838479 |
| classification_rvk | ST 270 ST 515 QP 345 |
| ctrlnum | (OCoLC)944019974 (DE-599)BSZ470243465 |
| dewey-full | 006.33 |
| dewey-hundreds | 000 - Computer science, information, general works |
| dewey-ones | 006 - Special computer methods |
| dewey-raw | 006.33 |
| dewey-search | 006.33 |
| dewey-sort | 16.33 |
| dewey-tens | 000 - Computer science, information, general works |
| discipline | Informatik Wirtschaftswissenschaften |
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| id | DE-604.BV043838479 |
| illustrated | Illustrated |
| indexdate | 2024-07-10T07:36:26Z |
| institution | BVB |
| isbn | 9781107122567 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-029249070 |
| oclc_num | 944019974 |
| open_access_boolean | |
| owner | DE-355 DE-BY-UBR DE-188 DE-Aug4 DE-521 |
| owner_facet | DE-355 DE-BY-UBR DE-188 DE-Aug4 DE-521 |
| physical | xxiv, 455 Seiten Illustrationen, Diagramme |
| publishDate | 2016 |
| publishDateSearch | 2016 |
| publishDateSort | 2016 |
| publisher | Cambridge University Press |
| record_format | marc |
| spelling | Tecuci, Gheorghe 1954- Verfasser (DE-588)1120151937 aut Knowledge engineering building cognitive assistants for evidence-based reasoning Gheorghe Tecuci (George Mason University), Dorin Marcu (George Mason University), Mihai Boicu (George Mason University), David A. Schum (George Mason University) New York, NY Cambridge University Press 2016 xxiv, 455 Seiten Illustrationen, Diagramme txt rdacontent n rdamedia nc rdacarrier Includes bibliographical references and index Datenverarbeitung Wissensextraktion (DE-588)4546354-2 gnd rswk-swf Wissenstechnik (DE-588)4192641-9 gnd rswk-swf Ontologie Wissensverarbeitung (DE-588)4827894-4 gnd rswk-swf Expert systems (Computer science) Knowledge, Theory of / Data processing Computational learning theory A priori / Data processing Wissenstechnik (DE-588)4192641-9 s Wissensextraktion (DE-588)4546354-2 s Ontologie Wissensverarbeitung (DE-588)4827894-4 s DE-604 Marcu, Dorin Verfasser aut Boicu, Mihai Verfasser aut Schum, David A. Verfasser aut Erscheint auch als Knowledge engineering Online-Ausgabe 978-1-316-38846-4 Digitalisierung UB Regensburg - ADAM Catalogue Enrichment application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=029249070&sequence=000003&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis Digitalisierung UB Regensburg - ADAM Catalogue Enrichment application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=029249070&sequence=000004&line_number=0002&func_code=DB_RECORDS&service_type=MEDIA Klappentext |
| spellingShingle | Tecuci, Gheorghe 1954- Marcu, Dorin Boicu, Mihai Schum, David A. Knowledge engineering building cognitive assistants for evidence-based reasoning Datenverarbeitung Wissensextraktion (DE-588)4546354-2 gnd Wissenstechnik (DE-588)4192641-9 gnd Ontologie Wissensverarbeitung (DE-588)4827894-4 gnd |
| subject_GND | (DE-588)4546354-2 (DE-588)4192641-9 (DE-588)4827894-4 |
| title | Knowledge engineering building cognitive assistants for evidence-based reasoning |
| title_auth | Knowledge engineering building cognitive assistants for evidence-based reasoning |
| title_exact_search | Knowledge engineering building cognitive assistants for evidence-based reasoning |
| title_full | Knowledge engineering building cognitive assistants for evidence-based reasoning Gheorghe Tecuci (George Mason University), Dorin Marcu (George Mason University), Mihai Boicu (George Mason University), David A. Schum (George Mason University) |
| title_fullStr | Knowledge engineering building cognitive assistants for evidence-based reasoning Gheorghe Tecuci (George Mason University), Dorin Marcu (George Mason University), Mihai Boicu (George Mason University), David A. Schum (George Mason University) |
| title_full_unstemmed | Knowledge engineering building cognitive assistants for evidence-based reasoning Gheorghe Tecuci (George Mason University), Dorin Marcu (George Mason University), Mihai Boicu (George Mason University), David A. Schum (George Mason University) |
| title_short | Knowledge engineering |
| title_sort | knowledge engineering building cognitive assistants for evidence based reasoning |
| title_sub | building cognitive assistants for evidence-based reasoning |
| topic | Datenverarbeitung Wissensextraktion (DE-588)4546354-2 gnd Wissenstechnik (DE-588)4192641-9 gnd Ontologie Wissensverarbeitung (DE-588)4827894-4 gnd |
| topic_facet | Datenverarbeitung Wissensextraktion Wissenstechnik Ontologie Wissensverarbeitung |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=029249070&sequence=000003&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=029249070&sequence=000004&line_number=0002&func_code=DB_RECORDS&service_type=MEDIA |
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